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Chenru Duan

machine learning for
quantum chemistry and chemical discovery
Chenru Duan.jpg

Chenru Duan

Research Scientist at Microsoft Quantum

Ph.D. at Chemistry, Chemical Engineering, MIT




Social media: twitter, linkedin

About Me

Hello! My name is Chenru Duan. I am currently a research scientist at Microsoft Quantum. I obtained Ph.D. in department of Chemirty at Massachusetts Institute of Technology and B.S. in Physics in Zhejiang University.

My research interest is integrating machine learning models in quantum chemistry calculations to achieve autonomous workflow for computational high throughput screening and materials discovery. My reserach covers building decision-making models to optimize cost-accuracy tradeoff in chemistry calculations and Bayesian optimization for chemiscal discovery. I have demonstrated this workflow on accelerating the chemical discovery of functional materials and molecules, such as redox couples in redox flow batteries, catalysts for methane-to-methanol conversion, and transition metal chromophores. 


I am passionate about computational chemistry, machine learning, and its combination for making impacts in our society. I am dedicated to make computation a more practically useful tool in chemistry exploration with machine learning. I am a community builder for #AI4Science and have organized #AI4Science workshops in ICML and NeurIPS in 2022 (

In my spare time, I enjoy playing  and electronic games, watching Japanese and Chinese anime, and hiking.

Honors and Awards

2022                   Excellence Award for Graduate Student, ACS Chemical Computing Group

                           MolSSI Software Fellow (NSF fund for open-source software development in MolSSI)

2021                   Best Poster Award, International Symposium on Machine Learning in Quantum Chemistry

                           Gold Award, MRS Graduate Student Award

                           Graduate Student Award, AIChE’s Computational Molecular Science and Engineering Forum


Ph.D. in Chemistry, MIT, Cambridge, MA

Doctoral advisor: Prof. Heather J. Kulik                                                                                                                            2017 - 2022


B.S. in Physics, Chu KoChen Honors college, Zhejiang University, Hangzhou, China

Honored degree,                                                                                                                                                               2013 - 2017

Research Experience and Skills
For more detailed descriptions, see "Projects" pages.

Aug 2022 - Now

Sept. 2017 - July 2022

July 2017 - Sept. 2017

July 2015 - June 2017

Microsoft, Azure Quantum, Redmond, WA

Research Scientist

Building machine learning and quantum solutions for chemistry and materials problems

Department of Chemistry, MIT, Cambridge, MA

Graduate Research Assistant; Advisor: Prof. Heather J. Kulik

Thesis Theme: High-throughput computational chemistry and machine learning for chemical discovery

  • Developed the first set of machine learning classifiers that monitor quantum chemistry calculations on the fly in computational high throughput screening, saving more than half of the computational resources and time that would have beed wasted on failed calculations

  • Developed the first semi-supervised learning classifier to identify strong static correlation in materials, achieving state-of-the-art for this classification task and avoiding computational data noises.

  • Integrated recommendation systems, transfer learning and uncertainty quantification in computational high throughput screening, reducing the error of machine learning accelerated chemical discovery to 1 kcal/mol chemical accuracy

  • Discovered functional materials with multi-objective active learning, such as redox couples in redox flow battery, single-site catalysts for methane-to-methanol conversion, and robust transition metal chromophores

  • Developed proficiency with programming languages (Python and C), high performance computing, machine learning packages (Pytorch, Tensorflow, and PyG), quantum chemistry packages (TeraChem, Psi4, ORCA, and QChem) and software for working efficiency (Jupyter, Plotly, Docker, Colab, etc.)

  • Published over 20 papers in peer-reviewed journals (ten first-authored); Received five prestigious awards from five international professional associations; Gave 13 formal presentations at conferences (three invited)

SMART, National University of Singapore, Singapore

Research Engineer; Advisor: Prof. Jianshu Cao

  • Uncovered novel heat transport behaviors in non-commutative quantum heat engine with heat-flux extended hierarchical equation of motion

  • Developed proficiency with Fortran, Matplotlib, and LaTex

  • Published two papers in peer-reviewed journals (one first authored) and gave two departmental presentations

Department of Physics, Zhejiang University, Hangzhou, China

Undergraduate Research Assistant; Advisor: Prof. Jianlan Wu

  • Enabled numerical-exact calculations of open quantum dynamics via extending the domain of applicability of hierarchical equation of motion, and  studied the quantum phase transition of the spin-boson model

  • Developed proficiency with Bash, Matlab, Mathematica, and OriginLab

  • Published three papers in peer-reviewed journals (two first authored) and defended one bachelor thesis


25 Ames Street,  Cambridge, MA 02142 

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