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RESEARCH PAPER · iScience · 2024

Machine learning interatomic potential: Bridge the gap between small-scale models and realistic device-scale simulations

Guanjie Wang, Changrui Wang, Xuanguang Zhang, Zefeng Li, Jian Xin Zhou, Zhimei Sun

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Machine learning interatomic potential: Bridge the gap between small-scale models and realistic device-scale simulationsOriginalKnowledge Graphs in Smart Digital Libraries3Self-Consistency Is Not Enough: A Framework for Error-Correcting ML Potentials with On-the-Fly Residual Learning0When Long-Range Interactions Break ML Potentials: A Failure Mode Study of Charge-Transfer Sensitivity0Review of External Field Effects on Electrocatalysis: Machine Learning Guided Design32Advancing food security: The role of machine learning in pathogen detection47Critical review of high-entropy alloys for catalysts: Design, synthesis, and applications27Diamond under extremes8Recent progress in mechanistic insights into cation effects on electrochemical CO2 reduction reactions12Sparse Gaussian process based machine learning first principles potentials for materials simulations: Application to batteries, solar cells, catalysts, and macromolecular systems30Recent machine learning-driven investigations into high entropy alloys: A comprehensive review37Machine Learning-Driven Advances in Metal-Organic Framework Nanomaterials for Wastewater Treatment: Developments and Challenges27A PCM Framework for Quantum Computing: Insights in Key Models and Algorithms0Efficient prediction of potential energy surface and physical properties with Kolmogorov-Arnold Networks9Is the Future of Materials Amorphous? Challenges and Opportunities in Simulations of Amorphous Materials37Chalcogenide phase-change materials: unveiling new horizons with big data and machine learning10Decoding complexity in chemical vapor deposition processes of two-dimensional materials via atomistic modeling6Efficient training of machine learning potentials for metallic glasses: CuZrAl validation0Etching-to-Deposition Transition in Sio2/Si3n4 Using Chxfy Ion-Based Plasma Etching: An Atomistic Study with Neural Network Potentials1Introduction to Nanoengineered Materials0MACHINE LEARNING FOR THERMAL PROPERTIES OF INORGANIC CRYSTALS2MACHINE LEARNING IN NANOSCALE THERMAL TRANSPORT2Prospects of Machine Learning-Driven Atomistic Simulations: A Review0The evolution of machine learning potentials for molecules, reactions and materials71A review of displacement cascade simulations using molecular dynamics emphasizing interatomic potentials for TPBAR components15Knowledge-guided large language model for material science16Lattice dynamics modeling of thermal transport in solids using machine-learned atomic cluster expansion potentials: A tutorial7Advances in modeling complex materials: The rise of neuroevolution potentials33Applications of machine learning in surfaces and interfaces30Machine Learning-Based Computational Design Methods for High-Entropy Alloys49Non-Nernstian Effects in Theoretical Electrocatalysis16Advances in high-pressure materials discovery enabled by machine learning10Shadow Molecular Dynamics with a Machine Learned Flexible Charge Potential5On simulating thin-film processes at the atomic scale using machine-learned force fields3Advancing electrocatalyst discovery through the lens of data science: State of the art and perspectives29Regressing the self-consistent Hamiltonian and density from the non-self-consistent Hamiltonian0Shadow Molecular Dynamics for a Charge-Potential Equilibration Model5Applications of density functional theory and machine learning in nanomaterials: A review51Enhancement of classical force field predictions of hydrogen bonding in water, methanol and their crossed dimers using machine learning1Δ-model correction of foundation model based on the model’s own understanding8Application of machine learning interatomic potentials in heterogeneous catalysis25TrIP2: Expanding the Transformer Interatomic Potential Demonstrates Architectural Scalability for Organic Compounds1From empirical to machine learning potentials: Unraveling the physical properties of grapheneplus —A novel half-auxetic 2D carbon nanosheet7Amorphous silicon structures generated using a moment tensor potential and the activation relaxation technique nouveau0Accelerating Data Set Population for Training Machine Learning Potentials with Automated System Generation and Strategic Sampling7Artificial intelligence in materials science and engineering: Current landscape, key challenges, and future trajectories33Efficient and Accurate Machine Learning Interatomic Potential for Graphene: Capturing Stress–Strain and Vibrational Properties4Artificial Intelligence for Materials Discovery, Development, and Optimization171Screening of Material Defects using Universal Machine‐Learning Interatomic Potentials8Opportunities and Challenges in Unsupervised Learning: The Case of Aqueous Electrolyte Solutions3Genetic algorithm-based inverse optimization of interaction potential for nucleon-deuteron scattering below break-up threshold3Machine learning interatomic potentials in biomolecular modeling: principles, architectures, and applications7Advances in Simulations of Thermal Transport in Carbon Nanotubes and Graphene Reinforced Polymer Composites: From Microscopic, Mesoscopic to Macroscopic6Revolutionizing pharmacology: AI-powered approaches in molecular modeling and ADMET prediction44Deep potential-driven molecular dynamics of CO ice analogs: Investigating desorption following vibrational excitation2A robust machine learned interatomic potential for Nb: collision cascade simulations with accurate non-equilibrium properties4Moving in the right direction: Emerging ideas in fitting accurate potentials1Etching-to-deposition transition in SiO2/Si 3 N 4 using CH x F y ion-based plasma etching: An atomistic study with neural network potentials3From Molecules to Medicine: Molecular Dynamics and Docking in Breast Cancer Therapeutics3Molecular Simulations of Polymer‐based Drug Nanocarriers: From Physical and Structural Properties to Controlled Release24Universal machine learning potentials for systems with reduced dimensionality5First principles and neural network-driven biochar spectral database: Raman, XPS, IR, and NMR8Development of a Machine Learning Interatomic Potential for Zirconium and Its Verification in Molecular Dynamics3A Comprehensive Review of Machine-Learning Approaches for Crystal Structure/Property Prediction12Machine Learning Approaches in Soft Matter Molecular Simulation and Materials Characterization: Challenges and Perspectives1Machine Learning Applied to High Entropy Alloys under Irradiation8Bridging accuracy and efficiency: assessing universal ML potentials for niobiumoxygen clusters0Machine learning pipelines for the design of solid-state electrolytes30Melting Transitions in Small Aluminum Clusters Simulated with Energies Approaching DFT Accuracy1Computational Workflow to Unravel the Structural Dynamics of Supramolecular Metallacages in Solution3Convergence of Computational Materials Science and AI for Next-Generation Energy Storage Materials6Bridging the synthesizability gap in perovskites by combining computations, literature data, and PU learning1Sparse multivariate regression with missing values in both predictors and responses0Grain boundary diffusion in compositionally complex alloys: A comprehensive review12Critical Composition Window for Co–P Bulk Metallic Glasses Revealed by Machine-Learned Interatomic Potentials2Structure and dynamics of sulfur vacancies in monolayer MoS2 studied by DFT-based machine learning potentials0AI‐Driven Decoding of Material Dynamics: From Machine Learning Potentials and Interpretability to Generative Prediction18Latest Advancements and Mechanistic Insights into High-Entropy Alloys: Design, Properties and Applications42Protonation dynamics of confined ethanol–water mixtures in H-ZSM-5 from machine learning-driven metadynamics2From O adsorption to Fe oxide growth: Benchmarking reactive force fields and universal machine learning interatomic potentials against DFT for BCC Fe surface oxidation1Machine-learned potentials for solvation modeling5A non-enantiopure H 8 -BINOL metal–organic framework for enantioselective fluorescence sensing0Computational tools in the development of new ceramic materials0Critique of De-Facto Standard Loss Functions for Force-Field Training: Why Energy–Force Balance Fails on Anharmonic Systems0Data-driven and machine learning-assisted research on ferroelectric materials0Data-efficient training of machine learning interatomic potentials for MAX-phase synthesizability prediction1Electronic structure and multiscale modelling of the hydroxyapatite–TiO 2 bio-interface: from self-interaction error to predictive coating design0Machine-learned interatomic potentials for battery materials: from fundamental methodology to emerging applications in electrodes, electrolytes, and interfaces0Neural-network deep potential models for lithium adsorption on C–N–O-modified carbon materials0Modeling Single-Crystal Battery Materials: From Fundamental Understanding to Performance Evaluation4Research progress on liquid-solid transition under synchrotron radiation X-ray and simulation1Multiscale simulations of Ge–Sb–Se–Te phase-change alloys for photonic memory applications1How Artificial Intelligence Can Advance Electrochemical Science and Identify Water Molecule Orientation on Platinum Electrodes0Melting line of silicon modelled with a SNAP machine-learning potential0Bridging thermodynamic models and dynamic surface behavior in the design of binary Pd-based alloy catalysts0Perspectives of Machine Learning for Ligand-Field Analyses in Lanthanide-Based Single Molecule Magnets0Review on improved neural network algorithms for battery state of energy estimation in smart grids26Graph atomic cluster expansion for foundational machine learning interatomic potentials36Foundation models for atomistic simulation of chemistry and materials32Shadow molecular dynamics for flexible multipole models0Machine-learning interatomic potentials for interfaces in all-solid-state batteries: Perspectives on training data, model selection, and validation4DFT-Based Multiscale Modeling of Heterogeneous (Electro)Catalytic Reactions12Computational dual-loop frameworks bridging single-enzyme design and cascade tunnel network engineering for next-generation biosynthetic systems3Few-Shot Ensemble Learning for Catalysis and Application to Trimetallics for Oxygen Reduction6Optimizing cross-domain transfer for universal machine learning interatomic potentials14Machine Learning Driven Advances in Molecular Dynamics of Bulk and Interfacial Aqueous Systems5Artificial Intelligence for Multiscale Modeling in Solid‐State Physics and Chemistry: A Comprehensive Review3Machine Learning for Designing Perovskites and Perovskite‐Inspired Solar Materials: Emerging Opportunities and Challenges7Structure and motion of tritium and helium in the breeding-blanket-relevant liquid lithium–lead eutectic alloy: ab-initio molecular dynamics simulations0Charting the Landscape of Oxygen Ion Conductors: A 60-Year Dataset with Interpretable Regression Models1Machine-Learned Extrapolation of Quantum Mechanical Energies in Implicit Solvent from Short to Long Oligopeptides0Lattice Thermal Transport in Polymers: Atomistic Insights, Modeling Advances, and Design Perspectives0Capturing long-range interactions with a reciprocal-space neural network1Static Local Lattice Distortion in BCC Refractory High-Entropy Alloys: A DFT Study of NbTaTiV, TiZrNbMo, and HfZrNbMo2Predicting Crystal Structures and Ionic Conductivities in Li 3 YCl 6−x Br x Halide Solid Electrolytes Using a Fine‐Tuned Machine Learning Interatomic Potential5Learning Electronic Polarization in Molecular Systems: Vibrational Spectroscopy of Ethanol–Water Mixtures1Multiscale Modeling and Machine Learning-Driven Characterization of Degradation Mechanism in Li-Ion Battery Materials0Structural Changes and Oxygen Dimerization in Li-Rich Layered Oxide Cathodes: An Atomic-Scale Study1Artificial intelligence in advanced polymer manufacturing1Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications14Theoretical Insights into Reaction-Induced Transformation and Tuning of Catalytic Behavior in Heterogenous Catalysis0How Silica Surface Chemistry Modulates Interfacial Water: Insights from Machine Learning Molecular Dynamics1Data-driven advances in micro-scale petroleum research: From digital rocks to molecular simulations0High-Pressure Phase Diagram of Fluorapatite and Anisotropic Fluorine Channel Motion via Machine Learning Molecular Dynamics0Machine-learned interatomic potentials for modeling multicomponent metallic systems: a comprehensive review2Stability of Polarons and Oxygen Vacancies in BiVO 4 Revealed by Machine-Learning-Driven Simulations0Software−hardware co-design accelerates materials simulations0Utilizing a high-accuracy machine learning potential for molecular dynamics studies of radiation damage in Al-Li alloys0Machine Learning-Empowered Electromagnetic Wave Absorbing Materials: From Forward Prediction to Generative Inverse Design4Transferable machine learning potential for modeling uranium nitrides across stoichiometries0Machine Learning in Molecular Dynamics Applications in Medicinal Chemistry0Machine‐Learning‐Assisted Elucidation of Ion Transport Mechanism in Heterogeneous Interphases Formed at the Electrolyte–Electrode Interfaces0From Migration–Healing Reconstruction to Chemical Segregation: Atomistic Origins of High-Temperature Evolution in Cubic Boron Nitride Nanoparticles Revealed by Machine Learning Potential Simulations0Vacancy-Driven Phase Separation Governs Li+ Transport Degradation in Li2– x O2: A Computational Study Relevant to Li–O2 Batteries0Benchmarking universal machine learning interatomic potentials on elemental systems0Li+ Transport in Ca-Substituted β -Li3PS4: Role of PS43– Vibrations and Rotations from Machine-Learning Molecular Dynamics0Molecular Property Prediction via Sparse Binary Matrix Representation and Convolutional Neural Networks0Accelerating SCR Catalyst Screening via DeepMD and Bayesian Optimization Reveals a Cross-Chemistry VOx Redox Bottleneck0AI-driven explorations of the key atomic mechanism of phase-change memory semiconductors0Machine learning interatomic potentials for high-entropy alloys: from atomic mechanisms to macroscopic design0Regulating Hybrid Na + /Zn 2+ ‐Ion Acetonitrile‐Water Electrolyte Enables High Rate and Long‐Life Na 3 V 2 (PO 4 ) 3 Cathode for Zn‐Ion Batteries0Cellulose Ionogels: Unraveling Structure–Property Relationships Through Multiscale In-Situ Characterization and Theoretical Modeling0Computational design of quantum and functional materials for next generation electronic and energy technologies0A Review of Atomistic Machine Learning for Grain Boundaries in Advanced Manufacturing: From Metastable Structures to Processing–Structure–Property Design0Thermal Stability, Negative Thermal Expansion, and Pore Accessibility of HKUST‐1 From M3GNet Machine Learning Molecular Dynamics0Charge‐Derived Polarization Fingerprints from Charge‐Aware Molecular Dynamics: A Descriptor Framework for BaTiO30Leveraging Symbolic Artificial Intelligence and Fuzzy Logic for Materials Science: A Review of Methods, Challenges, and Applications to Scarce and Imperfect Experimental Data0Fine-tuned machine-learned interatomic potential for oxygen adsorption on molybdenum surfaces0

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