CrystalFormer-CSP: Thinking Fast and Slow for Crystal Structure Prediction

• URL: http://arxiv.org/abs/2512.18251v1
• Date: Sat, 20 Dec 2025 07:22:58 GMT
• Title: CrystalFormer-CSP: Thinking Fast and Slow for Crystal Structure Prediction
• Authors: Zhendong Cao, Shigang Ou, Lei Wang,
• Abstract summary: We present CrystalFormerCSP, an efficient framework that unifies data-driven and physics-driven optimization approaches to predict stable crystal structures for given chemical compositions.<n>The approach combines pretrained generative models for space-group-informed structure generation and a universal machine learning force field for energy minimization.<n>We demonstrate the effectiveness of CrystalFormer-CSP on benchmark problems and showcase its usage via web interface and language model integration.
• Score: 2.110303171517621
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Crystal structure prediction is a fundamental problem in materials science. We present CrystalFormer-CSP, an efficient framework that unifies data-driven heuristic and physics-driven optimization approaches to predict stable crystal structures for given chemical compositions. The approach combines pretrained generative models for space-group-informed structure generation and a universal machine learning force field for energy minimization. Reinforcement fine-tuning can be employed to further boost the accuracy of the framework. We demonstrate the effectiveness of CrystalFormer-CSP on benchmark problems and showcase its usage via web interface and language model integration.

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