36 citations · 83 across the 10 of their papers we have counts for
4 papers · 1 filter
RS-CIDER: A non-local machine learning model for approximating screened hybrid functionals
Zhuotao Jin, Mohamed S. Abdallah, Boris Kozinsky +1
Screened hybrid functionals such as HSE06 improve the description of band gaps, charge localization, and redox energetics relative to semilocal approximations, but their explicit H…
Machine-learned exchange-correlation functionals for molecules, solids, and reactive surfaces
Mohamed S. Abdallah, Zhuotao Jin, Boris Kozinsky +1
The application of density functional theory to heterogeneous catalysis is hindered by the shortcomings of conventional density functional approximations. We combine machine learni…
Complexity of Many-Body Interactions in Transition Metals via Machine-Learned Force Fields from the TM23 Data Set
Cameron J. Owen, Steven B. Torrisi, Yu Xie +6
This work examines challenges associated with the accuracy of machine-learned force fields (MLFFs) for bulk solid and liquid phases of d-block elements. In exhaustive detail, we co…
Pawpyseed: Perturbation-extrapolation band shifting corrections for point defect calculations
Kyle Bystrom, Danny Broberg, Shyam Dwaraknath +2
Significant progress has been made recently in the automation and standardization of ab initio point defect calculations. However, the task of developing, implementing, and benchma…