ArticleNature communications2022
BIGDML-Towards accurate quantum machine learning force fields for materials.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Soft Computing Approaches toward Density-Based Analysis of Many-Electron Systems.ACS physical chemistry Au · 2026Review
- Physics-informed Hamiltonian learning for large-scale optoelectronic property prediction.Nature communications · 2026Article
- Molecular Simulation of Hydrogen Systems: From Properties and Methods to Applications and Future Directions.Chemical reviews · 2025Review
- Supersaturation, Nucleation, and Phase Separation of Mesoscopic Systems.Journal of the American Chemical Society · 2025Article
- Machine learning for nonadiabatic molecular dynamics: best practices and recent progress.Chemical science · 2025Review
- Crash testing machine learning force fields for molecules, materials, and interfaces: molecular dynamics in the TEA challenge 2023.Chemical science · 2025Article
- Crash testing machine learning force fields for molecules, materials, and interfaces: model analysis in the TEA Challenge 2023.Chemical science · 2025Article
- Complete and Efficient Covariants for Three-Dimensional Point Configurations with Application to Learning Molecular Quantum Properties.The journal of physical chemistry letters · 2024Article
- Rapid Characterization of Point Defects in Solid-State Ion Conductors Using Raman Spectroscopy, Machine-Learning Force Fields, and Atomic Raman Tensors.Journal of the American Chemical Society · 2024Article
- A Euclidean transformer for fast and stable machine learned force fields.Nature communications · 2024Article
- Insights into One-Dimensional Thermoelectric Materials: A Concise Review of Nanowires and Nanotubes.Nanomaterials (Basel, Switzerland) · 2024Review
- Continuum Robots and Magnetic Soft Robots: From Models to Interdisciplinary Challenges for Medical Applications.Micromachines · 2024Review
- Neural Network Corrections to Intermolecular Interaction Terms of a Molecular Force Field Capture Nuclear Quantum Effects in Calculations of Liquid Thermodynamic Properties.Journal of chemical theory and computation · 2024Article
- Force Field Analysis Software and Tools (FFAST): Assessing Machine Learning Force Fields under the Microscope.Journal of chemical theory and computation · 2023Article
- Accurate, interpretable predictions of materials properties within transformer language models.Patterns (New York, N.Y.) · 2023Article
- Universal machine learning for the response of atomistic systems to external fields.Nature communications · 2023Article
- Reconstructing Kernel-Based Machine Learning Force Fields with Superlinear Convergence.Journal of chemical theory and computation · 2023Article
- Efficient interatomic descriptors for accurate machine learning force fields of extended molecules.Nature communications · 2023Article
- Gaussian Process Regression Models for Predicting Atomic Energies and Multipole Moments.Journal of chemical theory and computation · 2023Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine-learning force fields (MLFF) should be accurate, computationally and data efficient, and applicable to molecules, materials, and interfaces thereof. Currently, MLFFs often introduce tradeoffs that restrict their practical applicability to small subsets of chemical space or require exhaustive datasets for training. Here, we introduce the Bravais-Inspired Gradient-Domain Machine Learning (BIGDML) approach and demonstrate its ability to construct reliable force fields using a training set with just 10-200 geometries for materials including pristine and defect-containing 2D and 3D semiconductors and metals, as well as chemisorbed and physisorbed atomic and molecular adsorbates on surfaces. The BIGDML model employs the full relevant symmetry group for a given material, does not assume artificial atom types or localization of atomic interactions and exhibits high data efficiency and state-of-the-art energy accuracies (errors substantially below 1 meV per atom) for an extended set of materials. Extensive path-integral molecular dynamics carried out with BIGDML models demonstrate the counterintuitive localization of benzene-graphene dynamics induced by nuclear quantum effects and their strong contributions to the hydrogen diffusion coefficient in a Pd crystal for a wide range of temperatures.
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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.