ArticleJournal of computational chemistry2022
Producing chemically accurate atomic Gaussian process regression models by active learning for molecular simulation.
Article in Journal of computational chemistry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Adaptive model-guided protein evolution with sparse data optimizes compact eukaryotic genome editors.Nature biotechnology · 2026Article
- Current developments and trends in quantum crystallography.Acta crystallographica Section B, Structural science, crystal engineering and materials · 2024Article
- Incorporating Noncovalent Interactions in Transfer Learning Gaussian Process Regression Models for Molecular Simulations.Journal of chemical theory and computation · 2024Article
- Construction of a Gaussian Process Regression Model of Formamide for Use in Molecular Simulations.The journal of physical chemistry. A · 2023Article
- Producing chemically accurate atomic Gaussian process regression models by active learning for molecular simulation.Journal of computational chemistry · 2022Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Machine learning is becoming increasingly more important in the field of force field development. Never has it been more vital to have chemically accurate machine learning potentials because force fields become more sophisticated and their applications expand. In this study a method for developing chemically accurate Gaussian process regression models is demonstrated for an increasingly complex set of molecules. This work is an extension to previous work showing the progression of the active learning technique in producing more accurate models in much less CPU time than ever before. The per-atom active learning approach has unlocked the potential to generate chemically accurate models for molecules such as peptide-capped glycine.
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Registered trials
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