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Chenru Duan
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2020 – today
- 2024
- [j4]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): 589-604 (2024) - [i19]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 abs/2404.13430 (2024) - [i18]Guanghao Wei, Yining Huang, Chenru Duan, Yue Song, Yuanqi Du:
Navigating Chemical Space with Latent Flows. CoRR abs/2405.03987 (2024) - [i17]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 abs/2406.16976 (2024) - 2023
- [j3]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): 121 (2023) - [j2]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): 38-47 (2023) - [j1]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): 1045-1055 (2023) - [c2]Yuanqi Du, Yingheng Wang, Yining Huang, Jianan Canal Li, Yanqiao Zhu, Tian Xie, Chenru Duan, John M. Gregoire, Carla Pedro Gomes:
M2Hub: Unlocking the Potential of Machine Learning for Materials Discovery. NeurIPS 2023 - [c1]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 - [i16]Chenru Duan, Yuanqi Du, Haojun Jia, Heather J. Kulik:
Accurate transition state generation with an object-aware equivariant elementary reaction diffusion model. CoRR abs/2304.06174 (2023) - [i15]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 abs/2306.09375 (2023) - [i14]Yuanqi Du, Yingheng Wang, Yining Huang, Jianan Canal Li, Yanqiao Zhu, Tian Xie, Chenru Duan, John M. Gregoire, Carla P. Gomes:
M2Hub: Unlocking the Potential of Machine Learning for Materials Discovery. CoRR abs/2307.05378 (2023) - 2022
- [i13]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 abs/2201.04243 (2022) - [i12]Chenru Duan, Aditya Nandy, Husain Adamji, Yuriy Román-Leshkov, Heather J. Kulik:
Machine learning models predict calculation outcomes with the transferability necessary for computational catalysis. CoRR abs/2203.01276 (2022) - [i11]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 abs/2205.02879 (2022) - [i10]Chenru Duan, Fang Liu, Aditya Nandy, Heather J. Kulik:
Putting Density Functional Theory to the Test in Machine-Learning-Accelerated Materials Discovery. CoRR abs/2205.02967 (2022) - [i9]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 abs/2207.10747 (2022) - [i8]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 abs/2208.05444 (2022) - [i7]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 abs/2209.08595 (2022) - [i6]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 abs/2210.14191 (2022) - 2021
- [i5]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 abs/2106.10768 (2021) - [i4]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 abs/2106.13109 (2021) - [i3]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 abs/2106.13327 (2021) - [i2]Aditya Nandy, Gianmarco Terrones, Naveen Arunachalam, Chenru Duan, David W. Kastner, Heather J. Kulik:
MOFSimplify: Machine Learning Models with Extracted Stability Data of Three Thousand Metal-Organic Frameworks. CoRR abs/2109.08098 (2021) - [i1]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 abs/2111.01905 (2021)
Coauthor Index
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