用对称性编码提升大晶体结构预测效率
CRYSIM: Prediction of Symmetric Structures of Large Crystals with GPU-based Ising Machines
- 将晶格对称性、位置组合与原子坐标分项建模，大幅压缩搜索空间
- 在150个以上原子的晶胞中，性能媲美CALYPSO和贝叶斯优化
- 为未来量子计算时代的晶体结构预测提供可扩展框架
利用伊辛机解决材料科学中的黑箱优化问题日益普遍，但其在晶体结构预测（CSP）中的应用仍受限于原子坐标的对称性无关编码。本文提出CRYSIM算法，将晶系空间群、Wyckoff位置组合及独立原子位点坐标分别作为变量进行编码，通过利用空间群的对称性显著缩减搜索空间。当与Fixstars Amplify（基于GPU的伊辛机）结合时，该方法在单元胞含超过150个原子的晶体预测任务中表现达到与CALYPSO和贝叶斯优化相当的水平。尽管当前小型量子设备难以直接对接CRYSIM，但其具备成为未来量子时代标准CSP算法的潜力。
原文摘要 · Abstract (English)
Solving black-box optimization problems with Ising machines is increasingly common in materials science. However, their application to crystal structure prediction (CSP) is still ineffective due to symmetry agnostic encoding of atomic coordinates. We introduce CRYSIM, an algorithm that encodes the space group, the Wyckoff positions combination, and coordinates of independent atomic sites as separate variables. This encoding reduces the search space substantially by exploiting the symmetry in space groups. When CRYSIM is interfaced to Fixstars Amplify, a GPU-based Ising machine, its prediction performance was competitive with CALYPSO and Bayesian optimization for crystals containing more than 150 atoms in a unit cell. Although it is not realistic to interface CRYSIM to current small-scale quantum devices, it has the potential to become the standard CSP algorithm in the coming quantum age.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。