Author Title Type [ Year(Asc)]
Filters: Author is Heather J. Kulik  [Clear All Filters]
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)
Machine Learning Accelerates the Discovery of Design Rules and Exceptions in Stable Metal-Oxo Intermediate Formation, Nandy, Aditya, Zhu Jiazhou, Janet Jon Paul, Duan Chenru, Getman Rachel B., and Kulik Heather J. , ACS Catalysis, Volume 9, p.8243-8255, (2019)
Non-empirical, low-cost recovery of exact conditions with model-Hamiltonian inspired expressions in jmDFT, Bajaj, Akash, Liu Fang, and Kulik Heather J. , Journal of Chemical Physics, Volume 150, p.154115, (2019)
Protection of tissue physicochemical properties using polyfunctional crosslinkers, Park, Young-Gyun, Sohn Chang Ho, Chen Ritchie, McCue Margaret, Drummond Gabrielle T., Ku Taeyun, Yun Dae Hee, Evans Nicholas B., Oak Hayeon Caitlyn, Trieu Wendy, et al. , 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, Mehmood, Rimsha, Qi Helena W., Steeves Adam H., and Kulik Heather J. , ACS Catalysis, Volume 9, p.4930-4943, (2019)
A quantitative uncertainty metric controls error in neural network-driven chemical discovery, Janet, Jon Paul, Duan Chenru, Yang Tzuhsiung, Nandy Aditya, and Kulik Heather J. , Chemical Science, Volume 10, p.7913-7922, (2019)
Quantum Mechanical Description of Electrostatics Provides a Unified Picture of Catalytic Action Across Methyltransferases, Yang, Zhongyue, Liu Fang, Steeves Adam H., and Kulik Heather J. , The Journal of Physical Chemistry Letters, Volume 10, p.3779-3787, (2019)
Revealing quantum mechanical effects in enzyme catalysis with large-scale electronic structure simulation, Yang, Zhongyue, Mehmood Rimsha, Wang Mengyi, Qi Helena W., Steeves Adam H., and Kulik Heather J. , Reaction Chemistry & Engineering, Volume 4, p.298-315, (2019)
Stable Surfaces that Bind too Tightly: Can Range Separated Hybrids or DFT+U Improve Paradoxical Descriptions of Surface Chemistry?, Zhao, Qing, and Kulik Heather J. , The Journal of Physical Chemistry Letters, Volume 10, p.5090-5098, (2019)
Accelerating Chemical Discovery with Machine Learning: Simulated Evolution of Spin Crossover Complexes with an Artificial Neural Network, Janet, Jon Paul, Chan Lydia, and Kulik Heather J. , The Journal of Physical Chemistry Letters, 02/2018, Volume 9, p.1064-1071, (2018)
Electronic Structure Origins of Surface-Dependent Growth in III-V Quantum Dots, Zhao, Qing, and Kulik Heather J. , Chemistry of Materials, 09/2018, Volume 30, p.7154-7165, (2018)
Large-scale QM/MM free energy simulations of enzyme catalysis reveal the influence of charge transfer, Kulik, Heather J. , Physical Chemistry Chemical Physics, 07/2018, Volume 20, p.20650-20660, (2018)
Modeling Mechanochemistry from First Principles, Kulik, Heather J. , Reviews in Computational Chemistry, Volume 31, (2018)
Quantifying electronic effects in QM and QM/MM biomolecular modeling with the Fukui function, Qi, Helena W., Karelina Maria, and Kulik Heather J. , Acta Physico-Chimica Sinica (invited article for Conceptual Density Functional Theory special issue, ed. Shubin Liu), Volume 34, p.81-91, (2018)
Strategies and Software for Machine Learning Accelerated Discovery in Transition Metal Chemistry, Nandy, Aditya, Duan Chenru, Janet Jon Paul, Gugler Stefan, and Kulik Heather J. , Industrial & Engineering Chemistry Research (invited for special issue), 09/2018, Volume 57, (2018)
Understanding and Breaking Scaling Relations in Single-Site Catalysis: Methane-to-methanol Conversion by Fe(IV)=O, Gani, Terry Z. H., and Kulik Heather J. , ACS Catalysis, Volume 8, p.975-986, (2018)


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: