Year: 2026
Type: preprint
Abstract: Data-driven materials discovery interpolates more reliably than it extrapolates and seldom reveals new structure types. We present MatEvolve, an agentic-AI framework that designs crystals in an interpretable \emph{language of motifs}, in which each crystal is written as a \emph{motif profile} that describes the recurring geometric patterns---the \emph{motifs}---composing the crystal. The motif profile serves not merely as a description of a material but as the medium for material design: the agent edits the profile with a stated rationale and constructs a crystal from the modified one; the most promising candidates are then validated by first-principles calculations. MatEvolve is built on the state-of-the-art language model Claude Fable~5 without fine-tuning. It is applied to the design of rare-earth-lean permanent-magnet compounds and finds novel (meta)stable crystals that are both new structural prototypes and magnet compounds; under an equal validation budget, it does so an order of magnitude more often than generative models. Beyond design, analyzing the human-readable profiles of the discovered crystals reveals their structure--property relationships.
Source: arXiv (Cornell University)
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: green
Awards: An active learning framework for modeling the formation mechanism of high-entropy alloys, G358964878, G6337325905, 第一原理網羅計算プラットホームの開発と説明可能な機械学習モデルによる法則の獲得, Magnetic materials exploration using data-driven AI that can explain structure-property mechanisms, Construction of a platform for visualization of microscopic non-equilibrium states using super ptychography
OpenAlex ID: W7203725837