Active learning of reactive Bayesian force fields applied to heterogeneous catalysis dynamics of H/Pt

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FINETUNA: fine-tuning accelerated molecular simulations

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CIDER: An Expressive, Nonlocal Feature Set for Machine Learning Density Functionals with Exact Constraints

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Effect of Cation Disorder on Lithium Transport in Halide Superionic Conductors

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How fast do defects migrate in halide perovskites: insights from on-the-fly machine-learned force fields

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Application of machine learning in chemical engineering: outlook and perspectives

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Sparse Gaussian process based machine learning first principles potentials for materials simulations: Application to batteries, solar cells, catalysts, and macromolecular systems

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The emergence of machine learning force fields in drug design

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Machine Learning Force Fields in Electrochemistry: From Fundamentals to Applications

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Cluster-MLP: An Active Learning Genetic Algorithm Framework for Accelerated Discovery of Global Minimum Configurations of Pure and Alloyed Nanoclusters

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Crash testing machine learning force fields for molecules, materials, and interfaces: molecular dynamics in the TEA challenge 2023

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Machine Learning, Density Functional Theory, and Experiments to Understand the Photocatalytic Reduction of CO2 on CuPt/TiO2

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Machine-learned acceleration for molecular dynamics in CASTEP

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Generating Minimal Training Sets for Machine Learned Potentials

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Electrolyte effects on reaction kinetics in electrochemical CO2 reduction: The roles of pH, cations, and anions

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Application of machine learning interatomic potentials in heterogeneous catalysis

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Kernel regression methods for prediction of materials properties: Recent developments

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Staged 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) Vicinal

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Spatially Resolved Uncertainties for Machine Learning Potentials

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Harnessing Cation Disorder for Enhancing Ionic Conductivity in Lithium Inverse Spinel Halides

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Discovering novel lead-free solder alloy by multi-objective Bayesian active learning with experimental uncertainty

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Room-Temperature Decomposition of the Ethaline Deep Eutectic Solvent

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Degeneration of kernel regression with Matern kernels into low-order polynomial regression in high dimension

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Next-Generation Computational and Experimental Tools for Understanding Nucleation and Growth of Metal–Organic Frameworks

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Applications and training sets of machine learning potentials

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Dielectric Saturation in Water from a Long-Range Machine Learning Model

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Data Efficient and Stability Indicated Sampling for Developing Reactive Machine Learning Potential to Achieve Ultralong Simulation in Lithium-Metal Batteries

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No Time for Nitrides: How Cobalt Alloying Promotes Iron Catalysts for Ammonia Decomposition

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Machine learning force fields for inorganic crystalline materials: principles, advances, and emerging applications

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Uncertainty driven active learning of coarse grained free energy models

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Reactive Machine Learning Interatomic Potentials for Chemistry and Materials Science

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Graphs and Kernelized Learning Applied to Interactions of Hydrogen with Doped Gold Nanoparticle Electrocatalysts

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Accurate Simulations of the Reaction of H2on a Curved Pt Crystal through Machine Learning

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Phase transitions of LaMnO3 and SrRuO3 from DFT+U based machine learning force fields simulations

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Accelerated Bayesian Inference for Molecular Simulations using Local Gaussian Process Surrogate Models

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Active sparse Bayesian committee machine potential for isothermal–isobaric molecular dynamics simulations

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A Molecular View of Methane Activation on Ni(111) through Enhanced Sampling and Machine Learning

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Graph neural networks reshaping the paradigm of electrocatalyst design for green hydrogen production

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Exploring model complexity in machine learned potentials for simulated properties

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Structural Coarse-Graining via Multiobjective Optimization with Differentiable Simulation

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Pressure-driven charge transfer and tunable superconductivity in intermetallic Li–Mg electrides

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Machine Learning Force Field beyond the Limits of Classical and First-Principles Molecular Dynamics Simulations: The Case of Kaolinite Hydration

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Uncertainty in the era of machine learning for atomistic modeling

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NMR spectrum prediction for dynamic molecules by machine learning: A case study of trefoil knot molecule

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Reactive Force Field Development for Propane Dehydrogenation on Platinum Surfaces

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What Makes Au Nanospheres Superior to Octahedral and Cubic Counterparts for the Deposition of a Pt Monolayer Shell?

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Solvation of furfural at metal–water interfaces: Implications for aqueous phase hydrogenation reactions

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Diffusion-Limited Crystal Growth of Gallium Nitride Using Active Machine Learning

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Generalization of graph-based active learning relaxation strategies across materials

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Enhanced Machine Learning Molecular Simulations for optimization of flotation selectivity: A perspective paper

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Machine Learning Nonadiabatic Dynamics: Eliminating Phase Freedom of Nonadiabatic Couplings with the State-Interaction State-Averaged Spin-Restricted Ensemble-Referenced Kohn–Sham Approach

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A sparse Bayesian Committee Machine potential for oxygen-containing organic compounds

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Transformative discovery of rare-earth functional materials for energy and electronics via machine learning

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Efficient ensemble uncertainty estimation in Gaussian processes regression

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Lowering the barrier to access information-rich transient kinetic data for machine learning methods

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Accelerating Materials Discovery Through Sparse Gaussian Process Machine Learning Potentials

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Transferable Force Field for Gallium Nitride Crystal Growth from the Melt Using On-The-Fly Active Learning

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Autonomous Output‐Oriented Aerosol Jet Printing Enabled by Hybrid Machine Learning

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Neural network based analysis of multimodal bond distributions using extended x-ray absorption fine structure spectra

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A machine-learned kinetic energy model for light weight metals and compounds of group III-V elements

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Efficient generation of stable linear machine-learning force fields with uncertainty-aware active learning

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PAL – parallel active learning for machine-learned potentials

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Computational Investigation of the Potential and Limitations of Machine Learning with Neural Network Circuits Based on Synaptic Transistors

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Overcoming the Chemical Complexity Bottleneck in on-the-Fly Machine Learned Molecular Dynamics Simulations

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Atomic Layer Restructuring of Gold Surfaces by N -Heterocyclic Carbenes over Large Surface Areas

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Nature of molybdenum carbide surfaces for catalytic hydrogen dissociation using machine-learned potentials: an ensemble-averaged perspective

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Molecular dynamics-driven global potential energy surfaces: Application to the AlF dimer

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Bayesian Optimization in Polymer Modeling: From Coarse‐Graining Foundations to Autonomous Inverse Design

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Commercialization path of data-driven green catalytic technology: the application of machine learning in technology lifecycle management

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Exploring fracture of H-BN and graphene by neural network force fields

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Gaussian Process Approach to Constructing Transferable Force Fields for Thiolate-Protected Gold Nanoclusters

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Evolutionary Monte Carlo of QM Properties in Chemical Space: Electrolyte Design

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CURATOR: Building Robust Machine Learning Potentials for Atomistic Simulations Autonomously with Batch Active LearningPreprint

Yang, X. L., Petersen, M. H., Sechi, R., Hansen, W. S., Norwood, S. W., Krishnan, Y., Vincent, S., Busk, J., Cornet, F., Winther, O., et al. (2024) · ChemRxiv · doi:10.26434/chemrxiv-2024-p5t3l · Cited by 5 · see citing works

Data-efficient modeling of catalytic reactions via enhanced sampling and on-the-fly learning of machine learning potentialsPreprint

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Atomistic Simulations of Thermal and Chemical Expansions of PrNi x Co 1‐x O 3‐δ Accelerated by Machine Learning Potentials

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Machine-Learning-Guided Insights into Solid-Electrolyte Interphase Conductivity: Are Amorphous Lithium Fluorophosphates the Key?

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Design, characterization, and dynamic behavior of atomically dispersed Pt catalysts for water splitting and oxygen reduction: From experimental insights to machine-learning-assisted analysis

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Micron-scale heterogeneous catalysis with Bayesian force fields from first principles and active learningPreprint

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On-the-fly active learning of interpretable Bayesian force fields for atomistic rare events

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On-the-Fly Bayesian Active Learning of Interpretable Force-Fields for Atomistic Rare EventsPreprint

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Bayesian active learning of interatomic force field for molecular dynamics simulation of Pt/Ag(111)Preprint

Xu, K., Yan, L., & You, B. (2021) · ChemRxiv · doi:10.26434/chemrxiv-2021-sk6lf-v2

Active learning for robust, high-complexity reactive atomistic simulations

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