Publications
Putting Density Functional Theory to the Test in Machine-Learning-Accelerated Materials Discovery,
, The Journal of Physical Chemistry Letters, (Submitted)
Quantum-mechanical/Molecular-mechanical (QM/MM) Simulations for Understanding Enzyme Dynamics,
, Methods in Molecular Biology, (In Press)
What’s Left for a Computational Chemist To Do in the Age of Machine Learning?,
, Israel Journal of Chemistry, (In Press)
Biochemical and crystallographic investigations into isonitrile formation by a non-heme iron-dependent oxidase/decarboxylase,
, The Journal of Biological Chemistry, Volume 296, p.100231, (2021)
Molecular Basis of C–S Bond Cleavage in the Glycyl Radical Enzyme Isethionate Sulfite-Lyase,
, Cell Chemical Biology, Volume 28, (2021)
Molecular DFT+U: A Transferable, Low-Cost Approach to Eliminate Delocalization Error,
, The Journal of Physical Chemistry Letters, Volume 12, p.3633–3640, (2021)
Navigating Transition-Metal Chemical Space: Artificial Intelligence for First-Principles Design,
, Accounts of Chemical Research, Volume 54, p.532-545, (2021)
When Are Two Hydrogen Bonds Better than One? Accurate First-Principles Models Explain the Balance of Hydrogen Bond Donors and Acceptors Found in Proteins,
, Chemical Science, Volume 12, p.1147-1162, (2021)
Accurate multi-objective design in a space of millions of transition metal complexes with neural-network-driven efficient global optimization,
, ACS Central Science, Volume 6, p.513-524, (2020)
Both Configuration and QM Region Size Matter: Zinc Stability in QM/MM Models of DNA Methyltransferase,
, Journal of Chemical Theory and Computation, Volume 16, p.3121-3134, (2020)
Data-Driven Approaches Can Overcome the Cost-Accuracy Tradeoff in Multireference Diagnostics,
, Journal of Chemical Theory and Computation, Volume 16, p.4373-4387, (2020)
Enumeration of de novo inorganic complexes for chemical discovery and machine learning,
, Molecular Systems Design & Engineering, Volume 5, p.139-152, (2020)
Impact of Approximate DFT Density Delocalization Error on Potential Energy Surfaces in Transition Metal Chemistry,
, Journal of Chemical Theory and Computation, Volume 16, Issue 1, p.264-277, (2020)
Ionization behavior of nanoporous polyamide membranes,
, Proceedings of the National Academy of Sciences, Volume 117, p.30191-30200, (2020)
Large-scale comparison of 3d and 4d transition metal complexes illuminates the reduced effect of exchange on second-row spin-state energetics,
, Physical Chemistry Chemical Physics, Volume 22, p.19326-19341, (2020)
Machine Learning in Chemistry,
, ACS In Focus Series, (2020)
Making machine learning a useful tool in the accelerated discovery of transition metal complexes,
, Wiley Interdisciplinary Reviews: Computational Molecular Science, Volume 10, Issue 1, (2020)
Rapid detection of strong correlation with machine learning for transition metal complex high-throughput screening,
, The Journal of Physical Chemistry Letters, Volume 11, (2020)
Seeing is Believing: Experimental Spin States from Machine Learning Model Structure Predictions,
, The Journal of Physical Chemistry A, Volume 124, p.3286-3299, (2020)
Semi-Supervised Machine Learning Enables the Robust Detection of Multireference Character at Low Cost,
, The Journal of Physical Chemistry Letters, Volume 11, p.6640-6648, (2020)
Uncovering Alternate Pathways to Nafion Membrane Degradation in Fuel Cells with First-Principles Modeling,
, The Journal of Physical Chemistry C, Volume 124, p.15094-15106, (2020)
Understanding the diversity of the metal-organic framework ecosystem,
, Nature Communications, Volume 11, (2020)
Why Conventional Design Rules for C-H Activation Fail for Open Shell Transition Metal Catalysts,
, ACS Catalysis, Volume 10, p.15033-15047, (2020)