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physics.comp-ph2020★ 7 cited
Simple and efficient algorithms for training machine learning potentials to force data
Justin S. Smith, Nicholas Lubbers, Aidan P. Thompson +1
Abstract Machine learning models, trained on data from ab initio quantum simulations, are yielding molecular dynamics potentials with unprecedented accuracy. One limiting factor is…
physics.comp-ph2020
Multi-fidelity machine-learning with uncertainty quantification and Bayesian optimization for materials design: Application to ternary random alloys
Anh Tran, Julien Tranchida, Tim Wildey +1
We present a scale-bridging approach based on a multi-fidelity (MF) machine-learning (ML) framework leveraging Gaussian processes (GP) to fuse atomistic computational model predict…