35 citations · 99 across the 6 of their papers we have counts for
6 papers
Emerging Microelectronic Materials by Design: Navigating Combinatorial Design Space with Scarce and Dispersed Data
Hengrui Zhang, Alexandru B. Georgescu, Suraj Yerramilli +5
The increasing demands of sustainable energy, electronics, and biomedical applications call for next-generation functional materials with unprecedented properties. Of particular in…
Do Graph Neural Networks Work for High Entropy Alloys?
Hengrui Zhang, Ruishu Huang, Jie Chen +2
Graph neural networks (GNNs) have excelled in predictive modeling for both crystals and molecules, owing to the expressiveness of graph representations. High-entropy alloys (HEAs),…
Adaptive Catalyst Discovery Using Multicriteria Bayesian Optimization with Representation Learning
Jie Chen, Pengfei Ou, Yuxin Chang +4
High-performance catalysts are crucial for sustainable energy conversion and human health. However, the discovery of catalysts faces challenges due to the absence of efficient appr…
MolSets: Molecular Graph Deep Sets Learning for Mixture Property Modeling
Hengrui Zhang, Jie Chen, James M. Rondinelli +1
Recent advances in machine learning (ML) have expedited materials discovery and design. One significant challenge faced in ML for materials is the expansive combinatorial space of…
ET-AL: Entropy-Targeted Active Learning for Bias Mitigation in Materials Data
Hengrui Zhang, Wei Wayne Chen, James M. Rondinelli +1
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 qu…
Uncertainty-Aware Mixed-Variable Machine Learning for Materials Design
Hengrui Zhang, Wei Wayne Chen, Akshay Iyer +2
Data-driven design shows the promise of accelerating materials discovery but is challenging due to the prohibitive cost of searching the vast design space of chemistry, structure,…