TCSP3.0：模板法晶体结构预测在 T180 基准上 Top-1 达 72.2%
TCSP3.0: Template-Based Crystal Structure Prediction with Coefficient-Preserving Element Substitution and Structural Diversity
这篇把晶体结构预测的 Top-1 从 28.3% 干到 72.2%，关键居然是修 bug——元素替换时没管化学计量系数，作者审计后重写流程，还比 CrystalFlow 快十倍。
TCSP3.0 针对模板法晶体结构预测（CSP）中的成分错误问题，采用保持化学计量系数的元素映射、同步替换与逐候选验证。在 T180 基准上，TCSP3.0 的 Top-1 和 Top-20 匹配率分别为 72.2% 和 90.6%，而 TCSP2.0 仅为 28.3% 和 45.6%；Top-1 成绩超过深度生成模型 CrystalFlow 和 MaskGXT（最高 63.9%）。不做弛豫时，它在两个 CPU 核心上每个目标中位耗时 28 秒，约为 CrystalFlow 的十分之一。消融实验显示，大部分提升来自正确性修复，结构去重显著提高了 Top-5 匹配率。
TCSP3.0: Template-Based Crystal Structure Prediction with Coefficient-Preserving Element Substitution and Structural Diversity
Template-based crystal structure prediction (CSP) transfers a known crystal structure to a new composition by element substitution, so its accuracy depends on retrieving suitable templates, mapping elements and ranking the resulting candidates correctly. Auditing the original TCSP2.0 implementation on the T180 benchmark, we found that 469 of its 2,347 selected structures (20%) have the wrong composition, caused by element mappings that ignore stoichiometric coefficients and by sequential text substitution that cascades or corrupts element symbols. We propose TCSP3.0, which enforces coefficient-preserving mappings, substitutes species simultaneously on parsed structures and validates every candidate's composition and cell. It further adds periodic-number retrieval, soft oxidation-state evidence, radius-compatibility scoring, structural deduplication, and ORB-v3 machine-learning-potential relaxation and energy ranking of its candidates, as used by deep generative CSP baselines. TCSP3.0 achieves Top-1 and Top-20 match rates of 72.2% and 90.6% on T180, compared with 28.3% and 45.6% for TCSP2.0, and a Top-20 match rate of 74.0% on a new 800-target benchmark (T800). On T180 it also outperforms PNcsp+ and deep generative models CrystalFlow and MaskGXT (Top-1 72.2% versus at most 63.9%). Even without relaxation, TCSP3.0 reaches 67.8% at Top-1 and 90.6% at Top-20 in a median of 28 s per target on two CPU cores, about ten times faster than CrystalFlow. Paired ten-arm ablations on T180 and T800 show that most of the improvement comes from the correctness fixes; structural deduplication significantly improves the Top-5 match rate, embedding-based retrieval is required for coverage, and the remaining scoring features have small or null effects.