Chenru Duan

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Strategies and Software for Machine Learning Accelerated Discovery in Transition Metal Chemistry

Chenru Duan, Aditya Nandy, Jon Paul Janet, 2018, Industrial & Engineering Chemistry Research.

Learning from Failure: Predicting Electronic Structure Calculation Outcomes with Machine Learning Models.

Fang Liu, Chenru Duan, Aditya Nandy, 2019, Journal of chemical theory and computation.

A Quantitative Uncertainty Metric Controls Error in Neural Network-Driven Chemical Discovery

H. Kulik, J. Janet, A. Nandy, 2019 .

Designing in the Face of Uncertainty: Exploiting Electronic Structure and Machine Learning Models for Discovery in Inorganic Chemistry.

F. Liu, H. Kulik, J. Janet, 2019, Inorganic chemistry.

Seeing Is Believing: Experimental Spin States from Machine Learning Model Structure Predictions

Michael G. Taylor, H. Kulik, J. Janet, 2020, The journal of physical chemistry. A.

Large-scale comparison of 3d and 4d transition metal complexes illuminates the reduced effect of exchange on second-row spin-state energetics.

H. Kulik, A. Nandy, Chenru Duan, 2020, Physical chemistry chemical physics : PCCP.

Accelerating inorganic discovery with meta-calculation filtering via a decision classifier

Fang Liu, Chenru Duan, Aditya Nandy, 2019 .

Zero-temperature localization in a sub-Ohmic spin-boson model investigated by an extended hierarchy equation of motion

Jianshu Cao, Chenru Duan, Jianlan Wu, 2017 .

Detection of multi-reference character imbalances enables a transfer learning approach for virtual high throughput screening with coupled cluster accuracy at DFT cost

H. Kulik, A. Nandy, Chenru Duan, 2022, Chemical science.

New Strategies for Direct Methane-to-Methanol Conversion from Active Learning Exploration of 16 Million Catalysts

H. Kulik, A. Nandy, Chenru Duan, 2022, JACS Au.

Rapid Detection of Strong Correlation with Machine Learning for Transition-Metal Complex High-Throughput Screening.

H. Kulik, Chenru Duan, Fang Liu, 2020, The journal of physical chemistry letters.

Accurate Multiobjective Design in a Space of Millions of Transition Metal Complexes with Neural-Network-Driven Efficient Global Optimization

H. Kulik, J. Janet, Chenru Duan, 2019, ACS central science.

A transferable recommender approach for selecting the best density functional approximations in chemical discovery

H. Kulik, A. Nandy, Chenru Duan, 2022, Nature Computational Science.

Data-Driven Approaches Can Overcome Limitations in Multireference Diagnostics

H. Kulik, A. Nandy, Chenru Duan, 2020 .

Strategies and Software for Machine Learning Accelerated Discovery in Transition Metal Chemistry

H. Kulik, J. Janet, A. Nandy, 2018, Industrial & Engineering Chemistry Research.

Rapid Exploration of a 32.5M Compound Chemical Space with Active Learning to Discover Density Functional Approximation Insensitive and Synthetically Accessible Transitional Metal Chromophores

H. Kulik, A. Nandy, Chenru Duan, 2022, ArXiv.

Large-Scale Screening Reveals That Geometric Structure Matters More Than Electronic Structure in the Bioinspired Catalyst Design of Formate Dehydrogenase Mimics

Michael G. Taylor, H. Kulik, A. Steeves, 2021, ACS Catalysis.

Exploiting Ligand Additivity for Transferable Machine Learning of Multireference Character Across Known Transition Metal Complex Ligands

Michael G. Taylor, H. Kulik, Chenru Duan, 2022, Journal of chemical theory and computation.

Learning from Failure: Predicting Electronic Structure Calculation Outcomes with Machine Learning Models.

H. Kulik, J. Janet, A. Nandy, 2019, Journal of chemical theory and computation.

Semi-Supervised Machine Learning Enables the Robust Detection of Multireference Character at Low Cost.

H. Kulik, A. Nandy, Chenru Duan, 2020, The journal of physical chemistry letters.

Seeing Is Believing: Experimental Spin States from Machine Learning Model Structure Predictions

Michael G. Taylor, H. Kulik, J. Janet, 2020, The journal of physical chemistry. A.

Active Learning Exploration of Transition-Metal Complexes to Discover Method-Insensitive and Synthetically Accessible Chromophores

H. Kulik, A. Nandy, Chenru Duan, 2022, JACS Au.

A quantitative uncertainty metric controls error in neural network-driven chemical discovery

H. Kulik, J. Janet, A. Nandy, 2019, Chemical science.

A Quantitative Uncertainty Metric Controls Error in Neural Network-Driven Chemical Discovery

H. Kulik, J. Janet, A. Nandy, 2019 .

Low-cost machine learning approach to the prediction of transition metal phosphor excited state properties

H. Kulik, A. Nandy, Chenru Duan, 2022, ArXiv.

A Database of Ultrastable MOFs Reassembled from Stable Fragments with Machine Learning Models

H. Kulik, A. Nandy, Chenru Duan, 2022, SSRN Electronic Journal.

Ligand additivity relationships enable efficient exploration of transition metal chemical space.

Michael G. Taylor, H. Kulik, J. Janet, 2022, The Journal of chemical physics.

DFT-Based Multireference Diagnostics in the Solid State: Application to Metal-Organic Frameworks.

H. Kulik, A. Nandy, Chenru Duan, 2022, Journal of chemical theory and computation.

Understanding the Role of Geometric and Electronic Structure in Bioinspired Catalyst Design: the Case of Formate Dehydrogenase

H. Kulik, A. Steeves, A. Nandy, 2021 .

Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning.

Michael G. Taylor, F. Liu, H. Kulik, 2021, Chemical reviews.

Putting Density Functional Theory to the Test in Machine-Learning-Accelerated Materials Discovery

F. Liu, H. Kulik, Chenru Duan, 2021, The journal of physical chemistry letters.

Data-Driven Approaches Can Overcome the Cost-Accuracy Trade-off in Multireference Diagnostics.

Chenru Duan, Aditya Nandy, Heather J Kulik, 2020, Journal of chemical theory and computation.

Machine learning to tame divergent density functional approximations: a new path to consensus materials design principles

Chenru Duan, Heather J. Kulik, Fang Liu, 2021, Chemical science.

Audacity of huge: overcoming challenges of data scarcity and data quality for machine learning in computational materials discovery pdf

Chenru Duan, Aditya Nandy, Heather J. Kulik, 2021, Current Opinion in Chemical Engineering.

Accurate transition state generation with an object-aware equivariant elementary reaction diffusion model

H. Kulik, Chenru Duan, Yuanqi Du, 2023, ArXiv.

Machine learning models predict calculation outcomes with the transferability necessary for computational catalysis

H. Kulik, Yuriy Román‐Leshkov, Chenru Duan, 2022, Journal of chemical theory and computation.

Two Wrongs Can Make a Right: A Transfer Learning Approach for Chemical Discovery with Chemical Accuracy pdf

Chenru Duan, Aditya Nandy, Heather J. Kulik, 2022, ArXiv.

Using Machine Learning and Data Mining to Leverage Community Knowledge for the Engineering of Stable Metal-Organic Frameworks

Chenru Duan, Aditya Nandy, Heather J. Kulik, 2021, Journal of the American Chemical Society.

MOFSimplify: Machine Learning Models with Extracted Stability Data of Three Thousand Metal-Organic Frameworks pdf

N. Arunachalam, G. Terrones, A. Nandy, 2021, ArXiv.

Molecular orbital projectors in non-empirical jmDFT recover exact conditions in transition-metal chemistry.

Michael G. Taylor, H. Kulik, Akash Bajaj, 2021, The Journal of chemical physics.

Representations and Strategies for Transferable Machine Learning Models in Chemical Discovery pdf

Chenru Duan, Aditya Nandy, Jon Paul Janet, 2021, The Journal of chemical physics.

Machine Learning Accelerates the Discovery of Design Rules and Exceptions in Stable Metal–Oxo Intermediate Formation

Rachel B. Getman, H. Kulik, J. Janet, 2019, ACS Catalysis.

Dynamical scaling in the Ohmic spin-boson model studied by extended hierarchical equations of motion.

Chenru Duan, Jianlan Wu, Zhihao Gong, 2019, The Journal of chemical physics.

Unusual Transport Properties with Non-Commutative System-Bath Coupling Operators.

Jianshu Cao, Chang-Yu Hsieh, Chenru Duan, 2019, The journal of physical chemistry letters.

Navigating Transition-Metal Chemical Space: Artificial Intelligence for First-Principles Design.

H. Kulik, J. Janet, Chenru Duan, 2021, Accounts of chemical research.

A Nonequilibrium Variational Polaron Theory to Study Quantum Heat Transport

Jianshu Cao, Chenru Duan, Junjie Liu, 2019, The Journal of Physical Chemistry C.

Assessing the performance of approximate density functional theory on 95 experimentally characterized Fe(II) spin crossover complexes.

Michael G. Taylor, H. Kulik, Chenru Duan, 2023, The Journal of chemical physics.

M2Hub: Unlocking the Potential of Machine Learning for Materials Discovery

Carla P. Gomes, J. Gregoire, Yanqiao Zhu, 2023, ArXiv.

Understanding the chemical bonding of ground and excited states of HfO and HfB with correlated wavefunction theory and density functional approximations.

H. Kulik, Chenru Duan, I. Ariyarathna, 2022, The Journal of chemical physics.

Symmetry-Informed Geometric Representation for Molecules, Proteins, and Crystalline Materials

Anima Anandkumar, Shengchao Liu, C. Borgs, 2023, NeurIPS.

Data‐Driven Fine Element Tuning of Halide Double Perovskite for Enhanced Photoluminescence

Chenru Duan, Haitao Zhao, Zijian Chen, 2023, Advanced Optical Materials.

MOFSimplify, machine learning models with extracted stability data of three thousand metal–organic frameworks

H. Kulik, Chenru Duan, N. Arunachalam, 2022, Scientific Data.

Computational Discovery of Codoped Single-Atom Catalysts for Methane-to-Methanol Conversion

Chenru Duan, Ilia Kevlishvili, Mingjie Liu, 2024, ACS Catalysis.

Accurate transition state generation with an object-aware equivariant elementary reaction diffusion model

H. Kulik, Chenru Duan, Yuanqi Du, 2023, Nat. Comput. Sci..

Machine Learning Prediction of the Experimental Transition Temperature of Fe(II) Spin-Crossover Complexes.

Chenru Duan, Vyshnavi Vennelakanti, Gianmarco G. Terrones, 2023, The journal of physical chemistry. A.