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RESEARCH PAPER · The Journal of Physical Chemistry Letters · 2020

On-the-Fly Active Learning of Interatomic Potentials for Large-Scale Atomistic Simulations

Ryosuke Jinnouchi, Kazutoshi Miwa, Ferenc Karsai, Georg Kresse, Ryoji Asahi

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On-the-Fly Active Learning of Interatomic Potentials for Large-Scale Atomistic SimulationsOriginalMachine learning for metallurgy I. A neural-network potential for Al-Cu87Advancing Physical Chemistry with Machine Learning73Theoretical study on conformational energies of transition metal complexes106Machine Learning in Computational Surface Science and Catalysis: Case Studies on Water and Metal–Oxide Interfaces26Mean-field density matrix decompositions14Ab initio machine learning in chemical compound space4A transferable active-learning strategy for reactive molecular force fields91Perspective on computational reaction prediction using machine learning methods in heterogeneous catalysis74Predicting phosphorescence energies and inferring wavefunction localization with machine learning27Anharmonic Correction to Adsorption Free Energy from DFT-Based MD Using Thermodynamic Integration50Transfer learning for materials informatics using crystal graph convolutional neural network96Automated discovery of a robust interatomic potential for aluminum97Dynamically polarisable force-fields for surface simulations via multi-output classification Neural Networks0Automated Development of Molten Salt Machine Learning Potentials: Application to LiCl48Multibaric sampling for machine learning potential construction4Strategies for the construction of machine-learning potentials for accurate and efficient atomic-scale simulations91α − β phase transition of zirconium predicted by on-the-fly machine-learned force field49Machine learning for metallurgy III: A neural network potential for Al-Mg-Si39Active learning of reactive Bayesian force fields: Application to heterogeneous hydrogen-platinum catalysis dynamics1Accelerating Metadynamics-Based Free-Energy Calculations with Adaptive Machine Learning Potentials45Machine-learning accelerated geometry optimization in molecular simulation61Dynamically Polarizable Force Fields for Surface Simulations via Multi-output Classification Neural Networks13Machine learning potentials for complex aqueous systems made simple224Machine Learning-Based Screening of Highly Stable and Active Ternary Pt Alloys for Oxygen Reduction Reaction18Achievements and Expectations in the Field of Computational Heterogeneous Catalysis in an Innovation Context28Ab Initio Machine Learning in Chemical Compound Space195Gaussian Process Regression for Materials and Molecules1221Physics-Based Feature Makes Machine Learning Cognizing Crystal Properties Simple4Physics-based, neural network force fields for reactive molecular dynamics: Investigation of carbene formation from [EMIM+][OAc−]12Thermal transport and phase transitions of zirconia by on-the-fly machine-learned interatomic potentials154Data-efficient iterative training of Gaussian approximation potentials: Application to surface structure determination of rutile IrO2 and RuO235Accurate Simulations of the Reaction of H2on a Curved Pt Crystal through Machine Learning14Molecular dynamics simulations of proton conducting media containing phosphoric acid13Structure and registry of the silica bilayer film on Ru(0001) as viewed by LEED and DFT3Permutationally invariant polynomial regression for energies and gradients, using reverse differentiation, achieves orders of magnitude speed-up with high precision compared to other machine learning methods50Recent Advances in First-Principles Based Molecular Dynamics88On the integration of molecular dynamics, data science, and experiments for studying solvent effects on catalysis16Phase transitions of zirconia: Machine-learned force fields beyond density functional theory62Machine Learning and Optoelectronic Materials Discovery: A Growing Synergy26Experimental Observations versus First‐Principles Calculations for Ni–Mn–Ga Ferromagnetic Shape Memory Alloys: A Review17Materials Data toward Machine Learning: Advances and Challenges62Improving the Accuracy of Atomistic Simulations of the Electrochemical Interface135Elucidation of Cu–Zn Surface Alloying on Cu(997) by Machine-Learning Molecular Dynamics9Facilitating ab initio configurational sampling of multicomponent solids using an on-lattice neural network model and active learning12Extending machine learning beyond interatomic potentials for predicting molecular properties167Active learning of reactive Bayesian force fields applied to heterogeneous catalysis dynamics of H/Pt146Hard antiphase domain boundaries in strontium titanate unravelled using machine-learned force fields9Constructing machine learning potentials with active learning10Producing chemically accurate atomic Gaussian process regression models by active learning for molecular simulation15Advances and challenges in multiscale characterizations and analyses for battery materials10A generalizable, uncertainty-aware neural network potential for GeSbTe with Monte Carlo dropout3Conical Intersection in Chemiluminescence of Cyclic Peroxides17Adaptive regularized Gaussian process regression for application in the context of hydrogen adsorption on graphene sheets1Deep coarse-grained potentials via relative entropy minimization49Accelerating explicit solvent models of heterogeneous catalysts with machine learning interatomic potentials51Allegro-Legato: Scalable, Fast, and Robust Neural-Network Quantum Molecular Dynamics via Sharpness-Aware Minimization15An Introduction to Machine Learning in Molecular Sciences0Efficient, Accurate, and Transferable Machine Learning Potentials: Application to Dislocations and Cracks in Iron0Systematic Collapse of Transfer Learning in Small-Data Materials Regimes: Why Fine-Tuning Fails0Adsorption at Nanoconfined Solid–Water Interfaces57A Survey on Active Learning: State-of-the-Art, Practical Challenges and Research Directions175Counterintuitive Oxidation of Alcohols at Air–Water Interfaces31Restricted multicanonical sampling for machine learning potential construction6Molecular Views on Mechanisms of Brønsted Acid-Catalyzed Reactions in Zeolites244Defect modeling and control in structurally and compositionally complex materials35Proton Transport in Perfluorinated Ionomer Simulated by Machine-Learned Interatomic Potential25Challenges for fluoride superionic conductors: fundamentals, design, and applications22Anharmonic Correction to Free Energy Barriers from DFT-Based Molecular Dynamics Using Constrained Thermodynamic Integration11Site-Averaged Ab Initio Kinetics: Importance Learning for Multistep Reactions on Amorphous Supports9Neural-network-backed effective harmonic potential study of the ambient pressure phases of hafnia5Challenges in modelling dynamic processes in realistic nanostructured materials at operating conditions27Computing Surface Reaction Rates by Adaptive Multilevel Splitting Combined with Machine Learning and Ab Initio Molecular Dynamics15Evaluation of Machine Learning Interatomic Potentials for Gold Nanoparticles—Transferability towards Bulk13Challenges for ab initio molecular dynamics simulations of electrochemical interfaces38Machine-learning driven global optimization of surface adsorbate geometries74Machine-learned acceleration for molecular dynamics in CASTEP26On-the-fly machine learning force field study of liquid-Al/α-Al2O3 interface19Melting of atomic materials under high pressures using computer simulations2Creation of crystal structure reproducing X-ray diffraction pattern without using database20Operando Modeling of Zeolite-Catalyzed Reactions Using First-Principles Molecular Dynamics Simulations80Staged Training of Machine-Learning Potentials from Small to Large Surface Unit Cells: Efficient Global Structure Determination of the RuO2(100)-c(2 × 2) Reconstruction and (410) Vicinal23Structural Dynamics Descriptors for Metal Halide Perovskites38Molecular dynamics simulations using machine learning potential for a-Si:H/c-Si interface: Effects of oxygen and hydrogen on interfacial defect states3Machine-learning surrogate model for accelerating the search of stable ternary alloys5Applications and Advances in Machine Learning Force Fields522023 Roadmap on molecular modelling of electrochemical energy materials40Molecular dynamics-driven global potential energy surfaces: Application to the AlF dimer6Transferable Force Field for Gallium Nitride Crystal Growth from the Melt Using On-The-Fly Active Learning8Stress and heat flux via automatic differentiation17Accurate melting point prediction through autonomous physics-informed learning11Phase Transitions, Dielectric Response, and Nonlinear Optical Properties of Aziridinium Lead Halide Perovskites48Machine-learned interatomic potentials: Recent developments and prospective applications41Low-Cost Vibrational Free Energies in Solid Solutions with Machine Learning Force Fields11High accuracy uncertainty-aware interatomic force modeling with equivariant Bayesian neural networks7Self-Consistency Is Not Enough: A Framework for Error-Correcting ML Potentials with On-the-Fly Residual Learning0Active machine learning model for the dynamic simulation and growth mechanisms of carbon on metal surface68Machine learned force-fields for an Ab-initio quality description of metal-organic frameworks77Machine Learning Potentials with the Iterative Boltzmann Inversion: Training to Experiment22Atomistic simulations of nuclear fuel UO 2 with machine learning interatomic potentials28Harmonizing mechanical and thermal properties in Al/SiC superlattices: Ab initio machine-learning-potential study6Nonadiabatic Dynamics in Two-Dimensional Perovskites Assisted by Machine Learned Force Fields11Efficiency, accuracy, and transferability of machine learning potentials: Application to dislocations and cracks in iron41Predicting Spin-Dependent Phonon Band Structures of HKUST-1 Using Density Functional Theory and Machine-Learned Interatomic Potentials5Comparing machine learning potentials for water: Kernel-based regression and Behler–Parrinello neural networks48On-the-fly machine learned force fields for the study of warm dense matter: Application to diffusion and viscosity of CH15Active learning for SNAP interatomic potentials via Bayesian predictive uncertainty4Machine learning-aided first-principles calculations of redox potentials35A moment tensor potential for lattice thermal conductivity calculations of α and β phases of Ga2O317Polymers simulation using machine learning interatomic potentials19Compositional transferability of deep learning potentials: a case study for LiCl–KCl melt6Machine Learning Interatomic Potentials for Heterogeneous Catalysis43Accelerated First‐Principles Exploration of Structure and Reactivity in Graphene Oxide16Accelerated First‐Principles Exploration of Structure and Reactivity in Graphene Oxide0On-the-Fly Machine Learning Force Field Study of Liquid-Al/Solid-TiB 2 Interfaces14Derivative learning of tensorial quantities—Predicting finite temperature infrared spectra from first principles21Grand-Canonical First Principles-Based Calculations of Electrochemical Reactions11Equivariant tensor network potentials7Leveraging Machine Learning Potentials for In-Situ Searching of Active sites in Heterogeneous Catalysis48Multikernel similarity‐based clustering of amorphous systems and machine‐learned interatomic potentials by active learning6Universal Neural Network Potential-Driven Molecular Dynamics Study of CO2/O2 Evolution at the Ethylene Carbonate/Charged–Electrode Interface6Shock Hugoniot calculations using on-the-fly machine learned force fields with ab initio accuracy7Integrating machine learning interatomic potentials with hybrid reverse Monte Carlo structure refinements in RMCProfile4Machine-learning surrogate models for particle insertions and element substitutions5Transferable performance of machine learning potentials across graphene–water systems of different sizes: Insights from numerical metrics and physical characteristics2Data Generation for Machine Learning Interatomic Potentials and Beyond176Density-based long-range electrostatic descriptors for machine learning force fields20Realization of Ideal Ba Promoter State by Simultaneous Incorporation with Co into Carbon‐protective Framework for Ammonia Synthesis Catalyst3Optimal design of experiments in the context of machine-learning inter-atomic potentials: improving the efficiency and transferability of kernel based methods0Theoretical Design and Synthesis of Metal–Inorganic Frameworks Using Host Atom‐Centered Building Blocks for Efficient Catalysis with Diverse Reactive Sites8Challenges and opportunities for machine learning potentials in transition path sampling: alanine dipeptide and azobenzene studies11Integrating Atomistic Insights With Circuit Simulations via Transformer-Driven Symbolic Regression3Introduction to Machine Learning for Predictive Modeling of Organic Materials0Microscopic insights into the ring-opening reaction of ethylene carbonate on LiCoO 2 by on-the-fly machine learning molecular dynamics1PAL – parallel active learning for machine-learned potentials6Point defect formation at finite temperatures with machine learning force fields17Atomic Scale Modeling via Molecular Dynamics and Machine Learning of Na2O-V2O5-TeO2 Glass-Based Cathodes0Role of the human-in-the-loop in emerging self-driving laboratories for heterogeneous catalysis48Protons in (Ga,Sc,In,Y)3+-doped BaFeO3 triple conductors — Site energies and migration barriers investigated by density functional theory calculations14Heat transport in crystalline organic semiconductors: coexistence of phonon propagation and tunneling12Machine Learning Model to Predict Free-Energy Landscape and Position-Dependent Diffusion Constant to Extend the Scale of Dynamic Monte Carlo Simulations6A practical guide to machine learning interatomic potentials – Status and future214MOLPIPx: An end-to-end differentiable package for permutationally invariant polynomials in Python and Rust3Applications of machine learning in surfaces and interfaces30Decoding the hidden dynamics of super-Arrhenius hydrogen diffusion in multi-principal element alloys via machine learning17Active learning of effective Hamiltonian for super-large-scale atomic structures20PowerModel-AI: A First On-the-Fly Machine-Learning Predictor for AC Power Flow Solutions2Enhancing fluoride-ion conduction via doping-induced anionic ordering in an n = 1 Ba-based Ruddlesden-Popper oxyfluoride structure0Transferable machine learning model for multi-target nanoscale simulations in hydrogen-carbon system from crystal to amorphous6Advancing the design of gold nanomaterials with machine-learned potentials5Small-cell-based fast active learning of machine learning interatomic potentials3

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