19 citations · 23 across the 6 of their papers we have counts for
6 papers
Synthesizing like a chemist: an iterative, feedback-driven loop for materials discovery
Fang Sheng, Steven B. Torrisi, Amanda Volk +4
Most computationally predicted materials are never synthesized because conventional synthesis optimization is slow, expertise-dependent, and iterative. Here we present a closed-loo…
ProDCARL: Reinforcement Learning-Aligned Diffusion Models for De Novo Antimicrobial Peptide Design
Fang Sheng, Mohammad Noaeen, Zahra Shakeri
Antimicrobial resistance threatens healthcare sustainability and motivates low-cost computational discovery of antimicrobial peptides (AMPs). De novo peptide generation must optimi…
A closed-loop AI framework for hypothesis-driven and interpretable materials design
Kangyu Ji, Tianran Liu, Fang Sheng +3
Scientific hypothesis generation is central to materials discovery, yet current approaches often emphasize either conceptual (idea-to-data) reasoning or data-driven (data-to-idea)…
Multimodal transformers with elemental priors for phase classification of X-ray diffraction spectra
Kangyu Ji, Fang Sheng, Tianran Liu +2
Classifying a crystalline solid's phase using X-ray diffraction (XRD) is a challenging endeavor, first because this is a poorly constrained problem as there are nearly limitless ca…
A Self-Supervised Robotic System for Autonomous Contact-Based Spatial Mapping of Semiconductor Properties
Alexander E. Siemenn, Basita Das, Kangyu Ji +2
Integrating robotically driven contact-based material characterization techniques into self-driving laboratories can enhance measurement quality, reliability, and throughput. While…
Using Scalable Computer Vision to Automate High-throughput Semiconductor Characterization
Alexander E. Siemenn, Eunice Aissi, Fang Sheng +4
High-throughput materials synthesis methods have risen in popularity due to their potential to accelerate the design and discovery of novel functional materials, such as solution-p…