ArticleNature communications2023
Universal machine learning for the response of atomistic systems to external fields.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
What it found
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Who cites it
26 citing papers in PubMed.
- High-throughput physics-based enzyme engineering.bioRxiv : the preprint server for biology · 2026Article
- Integrating Coupled-Cluster Theory Within AI Workflows for Accurate Molecular Geometries.Journal of computational chemistry · 2026Article
- Efficient Parallelization of Message Passing Neural Network Potentials for Large-Scale Molecular Dynamics.JACS Au · 2026Article
- Multiscale machine learning molecular mechanics for mechanism and stereoselectivity of Diels-Alderase catalysis.Nature communications · 2026Article
- Reactive Machine Learning Interatomic Potentials for Chemistry and Materials Science.Chemical reviews · 2026Review
- Transition from Vehicular to Structural Ionic Transport in Electrified Alkali Aqueous Solutions.The journal of physical chemistry. B · 2026Article
- Node-equivariant message passing for efficient and accurate machine learning interatomic potentials.Chemical science · 2026Article
- Long-range electrostatics for machine learning interatomic potentials is easier than we thought.The Journal of chemical physics · 2026Article
- Article
- A foundation machine learning potential with polarizable long-range interactions for materials modelling.Nature communications · 2025Article
- Redefining Computational Enzymology with Multiscale Machine Learning/Molecular Mechanics: Catalytic Mechanism and Stereoselectivity in Diels-Alderases.Research square · 2025Article
- Article
- Introducing Virtual Points in Equivariant Networks by Extending Atom Representation for Effective Prediction.Journal of chemical theory and computation · 2025Article
- Exploring the Design Space of Machine Learning Models for Quantum Chemistry with a Fully Differentiable Framework.Journal of chemical theory and computation · 2025Article
- Unveiling the intermediate hydrated proton in water through vibrational analysis on the 1750 cmNature communications · 2025Article
- Unified differentiable learning of electric response.Nature communications · 2025Article
- Crash testing machine learning force fields for molecules, materials, and interfaces: molecular dynamics in the TEA challenge 2023.Chemical science · 2025Article
- PiNN: Equivariant Neural Network Suite for Modeling Electrochemical Systems.Journal of chemical theory and computation · 2025Article
- Mechanism of Fe(II) Chemisorption on Hematite(001) Revealed by Reactive Neural Network Potential Molecular Dynamics.The journal of physical chemistry letters · 2025Article
- Machine Learning Quantum Mechanical/Molecular Mechanical Potentials: Evaluating Transferability in Dihydrofolate Reductase-Catalyzed Reactions.Journal of chemical theory and computation · 2025Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Machine learned interatomic interaction potentials have enabled efficient and accurate molecular simulations of closed systems. However, external fields, which can greatly change the chemical structure and/or reactivity, have been seldom included in current machine learning models. This work proposes a universal field-induced recursively embedded atom neural network (FIREANN) model, which integrates a pseudo field vector-dependent feature into atomic descriptors to represent system-field interactions with rigorous rotational equivariance. This "all-in-one" approach correlates various response properties like dipole moment and polarizability with the field-dependent potential energy in a single model, very suitable for spectroscopic and dynamics simulations in molecular and periodic systems in the presence of electric fields. Especially for periodic systems, we find that FIREANN can overcome the intrinsic multiple-value issue of the polarization by training atomic forces only. These results validate the universality and capability of the FIREANN method for efficient first-principles modeling of complicated systems in strong external fields.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.