ArticleChemical science2021
A transferable active-learning strategy for reactive molecular force fields.
Article in Chemical science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
- Interpretable Bayesian optimization for catalyst discovery.Faraday discussions · 2026Article
- aims-PAX: Parallel Active Exploration Enables Expedited Construction of Machine Learning Force Fields for Molecules and Materials.Journal of chemical information and modeling · 2026Article
- Reactive Machine Learning Interatomic Potentials for Chemistry and Materials Science.Chemical reviews · 2026Review
- Active learning meets metadynamics: automated workflow for reactive machine learning interatomic potentials.Digital discovery · 2026Article
- Towards Routine Condensed Phase Simulations with Delta-Learned Coupled Cluster Accuracy: Application to Liquid Water.Journal of chemical theory and computation · 2025Article
- Assessing zero-shot generalisation behaviour in graph-neural-network interatomic potentials.Digital discovery · 2025Article
- Reactive Active Learning: An Efficient Approach for Training Machine Learning Interatomic Potentials for Reacting Systems.Journal of chemical theory and computation · 2025Article
- An automated framework for exploring and learning potential-energy surfaces.Nature communications · 2025Article
- AI Approaches to Homogeneous Catalysis with Transition Metal Complexes.ACS catalysis · 2025Review
- Including Physics-Informed Atomization Constraints in Neural Networks for Reactive Chemistry.Journal of chemical information and modeling · 2025Article
- Room-Temperature Decomposition of the Ethaline Deep Eutectic Solvent.The journal of physical chemistry letters · 2025Article
- ArcaNN: automated enhanced sampling generation of training sets for chemically reactive machine learning interatomic potentials.Digital discovery · 2025Article
- Article
- Estimation of hydrogen solubility in aqueous solutions using machine learning techniques for hydrogen storage in deep saline aquifers.Scientific reports · 2024Article
- Multi-Level Protocol for Mechanistic Reaction Studies Using Semi-Local Fitted Potential Energy Surfaces.International journal of molecular sciences · 2024Article
- Overcoming the Pitfalls of Computing Reaction Selectivity from Ensembles of Transition States.The journal of physical chemistry letters · 2024Article
- Modelling chemical processes in explicit solvents with machine learning potentials.Nature communications · 2024Article
- Prebiotic chemical reactivity in solution with quantum accuracy and microsecond sampling using neural network potentials.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Machine learning interatomic potential: Bridge the gap between small-scale models and realistic device-scale simulations.iScience · 2024Review
- Exploring the frontiers of condensed-phase chemistry with a general reactive machine learning potential.Nature chemistry · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Predictive molecular simulations require fast, accurate and reactive interatomic potentials. Machine learning offers a promising approach to construct such potentials by fitting energies and forces to high-level quantum-mechanical data, but doing so typically requires considerable human intervention and data volume. Here we show that, by leveraging hierarchical and active learning, accurate Gaussian Approximation Potential (GAP) models can be developed for diverse chemical systems in an autonomous manner, requiring only hundreds to a few thousand energy and gradient evaluations on a reference potential-energy surface. The approach uses separate intra- and inter-molecular fits and employs a prospective error metric to assess the accuracy of the potentials. We demonstrate applications to a range of molecular systems with relevance to computational organic chemistry: ranging from bulk solvents, a solvated metal ion and a metallocage onwards to chemical reactivity, including a bifurcating Diels-Alder reaction in the gas phase and non-equilibrium dynamics (a model S
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Registered trials
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