Chenru Duan
Chenru Duan
ex-Microsoft; ex-MIT
E-mail confirmado em - Página inicial
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A quantitative uncertainty metric controls error in neural network-driven chemical discovery
JP Janet, C Duan, T Yang, A Nandy, HJ Kulik
Chemical science 10 (34), 7913-7922, 2019
Computational discovery of transition-metal complexes: from high-throughput screening to machine learning
A Nandy, C Duan, MG Taylor, F Liu, AH Steeves, HJ Kulik
Chemical reviews 121 (16), 9927-10000, 2021
Accurate multiobjective design in a space of millions of transition metal complexes with neural-network-driven efficient global optimization
JP Janet, S Ramesh, C Duan, HJ Kulik
ACS central science 6 (4), 513-524, 2020
Strategies and software for machine learning accelerated discovery in transition metal chemistry
A Nandy, C Duan, JP Janet, S Gugler, HJ Kulik
Industrial & Engineering Chemistry Research 57 (42), 13973-13986, 2018
Using machine learning and data mining to leverage community knowledge for the engineering of stable metal–organic frameworks
A Nandy, C Duan, HJ Kulik
Journal of the American Chemical Society 143 (42), 17535-17547, 2021
Designing in the face of uncertainty: exploiting electronic structure and machine learning models for discovery in inorganic chemistry
JP Janet, F Liu, A Nandy, C Duan, T Yang, S Lin, HJ Kulik
Inorganic chemistry 58 (16), 10592-10606, 2019
Zero-temperature localization in a sub-Ohmic spin-boson model investigated by an extended hierarchy equation of motion
C Duan, Z Tang, J Cao, J Wu
Physical Review B 95 (21), 214308, 2017
Machine learning accelerates the discovery of design rules and exceptions in stable metal–oxo intermediate formation
A Nandy, J Zhu, JP Janet, C Duan, RB Getman, HJ Kulik
Acs Catalysis 9 (9), 8243-8255, 2019
Learning from failure: predicting electronic structure calculation outcomes with machine learning models
C Duan, JP Janet, F Liu, A Nandy, HJ Kulik
Journal of Chemical Theory and Computation 15 (4), 2331-2345, 2019
Seeing is believing: Experimental spin states from machine learning model structure predictions
MG Taylor, T Yang, S Lin, A Nandy, JP Janet, C Duan, HJ Kulik
The Journal of Physical Chemistry A 124 (16), 3286-3299, 2020
MOFSimplify, machine learning models with extracted stability data of three thousand metal–organic frameworks
A Nandy, G Terrones, N Arunachalam, C Duan, DW Kastner, HJ Kulik
Scientific Data 9 (1), 74, 2022
Rapid detection of strong correlation with machine learning for transition-metal complex high-throughput screening
F Liu, C Duan, HJ Kulik
The journal of physical chemistry letters 11 (19), 8067-8076, 2020
Navigating transition-metal chemical space: artificial intelligence for first-principles design
JP Janet, C Duan, A Nandy, F Liu, HJ Kulik
Accounts of Chemical Research 54 (3), 532-545, 2021
New strategies for direct methane-to-methanol conversion from active learning exploration of 16 million catalysts
A Nandy, C Duan, C Goffinet, HJ Kulik
Jacs Au 2 (5), 1200-1213, 2022
Data-driven approaches can overcome the cost–accuracy trade-off in multireference diagnostics
C Duan, F Liu, A Nandy, HJ Kulik
Journal of Chemical Theory and Computation 16 (7), 4373-4387, 2020
Putting density functional theory to the test in machine-learning-accelerated materials discovery
C Duan, F Liu, A Nandy, HJ Kulik
The Journal of Physical Chemistry Letters 12 (19), 4628-4637, 2021
Semi-supervised machine learning enables the robust detection of multireference character at low cost
C Duan, F Liu, A Nandy, HJ Kulik
The Journal of Physical Chemistry Letters 11 (16), 6640-6648, 2020
Audacity of huge: overcoming challenges of data scarcity and data quality for machine learning in computational materials discovery
A Nandy, C Duan, HJ Kulik
Current Opinion in Chemical Engineering 36, 100778, 2022
Large-scale comparison of 3d and 4d transition metal complexes illuminates the reduced effect of exchange on second-row spin-state energetics
A Nandy, DBK Chu, DR Harper, C Duan, N Arunachalam, Y Cytter, ...
Physical Chemistry Chemical Physics 22 (34), 19326-19341, 2020
Machine learning to tame divergent density functional approximations: a new path to consensus materials design principles
C Duan, S Chen, MG Taylor, F Liu, HJ Kulik
Chemical Science 12 (39), 13021-13036, 2021
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