Publications

[ Author(Desc)] Title Type Year
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z 
D
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. , (In Press)
Mechanically triggered heterolytic unzipping of a low-ceiling-temperature polymer, Diesendruck, Charles E., Peterson Gregory I., Kulik Heather J., Kaitz Joshua A., Mar Brendan D., May Preston A., White Scott R., Martinez Todd J., Boydston Andrew J., and Moore Jeffrey S. , Nature Chemistry, Volume 6, p.623-628, (2014) PDF icon Reprint (1.79 MB)
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)
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)
Putting Density Functional Theory to the Test in Machine-Learning-Accelerated Materials Discovery, Duan, Chenru, Liu Fang, Nandy Aditya, and Kulik Heather J. , (Submitted)
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)
J
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)
Machine Learning in Chemistry, Janet, Jon Paul, and Kulik Heather J. , ACS In Focus Series, (2020)
Predicting Electronic Structure Properties of Transition Metal Complexes with Neural Networks, Janet, Jon Paul, and Kulik Heather J. , Chemical Science, Volume 8, p.5137-5152, (2017)
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)
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)
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)
Leveraging Cheminformatics Strategies for Inorganic Discovery: Application to Redox Potential Design, Janet, Jon Paul, Gani Terry Z. H., Steeves Adam H., Ioannidis Efthymios I., and Kulik Heather J. , Industrial & Engineering Chemistry Research, Invited Cover Article for "2017 Class of Influential Researchers" Virtual Issue, Volume 56, Issue 17, p.4898-4910, (2017)

Pages

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: