5 papers · 1 filter
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…
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…
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…