Fanchen Meng

Jul 29, 2023 | nist.gov | Matthew Carbone |Fanchen Meng |Christian Vorwerk |Benedikt Mauer

Author(s) Matthew Carbone, Fanchen Meng, Christian Vorwerk, Benedikt Mauer, Fabian Peschel, Xiaohui Qu, Eli Stavitski, Claudia Draxl, , Deyu Lu Abstract Spectroscopy simulations are a critical tool for the interpretation of experiment, the development of new theoretical understanding, and fast screening of new molecules and materials.

Mar 15, 2023 | link.aps.org | Animesh Ghose |Fanchen Meng |Zhu Liang |Mikhail Segal

Abstract As machine learning (ML) methods continue to be applied to a broad scope of problems in the physical sciences, uncertainty quantification is becoming correspondingly more important for their robust application. Uncertainty-aware machine learning methods have been used in select applications, but largely for scalar properties.