Publications

Export 112 results:
Author Title Type [ Year(Asc)]
Submitted
Computational Scaling Relationships Predict Experimental Activity and Rate Limiting Behavior in Homogenous Water Oxidation, Harper, Daniel R., and Kulik Heather J. , (Submitted)
Eliminating Delocalization Error to Improve Heterogeneous Catalysis Predictions with Molecular DFT+U, Bajaj, Akash, and Kulik Heather J. , (Submitted)
Machine Learning for the Discovery, Design, and Engineering of Materials, Duan, Chenru, Nandy Aditya, and Kulik Heather J. , (Submitted)
Modeling the roles of rigidity and dopants in single-atom methane-to-methanol catalysts, Jia, Haojun, Nandy Aditya, Liu Mingjie, and Kulik Heather J. , (Submitted)
MOFSimplify: Machine Learning Models with Extracted Stability Data of Three Thousand Metal–Organic Frameworks, Nandy, Aditya, Terrones Gianmarco, Arunachalam Naveen, Duan Chenru, Kastner David W., and Kulik Heather J. , (Submitted)
Representations and Strategies for Transferable Machine Learning Improve Model Performance in Chemical Discovery, Harper, Daniel R., Nandy Aditya, Arunachalam Naveen, Duan Chenru, Janet Jon Paul, and Kulik Heather J. , (Submitted)
In Press
Audacity of huge: overcoming challenges of data scarcity and data quality for machine learning in computational materials discovery, Nandy, Aditya, Duan Chenru, and Kulik Heather J. , Current Opinion in Chemical Engineering, (In Press)
The Effect of Hartree-Fock Exchange on Scaling Relations and Reaction Energetics for C–H Activation Catalysts, Vennelakanti, Vyshnavi, Nandy Aditya, and Kulik Heather J. , Topics in Catalysis, (In Press)
An Irreversible Synthetic Route to an Ultra-Strong Two-Dimensional Polymer, Zeng, Yuwen, Gordiichuk Pavlo, Ichihara Takeo, Zhang Ge, Sandoz-Rosado Emil, Wetzel Eric D., Tresback Jason, Yang Jing, Yang Zhongyue, Kozawa Daichi, et al. , Nature, (In Press)
Large-scale Screening Reveals Geometric Structure Matters More than Electronic Structure in Bioinspired Catalyst Design of Formate Dehydrogenase Mimics, Liu, Mingjie, Nazemi Azadeh, Taylor Michael G., Nandy Aditya, Duan Chenru, Steeves Adam H., and Kulik Heather J. , ACS Catalysis, (In Press)
Machine Learning Reveals Key Ion Selectivity Mechanisms in Polymeric Membranes, Ritt, Cody L., Liu Mingjie, Pham Tuan Anh, Epsztein Razi, Kulik Heather J., and Elimelech Menachem , Science Advances, (In Press)
What’s Left for a Computational Chemist To Do in the Age of Machine Learning?, Kulik, Heather J. , Israel Journal of Chemistry, (In Press)
2022
Harder, better, faster, stronger: large-scale QM and QM/MM for predictive modeling in enzymes and proteins, Vennelakanti, Vyshnavi, Nazemi Azadeh, Mehmood Rimsha, Steeves Adam H., and Kulik Heather J. , Current Opinion in Structural Biology, Volume 72, p.9-17, (2022)
Quantum-mechanical/Molecular-mechanical (QM/MM) Simulations for Understanding Enzyme Dynamics, Mehmood, Rimsha, and Kulik Heather J. , Enzyme Engineering: Methods and Protocols, p.227-248, (2022)
2021
Advancing Discovery in Chemistry with Artificial Intelligence: From Reaction Outcomes to New Materials and Catalysts, Kulik, Heather J., and Sigman Matthew S. , Accounts of Chemical Research, Volume 54, Issue 10, (2021)
Biochemical and crystallographic investigations into isonitrile formation by a non-heme iron-dependent oxidase/decarboxylase, Jonnalagadda, Rohan, Flores Antonio Del Rio, Cai Wenlong, Mehmood Rimsha, Narayanamoorthy Maanasa, Ren Chaoxiang, Zaragoza Jan Paulo T., Kulik Heather J., Zhang Wenjun, and Drennan Catherine L. , The Journal of Biological Chemistry, Volume 296, p.100231, (2021)
Computational Discovery of Transition-Metal Complexes: From High-throughput Screening to Machine Learning, Nandy, Aditya, Duan Chenru, Taylor Michael G., Liu Fang, Steeves Adam H., and Kulik Heather J. , Chemical Reviews, Volume 121, Issue 16, p.9927–10000, (2021)
Deciphering Cryptic Behavior in Bimetallic Transition Metal Complexes with Machine Learning, Taylor, Michael G., Nandy Aditya, Lu Connie C., and Kulik Heather J. , The Journal of Physical Chemistry Letters, Volume 12, p.9812-9820, (2021)
Machine learning to tame divergent density functional approximations: a new path to consensus materials design principles, Duan, Chenru, Chen Shuxin, Taylor Michael G., Liu Fang, and Kulik Heather J. , Chemical Science, Volume 12, (2021)
Mapping the Origins of Surface- and Chemistry-Dependent Doping Trends in III-V Quantum Dots with Density Functional Theory, Taylor, Michael G., and Kulik Heather J. , Chemistry of Materials, Volume 33, p.7113-7123, (2021)
Molecular Basis of C–S Bond Cleavage in the Glycyl Radical Enzyme Isethionate Sulfite-Lyase, Dawson, Christopher, Irwin Stephania, Backman Lindsey, Le Chip, Wang Jennifer X., Vennelakanti Vyshnavi, Yang Zhongyue, Kulik Heather J., Drennan Catherine L., and Balskus Emily P. , Cell Chemical Biology, Volume 28, (2021)
Molecular DFT+U: A Transferable, Low-Cost Approach to Eliminate Delocalization Error, Bajaj, Akash, and Kulik Heather J. , The Journal of Physical Chemistry Letters, Volume 12, p.3633–3640, (2021)
Navigating Transition-Metal Chemical Space: Artificial Intelligence for First-Principles Design, Janet, Jon Paul, Duan Chenru, Nandy Aditya, Liu Fang, and Kulik Heather J. , Accounts of Chemical Research, Volume 54, p.532-545, (2021)
Protein dynamics and substrate protonation state mediate the catalytic action of trans-4-hydroxy-L-proline dehydratase, Yang, Zhongyue, and Kulik Heather J. , The Journal of Physical Chemistry B, Volume 125, Issue 28, p.7774–7784, (2021)
Putting Density Functional Theory to the Test in Machine-Learning-Accelerated Materials Discovery, Duan, Chenru, Liu Fang, Nandy Aditya, and Kulik Heather J. , The Journal of Physical Chemistry Letters, Volume 12, p.4628-4637, (2021)

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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: