molSimplify: A toolkit for automating discovery in inorganic chemistry. - Efthymios I. Ioannidis; Terry Z. H. Gani; Heather J. Kulik (J. Comput. Chem., 2016)

Evaluating Unexpectedly Short Non-covalent Distances in X-ray Crystal Structures of Proteins with Electronic Structure Analysis. - Helena W. Qi; Heather J. Kulik (J. Chem. Inf. Model., 2019)

Reply to "Comment on 'Evaluating Unexpectedly Short Non-covalent Distances in X-ray Crystal Structures of Proteins with Electronic Structure Analysis'". - Helena W. Qi; Heather J. Kulik (J. Chem. Inf. Model., 2019)

Representations and Strategies for Transferable Machine Learning Models in Chemical Discovery. - Daniel R. Harper; Aditya Nandy; Naveen Arunachalam; Chenru Duan; Jon Paul Janet; Heather J. Kulik (CoRR, 2021)

Machine learning to tame divergent density functional approximations: a new path to consensus materials design principles. - Chenru Duan; Shuxin Chen; Michael G. Taylor; Fang Liu; Heather J. Kulik (CoRR, 2021)

Using Machine Learning and Data Mining to Leverage Community Knowledge for the Engineering of Stable Metal-Organic Frameworks. - Aditya Nandy; Chenru Duan; Heather J. Kulik (CoRR, 2021)

Deciphering Cryptic Behavior in Bimetallic Transition Metal Complexes with Machine Learning. - Michael G. Taylor; Aditya Nandy; Connie C. Lu; Heather J. Kulik (CoRR, 2021)

MOFSimplify: Machine Learning Models with Extracted Stability Data of Three Thousand Metal-Organic Frameworks. - Aditya Nandy; Gianmarco Terrones; Naveen Arunachalam; Chenru Duan; David W. Kastner; Heather J. Kulik (CoRR, 2021)

Audacity of huge: overcoming challenges of data scarcity and data quality for machine learning in computational materials discovery. - Aditya Nandy; Chenru Duan; Heather J. Kulik (CoRR, 2021)

Two Wrongs Can Make a Right: A Transfer Learning Approach for Chemical Discovery with Chemical Accuracy. - Chenru Duan; Daniel B. K. Chu; Aditya Nandy; Heather J. Kulik (CoRR, 2022)

Machine learning models predict calculation outcomes with the transferability necessary for computational catalysis. - Chenru Duan; Aditya Nandy; Husain Adamji; Yuriy Román-Leshkov; Heather J. Kulik (CoRR, 2022)

Exploiting Ligand Additivity for Transferable Machine Learning of Multireference Character Across Known Transition Metal Complex Ligands. - Chenru Duan; Adriana J. Ladera; Julian C.-L. Liu; Michael G. Taylor; Isuru R. Ariyarathna; Heather J. Kulik (CoRR, 2022)

Putting Density Functional Theory to the Test in Machine-Learning-Accelerated Materials Discovery. - Chenru Duan; Fang Liu; Aditya Nandy; Heather J. Kulik (CoRR, 2022)

Rapid Exploration of a 32.5M Compound Chemical Space with Active Learning to Discover Density Functional Approximation Insensitive and Synthetically Accessible Transitional Metal Chromophores. - Chenru Duan; Aditya Nandy; Gianmarco Terrones; David W. Kastner; Heather J. Kulik (CoRR, 2022)

Low-cost machine learning approach to the prediction of transition metal phosphor excited state properties. - Gianmarco Terrones; Chenru Duan; Aditya Nandy; Heather J. Kulik (CoRR, 2022)

A Database of Ultrastable MOFs Reassembled from Stable Fragments with Machine Learning Models. - Aditya Nandy; Shuwen Yue; Changhwan Oh; Chenru Duan; Gianmarco G. Terrones; Yongchul G. Chung; Heather J. Kulik (CoRR, 2022)

Accurate transition state generation with an object-aware equivariant elementary reaction diffusion model. - Chenru Duan; Yuanqi Du; Haojun Jia; Heather J. Kulik (Nat. Comput. Sci., 2023)

A transferable recommender approach for selecting the best density functional approximations in chemical discovery. - Chenru Duan; Aditya Nandy; Ralf Meyer; Naveen Arunachalam; Heather J. Kulik (Nat. Comput. Sci., 2023)

SESAMI APP: An Accessible Interface for Surface Area Calculation of Materials from Adsorption Isotherms. - Gianmarco G. Terrones; Yu Chen; Archit Datar; Li-Chiang Lin; Heather J. Kulik; Yongchul G. Chung (Zenodo, 2023)

SESAMI APP: An Accessible Interface for Surface Area Calculation of Materials from Adsorption Isotherms. - Gianmarco G. Terrones; Yu Chen; Archit Datar; Li-Chiang Lin; Heather J. Kulik; Yongchul G. Chung (Zenodo, 2023)

SESAMI APP: An Accessible Interface for Surface Area Calculation of Materials from Adsorption Isotherms. - Gianmarco G. Terrones; Yu Chen; Archit Datar; Li-Chiang Lin; Heather J. Kulik; Yongchul G. Chung (J. Open Source Softw., 2023)

Uncertain of uncertainties? A comparison of uncertainty quantification metrics for chemical data sets. - Maria H. Rasmussen; Chenru Duan; Heather J. Kulik; Jan H. Jensen (J. Cheminformatics, 2023)

React-OT: Optimal Transport for Generating Transition State in Chemical Reactions. - Chenru Duan; Guan-Horng Liu; Yuanqi Du; Tianrong Chen; Qiyuan Zhao; Haojun Jia; Carla P. Gomes; Evangelos A. Theodorou; Heather J. Kulik (CoRR, 2024)

Many-body Expansion Based Machine Learning Models for Octahedral Transition Metal Complexes. - Ralf Meyer; Daniel B. K. Chu; Heather J. Kulik (CoRR, 2024)

Protein3D: Enabling analysis and extraction of metal-containing sites from the Protein Data Bank with molSimplify. - Freya Edholm; Aditya Nandy; Clorice R. Reinhardt; David W. Kastner; Heather J. Kulik (J. Comput. Chem., 2024)

Ligand Many-Body Expansion as a General Approach for Accelerating Transition Metal Complex Discovery. - Daniel B. K. Chu; David A. González-Narváez; Ralf Meyer; Aditya Nandy; Heather J. Kulik (J. Chem. Inf. Model., 2024)

Assessing UFF and DFT-Tuned Force Fields for Predicting Experimental Isotherms of MOFs. - Yeongsu Cho; Jakob Teetz; Heather J. Kulik (J. Chem. Inf. Model., 2025)

Incorporating Anionic Ligands in Chemical Space Exploration with New Ligand Additivity Relationships. - Heather J. Kulik (J. Chem. Inf. Model., 2025)

Optimal transport for generating transition states in chemical reactions. - Chenru Duan; Guan-Horng Liu; Yuanqi Du; Tianrong Chen; Qiyuan Zhao; Haojun Jia; Carla P. Gomes; Evangelos A. Theodorou; Heather J. Kulik (Nat. Mac. Intell., 2025)

Enhancing Materials Discovery with Valence Constrained Design in Generative Modeling. - Mouyang Cheng; Weiliang Luo; Hao Tang; Bowen Yu; Yongqiang Cheng 006; Weiwei Xie; Ju Li; Heather J. Kulik; Mingda Li (CoRR, 2025)