2025/01/30 by Raul Ortega-Ochoa, Alán Aspuru-Guzik, Alán Aspuru‐Guzik +6 · 1 voice · 2 citations
Engineering · Materials Science · Physics and Astronomy · #Hydrocarbon exploration and reservoir analysis #Mineral Processing and Grinding #X-ray Diffraction in Crystallography #cond-mat.mtrl-sci #physics.comp-ph
paper · pdf · doi:10.48550/arxiv.2501.18163
openalex publication_date 2025/01/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Recent advancements in machine learning (ML) for materials have demonstrated that "simple" materials representations (e.g., the chemical formula alone without structural information) can sometimes achieve competitive property prediction performance in common-tasks. Our physics-based intuition would suggest that such representations are "incomplete", which indicates a gap in our understanding. This work proposes a tomographic interpretation of structure-property relations of materials to bridge that gap by defining what is a material representation, material properties, the material and the relationships between these three concepts using ideas from information theory. We verify this framework performing an exhaustive comparison of property-augmented representations on a range of material's property prediction objectives, providing insight into how different properties can encode complementary information.