生成式分层材料搜索

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

规模训练的生成模型现在能够生成文本、视频，以及最近甚至是科学数据，比如晶体结构。在将生成方法应用于材料科学，尤其是晶体结构的应用中，领域专家以高层指导形式对自动系统输出适用于下游研究的候选晶体的指导可能是至关重要的。在这项工作中，我们将端到端的语言到结构生成问题制定为多目标优化问题，并提出了用于可控生成晶体结构的生成式分层材料搜索（GenMS）。GenMS包括（1）一个接受高层自然语言输入并生成有关晶体的中间文本信息（例如化学式）的语言模型，以及（2）一个接受中间信息输入并生成低级连续值晶体结构的扩散模型。GenMS还使用图神经网络从生成的晶体结构中预测性质（例如形成能）。在推断过程中，GenMS利用这三个组件在可能结构空间上进行前向树搜索。实验证明，GenMS在满足用户请求和生成低能量结构方面均优于直接使用语言模型生成结构的其他替代方法。我们确认GenMS能够仅通过自然语言输入生成常见的晶体结构，如双钙钛矿或尖晶石，因此可以为不久的将来更复杂的结构生成奠定基础。

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.