Computational chemistry articles within Nature Communications

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  • Article
    | Open Access

    Computational material design often does not account for temperature effects. The present manuscript combines quantum-mechanics based calculations with a machine-learned correction to establish a unified thermodynamics framework for accurate prediction of high temperature reaction free energies in oxides.

    • Jose Antonio Garrido Torres
    • , Vahe Gharakhanyan
    •  & Alexander Urban

  • Article
    | Open Access

    The most common oxidation state for lanthanides is +3. Here the authors use photoelectron spectroscopy and theoretical calculations to study half-sandwich complexes where a lanthanide center in the oxidation state +1 is bound to an aromatic wheel-like B82- ligand.

    • Wan-Lu Li
    • , Teng-Teng Chen
    •  & Lai-Sheng Wang

  • Article
    | Open Access

    Layered boron compounds attract enormous interest in applications. This work reports first-principles calculations coupled with global optimization to show that the outer boron surface in MgB2 nanosheets undergo disordering and clustering, which is experimentally confirmed in synthesized MgB2 nanosheets.

    • Sichi Li
    • , Harini Gunda
    •  & Brandon C. Wood

  • Article
    | Open Access

    The present manuscript reports a Bayesian deep-learning approach for the automatic, robust classification of polycrystalline systems of both synthetic and experimental origin. The unsupervised analysis of the internal neural-network representations reveals physically understandable patterns.

    • Andreas Leitherer
    • , Angelo Ziletti
    •  & Luca M. Ghiringhelli

  • Article
    | Open Access

    Protonated water species have been the subject of numerous experimental and computational studies. Here the authors provide a nearly complete assignment of the experimental IR spectrum of the H+(H2O)21 water cluster based on high-level wavefunction theory and anharmonic vibrational quasi-degenerate perturbation theory.

    • Jinfeng Liu
    • , Jinrong Yang
    •  & Xiao He

  • Article
    | Open Access

    Experimental determination of new cocrystals remains challenging due to the need of a systematic screening with a large range of coformers. Here the authors develop a flexible deep learning framework based on graph neural network demonstrated to quickly predict the formation of co-crystals.

    • Yuanyuan Jiang
    • , Zongwei Yang
    •  & Xuemei Pu

  • Article
    | Open Access

    Aqueous solutions under nanoscale confinement exhibit interesting physicochemical properties. This work reports evidence on the spontaneous formation of two-dimensional alkali chloride crystalline/non-crystalline nanostructures in dilute aqueous solution under nanoscale confinement by computer simulations.

    • Wenhui Zhao
    • , Yunxiang Sun
    •  & Xiao Cheng Zeng

  • Article
    | Open Access

    Understanding the catalysts’ structure evolution under working conditions is challenging. Here the authors use a multiscale simulation approach and machine learning to study the structures and nucleation of CeO2-supported Pd clusters and single atoms at various catalyst loadings, temperatures, and exposures to CO.

    • Yifan Wang
    • , Jake Kalscheur
    •  & Dionisios G. Vlachos

  • Article
    | Open Access

    Commonly, large π-conjugated systems facilitate low-energy electronic transitions. Here, the authors demonstrate that the relief of excited-state antiaromaticity of the benzene core leads to large Stokes shifts, and allows the construction of emitters covering the entire visible spectrum without the need of extending π-conjugation.

    • Heechan Kim
    • , Woojin Park
    •  & Dongwhan Lee

  • Article
    | Open Access

    Neural Networks are known to perform poorly outside of their training domain. Here the authors propose an inverse sampling strategy to train neural network potentials enabling to drive atomistic systems towards high-likelihood and high-uncertainty configurations without the need for molecular dynamics simulations.

    • Daniel Schwalbe-Koda
    • , Aik Rui Tan
    •  & Rafael Gómez-Bombarelli

  • Article
    | Open Access

    Quantum mechanical calculations of molecular ionized states are computationally quite expensive. This work reports a successful extension of a previous deep-neural networks approach towards transferable neural-network models for predicting multiple properties of open shell anions and cations.

    • Roman Zubatyuk
    • , Justin S. Smith
    •  & Olexandr Isayev

  • Article
    | Open Access

    The concept of delocalization, resonance and aromaticity are commonly discussed within electronic structure frameworks relying on specific wave function expansions. Here the authors propose a redefinition of these concepts from first-principles by investigating saddle points of the all-electron probability density.

    • Leonard Reuter
    •  & Arne Lüchow

  • Article
    | Open Access

    Mechanically flexible single crystals are promising materials for advanced technological applications. Here, the authors study the high pressure response of a plastically flexible coordination polymer and provide indication of an overall disparate mechanical response of bulk flexibility and quasi-hydrostatic compression within the same crystal lattice.

    • Xiaojiao Liu
    • , Adam A. L. Michalchuk
    •  & Colin R. Pulham

  • Article
    | Open Access

    Despite considerable efforts, quantitative prediction of various molecular properties remains a challenge. Here, the authors propose an algebraic graph-assisted bidirectional transformer, which can incorporate massive unlabeled molecular data into molecular representations via a self-supervised learning strategy and assisted with 3D stereochemical information from graphs.

    • Dong Chen
    • , Kaifu Gao
    •  & Feng Pan

  • Article
    | Open Access

    Generating new sensible molecular structures is a key problem in computer aided drug discovery. Here the authors propose a graph-based molecular generative model that outperforms previously proposed graph-based generative models of molecules and performs comparably to several SMILES-based models.

    • Omar Mahmood
    • , Elman Mansimov
    •  & Kyunghyun Cho

  • Article
    | Open Access

    Efficient methods to calculate magnetically induced currents in metallic nanostructures are currently lacking. Here, the authors propose a theoretical method to compute and analyze magnetically induced currents in nanostructures validated for experimentally synthesized gold-based, hydrogen-containing ligand-protected clusters.

    • Omar López-Estrada
    • , Bernardo Zuniga-Gutierrez
    •  & Hannu Häkkinen

  • Article
    | Open Access

    Existing methods for organic semiconductor computational screening are limited by the computational demand of the process, leading to the identification of non-optimal material candidates. Here, the authors report machine learning method to guide the discovery of organic semiconductors.

    • Christian Kunkel
    • , Johannes T. Margraf
    •  & Karsten Reuter

  • Comment
    | Open Access

    Precise knowledge of chemical composition and atomic structure of functional nanosized systems, such as metal clusters stabilized by an organic molecular layer, allows for detailed computational work to investigate structure-property relations. Here, we discuss selected recent examples of computational work that has advanced understanding of how these clusters work in catalysis, how they interact with biological systems, and how they can make self-assembled, macroscopic materials. A growing challenge is to develop effective new simulation methods that take into account the cluster-environment interactions. These new hybrid methods are likely to contain components from electronic structure theory combined with machine learning algorithms for accelerated evaluations of atom-atom interactions.

    • Sami Malola
    •  & Hannu Häkkinen

  • Article
    | Open Access

    Single-atom metal alloys attract considerable interest as alternative metal hydrogenation catalysts. Here the authors combine first-principles calculations with compressed-sensing data-analytics approaches to develop stability and activity’s descriptors for screening single atom alloy catalysts.

    • Zhong-Kang Han
    • , Debalaya Sarker
    •  & Sergey V. Levchenko

  • Article
    | Open Access

    Machine learning algorithms offer new possibilities for automating reaction procedures. The present paper investigates automated reaction’s prediction with Molecular Transformer, the state-of-the-art model for reaction prediction, proposing a new debiased dataset for a realistic assessment of the model’s performance.

    • Dávid Péter Kovács
    • , William McCorkindale
    •  & Alpha A. Lee

  • Article
    | Open Access

    Understanding ice re-crystallization is key to improve the current cryopreservation technologies. Here, the authors bring together experiments and simulations to unravel the atomistic details of the ice re-crystallization inhibition (IRI) activity of poly(vinyl)alcohol—the most potent biomimetic IRI agent.

    • Fabienne Bachtiger
    • , Thomas R. Congdon
    •  & Gabriele C. Sosso

  • Article
    | Open Access

    The accuracy of a machine-learned potential is limited by the quality and diversity of the training dataset. Here the authors propose an active learning approach to automatically construct general purpose machine-learning potentials here demonstrated for the aluminum case.

    • Justin S. Smith
    • , Benjamin Nebgen
    •  & Kipton Barros

  • Article
    | Open Access

    The nature of the bulk hydrated electron has been a challenge for both experiment and theory. Here the authors use a machine-learning model trained on MP2 data to achieve an accurate determination of the structure, diffusion mechanisms, and vibrational spectroscopy of the solvated electron.

    • Jinggang Lan
    • , Venkat Kapil
    •  & Vladimir V. Rybkin

  • Article
    | Open Access

    Complex interatomic interactions and diverse structures make computing the phase diagram of water very challenging. Here, a combination of machine learning and advanced free-energy methods at three levels of hybrid DFT enables the prediction of the phase diagram in close agreement with experiment.

    • Aleks Reinhardt
    •  & Bingqing Cheng

  • Article
    | Open Access

    The inclusion of nuclear quantum effects (NQE) in atomistic simulations of chemical systems is of key importance. Here the authors use machine learned force fields trained on coupled cluster reference data to show the dynamical strengthening of covalent and non-covalent molecular interactions induced by NQE.

    • Huziel E. Sauceda
    • , Valentin Vassilev-Galindo
    •  & Alexandre Tkatchenko

  • Article
    | Open Access

    Semilocal density functionals, while computationally efficient, do not account for non-local correlation. Here, the authors propose a machine-learning approach to DFT that leads to non-local and transferable functionals applicable to non-covalent, ionic and covalent interactions across system of different sizes.

    • Johannes T. Margraf
    •  & Karsten Reuter

  • Article
    | Open Access

    High-level methods to describe van der Waals interactions are limited due to their computational cost. This work introduces a new theoretical approach, that extends the dipolar many-body dispersion formalism to higher-order contributions, demonstrated to be applicable to practically-relevant systems and nano-environments.

    • Martin Stöhr
    • , Mainak Sadhukhan
    •  & Alexandre Tkatchenko

  • Article
    | Open Access

    Computational catalysis would strongly benefit from general descriptors applicable for predicting adsorption energetics. Here the authors propose a machine-learning approach for adsorption energy predictions based on learning the relevant descriptors in a surface atom's density of states as part of the training.

    • Victor Fung
    • , Guoxiang Hu
    •  & Bobby G. Sumpter

  • Article
    | Open Access

    It is commonly accepted that electrolyte alkali metal cations modify the catalytic activity for oxygen evolution reaction. Here the authors challenge this assumption, showing that the activity is actually affected by a change in the electrolyte pH rather than a specific alkali cation.

    • Mikaela Görlin
    • , Joakim Halldin Stenlid
    •  & Oscar Diaz-Morales

  • Article
    | Open Access

    Accurate prediction of solubility represents a challenge for traditional computational approaches due to the complex nature of phenomena involved. Here the authors report a successful approach to solubility prediction in organic solvents and water using combination of machine learning and computational chemistry.

    • Samuel Boobier
    • , David R. J. Hose
    •  & Bao N. Nguyen

  • Article
    | Open Access

    Molecular understanding of water is challenging due to the structural complexity of liquid water and the large number of ice phases. Here the authors use a machine-learning potential trained on liquid water to demonstrate the structural similarity of liquid water and that of 54 real and hypothetical ice phases.

    • Bartomeu Monserrat
    • , Jan Gerit Brandenburg
    •  & Bingqing Cheng

  • Article
    | Open Access

    Development of algorithms to predict reactant and reagents given a target molecule is key to accelerate retrosynthesis approaches. Here the authors demonstrate that applying augmentation techniques to the SMILE representation of target data significantly improves the quality of the reaction predictions.

    • Igor V. Tetko
    • , Pavel Karpov
    •  & Guillaume Godin

  • Article
    | Open Access

    Application of machine-learning approaches to exploring chemical reaction networks is challenging due to need of including open-shell reaction intermediates. Here the authors introduce a density functional theory database of closed and open-shell molecules for machine-learning predictions of reaction energies.

    • Sina Stocker
    • , Gábor Csányi
    •  & Johannes T. Margraf

  • Article
    | Open Access

    Spectroscopic studies of water clusters provide insight into the hydrogen bond structure of water and ice. The authors measure infrared spectra of neutral water octamers using a threshold photoionization technique based on a tunable vacuum-UV free electron laser, identifying two cubic isomers in addition to those previously observed.

    • Gang Li
    • , Yang-Yang Zhang
    •  & Ling Jiang

  • Article
    | Open Access

    High-level ab initio quantum chemical methods carry a high computational burden, thus limiting their applicability. Here, the authors employ machine learning to generate coupled-cluster energies and forces at chemical accuracy for geometry optimization and molecular dynamics from DFT densities.

    • Mihail Bogojeski
    • , Leslie Vogt-Maranto
    •  & Kieron Burke

  • Article
    | Open Access

    Carbyne, a linear sp-hybridized carbon allotrope, is synthetically inaccessible and its properties are extrapolated from those of defined oligomers. Here the authors analyze weak optical bands in two series of oligoynes and reassess the optical and fundamental gap of carbyne to lower values than previously suggested.

    • Johannes Zirzlmeier
    • , Stephen Schrettl
    •  & Holger Frauenrath

  • Article
    | Open Access

    Extracting experimental operations for chemical synthesis from procedures reported in prose is a tedious task. Here the authors develop a deep-learning model based on the transformer architecture to translate experimental procedures from the field of organic chemistry into synthesis actions.

    • Alain C. Vaucher
    • , Federico Zipoli
    •  & Teodoro Laino

  • Article
    | Open Access

    The choice of molecular representations can severely impact the performances of machine-learning methods. Here the authors demonstrate a persistence homology based molecular representation through an active-learning approach for predicting CO2/N2 interaction energies at the density functional theory (DFT) level.

    • Jacob Townsend
    • , Cassie Putman Micucci
    •  & Konstantinos D. Vogiatzis

  • Article
    | Open Access

    Dioxolenium ion intermediates formed from remote positions are hypothesized to direct stereoselective glycosylations. Herein we combine infrared ion spectroscopy, DFT calculations and synthetic work to characterize and study these dioxolenium ions and their role in stereoselective glycosylation reactions.

    • Thomas Hansen
    • , Hidde Elferink
    •  & Thomas J. Boltje

  • Article
    | Open Access

    Bond dissociation enthalpies are key quantities in determining chemical reactivity, their computations with quantum mechanical methods being highly demanding. Here the authors develop a machine learning approach to calculate accurate dissociation enthalpies for organic molecules with sub-second computational cost.

    • Peter C. St. John
    • , Yanfei Guan
    •  & Robert S. Paton

  • Article
    | Open Access

    Metal-ligand δ and φ interactions, though considered weak, may be necessary for fully describing the electronic and geometric structures of certain compounds. Here, in actinide metallacycles, the authors discover two new types of M-L δ and φ back-bonds that contribute substantially to their unusual chemical behavior.

    • Morgan P. Kelley
    • , Ivan A. Popov
    •  & Ping Yang

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