Chenru Duan

Chenru Duan

Graduate Student

Massachusetts Institute of Technology

Chenru joined the Kulik group in November 2017 as a PhD student in Chemistry. He received his B.S. in the Physics Department, Zhejiang University, China in fall 2017. He is now focusing on inorganic catalysts design with machine learning techniques. Assuming one can build a good surrogate model with thousands of training data (proved to be true by works in our and other groups), he focuses on faster and more accurate data generation with first-principles method by building an automated and multi-fidelity job control system. Back in his undergraduate study, he worked on the dynamics of open quantum systems, heat transport and quantum phase transition of model systems.

  • machine learning
  • density functional theory
  • computational chemistry
  • BS in Physics, 2017

    Zhejiang University


  1. Exploiting Ligand Additivity for Transferable Machine Learning of Multireference Character Across Known Transition Metal Complex Ligands (2022)
  2. Machine learning models predict calculation outcomes with the transferability necessary for computational catalysis (2022)
  3. Audacity of huge: overcoming challenges of data scarcity and data quality for machine learning in computational materials discovery (2022)
  4. Detection of multi-reference character imbalances enables a transfer learning approach for chemical discovery with coupled cluster accuracy at DFT cost (2022)
  5. Large-scale Screening Reveals Geometric Structure Matters More than Electronic Structure in Bioinspired Catalyst Design of Formate Dehydrogenase Mimics (2022)
  6. Machine Learning for the Discovery, Design, and Engineering of Materials (2022)
  7. MOFSimplify, machine learning models with extracted stability data of three thousand metal–organic frameworks (2022)
  8. Molecular orbital projectors in non-empirical jmDFT recover exact conditions in transition-metal chemistry (2022)
  9. New Strategies for Direct Methane-to-Methanol Conversion from Active Learning Exploration of 16 Million Catalysts (2022)
  10. Representations and Strategies for Transferable Machine Learning Improve Model Performance in Chemical Discovery (2022)
  11. Understanding the chemical bonding of ground and excited states of HfO and HfB with correlated wavefunction theory and density functional approximations (2022)
  12. Navigating Transition-Metal Chemical Space: Artificial Intelligence for First-Principles Design (2021)
  13. Computational Discovery of Transition-Metal Complexes: From High-throughput Screening to Machine Learning (2021)
  14. Machine learning to tame divergent density functional approximations a new path to consensus materials design principles (2021)
  15. Putting Density Functional Theory to the Test in Machine-Learning-Accelerated Materials Discovery (2021)
  16. Using Machine Learning and Data Mining to Leverage Community Knowledge for the Engineering of Stable Metal-Organic Frameworks (2021)
  17. Rapid Detection of Strong Correlation with Machine Learning for Transition-Metal Complex High-Throughput Screening (2020)
  18. Semi-supervised Machine Learning Enables the Robust Detection of Multireference Character at Low Cost (2020)
  19. Data-Driven Approaches Can Overcome the Cost–Accuracy Trade-Off in Multireference Diagnostics (2020)
  20. Accurate Multiobjective Design in a Space of Millions of Transition Metal Complexes with Neural-Network-Driven Efficient Global Optimization (2020)
  21. Seeing Is Believing: Experimental Spin States from Machine Learning Model Structure Predictions (2020)
  22. Large-scale comparison of 3d and 4d transition metal complexes illuminates the reduced effect of exchange on second-row spin-state energetics (2020)
  23. Machine Learning Accelerates the Discovery of Design Rules and Exceptions in Stable Metal–Oxo Intermediate Formation (2019)
  24. Designing in the Face of Uncertainty: Exploiting Electronic Structure and Machine Learning Models for Discovery in Inorganic Chemistry (2019)
  25. Learning from Failure: Predicting Electronic Structure Calculation Outcomes with Machine Learning Models (2019)
  26. A quantitative uncertainty metric controls error in neural network-driven chemical discovery (2019)
  27. Strategies and Software for Machine Learning Accelerated Discovery in Transition Metal Chemistry (2018)