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)

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)

M2Hub: Unlocking the Potential of Machine Learning for Materials Discovery. - Yuanqi Du; Yingheng Wang; Yining Huang; Jianan Canal Li; Yanqiao Zhu 001; Tian Xie; Chenru Duan; John M. Gregoire; Carla Pedro Gomes (NeurIPS, 2023)

Symmetry-Informed Geometric Representation for Molecules, Proteins, and Crystalline Materials. - Shengchao Liu; Weitao Du; Yanjing Li; Zhuoxinran Li; Zhiling Zheng; Chenru Duan; Zhi-Ming Ma; Omar Yaghi; Animashree Anandkumar; Christian Borgs; Jennifer T. Chayes; Hongyu Guo; et al. (NeurIPS, 2023)

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)

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)

Doob's Lagrangian: A Sample-Efficient Variational Approach to Transition Path Sampling. - Yuanqi Du; Michael Plainer; Rob Brekelmans; Chenru Duan; Frank Noé; Carla P. Gomes; Alán Aspuru-Guzik; Kirill Neklyudov (NeurIPS, 2024)

Navigating Chemical Space with Latent Flows. - Guanghao Wei; Yining Huang; Chenru Duan; Yue Song; Yuanqi Du (NeurIPS, 2024)

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)

Efficient Evolutionary Search Over Chemical Space with Large Language Models. - Haorui Wang; Marta Skreta; Cher Tian Ser; Wenhao Gao 001; Lingkai Kong; Felix Streith-Kalthoff; Chenru Duan; Yuchen Zhuang; Yue Yu 001; Yanqiao Zhu 001; Yuanqi Du; Alán Aspuru-Guzik; et al. (CoRR, 2024)

Generative Design of Functional Metal Complexes Utilizing the Internal Knowledge of Large Language Models. - Jieyu Lu; Zhangde Song; Qiyuan Zhao; Yuanqi Du; Yirui Cao; Haojun Jia; Chenru Duan (CoRR, 2024)

Machine learning-aided generative molecular design. - Yuanqi Du; Arian R. Jamasb; Jeff Guo; Tianfan Fu; Charles Harris; Yingheng Wang; Chenru Duan; Pietro Liò; Philippe Schwaller; Tom L. Blundell (Nat. Mac. Intell., 2024)

Efficient Evolutionary Search Over Chemical Space with Large Language Models. - Haorui Wang; Marta Skreta; Cher Tian Ser; Wenhao Gao 001; Lingkai Kong; Felix Strieth-Kalthoff; Chenru Duan; Yuchen Zhuang; Yue Yu 001; Yanqiao Zhu 001; Yuanqi Du; Alán Aspuru-Guzik; et al. (ICLR, 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)

AlphaNet: Scaling Up Local Frame-based Atomistic Foundation Model. - Bangchen Yin; Jiaao Wang; Weitao Du; Pengbo Wang; Penghua Ying; Haojun Jia; Zisheng Zhang; Yuanqi Du; Carla P. Gomes; Chenru Duan; Hai Xiao; Graeme Henkelman (CoRR, 2025)

Large Language Models Are Innate Crystal Structure Generators. - Jingru Gan; Peichen Zhong; Yuanqi Du; Yanqiao Zhu 001; Chenru Duan; Haorui Wang; Carla P. Gomes; Kristin A. Persson; Daniel Schwalbe-Koda; Wei Wang 010 (CoRR, 2025)

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)