36 citations · 83 across the 10 of their papers we have counts for
12 papers
Near Chemical Accuracy From Size-Consistent Padé Resummed Møller-Plesset Perturbation Theory
Kyle Bystrom, Timothy C. Berkelbach, Diptarka Hait
Padé resummation of the correlation energy from Møller-Plesset (MP) perturbation theory is not size-consistent and is therefore seldom used. We present an orbital-based Padé resumm…
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…
The Python Simulations of Chemistry Framework: 10 years of an open-source quantum chemistry project
Qiming Sun, Matthew R Hermes, Xiaojie Wu +100
Over the past decade, the Python-based Simulations of Chemistry Framework (PySCF) has developed into a widely used open-source platform for electronic structure theory and quantum…
Size-Consistent Adiabatic Connection Functionals via Orbital-Based Matrix Interpolation
Kyle Bystrom, Timothy C. Berkelbach
We introduce a size-consistent and orbital-invariant formalism for constructing correlation functionals based on the adiabatic connection for density functional theory (DFT). By co…
A Recipe for Charge Density Prediction
Xiang Fu, Andrew Rosen, Kyle Bystrom +5
In density functional theory, charge density is the core attribute of atomic systems from which all chemical properties can be derived. Machine learning methods are promising in si…
Addressing the Band Gap Problem with a Machine-Learned Exchange Functional
Kyle Bystrom, Stefano Falletta, Boris Kozinsky
The systematic underestimation of band gaps is one of the most fundamental challenges in semilocal density functional theory (DFT). In addition to hindering the application of DFT…
Transferability and Accuracy of Ionic Liquid Simulations with Equivariant Machine Learning Interatomic Potentials
Zachary A. H. Goodwin, Malia B. Wenny, Julia H. Yang +11
Ionic liquids (ILs) are an exciting class of electrolytes finding applications in many areas from energy storage to solvents, where they have been touted as ``designer solvents'' a…
Nonlocal Machine-Learned Exchange Functional for Molecules and Solids
Kyle Bystrom, Boris Kozinsky
The design of better exchange-correlation functionals for Density Functional Theory (DFT) is a central challenge of modern electronic structure theory. However, current development…
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…
CIDER: An Expressive, Nonlocal Feature Set for Machine Learning Density Functionals with Exact Constraints
Kyle Bystrom, Boris Kozinsky
Machine learning (ML) has recently gained attention as a means to develop more accurate exchange-correlation (XC) functionals for density functional theory, but functionals develop…
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…