Collins Christopher M, Sayeed Hasan M, Darling George R, Claridge John B, Sparks Taylor D, Rosseinsky Matthew J
Department of Chemistry, University of Liverpool, Crown Street, Liverpool, L69 7ZD, UK.
Leverhulme Research Centre for Functional Materials Design, Materials Innovation Factory, University of Liverpool, Crown Street, Liverpool, L69 7ZD, UK.
Faraday Discuss. 2025 Jan 14;256(0):85-103. doi: 10.1039/d4fd00094c.
The prediction of new compounds crystal structure prediction may transform how the materials chemistry community discovers new compounds. In the prediction of inorganic crystal structures there are three distinct classes of prediction: performing crystal structure prediction heuristic algorithms, using a range of established crystal structure prediction codes, an emerging community using generative machine learning models to predict crystal structures directly and the use of mathematical optimisation to solve crystal structures exactly. In this work, we demonstrate the combination of heuristic and generative machine learning, the use of a generative machine learning model to produce the starting population of crystal structures for a heuristic algorithm and discuss the benefits, demonstrating the method on eight known compounds with reported crystal structures and three hypothetical compounds. We show that the integration of machine learning structure generation with heuristic structure prediction results in both faster compute times per structure and lower energies. This work provides to the community a set of eleven compounds with varying chemistry and complexity that can be used as a benchmark for new crystal structure prediction methods as they emerge.
新化合物晶体结构预测可能会改变材料化学界发现新化合物的方式。在无机晶体结构预测中，有三类不同的预测方法：使用启发式算法进行晶体结构预测、使用一系列既定的晶体结构预测代码、一个新兴的群体使用生成式机器学习模型直接预测晶体结构以及使用数学优化来精确求解晶体结构。在这项工作中，我们展示了启发式方法与生成式机器学习的结合，即使用生成式机器学习模型为启发式算法生成晶体结构的初始群体，并讨论其优点，在八个具有已报道晶体结构的已知化合物和三个假设化合物上演示了该方法。我们表明，将机器学习结构生成与启发式结构预测相结合，可使每个结构的计算时间更快且能量更低。这项工作为该领域提供了一组十一种具有不同化学性质和复杂性的化合物，可作为新出现的晶体结构预测方法的基准。
