Abstract
The development of spintronic devices demands the existence of materials with some kind of spin splitting (SS). In this Data Descriptor, we build a database of ab initio calculated SS in 2D materials. More than that, we propose a workflow for materials design integrating an inverse design approach and a Bayesian inference optimization. We use the prediction of SS prototypes for spintronic applications as an illustrative example of the proposed workflow. The prediction process starts with the establishment of the design principles (the physical mechanism behind the target properties), that are used as filters for materials screening, and followed by density functional theory (DFT) calculations. Applying this process to the C2DB database, we identify and classify 358 2D materials according to SS type at the valence and/or conduction bands. The Bayesian optimization captures trends that are used for the rationalized design of 2D materials with the ideal conditions of band gap and SS for potential spintronics applications. Our workflow can be applied to any other material property.
Measurement(s)  Spin polarized and spinorbit coupling band structures • Spinsplitting type at the valence and/or conduction bands  
Technology Type(s)  Density functional theory • Bayesian optimization and Highthroughput calculations  
Factor Type(s)  Atomic composition and stoichiometry of twodimensional compounds • Crystalline structure of twodimensional compounds 
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Background & Summary
The design of materials properties usually involves two main steps: (i) prediction and (ii) optimization. The first step is typically carried out by direct calculation or experimental measurement for all possible combinations of atomic identities, composition, and structures (ACS)^{1}. Although this process has been successfully implemented for functionalities such as ferroelectricity^{2,3,4} and twodimensional materials^{5,6,7}, this direct approach is usually tedious and expensive. The second step consists in the extrapolations of numerical correlations found with approaches such as machine learning^{8,9,10,11,12} or cluster expansion methods^{13}. However, numerical relations are not necessarily transferable (i.e., limited to the set of compounds used to train the models training set), preventing the rational design of material candidates with the optimized property out of the training set. In this work, we propose an inverse design process that integrates the rationalized prediction and optimization of materials properties as we illustrate it for the specific case of the functionality of spin splitting (SS) in 2D compounds.
Of special interest for spintronics device applications are the SS and spin polarization (SP). These functionalities are the cornerstone of spintronics, a promising, rapidly growing area that is based on the electron spin manipulation^{14,15,16}. In spintronic device prototypes, the interaction between the central region (a compound with SS bands) and electrodes (spin current source and detectors) can affect the SP properties^{16}. Indeed, although the interface states can intrinsically possess both SP and SS, not all interfaces allow the desirable controllability of the SS. Alternative device configurations minimizing the interface effects can consist of a Van der Waals heterojunction with a twodimensional (2D) material as central region^{17,18,19} or 2D compounds with intrinsic topological properties protecting spin currents against backscattering^{20,21,22}. Unlike 3D compounds, there is no database of 2D materials with SS^{23}. Thus, to illustrate the proposed design and optimization, we focus on 2D compounds possessing SS prototypes. These prototypes are historically classified by some kind of symmetry breaking (Fig. 1), namely: i) Rashba SS, with the electric breaking of inversion symmetry inducing SS^{24,25}, ii) the Dresselhaus SS resulting from the nonelectrical breaking of inversion symmetry^{26}, and iii) the Zeeman SS induced by the breaking of the timereversal symmetry^{27,28}. Here, we focus only on intrinsically induced SS, i.e., the effects that are originated from the material’s intrinsic electric dipoles and spinorbit coupling interactions, and consider a fourth additional SS prototype, namely HighOrder, as a category of observed SS effects that do not exactly follow the phenomenological SS models (see Methods). These SS prototypes are also characterized by different spin textures, i.e., the SP pattern in the reciprocal space (Fig. 1)^{29}.
We propose and implement a twostep automatic design of material properties: i) target property prediction based on the inverse design approach and ii) optimization of the target property based on Bayesian inference. Unlike the direct approach and machine learning, the inverse design approach requires first to define the design principles (DPs) (a physical mechanism) enabling the existence of the target properties, i.e., each of the SS prototypes above defined. We then develop a workflow that integrates a set of programs and scripts to automatically apply the design principles as filters, perform density functional band structure calculations including the spinorbit coupling, and analyze the physical properties characterizing spinpolarized bands (e.g., energy position with respect to the Fermi energy, SP values, and SS magnitude) for 2D materials. We integrate this workflow to the optimization process based on Bayesian inference, in which each desirable optimization condition is maximized. Applying this approach to 2D compounds, we calculate 436 2D materials and identify more than 1200 SS at the valence and/or conduction bands, that were then classified as Rashba, Dresselhaus, Zeeman, and HighOrder SS prototypes^{30,31}. The Bayesian analysis allowed us to find chemical and structural trends of three optimized properties relevant to spintronics: the Rashba parameter, the Zeeman spin splitting, and the band gap.
Methods
We propose a workflow based on the inverse design approach that automatically filters, selects, and designs compounds with specific functionalities^{32}, i.e., a controllable material property with potential device applications. Remarkably, in addition to the simple material prediction, commonly performed for diverse functionalities in quantum materials^{32,33,34}, we integrate the proposed workflow to the automatic optimization of the target functionality based on Bayesian statistics.
Unlike numerical correlation methods, such as machine learning and cluster expansion, the inverse design approach aims first to establish the physical mechanism (design principles) enabling the target property or functionality. The second step is to seek compounds using the design principles as filters or conditions for a rationalized design. Finally, the third step is the theoretical or experimental characterization of the magnitude of the target property for the predicted/selected compounds. In principle, unlike numerical predictive methods, in the inverse design method there is no selection of false positives, i.e., compounds that are selected but do not have the target property. The main idea of the inverse design approach is to perform an optimized materials prediction process that, compared to the “direct approach”, reduces the number of necessary experiments or calculations for the target property verification. The direct approach involves the direct computation or measure of the property for all possible combinations of atomic identities, composition, and structures (ACS) attributes.
In this section, we describe the abovenoted steps for the inverse design process in the context of spin splitting (SS) prototypes in twodimensional (2D) materials, namely: A. Definition of design principles, B. Implementing design principles as filters, and C. Target property magnitude characterization, including the SS Identification Algorithm (SSIA). Additionally, we also describe the automatic Bayesian optimization of the target properties as well as its integration with the inverse design process.
Automatic inverse design approach
A. Design principles for spin splitting prototypes
The inverse design process starts with the definition of the physical mechanism enabling the target property. The application of the inverse design process for more than one target property, e.g., different spin splitting prototypes, requires to divide them into: 1. design principles (DPs) enabling all spin splitting types, i.e., common DPs, and 2. unique enabling DPs for each spin splitting type, as indicated in Fig. 2. This design principles division is convenient for computational implementations and was also applied for the inverse design of cofunctionalities by Acosta et al.^{32}. It is important to note that the understanding of the mechanism behind the existence or absence of SS has evolved over time, from an orthodox description based on the crystal point group symmetry to a description based on the atomic site symmetry^{35}. The latter not only reproduces the wellknown description of the SS existence in compounds with noncentrosymmetric crystal point groups (e.g., Rashba and Dresselhaus SS), but also shows that compounds with local polar atomic site symmetry can have splitted split bands formed by orbital spatially localized at different material sectors^{35}. If local dipoles at different sectors cancel each other (i.e., centrosymmetric compounds), the band structure is spin degenerated. Despite the remarkable predictions and potential application of the hidden SS^{35,36,37}, in this work, we focus on materials that explicitly have energetically discriminating spin bands in its electronic structure. In this section, we then describe the common and unique enabling design principles for the SS prototypes classification here employed.

1.
Common enabling DPs for all SS types: The common design principles for the SS prototypes are represented in the central region of Fig. 2 (i.e., the intersection region). These include: a) noncentrosymmetry crystal symmetry, which is necessary for the SOCinduced breaking of spin degeneracy, b) nonvanishing SOC and c) nonmagnetic materials, which narrow the possible materials’ degrees of freedom in the analysis. As it is very well established, the breaking of both the inversion symmetry and timereversal symmetry lifts the spin degeneracy. Our work focuses on the breaking of the inversion symmetry (i.e., noncentrosymmetric compounds  DP a).The design principles ac are used as filters to screen materials from the original data source. Additionally, we also restricted our initial data set to compounds with a finite electronic band gap (i.e., larger than 1 meV). This is of special interest for applications in spin transistor devices.

2.
Unique enabling DPs for SS types:

DPs for Rashba spin splitting: Emmanuel Rashba determined that the breaking of the inversion symmetry induced by external electric fields in thin films leads to a shift in the momentum space of bands with opposite spin^{24}. This linearink shift near kpoints preserving the timereversal symmetry is also characterized by a splitting in the energy of the spin bands. Recently, this Rashba SS was confirmed to also exist in compounds with inversion symmetry breaking induced by the intrinsic electric dipole in 3D noncentrosymmetric bulk compounds^{38,39,40}. This discovery motivated the inverse design of the Rashba SS in 3D bulk compounds^{23}. Although intrinsic electric dipoles can also lead to the Rashba SS in 2D materials, even without external electric fields^{35}, there is not a list of 2D Rashba compounds. To organize the design and materials’ screening of this type of compounds, we first defined the enabling design principles. In addition to the common DPs previously defined, the Rashba SS in 2D compounds also requires a nonzero electric dipole (allowed by at least one polar atomic site and local dipoles that add up to nonzero, as described by Zhang et al.^{35}). The SS type depends on the wave vector point group^{29} and hence, the DPs also include the kpoint preserving the timereversal symmetry, as well as a linearink shift. The unique DPs for the Rashba SS (i.e., nonzero electric dipoles, timereversal symmetry, and linearink SS) are illustrated in the purple quadrant of Fig. 2. We note that the Rashba SS prototype classification employed in this work is equivalent to the R1 classification proposed by Zhang et al.^{35}, where the lack of global bulk inversion symmetry leads to nondegenerate, i.e., explicit spin split, electronic bands.

DPs for Dresselhaus spin splitting: Based on the k · p model, Gene Dresselhaus found that the SOC Hamiltonian describing states near a timereversal symmetry kpoint in a nonpolar noncentrosymmetric material (i.e., with zero electric dipole) is given by \(H(k)=\beta [({k}_{y}^{2}{k}_{z}^{2}){k}_{x}{J}_{x}+({k}_{z}^{2}{k}_{x}^{2}){k}_{y}{j}_{y}+({k}_{x}^{2}{k}_{y}^{2}){k}_{z}{J}_{z}]\), where J_{i} are the components of the total angular momentum operator^{26}. In 2D compounds, the Hamiltonian is \({H}_{2D}\left(k\right)={\beta }_{2D}\left({\sigma }_{x}{k}_{x}+{\sigma }_{y}{k}_{y}\right)\), where σ_{i} are the Pauli matrices. This SOC term leads to a linearink SS. A list of 3D compounds having Dresselhaus SS has been recently reported^{23}. Based on the enabling design principles, we extend the search of Dresselhaus SS to 2D materials. Besides the common DPs, as shown in Fig. 2, enabling DPs also include zero electric dipoles (i.e., nonpolar site symmetries or polar site symmetries that add up to zero, as established in ref. ^{35}), timereversal symmetry, and linearink SS. Unlike the Rashba SS allowed by noncentrosymmetric polar point groups, the Dresselhaus SS can be found in noncentrosymmetric nonpolar point groups, but it is not limited to these point groups (e.g., polar site symmetry can accidentally add up to zero). Similar to the previous case, this classification is equivalent to the D1 effect in ref. ^{35}

DPs for Zeemantype spin splitting: The existence of the electron spin was first elucidated by the magnetic discrimination of states^{41}. The breaking of timereversal symmetry via a magnetic field shifts in energy the Bloch bands with different spins, as in ferromagnetic compounds. More than one hundred years after Zeeman’s discovery, it was established that even nonmagnetic compounds can have a Zemmantype SS at kpoints breaking the timereversal symmetry^{27,28}. The SOC acts as an effective magnetic field that locally breaks the timereversal symmetry but globally preserves it. The Zeemantype SS can exist in polar and nonpolar compounds; however, it satisfies the linearink SOC Hamiltonians describing the Rashba and Dresselhaus SSs. In this Zeemantype SS, unlike the Dresselhaus and Rashba SS (Fig. 1), there are no degenerated bands. Summarizing, besides the common DPs, the Zeemantype SS also require the local breaking of the timereversal symmetry in the reciprocal space (i.e., wavevector point group symmetry without TRsymmetry), as indicated in Fig. 2.

DPs for highorder spin splitting: We define the highorder prototype as composed of SS whose band dispersion does not exactly follow the phenomenological linearink Rashba and Dresselhaus Hamiltonians but are degenerated at timereversal symmetry invariant kpoints. The design principles that apply here are polar and nonpolar noncentrosymmetric crystal point groups, SS near kpoints preserving the timereversal symmetry, and nonlinear SS. Highorder SS is also induced by the odd terms in k^{n} appearing in the SOC Hamiltonian (with n > 1). We acknowledge that this category is broadly defined and represents a way to report materials and spin splittings that do not follow the Rashba/Dresselhaus characteristic band dispersions but still present SS that can be investigated in future theoretical works.

Note that a given compound can present more than one SS prototype at different kpoints, as discussed by Acosta et al.^{29}, and thus the SS characterization should identify all SS types in a given compound. As both the Rashba and Dresselhaus SS prototypes share the common characteristic of having linearink Hamiltonians, it will be useful to label them as a Linear SS (LSS) when implementing the SS identification algorithm, as described below, while not losing the symmetry criteria that differentiate them. Zeeman and Highorder SS also have acronyms in the context of the algorithm: ZSS and HOSS, respectively). Additionally, the hidden SS can also constitute some of the potentially most promising classes of materials, which will be the focus of future works limited to only hidden SP in 3D and 2D materials.
B. Materials screening based on common design principles for spinsplitting types
In this section, we illustrate how design principles are translated into rules for compound selection, as well as the algorithms and computational implementations to perform the inverse design of SS prototypes in 2D compounds. The entire process detailed in this section is represented in the diagram shown in Fig. 3.
The starting point is the Computational 2D Materials Database (2020 version)^{42}, containing a total of 3814 unique entries generated by elemental substitution based on known 2D structural prototypes. Even though the cohesion of a given material in a twodimensional structure after relaxation is already checked throughout the workflow performed in the database, we rescan all the entries with a modified rank determination algorithm^{43}, implemented with the analysis.dimensionality module from Pymatgen^{44}. Although not being strictly mandatory, this step is intended to unify the criteria of the symmetry and dimensionality analysis if similar procedures are applied to other twodimensional databases that implement different strategies for 2D materials discovery. At this point, 3708 materials classified as 2D by the algorithm proceed in the screening workflow. For the list of 3708 2D compounds, the enabling DPs (See section A of Methods) are applied as screening filters based on the materials information provided by the C2DB database, distilling the materials landscape to be analyzed by firstprinciples calculations. Before implementing the common DPs, we use the DFT calculated GGAPBE band gap values in the C2DB database^{42} to remove the entries with nearzero band gap (i.e., E_{g} < 10^{−3} eV). This initial filtering results on 1020 nonzero bandgap materials. We note that throughout the DFT calculations workflow (see next subsection of Methods), the compound AuTe (identified in the C2DB with the uid: Au2Te2aafa8f843d5b has a bandgap of 0.04 eV, but it is identified as metallic according to the GGAPBE calculations implemented in the workflow of this work. This compound is then excluded from the SS identification analysis. The number of entries in this screening stage is then corrected to 1019.
For the selection of noncentrosymmetric materials (common DPa  see intersection region in Fig. 2), the structure space group number is determined via the symmetry.analyzer module from Pymatgen^{44}. Here, a strict tolerance parameter for the space group classification is employed (symprec = 0.001), to determine the structure’s symmetry. Such tight parameter, although being too strict for general purposes and possibly enabling the selection of false positives SS materials, is intended to select the largest group of materials that can potentially display the aforementioned SS effects in its band edges. We note, retrospectively, that if the tolerance parameter employed at this stage was the default value (symprec = 0.01), 21 materials that possess some type of SS in their band edges (determined by the next stages of this work) would not be identified. The number of materials that proceed at this stage of the screening process is then 500.
To guarantee the global preservation of timereversal symmetry (common DPb  see intersection region in Fig. 2), magnetic compounds are eliminated from the previous list of 500 noncentrosymmetric compounds. The timereversal symmetry breaking can induce SS that are not necessarily induced by the spinorbit coupling (e.g., the Zeeman effect^{45} and the antiferromagneticinduced SS^{46,47}). Naturally, our approach can be extended to magnetic compounds by considering a complete analysis of magnetic point group symmetry. In this case, the SOC is not necessarily a common design principle. Here, we use the magnetic ground state reported by the C2DB database, in which the antiferromagnetic configuration is restricted to duplicated unit cells. The detailed study of the magnetic ground states usually requires the DFT total energy study of multiple spin configurations and larger supercells^{48}. Alternatively, machine learning algorithms can be used to predict the magnetic ground states, as we demonstrated in ref. ^{49}. This filtering process based on common DPs then results in 436 nonmagnetic noncentrosymmetric semiconductors. Table 1 summarizes the number of entries that proceed at each step of the screening process.
Although a large atomic SOC is usually desired, an extra filter for compounds with high atomic numbers has not been applied, so a larger set of atomic species can be analyzed in the optimization step. At this point, in principle, all the selected compounds can potentially have at least one SS type. Thus, the final materials selection according to the unique design principles is accompanied by the characterization of the magnitude of the spin splitting. We then proceed with the selected 436 compounds for the highthroughput calculations required to evaluate the unique enabling design principles (Fig. 2).
C. Target property verification: Highthroughput calculations
To classify the 436 2D compounds potentially having SS according to their SS prototype, the implementation of the unique design principles is required. This requires the calculation of all band structures. Thus, in the specific case in which the target property is the SS type, it is convenient to design an algorithm that not only filters compounds using the unique design principles according to the band shape, but also extracts the magnitude of the specific SS type. A workflow of abinitio calculations is then developed to generate band structures with a spinpolarization resolution that are analyzed in the SS identification algorithm. DFT + SOC calculations are performed using the Vienna Ab initio Simulation Package (VASP) with the projectoraugmented wave (PAW) method^{50,51} and GGAPBE^{52} parameterization for the exchangecorrelation functional. The workflow is prepared and managed using the Atomic Simulation Environment (ASE) package^{53,54}, which is integrated with VASP.
For each entry (i.e., each of the 436 selected 2D compounds), a set of three calculations is performed to i) determine the ground state charge density in a selfconsistent scheme, ii) optimize the charge density in a noncollinear scf calculation, and iii) perform a band structure, noncollinear calculation (Fig. 3). No additional relaxation procedure is performed since the available structures in the C2DB already correspond to an energy minimum. The same exchangecorrelation functional is employed throughout the calculations in both the database and this work. We have verified this for a set of aleatory selected compounds at the start of the workflow. For all calculations, the cutoff energy for the planewave expansion was set to 520 eV. The choice of potentials employed in the workflow follows the Materials Project recommended PAW setup^{55}, implemented through ASE^{54}. Relevant parameters for the calculations i) and ii) are presented in Table 2. For the sampling of Brillouin Zone according to the specific kpaths in calculation iii), a density parameter of 80 kpoints per Å^{−1} was set for all entries.
SS Identification Algorithm (SSIA)
Based on the data generated from the band structures with orbital and spin resolution, an algorithm is designed to analyze the energy dispersion at the valence and conduction bands leading to the identification of the SS type (i.e., Rashba, Dresselhaus, Zeeman, and Highorder prototypes). In this process, the unique DPs are applied as criteria used by the algorithm to evaluate and identify SSs according to i) symmetry of the kpoint where the SS occurs, ii) band dispersion in the region of the SS, and iii) estimation of structure electric dipole. The code is built upon Pymatgen and ASE functionalities, and its underlying algorithm is detailed in this section. The algorithm of SS identification is solely based on the band structure data, obtained by the DFT calculations. It consists of looping the analysis over the eigenvalues of the valence and conduction bands and the immediate next bands (which would represent the spin degenerated copy in a nonpolarized scheme) on the highsymmetry kpoints, that are labeled as timereversal invariant momentum (TRIM) or nonTRIM kpoints. For the 5 different Brillouin zones in 2 dimensions, the TRIM kpoints can be directly determined and passed as a list to the SS algorithm (see section Kpaths along highsymmetry lines in the Supplementary file 1  Band Structures material). Any highsymmetry kpoint which is not in this list is treated as nonTRIM by the code, in the sense that it checks the possibility of having nondegenerate bands at this kpoint (Zeeman SS). When analyzing the proximity of those points in each direction, three possibilities may arise:

1.
The pair of bands are energy degenerated on the highsymmetry kpoint and in its vicinity: No SS is present;

2.
The pair of bands are not energy degenerated in a nonTRIM kpoint: There is a SS gap, which is measured, and the SS is classified as belonging to the Zeeman prototype;

3.
The pair of bands is energy degenerated in the highsymmetry kpoint, but not in its vicinity: A SS happens on the kpath segment between highsymmetry kpoints. The SS prototype classification will then follow the characteristics of the dispersion of the bands: if both SS bands follow the same direction (the sign of the first derivative is the same in the region next to the highsymmetry kpoint) the SS is immediately classified in the highorder prototype, as it do not completely obey the phenomenological Rashba/Dresselhaus linearink Hamiltonian. If the bands follow opposite directions, a Rashba/Dresselhaustype SS is observed, and the classification of the SS in one of those groups will follow the result from the structure based estimation of electric dipole. Crystal structures displaying zero (nonzero) net electric dipole are classified in the Dresselhaus (Rashba) prototype. For the last two cases, the Rashba/Dresselhaus coefficient α_{R, D} is computed according to Eq. 1:
where ΔE_{R,D}, k_{R,D}, stand for the energy difference of the spin splitted bands (SS magnitude) and the kpath interval from the SS to its correspondent highsymmetry kpoint in reciprocal space, respectively.
Figure 3 summarizes the SS classification heuristics adopted in the algorithm. For all SS prototypes, the energy difference between the SS bands and between the SS and the VBM/CBM is computed. The result is an extensive list of SS identified at the valence and/or conduction bands for 358 materials, presented in the tables in the Supplementary files 2–5 for the Rashba, Dresselhaus, Zeeman, and highorder SSs, respectively, whose distribution is also represented in Fig. 4. We note that single materials can have multiple, nonexcluding SS prototypes at the valence/conduction bands, which are reported in the tables accordingly.
Inverse optimization process
Besides the enabling (common and unique) design principles, there are other conditions that are not required for the existence of the target property, but are important for its optimization towards specific device applications. Unlike the enabling design principles, the optimizing design principles are not necessarily physical mechanisms, but characteristics related to the chemical compositions and structure (that do not affect the existence of the target property). For SS and SP prototypes, for instance, one would like to have a compound with (i) a large enough band gap to allow gatecontrollable position of the Fermi energy, and (ii) effective masses set to increase the charge carrier mobility to provide control over the performance of semiconductor devices. Additionally, it is also desirable to optimize some other properties characterizing SS prototypes and the efficiency of spintronic devices, namely: (iii) large SS (i.e., larger than 25 meV), (iv) large SP coefficient (i.e., larger than 1.3 meV), and v) position of the SS with respect to the Fermi energy.
In an ideal scenario, properties iv should have optimized values. However, these optimum values could be physically contradictory according to the usual trends defined by the chemical composition. For instance, while large SS are usually expected in compounds formed by atoms with large atomic numbers, large bandgaps tend to be found in compounds formed by atoms with small atomic numbers. This suggests that large bandgaps and large SSs are in some sense contradictory. The question arising from these apparent contradictions is: How to find the optimal candidate? This problem is also evident in other areas. For instance, the apparent contradiction between a high thermal insulation and electrical conductivity, which is desirable for thermoelectric applications. Here, we illustrate a strategy to address this problem for the specific case of compounds with SS prototypes for spintronic device applications, as shown in section Technical Validation. As we explain below, the inverse optimization process is based on Bayesian statistics using the materials’ composition and crystal structure.
Bayesian statistics for materials property optimization
Bayesian Inference (BI) is a statistical method of inference based on Bayes’ Theorem. The Bayes’ Theorem specifies how one should update the probabilities when new information is given. Starting from a hypothesis H, such as a material belonging to class h, and a property of this material, one can define P(HA), i.e., the probability of an material belonging to h given that it has the property A. This is called posterior probability and it is given by the Bayes Theorem:
where the three rightside terms are:

P(A): Probability of a material having the A property;

P(H): The prior probability. This is the probability of H without knowing anything regarding the material. It is simply given by the ratio of materials belonging to h;

P(AH): The likelihood function. The probability of a material with property A given that it belongs to h.
Equation 2 shows how to update the probability of given new information regarding A. Observing how the knowledge of a material’s property A updates the prior probability allows to infer about correlations between A and h. Therefore, an increasing probability update shows a strong correlation between A and h.
The likelihood term P(AH) is a calculated probability given by feature/property A and its distribution in materials within h. Properties can be continuous, discrete, or categorical, which implies that different probability distributions should be used accordingly to A’s nature.
For the case of A as a categorical feature, such as for structural cluster, we are interested in the posterior probabilities given that the material belongs to a structural cluster t from the n possible structural clusters, i.e. we want to calculate P(HA = t). One should use the Categorical distribution for the likelihood term P(A = tH), given by:
where N_{th} is the number of times the materials from structural cluster appears t in the class h, N_{h} is the total number of materials belonging to the class h, and α is a regularization term. The regularization term prevents P(A = tH) from having absolute values (0 or 100%) when the category t has all materials outside or within the class h.
For the case of A as the presence of an element in the material’s composition, A can assume False or True values. We wish to calculate P(HX element in composition = True) due to the boolean and nonexclusive nature of this feature (a material can have more than one element in its chemical composition). One then should use the Bernoulli distribution:
where A_{X} is the event of having element X in its composition and p_{X} is similar to the probability given by Eq. 3:
where N_{X=True,h} is the number of materials with X in the composition that belongs to class h, and N_{h} the total number of materials belonging to class h. Once again, α acts as a regularization term.
The Categorical and Bernoulli distributions were used to infer the effect of crystal structure and composition on the calculated spinsplitting properties, respectively, as presented in the Technical Validation Section. We used the scikitlearn^{56} implementations, with α = 1 as regularization.
Data Records
As each material may present multiple SSs in its band structure (in the valence and/or conduction band) classified into different SS types, the generated data shows a highly unstructured nature. For this reason, we opted to provide the complete data in multiple formats, suited for the different types of data structure and use cases.
Firstly, an overview of this work’s main findings is contained in tables available in the Supplementary files 2–5 and in an Excelcompatible.csv file. There, the reader may find the full list of SSs identified in this work, separated by the SS prototypes (Rashba, Dresselhaus, Zeeman, and Highorder) (See Design principles for spin splitting prototypes section), as well as the materials general information (e.g. id, symmetry, band gap, energy above convex hull) and SS related information (e.g. SS magnitude and localization in the band structure). As each line in the table represents a single SS, one compound may be repeated several times in each SS category. For a visual representation of the data, the Supplementary file 1 contains the rendered image of the structure and band structure with spin polarization resolution for all materials calculated in this work. The reader is then able to correspond the SS reported in the tables with its localization in the plotted band structure.
These images are also available in the Materials Cloud^{57} repository for this work^{58}, subdivided into folders for each compound. There, the reader also finds a .cif file containing the material’s crystal structure information, that determines the choice of the unit cell and origin of the coordinate system of the structure representations employed in the calculations.
Regarding the raw results of the calculations, these are available in two sources. For data provenance, we store the main input and output files of the VASP DFT band structure calculations in a NOMAD repository^{59}. In this manner, the reader can find the necessary raw files that would reproduce the calculations in this work. For accessing the band structure results, on the other hand, we alternatively provide a DFTcodeagnostic format which mainly relies on Pymatgen^{44} and ASE^{54} python objects. These are available in a pickle file inside each compound’s folder in the Materials Cloud repository^{58}. There, the reader may get the full list of kpoint coordinates, eigenvalues, orbital projections, spin polarization, and other relevant data of all the band structure calculations in this work. The README.md file available in the repository contains detailed instructions to open the data using Python.
Regarding the main results of this work, which represent the full postprocessed data with the SS identification and description generated by the developed algorithm for all screened materials, these are available in a single dataset as a JSON file in the Materials Cloud repository. Alternatively, we also made it available as a binary export dump file that can be imported directly to a MongoDB database (www.mongodb.com). These two files contain the same information and may suit different preferences and use cases. Detailed instructions for accessing both data files are also available in the README.md file.
In this dataset, each entry corresponds to one material and provides three classes of information: the materialspecific data, the band structure description data, and the spinsplitting description data.
The former provides the material’s general description regarding its composition, crystal structure, band gap, and other properties. Box 1 shows a complete overview of this data. The band structure data (Box 2 gives information about the band structure calculations, such as the number of calculated bands, the number of kpoints, the Brillouin zone, and all energy eigenvalues, Here, the key NOMAD_files has the URLs to the materialspecific files for the band structure calculation at the NOMAD repository.
The spin splitting data, as the name suggests, provides information of all spin splittings occurring in the material’s valence and conduction band, which compiles all the data resulting from SSIA into SS types (LSS, ZSS, or HOSS) and their specific parameters. Box 3 details all keys describing the SSs.
The user can easily query the data by importing it to MongoDB (check the MongoDB website on setting up a database in your specific system) or any other NoSQL database option. Alternatively, one can also convert the JSON file to a simple table, with special care to normalize the vb/cb keys and their subkeys. By using the MongoDB approach, one can query the unstructured spin splitting data for further analysis by using the many programming languages which MongoDB provides support, or by using the mongosh (shell environment) or even the Compass program (MongoDB’s graphical user interface environment). In the README.md file from the Materials Cloud repository we also provide instructions for doing so. As an example, the simple query shown in Box 4 retrieves the cations, anions, and the structural cluster of all compounds with a Zeeman spin splitting greater than 0.1 eV in the valence or the conduction band. The resulting data will be used in the next section of Technical Validation.
Technical Validation
The data generated in this work opens the way for two possible direct applications: i) the selection of optimized compound, i.e., the best of a class for a given application and ii) the understanding of the interplay between the physical mechanisms behind a given target property based on data trends that can be analyzed with the Bayesian optimization of materials properties. We then highlight in this technical validation section how the proposed workflow for selection and optimization leads to the best of a class for a 2D semiconductor with large SS, illustrating the use of the Bayesian optimization applied for the specific case of the Zeeman SS.
Bayesian inference
As symmetry and structure are major components of bands behavior, it is fundamental that we convey these properties into data in an appropriate way. The C2DB provides a label called crystal prototype, with a stoichiometry  space group  occupied Wyckoff positions format. The Hphase of monolayer MoS_{2} is described as AB_{2}187ai, for example. We have observed that grouping the materials using this label leads to some erroneous clusters. Some materials that should be clustered together are separated, resulting in a sparse space of crystal prototypes. Figure 1 in Supplementary file 6 illustrates this and some other limitations of using space group or crystal prototype for grouping materials. To bypass these limitations and also to get to more intuitive conclusions regarding the structure degree of freedom, we used the methodology described in ref. ^{60} to generate crystal fingerprints (CF) for each of the materials in the C2DB. As the resulting CFs have large dimensionality, we then used them as input for the UMAP^{61} embedding technique. The result is a 2d embedding that characterizes the structural differences and similarities of C2DB’s materials. We used DBSCAN^{62} to define the clusters in the embedding (Fig. 2 in Supplementary file 6) and then further subdivided them accordingly to the materials’ stoichiometry. As an example, MoS_{2} is labeled as AB_{2}c22 because of its binary stoichiometry and its given cluster number (22). A total of 23 structural clusters were found for the 436 2D materials. Further details of this process are presented in the Section 1 of Supplementary file 6. Due to the categorical nature of the structural cluster (i.e., the material can only be at one specific cluster), we used the categorical distribution implementation of Naive Bayes for evaluating how being a member of such clusters weights for a given material regarding a target property.
As an illustrative example, we used the Zeeman spinsplitting (ZSS) as a target property to be analyzed. Setting a bottom limit of 100 meV for materials with “large ZSS”, we then proceeded to use Bayesian inference to generate probabilities for each structural cluster. These probabilities are the posterior probabilities, i.e., the probability that a material has a large ZSS given that it is in a given structural cluster. They were calculated using the categorical distribution given by Eq. 3. The results are presented in Fig. 5. Only structural clusters with at least 5 materials are shown and analyzed.
The posterior probabilities shows that the only structural clusters associated with the target are ABc25 (Buckled Hexagonal AB), ABCc3 (TTMDC MXY Janus), ABCc4 (HTMDC MXY Janus), and AB_{2}c4 (HTMDC). All of them are hexagonal and present ZSS at the K points of the Brillouin Zone. The Zeemantype spin splitting in these structures, except for ABCc3, has been extensively studied in the context of valleytronics^{63}. In AB_{2}C4 (HTMDCs), the nondegeneracy of spin states at ±Kpoints combined with timereversal symmetry requires that spin splittings at +K and −K to be opposite^{64,65}. This condition gives rise to the called valley Zeeman effect^{66,67,68,69}, which happens when the degeneracy between valleys +K and −K is broken by applying a perpendicular magnetic field. The same characteristic can be found in the literature for ABCc4 (HTMDC MXY Janus) systems^{70,71,72} and ABc25 (Buckled Honeycomb AB) structures^{73}, with the latter presenting ferroelectricity as a cofunctionality. For ABCc3 (TTMDC MXY Janus), the spinsplitting arises uniquely from the intrinsic outofplane dipole originated from the electronegativity difference between X and Y anions. Materials with this structure have been studied primarily because of their large Rashbatype spin splittings located around Γ^{74,75,76}. The ZSS localized at K shows to be inaccessible as it is usually distant from the VBM/CBM around Γ.
To evaluate the composition influence on spin splitting properties, we first separated the composition of each material into cations and anions. This separation is performed according to the calculated Bader charges^{77} of each ion in the unit cell, which is currently available in C2DB. If the cation/anion X is (not) present at the material composition, the feature cation/anion X is set to (False) True. The distribution of cations and anions in the composition of all 436 materials is shown in Fig. 5 from Supplementary file 6. We used a Naive Bayes implemented with the Bernoulli distribution (Eq. 4) as these features are not exclusive, i.e., one entry can have more than one kind of cation/anion. The results are presented in Fig. 6. Only the cations/anions present in at least five materials are shown. We can see that the cations have a larger range of posterior probabilities, indicating that they are more important than anions regarding the Zeeman spin splitting in those systems. One can also notice the topdown trend in the periodic table groups: heavier atoms are associated with higher ZSS due to their higher spinorbit coupling. The most significant cations were Bi, Sb, As, Ir and Hf, while the most important anions were the heaviest ones: Te and I.
This same methodology is then applied to Rashba/Dresselhaus spinsplitting, Rashba parameter, and band gap as target properties. The results of the Bayesian Inference analysis are provided by Table 3. The analogous heatmaps are presented in the Supplementary file 6 (Section 3  Target properties structural and compositional heatmaps).
Case study for optimizing Zeeman spin splitting
Combining the optimal compositions and structures found in the previous section might lead to the exploration of novel spinbased materials by simple ion substitution. We used the results in Table 3 for ZSS and combined the optimal structural clusters, cations, and anions to generate a set of materials that are likely to show large ZSS (defined above as larger than 100 meV). The result is a set of 30 materials, from which nine were already in the screened dataset of 436 nonmetal materials (Duplicated NM), seven were in the general C2DB database as metals (Duplicated M), and 14 are new combinations (New). Figure 7a illustrates the combinatorial aspect of this ZSS optimization.
The new ionsubstituted combinations were then structurally relaxed until the HellmannFeynman forces on each ion were less than 1.0 meV/Å, and then their respective band structures were calculated. All of them maintained their structural characteristics. Also, all of them presented a ZSS larger than 100 meV, the target of the optimization process. However, except for two materials (IrTeI on the two Janus phases), almost all of them resulted in metallic band structures. The set Duplicated M consisting of 7 metals in C2DB that fits the criteria also validates our optimization process, except for only one material with ZSS below the threshold. Figure 7b shows the ZSS distribution over these different sets of materials.
Best of a class: the case of Zeeman SS Materials
To present a list of “best of a class” materials from the resulting screened C2DB database, one first needs to specify what “best” means with welldefined objectives. For such, we delineated three parameters:

SS intensity: Given by the energy delta of spin splitting. It is defined as Zeeman spin splitting for nonTRIM points and as Rashba/Dresselhaus spin splitting for TRIM points;

SS accessibility: The energy delta of the spin splitting to the VBM or CBM. It measures how accessible the SS is for exploitation and experimental verification;

Energy above hull (ehull): It measures the material stability to its decomposition to other structures with the same composition. An ehull of 0 eV means that the material is thermodynamically stable in comparison to other phases.
We described the dataset in terms of these three parameters so one can obtain materials with a large and accessible SS but, at the same time, that are thermodynamically stable in comparison to other competing phases. By filtering out the materials with ehull >30 meV, with accessibility <100 meV and Zeeman spin splitting >30 meV, we obtained the materials presented at Table 4.
Effect of anticrossing bands
Acosta et al. were the first to propose a causal relationship between the anticrossing orbital character of energy bands and a large Rashba coefficient for 3Dbulk compounds^{23}. In this work, we investigate the extension of this concept in the context of twodimensional materials.
An anticrossing analysis procedure is implemented in the SSIA and consists of two steps: i) identifying aligned pairs of SS between valence and conduction bands and ii) measuring the change in the orbital character of the selected bands in the kinterval between the highsymmetry kpoint and the inflection point where the SS is maximum. If a monotonic and inverse change on the orbital contribution is present in the pair of SSs at the valence and conduction bands, the anticrossing of SS bands (ACSS) is verified.
Figure 8a illustrates this analysis for the 2D compounds with SS identified in this work. In such context, the presence of anticrossing bands may indicate a sufficient condition, as all ACSS present Rashba coefficients larger than 0.862 eV/Å^{−1}. But they are not necessary, as far as other large Rashba compounds do not display a measurable anticrossing between valence and conduction bands. While we understand that other symmetrydriven physical mechanisms can play an important role in SS in 2D compounds, an indirect effect of anticrossing bands is also noted, as it is illustrated for the AsIS (MXY Janus) compound in Fig. 8b. For its respective band structure, anticrossing is verified among the valence and other deeper bands, leading to a large Rashba coefficient for such SS that may have a consequential effect on the SS observed at the valence band.
Usage Notes
As noted in the Data Records section, the complete data generated for all materials throughout this work are available in various formats, that may suit the reader’s different needs and use cases. A complete guide to accessing and working with the data using python is available in a README markdown file in the Materials Cloud repository^{58} for this work.
The computational code, i.e. a python class, used for all the SS analysis from the output of the DFT calculations is also provided (see Code Availability section) and can be used to identify and measure SS effects of other 2D materials calculations. Due to the context of this work, the algorithm currently supports noncollinear band structure calculations performed with VASP, whose kpaths over the reciprocal space have been generated with ASE’s Brillouin zone sampling automatic scheme. The code initialization requires only the specification of the folder path where the band structure calculation was performed, and presents different methods to identify SS effects in materials valence/conduction bands and the presence of anticrossing bands, and also offers tools for plotting band structures with spin texture resolution. A more detailed description of the current functionalities of the code are available in its GitHub repository.
Code availability
The entire computational code employed in the SS analysis within this work is openly available at the GitHub repository github.com/simcomat/SS_2D_Materials. It is intensely built upon tools and methods from Pymatgen^{44} and ASE^{54}, and provide functions to identify, measure and classify SS effects that appear valence/conduction bands of 2D materials band structure calculations.
Change history
19 August 2022
A Correction to this paper has been published: https://doi.org/10.1038/s41597022016417
References
Zunger, A. Inverse design in search of materials with target functionalities. Nat. Rev. Chem. 2 (2018).
Smidt, T. E., Mack, S. A., ReyesLillo, S. E., Jain, A. & Neaton, J. B. An automatically curated firstprinciples database of ferroelectrics. Sci. Data 7 (2020).
Garrity, K. F. Highthroughput firstprinciples search for new ferroelectrics. Phys. Rev. B 97, 024115 (2018).
Acharya, M. et al. Searching for new ferroelectric materials using highthroughput databases: An experimental perspective on BiAlO3 and BiInO3. Chem. Mater. 32, 7274–7283 (2020).
Mounet, N. et al. Twodimensional materials from highthroughput computational exfoliation of experimentally known compounds. Nat. Nanotechnol. 13, 246–252 (2018).
Zhou, J. et al. 2dmatpedia, an open computational database of twodimensional materials from topdown and bottomup approaches. Sci. Data 6 (2019).
Gjerding, M. N. et al. Recent progress of the computational 2d materials database (c2db). 2D Mater. 8, 044002 (2021).
Schleder, G. R., Padilha, A. C. M., Acosta, C. M., Costa, M. & Fazzio, A. From DFT to machine learning: recent approaches to materials sciencea review. J. Physics: Mater. 2, 032001 (2019).
Rodrigues, J. F., Florea, L., de Oliveira, M. C. F., Diamond, D. & Oliveira, O. N. Big data and machine learning for materials science. Discov. Mater. 1 (2021).
Schleder, G. R., Acosta, C. M. & Fazzio, A. Exploring twodimensional materials thermodynamic stability via machine learning. ACS Appl. Mater. & Interfaces 12, 20149–20157 (2019).
Acosta, C. M. et al. Analysis of topological transitions in twodimensional materials by compressed sensing. ArXiv 1805.10950 (2018).
Schleder, G. R., Padilha, A. C. M., Rocha, A. R., Dalpian, G. M. & Fazzio, A. Ab initio simulations and materials chemistry in the age of big data. J. Chem. Inf. Model. 60, 452–459 (2019).
Laks, D. B., Ferreira, L. G., Froyen, S. & Zunger, A. Efficient cluster expansion for substitutional systems. Phys. Rev. B 46, 12587–12605 (1992).
Zutic, I., Fabian, J. & Das Sarma, S. Spintronics: Fundamentals and applications. Rev. Mod. Phys. 76, 323–410 (2004).
Yang, S.H., Naaman, R., Paltiel, Y. & Parkin, S. S. P. Chiral spintronics. Nat. Rev. Phys. 3, 328–343 (2021).
Acosta, C. A. M. Transistor spintronico: descoberta e caracterizagao de isolantes topologicos. Ph.D. thesis, Universidade de Sao Paulo, Agencia USP de Gestao da Informacao Academica (AGUIA) (2018).
Sierra, J. F., Fabian, J., Kawakami, R. K., Roche, S. & Valenzuela, S. O. Van der waals heterostructures for spintronics and optospintronics. Nat. Nanotechnol. 16, 856–868 (2021).
Xia, W. et al. Recent progress in van der waals heterojunctions. Nanoscale 9, 4324–4365 (2017).
Kamalakar, M. V., Dankert, A. & Dash, S. P. Spintronics with graphene and van der waals heterostructures. In Contemporary Topics in Semiconductor Spintronics, 241258, (WORLD SCIENTIFIC, 2017).
Acosta, C. M. & Fazzio, A. Spinpolarization control driven by a rashbatype effect breaking the mirror symmetry in twodimensional dual topological insulators. Phys. Rev. Lett. 122, 036401 (2019).
Mera Acosta, C., Babilonia, O., Abdalla, L. & Fazzio, A. Unconventional spin texture in a noncentrosymmetric quantum spin hall insulator. Phys. Rev. B 94, 041302 (2016).
Pan, H., Wu, M., Liu, Y. & Yang, S. A. Electric control of topological phase transitions in dirac semimetal thin films. Sci. Reports 5 (2015).
Acosta, C. M., Ogoshi, E., Fazzio, A., Dalpian, G. M. & Zunger, A. The rashba scale: Emergence of band anticrossing as a design principle for materials with large rashba coefficient. Matter 3, 145–165 (2020).
Bychkov, Y. A. & Rashba, E. I. Properties of a 2D electron gas with lifted spectral degeneracy. Sov. J. Exp. Theor. Phys. Lett. 39, 78 (1984).
Manchon, A., Koo, H. C., Nitta, J., Frolov, S. M. & Duine, R. A. New perspectives for rashba spinorbit coupling. Nat. Mater. 14, 871–882 (2015).
Dresselhaus, G. Spinorbit coupling effects in zinc blende structures. Phys. Rev. 100, 580–586 (1955).
Acosta, C. M., Fazzio, A. & Dalpian, G. M. Zeemantype spin splitting in nonmagnetic threedimensional compounds. npj Quantum Mater. 4 (2019).
Yuan, H. et al. Zeemantype spin splitting controlled by an electric field. Nat. Phys. 9, 563–569 (2013).
Acosta, C. M., Yuan, L., Dalpian, G. M. & Zunger, A. Different shapes of spin textures as a journey through the brillouin zone. Phys. Rev. B 104 (2021).
Vajna, S. et al. Higherorder contributions to the rashbabychkov effect with application to the bi/ag(111) surface alloy. Phys. Rev. B 85, 075404 (2012).
Cartoixa, X., Wang, L., Ting, D. & Chang, Y. Higherorder contributions to rashba and dresselhaus effects. Phys. Rev. B 73, 205341 (2006).
Acosta, C. M., Fazzio, A., Dalpian, G. M. & Zunger, A. Inverse design of compounds that have simultaneously ferroelectric and rashba cofunctionality. Phys. Rev. B 102, 144106 (2020).
Zunger, A. & Malyi, O. I. Understanding doping of quantum materials. Chem. Rev. 121, 3031–3060 (2021).
Malyi, O. I. & Zunger, A. False metals, real insulators, and degenerate gapped metals. Appl. Phys. Rev. 7, 041310 (2020).
Zhang, X., Liu, Q., Luo, J.W., Freeman, A. J. & Zunger, A. Hidden spin polarization in inversionsymmetric bulk crystals. Nat. Phys. 10, 387–393 (2014).
Liu, Q., Guo, Y. & Freeman, A. J. Tunable rashba effect in twodimensional LaOBiS2 films: Ultrathin candidates for spin field effect transistors. Nano Lett. 13, 5264–5270 (2013).
Zhang, R., Marrazzo, A., Verstraete, M. J., Marzari, N. & Sohier, T. D. P. Gate control of spinlayerlocking FETs and application to monolayer LuIO. Nano Lett. 21, 7631–7636 (2021).
MaaB, H. et al. Spintexture inversion in the giant rashba semiconductor BiTeI. Nat. Commun. 7 (2016).
Ishizaka, K. et al. Giant rashbatype spin splitting in bulk BiTeI. Nat. Mater. 10, 521–526 (2011).
Feng, Y. et al. Rashbalike spin splitting along three momentum directions in trigonal layered PtBi2. Nat. Commun. 10 (2019).
Zeeman, P. On the influence of magnetism on the nature of the light emitted by a substance. The London, Edinburgh, Dublin Philos. Mag. J. Sci. 43, 226–239 (1897).
Haastrup, S. et al. The computational 2d materials database: highthroughput modeling and discovery of atomically thin crystals. 2D Mater. 5, 042002 (2018).
Larsen, P. M., Pandey, M., Strange, M. & Jacobsen, K. W. Definition of a scoring parameter to identify lowdimensional materials components. Phys. Rev. Mater. 3, 034003 (2019).
Ong, S. P. et al. Python materials genomics (pymatgen): A robust, opensource python library for materials analysis. Comput. Mater. Sci. 68, 314–319 (2013).
Tao, L. & Tsymbal, E. Y. Insulatortoconductor transition driven by the rashbazeeman effect. npj Comput. Mater. 6 (2020).
Pekar, S. & Rashba, E. Combined resonance in crystals in inhomogeneous magnetic fields. Sov. Phys.JETP 20, 1295 (1965).
Naka, M. et al. Spin current generation in organic antiferromagnets. Nat. Commun. 10 (2019).
Kabiraj, A., Kumar, M. & Mahapatra, S. Highthroughput discovery of high curie point twodimensional ferromagnetic materials. npj Comput. Mater. 6 (2020).
Acosta, C. M., Ogoshi, E., Souza, J. A. & Dalpian, G. M. Machine learning study of the magnetic ordering in 2d materials. ACS Appl. Mater. & Interfaces. 14, 9418–9432 (2022).
Kresse, G. & Hafner, J. Ab initio molecular dynamics for liquid metals. Phys. Rev. B 47, 558–561 (1993).
Kresse, G. & Furthmiiller, J. Efficiency of abinitio total energy calculations for metals and semiconductors using a planewave basis set. Comput. Mater. Sci. 6, 15–50 (1996).
Perdew, J. P., Burke, K. & Ernzerhof, M. Generalized gradient approximation made simple. Phys. Rev. Lett. 77, 3865–3868 (1996).
Bahn, S. R. & Jacobsen, K. W. An objectoriented scripting interface to a legacy electronic structure code. Comput. Sci. Eng. 4, 56–66 (2002).
Larsen, A. H. et al. The atomic simulation environment—a python library for working with atoms. J. Physics: Condens. Matter 29, 273002 (2017).
Jain, A. et al. Commentary: The materials project: A materials genome approach to accelerating materials innovation. APL Mater. 1, 011002 (2013).
Pedregosa, F. et al. Scikitlearn: Machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).
Talirz, L. et al. Materials cloud, a platform for open computational science. Sci. Data 7 (2020).
Nascimento, G. M., Ogoshi, E., Fazzio, A., Acosta, C. M. & Dalpian, G. M. High throughput inverse design and bayesian optimization of functionalities: spin splitting in twodimensional compounds. Mater. Cloud Arch. 2021.224, https://doi.org/10.24435/materialscloud:kr7s (2021).
Nascimento, G. M., Ogoshi, E., Fazzio, A., Acosta, C. M. & Dalpian, G. M. 2d ss materials. NOMAD https://doi.org/10.17172/NOMAD/2021.09.202 (2021).
Zimmermann, N. E. & Jain, A. Local structure order parameters and site fingerprints for quantification of coordination environment and crystal structure similarity. RSC Adv. 10, 6063–6081 (2020).
McInnes, L., Healy, J. & Melville, J. Umap: Uniform manifold approximation and projection for dimension reduction. arXivpreprint arXiv:1802.03426 (2018).
Ester, M. et al. A densitybased algorithm for discovering clusters in large spatial databases with noise. Kdd 96, 226–231 (1996).
Schaibley, J. R. et al. Valleytronics in 2d materials. Nat. Rev. Mater. 1, 1–15 (2016).
Xiao, D., Liu, G.B., Feng, W., Xu, X. & Yao, W. Coupled spin and valley physics in monolayers of mos 2 and other groupvi dichalcogenides. Phys. review letters 108, 196802 (2012).
Jones, A. M. et al. Optical generation of excitonic valley coherence in monolayer wse 2. Nat. nanotechnology 8, 634–638 (2013).
Aivazian, G. et al. Magnetic control of valley pseudospin in monolayer wse 2. Nat. Phys. 11, 148–152 (2015).
Srivastava, A. et al. Valley zeeman effect in elementary optical excitations of monolayer wse 2. Nat. Phys. 11, 141–147 (2015).
MacNeill, D. et al. Breaking of valley degeneracy by magnetic field in monolayer mose 2. Phys. review letters 114, 037401 (2015).
Li, Y. et al. Valley splitting and polarization by the zeeman effect in monolayer mose2. Phys. review letters 113, 266804 (2014).
Peng, R., Ma, Y., Zhang, S., Huang, B. & Dai, Y. Valley polarization in janus singlelayer mosse via magnetic doping. The journal physical chemistry letters 9, 3612–3617 (2018).
Hu, T. et al. Intrinsic and anisotropic rashba spin splitting in janus transitionmetal dichalcogenide monolayers. Phys. Rev. B 97, 235404 (2018).
Cheng, Y., Zhu, Z., Tahir, M. & Schwingenschlogl, U. Spinorbitinduced spin splittings in polar transition metal dichalcogenide monolayers. EPL (Europhysics Lett. 102, 57001 (2013).
Di Sante, D., Stroppa, A., Barone, P., Whangbo, M.H. & Picozzi, S. Emergence of ferroelectricity and spinvalley properties in twodimensional honeycomb binary compounds. Phys. Rev. B 91, 161401 (2015).
Ma, Y., Dai, Y., Wei, W., Li, X. & Huang, B. Emergence of electric polarity in bitex (x= br and i) monolayers and the giant rashba spin splitting. Phys. Chem. Chem. Phys. 16, 17603–17609 (2014).
Zhuang, H. L. et al. Rashba effect in singlelayer antimony telluroiodide sbtei. Phys. Rev. B 92, 115302 (2015).
RiisJensen, A. C., Deilmann, T., Olsen, T. & Thygesen, K. S. Classifying the electronic and optical properties of janus monolayers. ACS nano 13, 13354–13364 (2019).
Tang, W., Sanville, E. & Henkelman, G. A gridbased bader analysis algorithm without lattice bias. J. Physics: Condens. Matter 21, 084204 (2009).
Acknowledgements
The authors thank Brazilian agencies FAPESP (2017/023172, 2018/116410, 2018/118567 and 2019/041762) and CNPq for financial support. High throughput calculations were performed using the Santos Dumont supercomputer at LNCC.
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G.M.N. performed the ab initio calculations, wrote the code that calculated the spin splittings and generated band structures and datasets. E.O. performed the inverse optimization process, with the Bayes analysis and organized the MongoDB database. C.M.A. and G.M.D. conceived the idea. A.F., C.M.A. and G.M.D. supervised the work. All authors helped writing and reviewing the manuscript.
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Nascimento, G.M., Ogoshi, E., Fazzio, A. et al. Highthroughput inverse design and Bayesian optimization of functionalities: spin splitting in twodimensional compounds. Sci Data 9, 195 (2022). https://doi.org/10.1038/s41597022012928
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DOI: https://doi.org/10.1038/s41597022012928