Year: 2022
Type: article
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.
Source: Nature Communications
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $6,016
Funders: Fonds National de la Recherche Luxembourg, Bundesministerium für Bildung und Forschung, Korea University, Consejo Nacional de Ciencia y Tecnología, Technische Universität Berlin, Ministry of Science and ICT, South Korea, Dirección General de Asuntos del Personal Académico, Universidad Nacional Autónoma de México, Institute for Information and Communications Technology Promotion
Awards: G1004095800, G1067038318, G1395773357, G1668956305, G2804209865, G2953128317, G3515117348, G3803585159, Verbundprojekt: Bernstein Fokus Neurotechnologie - Nichtinvasive Neurotechnologie für Mensch-Maschine Interaktion - Teilprojekte A1, A3, A4, B4, W3, Zentrum, G4533892997, G4700831490, G4711502783, G5425843222, G5454750614, D-JPN Verbund: Adaptive Gehirn-Computer-Schnittstellen (BCI) in nichtstationären Umgebungen, G634377861, G6390159699, Verbundprojekt BIFOLD-BBDC: Berlin Institute for the Foundations of Learning and Data, G8230155105, G8436554242, G8613544847
OpenAlex ID: W4283699517