Chenru Duan

Publication Activity (10 Years)

Years Active: 2021-2024
Publications (10 Years): 24

Top Topics

Learning Models

Chemical Reactions

Geometric Shapes

Top Venues

Nat. Comput. Sci.

J. Cheminformatics

Publications

Chenru Duan, Guan-Horng Liu, Yuanqi Du, Tianrong Chen, Qiyuan Zhao, Haojun Jia, Carla P. Gomes, Evangelos A. Theodorou, Heather J. Kulik
React-OT: Optimal Transport for Generating Transition State in Chemical Reactions. CoRR (2024)
Guanghao Wei, Yining Huang, Chenru Duan, Yue Song, Yuanqi Du
Navigating Chemical Space with Latent Flows. CoRR (2024)
Haorui Wang, Marta Skreta, Cher-Tian Ser, Wenhao Gao, Lingkai Kong, Felix Streith-Kalthoff, Chenru Duan, Yuchen Zhuang, Yue Yu, Yanqiao Zhu, Yuanqi Du, Alán Aspuru-Guzik, Kirill Neklyudov, Chao Zhang
Efficient Evolutionary Search Over Chemical Space with Large Language Models. CoRR (2024)
Yuanqi Du, Arian R. Jamasb, Jeff Guo, Tianfan Fu, Charles Harris, Yingheng Wang, Chenru Duan, Pietro Liò, Philippe Schwaller, Tom L. Blundell
Machine learning-aided generative molecular design. Nat. Mac. Intell. 6 (6) (2024)
Chenru Duan, Aditya Nandy, Ralf Meyer, Naveen Arunachalam, Heather J. Kulik
A transferable recommender approach for selecting the best density functional approximations in chemical discovery. Nat. Comput. Sci. 3 (1) (2023)
Yuanqi Du, Yingheng Wang, Yining Huang, Jianan Canal Li, Yanqiao Zhu, Tian Xie, Chenru Duan, John M. Gregoire, Carla P. Gomes
Hub: Unlocking the Potential of Machine Learning for Materials Discovery. CoRR (2023)
Chenru Duan, Yuanqi Du, Haojun Jia, Heather J. Kulik
Accurate transition state generation with an object-aware equivariant elementary reaction diffusion model. Nat. Comput. Sci. 3 (12) (2023)
Shengchao Liu, Weitao Du, Yanjing Li, Zhuoxinran Li, Zhiling Zheng, Chenru Duan, Zhiming Ma, Omar Yaghi, Anima Anandkumar, Christian Borgs, Jennifer T. Chayes, Hongyu Guo, Jian Tang
Symmetry-Informed Geometric Representation for Molecules, Proteins, and Crystalline Materials. CoRR (2023)
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, Jian Tang
Symmetry-Informed Geometric Representation for Molecules, Proteins, and Crystalline Materials. NeurIPS (2023)
Maria H. Rasmussen, Chenru Duan, Heather J. Kulik, Jan H. Jensen
Uncertain of uncertainties? A comparison of uncertainty quantification metrics for chemical data sets. J. Cheminformatics 15 (1) (2023)
Chenru Duan, Yuanqi Du, Haojun Jia, Heather J. Kulik
Accurate transition state generation with an object-aware equivariant elementary reaction diffusion model. CoRR (2023)
Yuanqi Du, Yingheng Wang, Yining Huang, Jianan Canal Li, Yanqiao Zhu, Tian Xie, Chenru Duan, John M. Gregoire, Carla Pedro Gomes
Hub: Unlocking the Potential of Machine Learning for Materials Discovery. NeurIPS (2023)
Chenru Duan, Aditya Nandy, Husain Adamji, Yuriy Roman-Leshkov, Heather J. Kulik
Machine learning models predict calculation outcomes with the transferability necessary for computational catalysis. CoRR (2022)
Chenru Duan, Daniel B. K. Chu, Aditya Nandy, Heather J. Kulik
Two Wrongs Can Make a Right: A Transfer Learning Approach for Chemical Discovery with Chemical Accuracy. CoRR (2022)
Chenru Duan, Aditya Nandy, Ralf Meyer, Naveen Arunachalam, Heather J. Kulik
A Transferable Recommender Approach for Selecting the Best Density Functional Approximations in Chemical Discovery. CoRR (2022)
Gianmarco Terrones, Chenru Duan, Aditya Nandy, Heather J. Kulik
Low-cost machine learning approach to the prediction of transition metal phosphor excited state properties. CoRR (2022)
Chenru Duan, Adriana J. Ladera, Julian C.-L. Liu, Michael G. Taylor, Isuru R. Ariyarathna, Heather J. Kulik
Exploiting Ligand Additivity for Transferable Machine Learning of Multireference Character Across Known Transition Metal Complex Ligands. CoRR (2022)
Chenru Duan, Fang Liu, Aditya Nandy, Heather J. Kulik
Putting Density Functional Theory to the Test in Machine-Learning-Accelerated Materials Discovery. CoRR (2022)
Aditya Nandy, Shuwen Yue, Changhwan Oh, Chenru Duan, Gianmarco G. Terrones, Yongchul G. Chung, Heather J. Kulik
A Database of Ultrastable MOFs Reassembled from Stable Fragments with Machine Learning Models. CoRR (2022)
Chenru Duan, Aditya Nandy, Gianmarco Terrones, David W. Kastner, Heather J. Kulik
Rapid Exploration of a 32.5M Compound Chemical Space with Active Learning to Discover Density Functional Approximation Insensitive and Synthetically Accessible Transitional Metal Chromophores. CoRR (2022)
Daniel R. Harper, Aditya Nandy, Naveen Arunachalam, Chenru Duan, Jon Paul Janet, Heather J. Kulik
Representations and Strategies for Transferable Machine Learning Models in Chemical Discovery. CoRR (2021)
Aditya Nandy, Chenru Duan, Heather J. Kulik
Using Machine Learning and Data Mining to Leverage Community Knowledge for the Engineering of Stable Metal-Organic Frameworks. CoRR (2021)
Aditya Nandy, Chenru Duan, Heather J. Kulik
Audacity of huge: overcoming challenges of data scarcity and data quality for machine learning in computational materials discovery. CoRR (2021)
Chenru Duan, Shuxin Chen, Michael G. Taylor, Fang Liu, Heather J. Kulik
Machine learning to tame divergent density functional approximations: a new path to consensus materials design principles. CoRR (2021)