AI助手从视觉模型中发现新晶体结构预测方法，准确率提升至79.06%。
Discovering Crystal Structure Prediction Algorithms with an AI Co-Scientist
- 跨领域搜索结合人类少量指导，将视觉生成模型转用于晶体结构预测
- 在MP-20数据集上达79.06%匹配率，优于最强基线70.87%
- 适合关注跨领域迁移与人机协作的科研人员
我们提出人机协同发现系统（HACO），通过跨领域搜索与稀疏人类引导，实现科学算法发现。目标是从化学组成生成晶体结构，HACO在多个领域的生成模型中识别出视觉领域的掩码生成模型MaskGIT，作为晶体结构预测（CSP）的潜在框架。将该掩码形式转化为晶体结构的离散符号模型，再在稀疏高层人类目标指导下，引入晶格对称性符号、空间群分层采样以覆盖多晶型，并进行子区间坐标精修，构建出掩码生成晶体变压器（MaskGXT）。在MP-20多晶型划分数据集上，MaskGXT达到79.06%的match-everyone-to-reference（METRe）准确率，优于最强基线70.87%。在标准MP-20和MPTS-52 CSP基准测试中，也取得最佳匹配率。结果表明，在具备低成本、快速且对齐良好验证条件的领域，基于迁移引导的交互式人机共研AI可助力科学算法发现，识别可迁移建模原理并融合针对性的人类领域指导。
原文摘要 · Abstract (English)
We introduce Human-AI Co-discovery system (HACO) for scientific algorithm discovery through cross-domain search and sparse human steering. Starting from the goal of generating crystal structures from chemical compositions, HACO searched across generative modeling methodologies from multiple fields and identified MaskGIT, a masked generative model from vision, as a promising framework for crystal structure prediction (CSP). HACO instantiated this masked formulation as a discrete token model of crystal structure; guided by sparse high-level human objectives, it then added crystallographic symmetry tokens, space group stratified sampling for polymorph coverage, and sub-bin coordinate refinement, yielding the Masked Generative Crystal Transformer (MaskGXT). On the MP-20 polymorph split, MaskGXT reaches 79.06% match-everyone-to-reference (METRe) accuracy, compared with 70.87% for the strongest evaluated baseline. MaskGXT also attains the best match rate on standard MP-20 and MPTS-52 CSP benchmarks. These results provide evidence that, in domains offering cheap, fast, and well-aligned validation, transfer-guided interactive AI co-scientists can contribute to scientific algorithm discovery by identifying transferable modeling principles and combining them with targeted human domain guidance.
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