Kyle Bystrom

Harvard University — United States

Materials Science · Physical Sciences

7h-index1.1kcitations13works10.62yr avg
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Research Topics

Machine Learning in Materials Science(8), X-ray Diffraction in Crystallography(4), Computational Drug Discovery Methods(3), Ionic liquids properties and applications(2), Electrochemical Analysis and Applications(2)

Publications13 total

Training Machine-Learned Density Functionals on Band Gaps
Journal of Chemical Theory and Computation·2024· 10 cited
Nonlocal machine-learned exchange functional for molecules and solids
Physical review. B./Physical review. B·2024· 12 cited
Transferability and Accuracy of Ionic Liquid Simulations with Equivariant Machine Learning Interatomic Potentials
The Journal of Physical Chemistry Letters·2024· 26 cited
A Recipe for Charge Density Prediction
arXiv (Cornell University)·2024· 3 citedOpen Access
Complexity of many-body interactions in transition metals via machine-learned force fields from the TM23 data set
npj Computational Materials·2024· 34 citedOpen Access
Addressing the Band Gap Problem with a Machine-Learned Exchange Functional
arXiv (Cornell University)·2024Open Access
Transferability and Accuracy of Ionic Liquid Simulations with Equivariant Machine Learning Interatomic Potentials
arXiv (Cornell University)·2024Open Access
A Recipe for Charge Density Prediction
2024
High-throughput calculations of charged point defect properties with semi-local density functional theory—performance benchmarks for materials screening applications
npj Computational Materials·2023· 42 citedOpen Access
Nonlocal Machine-Learned Exchange Functional for Molecules and Solids
arXiv (Cornell University)·2023Open Access
CIDER: An Expressive, Nonlocal Feature Set for Machine Learning Density Functionals with Exact Constraints
Journal of Chemical Theory and Computation·2022Open Access
Pawpyseed: Perturbation-extrapolation band shifting corrections for point defect calculations
arXiv (Cornell University)·2019· 7 citedOpen Access
Matminer: An open source toolkit for materials data mining
Computational Materials Science·2018· 928 citedOpen Access

Frequent Co-authors

Boris Kozinsky(10), Albert Musaelian(5), Mark Asta(3), Kristin A. Persson(3), Simon Batzner(3), Lixin Sun(3), Danny Broberg(2), Shyam Dwaraknath(2), Zachary A. H. Goodwin(2), Malia B. Wenny(2), Julia H. Yang(2), Andrea Cepellotti(2), Jarad A. Mason(2), Nicola Molinari(2), Stefano Falletta(2), Xiang Fu(2), Andrew Rosen(2), Rui Wang(2), Tess Smidt(2), Tommi Jaakkola(2)