用于成分驱动晶体结构发现的傅里叶神经算子
Fourier Neural Operators for Composition-Driven Crystal Structure Discovery
- University of Science and Technology of China（中国科学技术大学）
- Suzhou Institute for Advanced Research, University of Science and Technology of China（中国科学技术大学苏州高等研究院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究开发基于傅里叶神经算子的晶体场求解器，构建耦合生成-求解框架，在104种化学式上生成新型晶体结构，为成分驱动的晶体结构发现提供可扩展途径。
AI中文摘要：
晶体材料发现对能源、电子和催化领域至关重要，但庞大的化学与结构空间使得穷尽筛选不可行。现有基于体素的方法受限于三维卷积神经网络的局部感受野，以及高维变分自编码器的后验崩溃问题。本文开发了一种基于傅里叶神经算子（FNO）的晶体场求解器，可将给定的化学式和晶格参数映射为周期性数密度场与电子密度场。该求解器通过对全局傅里叶模态进行操作，能够捕捉超出传统局部卷积范围的周期性晶体场中的长程相关性。基于此求解器，本文构建了耦合的生成-求解框架：其中条件变分自编码器在低维基系数空间中生成多样化的候选晶格参数，随后通过峰值检测、位置优化和权重优化实现密度场预测与原子重构。重构的结构进一步通过体素级过滤、机器学习原子间势弛豫和第一性原理计算进行筛选。该框架在104种化学式上生成了具有竞争力重构精度的新型结构，展现出高生成多样性与结构有效性。通过将傅里叶神经算子扩展至周期性晶体场，并将其与成分条件晶格生成耦合，本文的方法为从给定化学成分出发的晶体结构发现提供了可扩展的途径。
英文摘要：
Crystalline materials discovery is essential for energy, electronics, and catalysis, but the vast chemical and structural space makes exhaustive screening infeasible. Existing voxel-based methods are limited by the local receptive fields of three-dimensional convolutional neural networks and the posterior collapse of high-dimensional variational autoencoders. Here, we develop a Fourier Neural Operator (FNO)-based crystal-field solver that maps a prescribed chemical formula and lattice parameters to periodic number-density and electron-density fields. By operating on global Fourier modes, the solver captures long-range correlations in periodic crystal fields beyond conventional local convolutions. Building on this solver, we construct a coupled generation-solving framework in which a conditional variational autoencoder generates diverse candidate lattice parameters in a low-dimensional basis-coefficient space, followed by density-field prediction and atomic reconstruction through peak detection, position optimization, and weight optimization. The reconstructed structures are further screened using voxel-level filtering, machine-learning interatomic-potential relaxation, and first-principle calculations. The framework generates novel structures across 104 chemical formulas with competitive reconstruction accuracy, demonstrating high generative diversity and structural validity. By extending Fourier neural operators to periodic crystal fields and coupling them with composition-conditioned lattice generation, our approach provides a scalable route to crystal structure discovery from prescribed chemical compositions.
