Henry Zhang (张恒睿)

Evanston, IL, USA

hrzhang(at)u.northwestern.edu

Greetings! I am a PhD candidate and Ryan Fellow at Northwestern working with Profs. Wei Chen and James Rondinelli. I have a broad interest in AI for science, with a focus on materials design.

Before starting graduate school, I earned my BS in Materials Science and Computer Science from Shanghai Jiao Tong University. As an undergraduate researcher, I worked in MaGIC at SJTU, where I developed informatics tools for high-throughput experiments. I was a summer research intern at Oxford (2018), Northwestern (2019), and MERL (2023).

Education

Northwestern University
PhD, Mechanical Engineering, 2020–2025
Research Intern, 2019

Shanghai Jiao Tong University
BS, Materials Science & Engineering, 2016–2020
BS, Computer Science, 2017–2020

University of Oxford
Visiting Scholar, Department of Materials, 2018

News

Nov 08, 2023	Passed my thesis proposal exam!
Sep 15, 2023	Glad to have our proposal funded by NSF DMREF.
Sep 08, 2023	Wrapped up an enjoyable 3-month internship at MERL.
Jun 08, 2023	Honored to receive the Ryan Fellowship!

Selected publications

Automated crystal system identification from electron diffraction patterns using multiview opinion fusion machine learning

Jie Chen, Hengrui Zhang, Carolin B. Wahl, Wei Liu, Chad A. Mirkin, Vinayak P. Dravid, Daniel W. Apley, and Wei Chen

Proceedings of the National Academy of Sciences, 2023

Abs HTML

A bottleneck in high-throughput nanomaterials discovery is the pace at which new materials can be structurally characterized. Although current machine learning (ML) methods show promise for the automated processing of electron diffraction patterns (DPs), they fail in high-throughput experiments where DPs are collected from crystals with random orientations. Inspired by the human decision-making process, a framework for automated crystal system classification from DPs with arbitrary orientations was developed. A convolutional neural network was trained using evidential deep learning, and the predictive uncertainties were quantified and leveraged to fuse multiview predictions. Using vector map representations of DPs, the framework achieves a testing accuracy of 0.94 in the examples considered, is robust to noise, and retains remarkable accuracy using experimental data. This work highlights the ability of ML to be used to accelerate experimental high-throughput materials data analytics.
ET-AL: Entropy-targeted active learning for bias mitigation in materials data

Hengrui Zhang, Wei Chen, James M. Rondinelli, and Wei Chen

Applied Physics Reviews, 2023

Abs arXiv HTML

Growing materials data and data-driven informatics drastically promote the discovery and design of materials. While there are significant advancements in data-driven models, the quality of data resources is less studied despite its huge impact on model performance. In this work, we focus on data bias arising from uneven coverage of materials families in existing knowledge. Observing different diversities among crystal systems in common materials databases, we propose an information entropy-based metric for measuring this bias. To mitigate the bias, we develop an entropy-targeted active learning (ET-AL) framework, which guides the acquisition of new data to improve the diversity of underrepresented crystal systems. We demonstrate the capability of ET-AL for bias mitigation and the resulting improvement in downstream machine learning models. This approach is broadly applicable to data-driven materials discovery, including autonomous data acquisition and dataset trimming to reduce bias, as well as data-driven informatics in other scientific domains.