Chenru Duan
According to our database1,
Chenru Duan
authored at least 17 papers
between 2021 and 2023.
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Bibliography
2023
Uncertain of uncertainties? A comparison of uncertainty quantification metrics for chemical data sets.
J. Cheminformatics, December, 2023
A transferable recommender approach for selecting the best density functional approximations in chemical discovery.
Nat. Comput. Sci., 2023
Accurate transition state generation with an object-aware equivariant elementary reaction diffusion model.
Nat. Comput. Sci., 2023
Symmetry-Informed Geometric Representation for Molecules, Proteins, and Crystalline Materials.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023
M<sup>2</sup>Hub: Unlocking the Potential of Machine Learning for Materials Discovery.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023
2022
A Database of Ultrastable MOFs Reassembled from Stable Fragments with Machine Learning Models.
CoRR, 2022
Low-cost machine learning approach to the prediction of transition metal phosphor excited state properties.
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.
CoRR, 2022
Putting Density Functional Theory to the Test in Machine-Learning-Accelerated Materials Discovery.
CoRR, 2022
Exploiting Ligand Additivity for Transferable Machine Learning of Multireference Character Across Known Transition Metal Complex Ligands.
CoRR, 2022
Machine learning models predict calculation outcomes with the transferability necessary for computational catalysis.
CoRR, 2022
Two Wrongs Can Make a Right: A Transfer Learning Approach for Chemical Discovery with Chemical Accuracy.
CoRR, 2022
2021
Audacity of huge: overcoming challenges of data scarcity and data quality for machine learning in computational materials discovery.
CoRR, 2021
MOFSimplify: Machine Learning Models with Extracted Stability Data of Three Thousand Metal-Organic Frameworks.
CoRR, 2021
Using Machine Learning and Data Mining to Leverage Community Knowledge for the Engineering of Stable Metal-Organic Frameworks.
CoRR, 2021
Machine learning to tame divergent density functional approximations: a new path to consensus materials design principles.
CoRR, 2021
Representations and Strategies for Transferable Machine Learning Models in Chemical Discovery.
CoRR, 2021