800 citations · 947 across the 25 of their papers we have counts for
14 papers · 1 filter
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…
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)…
Disentangling the Effects of Simultaneous Environmental Variables on Perovskite Synthesis and Device Performance via Interpretable Machine Learning
Tianran Liu, Nicky Evans, Kangyu Ji +12
Despite the rapid rise in perovskite solar cell efficiency, poor reproducibility remains a major barrier to commercialization. Film crystallization and device performance are highl…
A tomographic interpretation of structure-property relations for materials discovery
Raul Ortega-Ochoa, Alán Aspuru-Guzik, Tejs Vegge +1
Recent advancements in machine learning (ML) for materials have demonstrated that "simple" materials representations (e.g., the chemical formula alone without structural informatio…
Exploring material compositions for synthesis using oxidation states
Maung Thway, Andy Paul Chen, Haiwen Dai +9
Recent advances in machine learning techniques have made it possible to use high-throughput screening to identify novel materials with specific properties. However, the large numbe…
Long-Term Research & Design Strategies for Fusion Energy Materials
David Cohen-Tanugi, Myles G. Stapelberg, Michael P. Short +4
Fusion energy is at an important inflection point in its development: multiple government agencies and private companies are now planning fusion pilot plants to deliver electricity…