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RESEARCH PAPER · The Journal of Chemical Physics · 2021

Perspective on integrating machine learning into computational chemistry and materials science

Julia Westermayr, Michael Gastegger, Kristof T. Schütt, Reinhard J. Maurer

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Perspective on integrating machine learning into computational chemistry and materials scienceOriginalPhysically inspired deep learning of molecular excitations and photoemission spectra65Machine Learning in Chemical Product Engineering: The State of the Art and a Guide for Newcomers61Artificial intelligence: A powerful paradigm for scientific research1702Machine Learning with Multilevel Descriptors for Screening of Inorganic Nonlinear Optical Crystals35Cross-property deep transfer learning framework for enhanced predictive analytics on small materials data198A data-driven approach to approximate the correlation functions in cluster variation method5Charge-Transfer Landscape Manifesting the Structure–Rate Relationship in the Condensed Phase Via Machine Learning23Chemistry of Functionalized Reactive Organic Intermediates in the Earth’s Atmosphere: Impact, Challenges, and Progress18Artificial intelligence-enhanced quantum chemical method with broad applicability143Assessment of DFT approaches in noble gas clathrate-like clusters: stability and thermodynamics10Predicting trajectory behaviour via machine-learned invariant manifolds4Beyond structural insight: a deep neural network for the prediction of Pt L 2/3 -edge X-ray absorption spectra12Bibliography0Catalytic properties of the ferryl ion in the solid state: a computational review2Data-driven chemistry0ICHOR: a modern pipeline for producing Gaussian process regression models for atomistic simulations16Understanding and controlling the formation of surface anion vacancies for catalytic applications12Permutationally invariant polynomial regression for energies and gradients, using reverse differentiation, achieves orders of magnitude speed-up with high precision compared to other machine learning methods50Dimensionality reduction in machine learning for nonadiabatic molecular dynamics: Effectiveness of elemental sublattices in lead halide perovskites14Arbitrary image reinflation: A deep learning technique for recovering 3D photoproduct distributions from a single 2D projection11Inverse design of 3d molecular structures with conditional generative neural networks188A Benchmark Protocol for DFT Approaches and Data-Driven Models for Halide-Water Clusters8Kinetic Monte Carlo simulations for heterogeneous catalysis: Fundamentals, current status, and challenges118Accurate, affordable, and generalizable machine learning simulations of transition metal x-ray absorption spectra using the XANESNET deep neural network43NQCDynamics.jl: A Julia package for nonadiabatic quantum classical molecular dynamics in the condensed phase20Toward Chemical Accuracy in Predicting Enthalpies of Formation with General-Purpose Data-Driven Methods53Modelling ultrafast dynamics at a conical intersection with regularized diabatic states: An approach based on multiplicative neural networks8Modeling Radiation Damage in Materials Relevant for Exploration and Settlement on the Moon2The MD17 datasets from the perspective of datasets for gas-phase “small” molecule potentials34Graph representation-based machine learning framework for predicting electronic band structures of quantum-confined nanostructures21Ab initio machine learning of phase space averages18Exploiting Ligand Additivity for Transferable Machine Learning of Multireference Character across Known Transition Metal Complex Ligands18Equivariant analytical mapping of first principles Hamiltonians to accurate and transferable materials models53UV–Visible Absorption Spectra of Solvated Molecules by Quantum Chemical Machine Learning22Molecular dipole moment learning via rotationally equivariant derivative kernels in molecular-orbital-based machine learning7Stability and distortion of fcc LaH10 with path-integral molecular dynamics7Learning excited-state properties2Learning from multiple quantum chemical methods: Δ-learning, transfer learning, co-kriging, and beyond16Exact constraints and appropriate norms in machine-learned exchange-correlation functionals22Towards fully ab initio simulation of atmospheric aerosol nucleation31Ligand additivity relationships enable efficient exploration of transition metal chemical space22The fourth-order expansion of the exchange hole and neural networks to construct exchange–correlation functionals4NeuralNEB—neural networks can find reaction paths fast38Reliable emulation of complex functionals by active learning with error control20Exploiting Machine Learning in Multiscale Modelling of Materials12Electronic-Structure Properties from Atom-Centered Predictions of the Electron Density25Which Deep Learning Framework Should I Use: A Comparative Study For Deep Regression Modeling3Machine Learning Accelerated Nonadiabatic Dynamics at Metal Surfaces0Terahertz Circular Dichroism Spectroscopy of Molecular Assemblies and Nanostructures65Recent advances in machine learning for electronic excited state molecular dynamics simulations2Transition1x - a dataset for building generalizable reactive machine learning potentials123An equivariant generative framework for molecular graph-structure Co-design21Classical and quantum machine learning applications in spintronics9Computational investigations of stable multiple-cage-occupancy He clathrate-like hydrostructures2Machine learning based implicit solvent model for aqueous-solution alanine dipeptide molecular dynamics simulations32Prediction of sodium binding energy on 2D VS 2 via machine learning: a robust accompanying method to ab initio random structure searching3Self-Supervised Representation Learning of Microstructures from Electron Microscopy for Property Prediction0Perspectives and Current Trends on Hybrid Nanocomposite Materials for Photocatalytic Applications11Designing sulfonated polyimide-based fuel cell polymer electrolyte membranes using machine learning approaches18PESPIP: Software to fit complex molecular and many-body potential energy surfaces with permutationally invariant polynomials42Efficient implementation and performance analysis of the independent electron surface hopping method for dynamics at metal surfaces19Benchmark of general-purpose machine learning-based quantum mechanical method AIQM1 on reaction barrier heights24Unicorns, Rhinoceroses and Chemical Bonds3A non-self-consistent tight-binding electronic structure potential in a polarized double-ζ basis set for all spd-block elements up to Z = 8625Active sampling for neural network potentials: Accelerated simulations of shear-induced deformation in Cu–Ni multilayers5Semiclassical Dynamics on Machine-Learned Coupled Multireference Potential Energy Surfaces: Application to the Photodissociation of the Simplest Criegee Intermediate6How to validate machine-learned interatomic potentials130Improving Accuracy and Transferability of Machine Learning Chemical Activation Energies by Adding Electronic Structure Information10Integrating Machine Learning in the Coarse-Grained Molecular Simulation of Polymers39Simulations in the era of exascale computing66SchNetPack 2.0: A neural network toolbox for atomistic machine learning90Accelerating the design of compositionally complex materials via physics-informed artificial intelligence110Multifidelity Neural Network Formulations for Prediction of Reactive Molecular Potential Energy Surfaces5Data-driven-aided strategies in battery lifecycle management: Prediction, monitoring, and optimization87Hybrid Machine Learning-Enabled Potential Energy Model for Atomistic Simulation of Lithium Intercalation into Graphite from Plating to Overlithiation10Predicting model of I–V characteristics of quantum-confined GaAs nanotube: a machine learning and DFT-based combined framework7Structure and thermodynamics of liquid ruthenium and ruthenium-based alloys from ab initio and classical molecular dynamics with embedded atom model potentials1A machine learning potential for simulating infrared spectra of nanosilicate clusters17Lifelong Machine Learning Potentials46Coupled cluster theory on modern heterogeneous supercomputers4Structural transformations in single-crystalline AgPd nanoalloys from multiscale deep potential molecular dynamics6Machine-learned dynamic disorder of electron transfer coupling5Data-driven machine learning for alloy research: Recent applications and prospects21Corrigendum: Coupled cluster theory on modern heterogeneous supercomputers0Evolution of artificial intelligence for application in contemporary materials science27Automatic Clustering of Excited-State Trajectories: Application to Photoexcited Dynamics14Machine learning electronic structure methods based on the one-electron reduced density matrix50Exploring exotic configurations with anomalous features with deep learning: Application of classical and quantum-classical hybrid anomaly detection1Machine learning and DFT-based combined framework for predicting transmission spectra of quantum-confined bio-molecular nanotube4Decoding energy decomposition analysis: Machine‐learned Insights on the impact of the density functional on the bonding analysis9Insight into the crystal structure analysis, vibrational studies, reactivities (MESP, HOMO-LUMO, NBO), and the anticancer activities of ruthenium diazide [Ru(POP)(PPh3)(N3)2] complex by molecular docking approach5A perspective on data-driven screening and discovery of polymer membranes for gas separation, from the molecular structure to the industrial performance11Advances of machine learning in materials science: Ideas and techniques105On the use of Machine Learning modeling tools in chemical process and product engineering0Application and Challenges of Machine Learning Techniques in Mining Engineering and Material Science4Atoms, dimers, and nanoparticles from orbital-free density-potential-functional theory5Training machine learning potentials for reactive systems: A Colab tutorial on basic models9AI in computational chemistry through the lens of a decade-long journey30Artificial intelligence and data-driven computational simulation0Deep Mind 21 functional does not extrapolate to transition metal chemistry25Dft Study of Mechanistic Routes in the Electrochemical Reaction between Nitrostyrene and Benzaldehyde0Machine learning for hypothesis generation in biology and medicine: exploring the latent space of neuroscience and developmental bioelectricity17Partial density of states representation for accurate deep neural network predictions of X-ray spectra7CoRe optimizer: an all-in-one solution for machine learning10ChatGPT in the Material Design: Selected Case Studies to Assess the Potential of ChatGPT36Machine learning advancements in organic synthesis: A focused exploration of artificial intelligence applications in chemistry93Mixtures Recomposition by Neural Nets: A Multidisciplinary Overview14Theory of moment propagation for quantum dynamics in single-particle description1Optimized multifidelity machine learning for quantum chemistry16Structural Systems Powered by AI and Machine Learning Technologies3Analyzing Multimodal Probability Measures with Autoencoders13Translational eigenstates of He@C60 from four-dimensional ab initio potential energy surfaces interpolated using Gaussian process regression3X-ray-induced atomic transitions via machine learning: A computational investigation0The Future of Material Scientists in an Age of Artificial Intelligence76Revisiting Stereoselective Propene Polymerization Mechanisms: Insights through the Activation Strain Model12An Unsupervised Machine Learning Approach for the Automatic Construction of Local Chemical Descriptors8Artificial Intelligence (AI) and Machine Learning (ML) Technology-Driven Structural Systems5Relay Catalysis of Fe and Co with Multi‐Active Sites for Specialized Division of Labor in Electrocatalytic Nitrate Reduction Reaction177Revealing the reconstruction mechanism of AgPd nanoalloys under fluorination based on a multiscale deep learning potential7Plugin-based interoperability and ecosystem management for the MolSSI Driver Interface Project3Learning Molecular Mixture Property Using Chemistry-Aware Graph Neural Network19Integrated workflows and interfaces for data-driven semi-empirical electronic structure calculations5Whispering gallery mode sensing through the lens of quantum optics, artificial intelligence, and nanoscale catalysis11Modelling ligand exchange in metal complexes with machine learning potentials12Quantum chemical package Jaguar: A survey of recent developments and unique features62Decoherence ensures convergence of non-adiabatic molecular dynamics with number of states25Prediction rigidities for data-driven chemistry10A mechanistic study of the electrochemical reaction between nitrostyrene and benzaldehyde: DFT calculations on all possible routes and intermediates6Predicting Model for Device Density of States of Quantum-Confined SiC Nanotube with Magnetic Dopant: An Integrated Approach Utilizing Machine Learning and Density Functional Theory17Assessing non-nested configurations of multifidelity machine learning for quantum-chemical properties3Structure, Properties, and Applications of Silica Nanoparticles: Recent Theoretical Modeling Advances, Challenges, and Future Directions32Artificial Neural Network–Particle Swarm Optimization Approach for Predictive Modeling of Kovats Retention Index in Essential Oils0AI Tools for Teaching-Learning Chemistry1Data Generation for Machine Learning Interatomic Potentials and Beyond176Ligand Many-Body Expansion as a General Approach for Accelerating Transition Metal Complex Discovery6Combining Transfer Learning and Representation Learning to Improve Predictive Analytics on Small Materials Data2Hybrid-LLM-GNN: integrating large language models and graph neural networks for enhanced materials property prediction21Machine-Learning Electron Dynamics with Moment Propagation Theory: Application to Optical Absorption Spectrum Computation Using Real-Time TDDFT7Accelerating materials property discovery in uncharted domains through the integration of high-throughput computation and machine learning2Condensed‐Phase Quantum Chemistry5Machine learning for nonadiabatic molecular dynamics: best practices and recent progress20Predicting pesticide vapour pressures: the power of functional groups0Energy Landscapes in Chemical Reactions and Transport2Simple approach to more efficient density functional theory simulations4Machine learning approach to predict L -edge x-ray absorption spectra of light transition metal ion compounds5Challenges and Future Prospects in the Field of Computational Chemistry1Comprehensive Study on Synthesis, Quantum Chemical Calculations, Molecular Modeling Studies, and Cytotoxic Activities of Metal(II) Schiff Base Complexes10Advanced theoretical modeling methodologies for electrocatalyst design in sustainable energy conversion28Integrating chemical artificial intelligence and cognitive computing for predictive analysis of biological pathways: a case for intrinsically disordered proteins4Prediction of hydration energies of adsorbates at Pt(111) and liquid water interfaces using machine learning5

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  5. Cross-property deep transfer learning framework for enhanced predictive analytics on small materials data

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  48. Machine Learning Accelerated Nonadiabatic Dynamics at Metal Surfaces

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  49. Terahertz Circular Dichroism Spectroscopy of Molecular Assemblies and Nanostructures

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  50. Recent advances in machine learning for electronic excited state molecular dynamics simulations

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