生成式階層材料搜索

Generative Hierarchical Materials Search

September 10, 2024

作者: Sherry Yang, Simon Batzner, Ruiqi Gao, Muratahan Aykol, Alexander L. Gaunt, Brendan McMorrow, Danilo J. Rezende, Dale Schuurmans, Igor Mordatch, Ekin D. Cubuk

cs.AI

摘要

目前，規模訓練的生成模型能夠產生文本、視頻，以及最近甚至科學數據，如晶體結構。在將生成方法應用於材料科學，特別是晶體結構方面，領域專家以高層指導形式對自動系統輸出適合下游研究的候選晶體可能至關重要。在這項工作中，我們將端到端的語言到結構生成定義為多目標優化問題，並提出了用於可控生成晶體結構的生成式分層材料搜索（GenMS）。GenMS包括（1）一個語言模型，接受高層自然語言作為輸入並生成有關晶體的中間文本信息（例如，化學式），以及（2）一個擴散模型，接受中間信息作為輸入並生成低層連續值晶體結構。GenMS還使用圖神經網絡從生成的晶體結構中預測性質（例如，形成能）。在推斷過程中，GenMS利用這三個組件在可能結構空間上進行正向樹搜索。實驗表明，GenMS在滿足用戶需求和生成低能量結構方面均優於直接使用語言模型生成結構的其他替代方法。我們確認GenMS能夠僅通過自然語言輸入生成常見的晶體結構，如雙鈣鈦礦或尖晶石，因此可以為不久的將來更複雜的結構生成奠定基礎。

English

Generative models trained at scale can now produce text, video, and more recently, scientific data such as crystal structures. In applications of generative approaches to materials science, and in particular to crystal structures, the guidance from the domain expert in the form of high-level instructions can be essential for an automated system to output candidate crystals that are viable for downstream research. In this work, we formulate end-to-end language-to-structure generation as a multi-objective optimization problem, and propose Generative Hierarchical Materials Search (GenMS) for controllable generation of crystal structures. GenMS consists of (1) a language model that takes high-level natural language as input and generates intermediate textual information about a crystal (e.g., chemical formulae), and (2) a diffusion model that takes intermediate information as input and generates low-level continuous value crystal structures. GenMS additionally uses a graph neural network to predict properties (e.g., formation energy) from the generated crystal structures. During inference, GenMS leverages all three components to conduct a forward tree search over the space of possible structures. Experiments show that GenMS outperforms other alternatives of directly using language models to generate structures both in satisfying user request and in generating low-energy structures. We confirm that GenMS is able to generate common crystal structures such as double perovskites, or spinels, solely from natural language input, and hence can form the foundation for more complex structure generation in near future.