May 05, 2017 article Open access Machine learning of accurate energy-conserving molecular force fields Science Advances DOI: 10.1126/sciadv.1603015 Full text (OA) OpenAlex Authors 0 Author list not loaded yet. Cited by 3 stored of 1,244 Search Sort Most cited Newest Oldest Patent citations Title Any typearticle review book-chapter conference-paper preprint dissertation book dataset other Any fieldAgricultural and Biological Sciences Arts and Humanities Biochemistry, Genetics and Molecular Biology Business, Management and Accounting Chemical Engineering Chemistry Computer Science Decision Sciences Dentistry Earth and Planetary Sciences Economics, Econometrics and Finance Energy Engineering Environmental Science Health Professions Immunology and Microbiology Materials Science Mathematics Medicine Neuroscience Nursing Pharmacology, Toxicology and Pharmaceutics Physics and Astronomy Psychology Social Sciences Veterinary Open access Towards sustainable exploration of chemical spaces with machine learning 2026 Digital Discovery article Materials Science Machine Learning in Materials Science Open access 0 citations Selecting fitted models under epistemic uncertainty using a stochastic process on quantile functions 2025 Nature Communications article Mathematics Statistical Methods and Inference Open access 2 citations Grassmann Extrapolation of Density Matrices for Born–Oppenheimer Molecular Dynamics 2021 Journal of Chemical Theory and Computation article Physics and Astronomy Advanced Chemical Physics Studies Open access 28 citations 3 results References 0