Information theory unifies atomistic machine learning, uncertainty quantification, and materials thermodynamics

1 code implementation • 18 Apr 2024 • Daniel Schwalbe-Koda, Sebastien Hamel, Babak Sadigh, Fei Zhou, Vincenzo Lordi

An accurate description of information is relevant for a range of problems in atomistic modeling, such as sampling methods, detecting rare events, analyzing datasets, or performing uncertainty quantification (UQ) in machine learning (ML)-driven simulations.

Active Learning Uncertainty Quantification