Integrating machine learning interatomic potentials with batched optimization for crystal structure prediction

Abstract

Molecular crystal structure prediction (CSP) faces a persistent computational bottleneck: it requires exhaustive sampling of vast packing landscapes while resolving energy differences of only a few kJ mol⁻¹. We introduce BOMLIP-CSP, an open-source Python framework that integrates machine learning interatomic potentials (MLIPs) with a tailored batched optimization strategy, enabling rapid, unbiased structure prediction across the full crystal density range. By introducing tailored parallelism into modern MLIPs, BOMLIP-CSP achieves a ∼2.1–2.3× acceleration in large-scale CSP searches without compromising accuracy. In benchmarks covering 34 experimental structures from six CSP blind tests, more than 50% of the experimental crystal structures can be recovered using foundation MLIPs when the correct space group and Z′ are included in the search, with more than 70% of the experimental structures recovered by at least one of the tested MLIPs under the present benchmark conditions. Importantly, our results suggest that MLIPs with comparable equilibrium-energy accuracy can yield strikingly different CSP outcomes, indicating that predictive success may depend not only on local energy fidelity but also on how the MLIP energy surface is organised. Together, these results establish BOMLIP-CSP as a broadly accessible platform for accelerated CSP and provide new insight into the interplay between MLIP characteristics and crystal structure discovery.