Publications
] Data-Driven Approaches Can Overcome the Cost-Accuracy Tradeoff in Multireference Diagnostics,
, (Submitted)
Ionization behavior of nanoporous polyamide membranes: implications for the permeability-selectivity tradeoff,
, (Submitted)
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)
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)
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)
Seeing is Believing: Experimental Spin States from Machine Learning Model Structure Predictions,
, The Journal of Physical Chemistry A, Volume 124, p.3286-3299, (2020)
Anthracene as a Launchpad for a Phosphinidene Sulfide and for Generation of a Phosphorus-Sulfur Material having the Composition P2S, a Vulcanized Red Phosphorus that is Yellow,
, Journal of the American Chemical Society, Volume 141, p.431-440, (2019)
Bridging the homogeneous-heterogeneous divide: modeling spin and reactivity in single atom catalysis,
, Frontiers In Chemistry, Volume 7, p.219, (2019)
Critical Knowledge Gaps in Mass Transport Through Single- Digit Nanopores: A Review and Perspective,
, The Journal of Physical Chemistry C, Volume 123, p.21309-21326, (2019)
Designing in the Face of Uncertainty: Exploiting Electronic Structure and Machine Learning Models for Discovery in Inorganic Chemistry,
, Inorganic Chemistry, Volume 58, p.10592-10606, (2019)
Evaluating Unexpectedly Short Non-covalent Distances in X-ray Crystal Structures of Proteins with Electronic Structure Analysis,
, Journal of Chemical Information and Modeling, Volume 59, p.2199-2211, (2019)
Exploiting graphical processing units to enable quantum chemistry calculation of large solvated molecules with conductor-like polarizable continuum models,
, International Journal of Quantum Chemistry , 10/2018, Volume 119, Issue 1, (2019)
Learning from Failure: Predicting Electronic Structure Calculation Outcomes with Machine Learning Models,
, Journal of Chemical Theory and Computation, Volume 15, p.2331-2345, (2019)
Machine Learning Accelerates the Discovery of Design Rules and Exceptions in Stable Metal-Oxo Intermediate Formation,
, ACS Catalysis, Volume 9, p.8243-8255, (2019)
Non-empirical, low-cost recovery of exact conditions with model-Hamiltonian inspired expressions in jmDFT,
, Journal of Chemical Physics, Volume 150, p.154115, (2019)
Protection of tissue physicochemical properties using polyfunctional crosslinkers,
, Nature Biotechnology, Volume 37, p.73-83, (2019)
The Protein’s Role in Substrate Positioning and Reactivity for Biosynthetic Enzyme Complexes: the Case of SyrB2/SyrB1,
, ACS Catalysis, Volume 9, p.4930-4943, (2019)
A quantitative uncertainty metric controls error in neural network-driven chemical discovery,
, Chemical Science, Volume 10, p.7913-7922, (2019)
Quantum Mechanical Description of Electrostatics Provides a Unified Picture of Catalytic Action Across Methyltransferases,
, The Journal of Physical Chemistry Letters, Volume 10, p.3779-3787, (2019)