Quantum-accurate magneto-elastic predictions with classical spin-lattice dynamics

no code implementations • 18 Jan 2021 • Svetoslav Nikolov, Mitchell A. Wood, Attila Cangi, Jean-Bernard Maillet, Mihai-Cosmin Marinica, Aidan P. Thompson, Michael P. Desjarlais, Julien Tranchida

Together they represent a potential energy surface from which the mechanical forces on the atoms and the precession dynamics of the atomic spins are computed.

Materials Science

Accelerating Finite-temperature Kohn-Sham Density Functional Theory with Deep Neural Networks

no code implementations • 10 Oct 2020 • J. Austin Ellis, Lenz Fiedler, Gabriel A. Popoola, Normand A. Modine, J. Adam Stephens, Aidan P. Thompson, Attila Cangi, Sivasankaran Rajamanickam

We present a numerical modeling workflow based on machine learning (ML) which reproduces the the total energies produced by Kohn-Sham density functional theory (DFT) at finite electronic temperature to within chemical accuracy at negligible computational cost.

BIG-bench Machine Learning

Simple and efficient algorithms for training machine learning potentials to force data

no code implementations • 9 Jun 2020 • Justin S. Smith, Nicholas Lubbers, Aidan P. Thompson, Kipton Barros

These forces provide much more information than the energy alone.

Atomic Forces BIG-bench Machine Learning +1