Xiangyu Yin

Xiangyu Yin

I am a Postdoctoral Appointee at the Argonne National Laboratory. My research interests include AI4science, Physics4ML, and autonomous scientific discovery. My overall research themes include integrating physics with data and algorithms, improving scientific discovery with new methodologies, as well as bridging gaps between different scientific domains. My typical work involves communicating with scientists and engineers, defining and analyzing various scientific and engineering problems, proposing, and implementing solutions by researching the latest technologies in optimization, statistics, data science, machine learning, and software development, and finally reporting and publishing results as well as following up with stakeholders and executors. Before joining Argonne, I obtained my Ph.D. in Chemical Engineering from Carnegie Mellon University, where I carried out research in computational materials discovery.


Postdoctoral Researcher
Oct 2023 – Present Lemont IL
  • Working across various projects under the common theme of automatic parameter tuning for computational imaging technologies
Research Intern
May 2022 – Aug 2022 Los Altos CA
  • Proposed a reinforcement learning (RL) framework for designing dopants and demonstrated its efficiency and scalability
  • Developed a package for RL-based dopant design, including customized environment, agent, and trainer components
  • Conducted experimental data analysis to develop predictive models, performed explainability studies, and optimized properties
Graduate Researcher
Sep 2018 – Sep 2023 Pittsburgh PA
  • Coursework: Optimization, Statistics, Machine Learning, MLOps, Deep Learning
  • Teaching Assistant: Chemical Product Design, Introduction to Chemical Engineering
  • Award: Neil and Jo Bushnll Fellowship in Engineering


(2022). Matopt: A python package for nanomaterials design using discrete optimization. In J. Chem. Inf. Model..

PDF Cite Code DOI

(2021). Search methods for inorganic materials crystal structure prediction. In Curr. Opin. Chem. Eng..


(2021). Designing stable bimetallic nanoclusters via an iterative two-step optimization approach. In Mol. Syst. Des. Eng..



Programming: Python, C++, SQL, MATLAB, Shell

Libs/Frameworks: Numpy, SciPy, SciKit-learn, PySpark, Ray, CUDA, PyTorch, TensorFlow, JAX, Gymnasium, Hugging Face, LangChain, AutoGluon, Docker, Pyomo, Gurobi, IPOPT, BARON, NOMAD, RASPA

Platforms: Github, AWS, Azure, GCP(Google Data Analytics), OpenAI


X. Yin, R. Lei, W. Ye, and J. Montoya. Deep Reinforcement Learning for Dopants Design in Crystalline Materials. MRS Fall Meeting, 2023

X. Yin, L. T. Biegler, and C. E. Gounaris. Incorporating Materials Surrogate Models into Process Models for Adsorption-Based Gas Separations. AIChE Annual Meeting, 2022

X. Yin, L. T. Biegler, and C. E. Gounaris. Enabling Process-materials Co-optimization via Surrogate Modeling for Adsorption-Based Gas Separations. INFORMS Annual Meeting, 2022

X. Yin, L. T. Biegler, and C. E. Gounaris. Towards process-materials co-optimization: Automatic generation of optimizable structure-function relationships. AIChE Annual Meeting, 2021

X. Yin, C. L. Hanselman, and C. E. Gounaris. Designing stable semiconductor nanowires via a generic materials optimization toolkit. AIChE Annual Meeting, 2020

X. Yin, C. L. Hanselman, D. C. Miller, and C. E. Gounaris. Nanomaterials design using Pyomo. INFORMS Annual Meeting, 2020

X. Yin, C. L. Hanselman, D. C. Miller, and C. E. Gounaris. Matopt: A mathematical optimization-based nanomaterials design toolkit. INFORMS Annual Meeting, 2020

X. Yin, N. M. Isenberg, M. G. Taylor, G. Mpourmpakis, and C. E. Gounaris. Identification of stable bimetallic nanoclusters via a mathematical optimization framework. AIChE Annual Meeting, 2019