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Density functional theory for modelling large molecular adsorbate-surface interactions: a mini-review and worked example, Janet, Jon Paul, Zhao Qing, Ioannidis Efthymios I., and Kulik Heather J. , Molecular Simulation, Invited Cover article in special "Surface Chemistry" issue, Volume 43, p.327-345, (2017) PDF icon Reprint (1.36 MB)
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)
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)
Resolving Transition Metal Chemical Space: Feature Selection for Machine Learning and Structure-Property Relationships, Janet, Jon Paul, and Kulik Heather J. , The Journal of Physical Chemistry A, Volume 121 , p.8939-8954, (2017)
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)
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)
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)
Machine Learning in Chemistry, Janet, Jon Paul, and Kulik Heather J. , ACS In Focus Series, (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)
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)

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: