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Found 4 papers, 2 papers with code
Accelerating the Training and Improving the Reliability of Machine-Learned Interatomic Potentials for Strongly Anharmonic Materials through Active Learning
Molecular dynamics (MD) employing machine-learned interatomic potentials (MLIPs) serve as an efficient, urgently needed complement to ab initio molecular dynamics (aiMD).
Extrapolation to complete basis-set limit in density-functional theory by quantile random-forest models
The ultimate precision is reached with an infinitely large basis set, i. e., in the limit of a complete basis set (CBS).
Heat flux for semi-local machine-learning potentials
The Green-Kubo (GK) method is a rigorous framework for heat transport simulations in materials.
A machine learning route between band mapping and band structure
Electronic band structure (BS) and crystal structure are the two complementary identifiers of solid state materials.
Data Analysis, Statistics and Probability Materials Science Computational Physics
