Crystal Structure Prediction (CSP) of molecular crystals plays a central role in applications, such as pharmaceuticals and organic electronics. CSP is challenging and computationally expensive due to the need to explore a large search space with sufficient accuracy to capture energy differences of a few kJ/mol between polymorphs. Dispersion-inclusive density functional theory (DFT) provides the required accuracy but its computational cost is impractical for a large number of putative structures. We introduce FastCSP, an open-source, high-throughput CSP workflow based on machine learning interatomic potentials (MLIPs). FastCSP combines random structure generation using Genarris 3.0 with geometry relaxation and free energy calculations powered entirely by the Universal Model for Atoms (UMA) MLIP. We benchmark FastCSP on a curated set of 28 mostly rigid molecules, demonstrating that our workflow consistently generates known experimental structures and ranks them within 5 kJ/mol per molecule of the global minimum. Our results demonstrate that universal MLIPs can be used across diverse compounds without requiring system-specific tuning. Moreover, the speed and accuracy afforded by UMA eliminate the need for classical force fields in the early stages of CSP and for final re-ranking with DFT. The open-source release of the entire FastCSP workflow significantly lowers the barrier to accessing CSP. CSP results for a single system can be obtained within hours on tens of modern GPUs, making high-throughput crystal structure prediction feasible for a broad range of scientific applications.

翻译：分子晶体结构预测（CSP）在药物和有机电子学等应用中具有核心作用。由于需要在足够精度下探索巨大的搜索空间以捕捉多晶型之间仅数kJ/mol的能量差异，CSP极具挑战性且计算成本高昂。包含色散校正的密度泛函理论（DFT）虽能提供所需精度，但其计算成本对于大量候选结构而言并不实用。本文介绍FastCSP——一个基于机器学习原子间势（MLIPs）的开源高通量CSP工作流程。FastCSP将Genarris 3.0的随机结构生成与完全由通用原子模型（UMA）MLIP驱动的几何弛豫和自由能计算相结合。我们在精选的28个（主要为刚性）分子数据集上对FastCSP进行基准测试，证明该工作流程能稳定生成已知实验结构，并将其排序在全局最小值每分子5 kJ/mol范围内。研究结果表明，通用MLIPs无需针对特定体系调参即可适用于多样化合物。此外，UMA提供的速度与精度优势使得在CSP早期阶段无需使用经典力场，也无需通过DFT进行最终重排序。整套FastCSP工作流程的开源发布显著降低了使用CSP技术的门槛。单个体系的CSP结果可在数十个现代GPU上数小时内获得，使得高通量晶体结构预测在广泛科学应用中成为可行。