Work

GPU-accelerated approximate kernel method for quantum machine learning

Year: 2022

Type: article

Abstract: We introduce Quantum Machine Learning (QML)-Lightning, a PyTorch package containing graphics processing unit (GPU)-accelerated approximate kernel models, which can yield trained models within seconds. QML-Lightning includes a cost-efficient GPU implementation of FCHL19, which together can provide energy and force predictions with competitive accuracy on a microsecond per atom timescale. Using modern GPU hardware, we report learning curves of energies and forces as well as timings as numerical evidence for select legacy benchmarks from atomistic simulation including QM9, MD-17, and 3BPA.

Source: The Journal of Chemical Physics

DOI: 10.1063/5.0108967

Authors: Nicholas J. Browning, Felix A. Faber, O. Anatole von Lilienfeld

Institutions: University of Basel, University of Cambridge, Berlin Institute for the Foundations of Learning and Data, Technische Universität Berlin

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,500

Funders: National Science Foundation, Universität Basel, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, National Center of Competence in Research Materials’ Revolution: Computational Design and Discovery of Novel Materials