Sign in
Chenru Duan
Publication Activity (10 Years)
Years Active: 2021-2023
Publications (10 Years): 18
Top Topics
Reaction Diffusion
Machine Learning
Exploration Exploitation
Machine Learning Models
Top Venues
CoRR
NeurIPS
J. Cheminformatics
</>
Publications
</>
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)
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)