Publications

Author Title Type [ Year(Asc)]
2020
Accurate multi-objective design in a space of millions of transition metal complexes with neural-network-driven efficient global optimization, Janet, Jon Paul, Ramesh Sahasrajit, Duan Chenru, and Kulik Heather J. , 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, Mehmood, Rimsha, and Kulik Heather J. , Journal of Chemical Theory and Computation, Volume 16, p.3121-3134, (2020)
Data-Driven Approaches Can Overcome the Cost-Accuracy Tradeoff in Multireference Diagnostics, Duan, Chenru, Liu Fang, Nandy Aditya, and Kulik Heather J. , Journal of Chemical Theory and Computation, Volume 16, p.4373-4387, (2020)
Enumeration of de novo inorganic complexes for chemical discovery and machine learning, Gugler, Stefan, Janet Jon Paul, and Kulik Heather J. , 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, Liu, Fang, and Kulik Heather J. , Journal of Chemical Theory and Computation, Volume 16, Issue 1, p.264-277, (2020)
Large-scale comparison of 3d and 4d transition metal complexes illuminates the reduced effect of exchange on second-row spin-state energetics, Nandy, Aditya, Chu Daniel B. K., Harper Daniel R., Duan Chenru, Arunachalam Naveen, Cytter Yael, and Kulik Heather J. , Physical Chemistry Chemical Physics, Volume 22, p.19326-19341, (2020)
Machine Learning in Chemistry, Janet, Jon Paul, and Kulik Heather J. , ACS In Focus Series, (2020)
Making machine learning a useful tool in the accelerated discovery of transition metal complexes, Kulik, Heather J. , 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, Liu, Fang, Duan Chenru, and Kulik Heather J. , The Journal of Physical Chemistry Letters, Volume 11, (2020)
Seeing is Believing: Experimental Spin States from Machine Learning Model Structure Predictions, Taylor, Michael G., Yang Tzuhsiung, Lin Sean, Nandy Aditya, Janet Jon Paul, Duan Chenru, and Kulik Heather J. , 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, Duan, Chenru, Liu Fang, Nandy Aditya, and Kulik Heather J. , 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, Bajaj, Akash, Liu Fang, and Kulik Heather J. , The Journal of Physical Chemistry C, Volume 124, p.15094-15106, (2020)
Understanding the diversity of the metal-organic framework ecosystem, Moosavi, Seyed Mohamad, Nandy Aditya, Jablonka Kevin M., Ongari Daniele, Janet Jon Paul, Boyd Peter G., Lee Yongjin, Smit Berend, and Kulik Heather J. , Nature Communications, Volume 11, (2020)
2019
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, Transue, Wesley, Nava Matthew, Terban Maxwell, Yang Jing, Greenberg Matthew, Wu Gang, Mustoe Chantal, Kennepohl Pierre, Owen Jonathan, Billinge Simon, et al. , 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, Liu, Fang, Yang Tzuhsiung, Yang Jing, Xu Eve, Bajaj Akash, and Kulik Heather J. , Frontiers In Chemistry, Volume 7, p.219, (2019)
Critical Knowledge Gaps in Mass Transport Through Single- Digit Nanopores: A Review and Perspective, Faucher, Samuel, Aluru Narayana, Bazant Martin, Blankschtein Daniel, Brozena Alexandra, Cumings John, J. de Souza Pedro, Menachem Elimelech, Epsztein Razi, Fourkas John, et al. , 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, Janet, Jon Paul, Liu Fang, Nandy Aditya, Duan Chenru, Yang Tzuhsiung, Lin Sean, and Kulik Heather J. , 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, Qi, Helena W., and Kulik Heather J. , 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, Liu, Fang, Sanchez David M., Kulik Heather J., and Martínez Todd J. , International Journal of Quantum Chemistry , 10/2018, Volume 119, Issue 1, (2019)
Learning from Failure: Predicting Electronic Structure Calculation Outcomes with Machine Learning Models, Duan, Chenru, Janet Jon Paul, Liu Fang, Nandy Aditya, and Kulik Heather J. , Journal of Chemical Theory and Computation, Volume 15, p.2331-2345, (2019)

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About Us

The Kulik group focuses on the development and application of new electronic structure methods and atomistic simulations tools in the broad area of catalysis.

Our Interests

We are interested in transition metal chemistry, with applications from biological systems (i.e. enzymes) to nonbiological applications in surface science and molecular catalysis.

Our Focus

A key focus of our group is to understand mechanistic features of complex catalysts and to facilitate and develop tools for computationally driven design.

Contact Us

Questions or comments? Let us know! Contact Dr. Kulik: