ArticleNature communications2023
Efficient interatomic descriptors for accurate machine learning force fields of extended molecules.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Using structure to evaluate the accuracy and transferability of machine learning potentials of biomolecules.RSC advances · 2026Article
- A Quick and Simple Chromatographic Method to Investigate the Adsorption of Glufosinate on a Subtropical Oxisol: Affinity Comparisons With Glyphosate.Journal of separation science · 2026Article
- AGAPE (Computational G‑Quadruplex Stabilization Prediction): The First Machine Learning Workflow for G‑Quadruplex Stabilization Prediction.ACS omega · 2026Article
- Transition from Vehicular to Structural Ionic Transport in Electrified Alkali Aqueous Solutions.The journal of physical chemistry. B · 2026Article
- A scalable and quantum-accurate foundation model for biomolecular force fields via linearly tensorized quadrangle attention.Nature communications · 2026Article
- Automated Learning of a Dense Manifold of Electronic States and Electronic Energy Transfer and Reactions in Singlet O Collisions with NResearch (Washington, D.C.) · 2026Article
- TrIP2: Expanding the Transformer Interatomic Potential Demonstrates Architectural Scalability for Organic Compounds.The journal of physical chemistry. A · 2025Article
- MACE-OFF: Short-Range Transferable Machine Learning Force Fields for Organic Molecules.Journal of the American Chemical Society · 2025Article
- Interpolating numerically exact many-body wave functions for accelerated molecular dynamics.Nature communications · 2025Article
- Crash testing machine learning force fields for molecules, materials, and interfaces: model analysis in the TEA Challenge 2023.Chemical science · 2025Article
- Force Fields, Quantum-Mechanical- and Molecular-Dynamics-Based Descriptors of Radiometal-Chelator Complexes.Molecules (Basel, Switzerland) · 2024Article
- Article
- Stable and accurate atomistic simulations of flexible molecules using conformationally generalisable machine learned potentials.Chemical science · 2024Article
- Multi-Level Protocol for Mechanistic Reaction Studies Using Semi-Local Fitted Potential Energy Surfaces.International journal of molecular sciences · 2024Article
- Combining Force Fields and Neural Networks for an Accurate Representation of Chemically Diverse Molecular Interactions.Journal of the American Chemical Society · 2023Article
- Efficient interatomic descriptors for accurate machine learning force fields of extended molecules.Nature communications · 2023Article
Corrections and comments
- Erratum issued
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning force fields (MLFFs) are gradually evolving towards enabling molecular dynamics simulations of molecules and materials with ab initio accuracy but at a small fraction of the computational cost. However, several challenges remain to be addressed to enable predictive MLFF simulations of realistic molecules, including: (1) developing efficient descriptors for non-local interatomic interactions, which are essential to capture long-range molecular fluctuations, and (2) reducing the dimensionality of the descriptors to enhance the applicability and interpretability of MLFFs. Here we propose an automatized approach to substantially reduce the number of interatomic descriptor features while preserving the accuracy and increasing the efficiency of MLFFs. To simultaneously address the two stated challenges, we illustrate our approach on the example of the global GDML MLFF. We found that non-local features (atoms separated by as far as 15 Å in studied systems) are crucial to retain the overall accuracy of the MLFF for peptides, DNA base pairs, fatty acids, and supramolecular complexes. Interestingly, the number of required non-local features in the reduced descriptors becomes comparable to the number of local interatomic features (those below 5 Å). These results pave the way to constructing global molecular MLFFs whose cost increases linearly, instead of quadratically, with system size.
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