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Computational chemistry methods with an optimal balance between predictive accuracy and computational cost hold major promise for accelerating the discovery of new molecules and materials. We at Nature Communications are eager to continue our engagement in this exciting and rapidly evolving field.
The diffusion of fluids in complex nanoporous geometries represents a challenge for modelling approaches. Here, the authors describe the macroscopic diffusivity of a simple fluid in disordered nanoporous materials by bridging microscopic and mesoscopic dynamics with parameters obtained from simple physical laws.
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.
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.
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.
The origin of the covalent H–H bond is understood to be driven by kinetic energy lowering. Here the authors show this is not the case for bonds between heavier elements likely due to the presence of core electrons, and that constructive quantum interference instead drives bond formation.
Accurate interpretation of molecular vibrational spectroscopic signals is key to understand chemical processes. Here the authors introduce a new computational approach to represent vibrational modes in terms of nuclear densities that captures anharmonic effects in protonated glycine.
Protein-ligand unbinding processes are out of reach for atomistic simulations due to time-scale involved. Here the authors demonstrate an approach relying on dissipation-corrected targeted molecular dynamics that enables to provide binding and unbinding rates with a speed-up of several orders of magnitude.
The unexpectedly long-ranged interface stress observed in recent delamination experiments is yet to be clarified. Here, the authors develop an analytical approach to show the wavelike atomic deformation as the origin for the observed ultra long-range stress in delamination of graphene from various substrates.
The electronic structure of benzene has been a test bed for competing theories along the years. Here the authors show via quantum chemistry calculations that the wavefunction of benzene can be partitioned into tiles which show that the two electron spins exhibit staggered Kekulé structures.
Despite the fact that layered materials are often employed as lubricants, many of the underlying mechanisms are still controversial. Here the authors present a fundamental model for describing friction on atomically thin sheets that reveals the dynamics of strengthening and layer-number dependence of the friction.
The determination of the speciation of ions and molecules in supercritical aqueous fluids under pressure is key to understanding their mass transport in the Earth’s interior. Here the authors present a strategy based on ab-initio molecular dynamics to determine the speciation of carbonates in aqueous fluids.
The inverse DFT problem of mapping the ground-state density to its exchange correlation potential has been numerically challenging so far. Here, the authors propose an approach for an accurate solution to the inverse DFT problem, enabling the evaluation of exact exchange and correlation potential from an ab initio density.
Quantum algorithms for simulating chemical systems are limited because of the a priori assumption about the form of the target wavefunction. Here the authors present a new variational hybrid quantum-classical algorithm which allows the system being simulated to determine its own optimal state.
Rational design of metal organic frameworks (MOFs) with shape-memory nanopores is a formidable challenge. Here the authors use an accurate theoretical approach to design thermo-responsive MOFs based on a balance of van der Waals and entropy contributions.
The description of van der Waals interactions should often account for coupling with pervasive electric fields, but this effect has been omitted in atomistic simulations. Here, the authors develop a model to study the effects of external charge on long-range van der Waals interactions.
Despite being essential to organic chemistry, the curly arrow notation of reaction mechanisms has been treated with suspicion due to its unclear connection with quantum mechanics. Here, the authors show that analysis of wavefunction 'tiles' along a reaction coordinate reveals the electron motion depicted by curly arrows.
The electron affinity of liquid water is a fundamental property which has not yet been accurately measured. Here, the authors predict this property by coupling path-integral molecular dynamics with ab initio potentials and electronic structure calculations, revisiting several estimates used in the literature.
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.
Colloidal CdSe nanocrystals hold great promise in applications due to their tunable optical spectrum. Using hybrid time-dependent density functional theory, the authors show that colloidal CdSe nanocrystals are inherently defective with a low energy spectrum dominated by dark, surface-associated excitations.
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.
Computational approaches to predict water’s role in host-ligand binding attract a great deal of attention. Here the authors use a metadynamics enhanced sampling method and machine learning to compute binding energies for host-guest systems from the SAMPL5 challenge and provide details of water structural changes.
Structural lubricity is one of the most interesting concepts in modern tribology, which promises to achieve ultra-low friction over a wide range of length-scales. Here the authors highlight novel research lines in this area achievable by combining theoretical and experimental efforts on hard two-dimensional materials and soft colloidal and cold ion systems.
Salts in water at extreme conditions play a fundamental role in determining the properties of the Earthʼs mantle constituents. Here the authors shed light on ion-water and ion-ion interactions for NaCl dissolved in water at conditions relevant to the Earthʼs upper mantle by molecular dynamics simulations.
Graphene oxide holds great promise for water purification applications, though its chemical reactivity in water is yet to be clarified. Here the authors show by first principles molecular dynamics that graphene oxide structures with correlated functional groups and regions of pristine graphene are the most stable in liquid water.
The accumulation of negative charge at hydrophobic–water interfaces has been a source of debate for a long time. Here the authors use ab initio calculations to show that the charge accumulation at air–water and oil–water interfaces is caused by subtle charge transfer processes.
Supramolecular catalytic assemblies attract enormous interest due to their activity that rivals natural enzymes. Using ab initio molecular dynamics, the authors show that a gold catalyst in a Ga4L612- nanocage, while impeded by reorganization energy, is accelerated by hosting a catalytic water molecule.
Soft porous crystals hold big promise as functional nanoporous materials due to their stimuli responsive flexibility. Here, molecular dynamics simulations reveal a new type of spatial disorder in mesoscale crystals that helps to understand the size-dependency of their phase transition behavior.
The properties of water under confinement are significantly altered with respect to the bulk phase. Here the authors use infrared spectroscopy and many-body molecular dynamics simulations to show the structure and dynamics of confined water as a function of relative humidity within a metal-organic framework.
Criegee intermediates have received much attention in atmospheric chemistry because of their importance in ozonolysis mechanisms. Here, using quantum mechanical kinetics, the authors reveal an unexpectedly fast mechanistic pathway for unimolecular reactions of large stabilized Criegee intermediates.
The origins of the different charging processes observed in graphene and boron-nitride nanofluidics are still under debate. Here, using ab-initio molecular dynamics, the authors show that hydroxide species in water exhibits physisorption on graphene but strong chemisorption on boron-nitride.
Surface roughness evolution with time is key for tribological applications. Here, the authors demonstrate by numerical simulations the evolution of sliding surfaces into self-affine morphologies during adhesive wear due to the formation of a third body trapped at the interface.
The mechanism underlying the superlubricity of tetrahedral amorphous carbon coatings lubricated with organic friction modifiers is still under debate. Here the authors combine experiments and simulations to reveal that superlubricious layers form due the mechano-chemical decomposition of friction modifiers.
Manipulation of the photochemistry of molecules is traditionally achieved through synthetic chemical modifications. Here the authors use computational photochemistry to show how to control azobenzene photoisomerization through hybrid light–molecule states (polaritons).
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.
Machine learning potentials do not account for long-range charge transfer. Here the authors introduce a fourth-generation high-dimensional neural network potential including non-local information of charge populations that is able to provide forces, charges and energies in excellent agreement with DFT data.
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.
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.
Atomistic simulations of phosphorus represent a challenge due to the element’s highly diverse allotropic structures. Here the authors propose a general-purpose machine-learning force field for elemental phosphorus, which can describe a broad range of relevant bulk and nanostructured allotropes.
Machine learning models insufficient for certain screening tasks can still provide valuable predictions in specific sub-domains of the considered materials. Here, the authors introduce a diagnostic tool to detect regions of low expected model error as demonstrated for the case of transparent conducting oxides.
At present there are databases with over 500,000 predicted or synthesized MOF structures, yet a method to establish whether a new material adds new information does not exist. Here the authors propose a machine-learning based approach to quantify the structural and chemical diversity in common MOF databases.
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.
Increasing the non-locality of the exchange and correlation functional in DFT theory comes at a steep increase in computational cost. Here, the authors develop NeuralXC, a supervised machine learning approach to generate density functionals close to coupled-cluster level of accuracy yet computationally efficient.
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.
Exploring nucleation processes of gallium by molecular simulation is extremely challenging due to its structural complexity. Here the authors demonstrate a neural network potential trained on a multithermal–multibaric DFT data for the study of the phase diagram of gallium in a wide temperature and pressure range.
Despite the importance of neural-network quantum states, representing fermionic matter is yet to be fully achieved. Here the authors map fermionic degrees of freedom to spin ones and use neural-networks to perform electronic structure calculations on model diatomic molecules to achieve chemical accuracy.
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.
Machine learning models can accurately predict atomistic chemical properties but do not provide access to the molecular electronic structure. Here the authors use a deep learning approach to predict the quantum mechanical wavefunction at high efficiency from which other ground-state properties can be derived.
Computational modelling of chemical systems requires a balance between accuracy and computational cost. Here the authors use transfer learning to develop a general purpose neural network potential that approaches quantum-chemical accuracy for reaction thermochemistry, isomerization, and drug-like molecular torsions.
Understanding local dynamical processes in materials is challenging due to the complexity of the local atomic environments. Here the authors propose a graph dynamical networks approach that is shown to learn the atomic scale dynamics in arbitrary phases and environments from molecular dynamics simulations.
Traditional machine learning potentials suffer from poor transferability to unknown structures. Here the authors present an approach to improve the transferability of machine-learning potentials by including information on the physical nature of interatomic bonding.
A computationally efficient description of ice-water systems at the mesoscopic scale is challenging due to system size and timescale limitations. Here the authors develop a machine-learned coarse-grained water model to elucidate the ice nucleation process much more efficiently than previous models.
Simultaneous accurate and efficient prediction of molecular properties relies on combined quantum mechanics and machine learning approaches. Here the authors develop a flexible machine-learning force-field with high-level accuracy for molecular dynamics simulations.
Machine learning allows electronic structure calculations to access larger system sizes and, in dynamical simulations, longer time scales. Here, the authors perform such a simulation using a machine-learned density functional that avoids direct solution of the Kohn-Sham equations.
Identifying optimal materials in multiobjective optimization problems represents a challenge for new materials design approaches. Here the authors develop an active-learning algorithm to optimize the Pareto-optimal solutions successfully applied to the in silico polymer design for a dispersant-based application.
Energy–structure–function (ESF) maps can facilitate functional materials discovery. Here the authors provide a protocol for the digital navigation of ESF maps, as demonstrated for hydrogen-bonded organic frameworks constructed from triptycene- and spiro-biphenyl-based molecular building block.
Predicting the structure of unknown materials’ compositions represents a challenge for high-throughput computational approaches. Here the authors introduce a new stoichiometry-based machine learning approach for predicting the properties of inorganic materials from their elemental compositions.
Machine learning driven research holds big promise towards accelerating materials’ discovery. Here the authors demonstrate CAMEO, which integrates active learning Bayesian optimization with practical experiments execution, for the discovery of new phase- change materials using X-ray diffraction experiments.
Over the last decade, we have witnessed the emergence of ever more machine learning applications in all aspects of the chemical sciences. Here, we highlight specific achievements of machine learning models in the field of computational chemistry by considering selected studies of electronic structure, interatomic potentials, and chemical compound space in chronological order.
Discovering chemicals with desired attributes is a long and painstaking process. Curated datasets containing reliable quantum-mechanical properties for millions of molecules are becoming increasingly available. The development of novel machine learning tools to obtain chemical knowledge from these datasets has the potential to revolutionize the process of chemical discovery. Here, I comment on recent breakthroughs in this emerging field and discuss the challenges for the years to come.
Photon-induced charge separation phenomena are at the heart of light-harvesting applications but challenging to be described by quantum mechanical models. Here the authors illustrate the potential of machine-learning approaches towards understanding the fundamental processes governing electronic excitations.
The stability of perovskite solar cells can be improved by using hybrid-organic perovskites capping-layers atop the active material. Here the authors use machine learning to optimize capping layers by monitoring time to degradation of differently capped lead-halide perovskite solar cells.
Symbolic regression holds big promise for guiding materials design, yet its application in materials science is still limited. Here the authors use symbolic regression to introduce an activity descriptor predicting new oxide perovskites with improved oxygen evolution activity as corroborated by experimental validation.
Crystallization is a challenging process to model quantitatively. Here the authors use machine learning and atomistic simulations together to uncover the role of the liquid structure on the process of crystallization and derive a predictive kinetic model of crystal growth.
Knowing compositional motifs of nanoparticle catalysts in operando conditions is crucial towards understanding their catalytic behavior. Here, the authors develop a physics-driven machine learning approach to predict adsorption sites for a CO molecule over platinum nanoparticles in a multitude of coordination environments.
Machine-learning approaches based on DFT computations can greatly enhance materials discovery. Here the authors leverage existing large DFT-computational data sets and experimental observations by deep transfer learning to predict the formation energy of materials from their elemental compositions with high accuracy.
Predictions of new solid-state Li-ion conductors are challenging due to the diverse chemistries and compositions involved. Here the authors combine unsupervised learning techniques and molecular dynamics simulations to discover new compounds with high Li-ion conductivity.
Predicting the synthesizability of inorganic materials is challenging due to the many variables and complex phenomena involved in synthesis. Here, the authors combine material stabilities with a historical analysis of experimental discovery timelines as a temporal network to predict the synthesizability of hypothetical materials.
While the conversion of greenhouse CO2 to chemical fuels offers a promising renewable energy technology, there is a dire need for new materials. Here, authors report the largest CO2 photocathode search using a first-principles approach to identify both known and unreported candidate photocatalysts.
Solid-state nuclear magnetic resonance combined with quantum chemical shift predictions is limited by high computational cost. Here, the authors use machine learning based on local atomic environments to predict experimental chemical shifts in molecular solids with accuracy similar to density functional theory.
Crystal stability prediction is of paramount importance for novel material discovery, with theoretical approaches alternative to expensive standard schemes highly desired. Here the authors develop a deep learning approach which, just using two descriptors, provides crystalline formation energies with very high accuracy.