Publications

Author Title Type [ Year(Asc)]
Filters: Author is Jon Paul Janet  [Clear All Filters]
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)
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)
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)
Machine Learning in Chemistry, Janet, Jon Paul, and Kulik Heather J. , ACS In Focus Series, (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)
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
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)
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)
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)
2018
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)
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)
2017
Communication: Recovering the flat plane condition in electronic structure theory at semi-local DFT cost, Bajaj, Akash, Janet Jon Paul, and Kulik Heather J. , Journal of Chemical Physics, Volume 147, p.191101, (2017)
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)
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)
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)
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)

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: