machine learning for
quantum chemistry and chemical discovery
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 (https://ai4sciencecommunity.github.io/neurips22.html).
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
Building machine learning and quantum solutions for chemistry and materials problems
Product: Azure Quantum Elements
Large-scale foundation model in chemistry
Generative AI for chemical design
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