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)

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)

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)

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)

Score-based generative diffusion with "active" correlated noise sources. - Alexandra Lamtyugina; Agnish Kumar Behera; Aditya Nandy; Carlos Floyd; Suriyanarayanan Vaikuntanathan (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)

Building-Block Aware Generative Modeling for 3D Crystals of Metal Organic Frameworks. - Chenru Duan; Aditya Nandy; Sizhan Liu; Yuanqi Du; Liu He; Yi Qu; Haojun Jia; Jin-Hu Dou (CoRR, 2025)