Integrating machine learning interatomic potentials with batched optimization for crystal structure prediction Article Swipe
Chengxi Zhao
,
Zhaojia Ma
,
Daojin Fan
,
Siyu Hu
,
Leping Wang
,
Weile Jia
,
En Shao
,
Guangming Tan
,
Jun Jiang
,
Linjiang Chen
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.26434/chemrxiv-2025-fqs7n
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.26434/chemrxiv-2025-fqs7n
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, over 50% of experimental crystals are recovered with foundational MLIPs (namely, MACE-OFF-small and SevenNet-0-D3), rising above 70% with judicious MLIP selection. Importantly, we show that MLIPs with comparable equilibrium energy accuracy can yield strikingly different CSP outcomes, underscoring that not only local energy fidelity but also the global topology of the crystal lattice energy landscape governs predictive success. 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.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.26434/chemrxiv-2025-fqs7n
- https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/68a81025a94eede1540983b7/original/integrating-machine-learning-interatomic-potentials-with-batched-optimization-for-crystal-structure-prediction.pdf
- OA Status
- gold
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4413726678
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https://openalex.org/W4413726678Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.26434/chemrxiv-2025-fqs7nDigital Object Identifier
- Title
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Integrating machine learning interatomic potentials with batched optimization for crystal structure predictionWork title
- Type
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preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-08-26Full publication date if available
- Authors
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Chengxi Zhao, Zhaojia Ma, Daojin Fan, Siyu Hu, Leping Wang, Weile Jia, En Shao, Guangming Tan, Jun Jiang, Linjiang ChenList of authors in order
- Landing page
-
https://doi.org/10.26434/chemrxiv-2025-fqs7nPublisher landing page
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https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/68a81025a94eede1540983b7/original/integrating-machine-learning-interatomic-potentials-with-batched-optimization-for-crystal-structure-prediction.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/68a81025a94eede1540983b7/original/integrating-machine-learning-interatomic-potentials-with-batched-optimization-for-crystal-structure-prediction.pdfDirect OA link when available
- Concepts
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Interatomic potential, Computer science, Crystal structure, Crystal structure prediction, Computational science, Artificial intelligence, Machine learning, Algorithm, Crystallography, Chemistry, Computational chemistry, Molecular dynamicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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