Kyle Bystrom
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View article: A Recipe for Charge Density Prediction
A Recipe for Charge Density Prediction Open
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 significantly accelerating charge density prediction, yet exi…
View article: Complexity of many-body interactions in transition metals via machine-learned force fields from the TM23 data set
Complexity of many-body interactions in transition metals via machine-learned force fields from the TM23 data set Open
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 contrast the performance of force, energy, and stress predic…
View article: Addressing the Band Gap Problem with a Machine-Learned Exchange Functional
Addressing the Band Gap Problem with a Machine-Learned Exchange Functional Open
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 to predicting electronic properties, the band gap problem i…
View article: Transferability and Accuracy of Ionic Liquid Simulations with Equivariant Machine Learning Interatomic Potentials
Transferability and Accuracy of Ionic Liquid Simulations with Equivariant Machine Learning Interatomic Potentials Open
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'' as they can be mixed to precisely tailor the physiochemical …
View article: Nonlocal Machine-Learned Exchange Functional for Molecules and Solids
Nonlocal Machine-Learned Exchange Functional for Molecules and Solids Open
The design of better exchange-correlation functionals for Density Functional Theory (DFT) is a central challenge of modern electronic structure theory. However, current developments are limited by the mathematical form of the functional, w…
View article: CIDER: An Expressive, Nonlocal Feature Set for Machine Learning Density Functionals with Exact Constraints
CIDER: An Expressive, Nonlocal Feature Set for Machine Learning Density Functionals with Exact Constraints Open
Machine learning (ML) has recently gained attention as a means to develop more accurate exchange-correlation (XC) functionals for density functional theory, but functionals developed thus far need to be improved on several metrics, includi…
View article: Pawpyseed: Perturbation-extrapolation band shifting corrections for point defect calculations
Pawpyseed: Perturbation-extrapolation band shifting corrections for point defect calculations Open
Significant progress has been made recently in the automation and standardization of ab initio point defect calculations. However, the task of developing, implementing, and benchmarking charge corrections for density functional theory (DFT…