发表机构
Bredesen Center for Interdisciplinary Research; University of Tennessee; University of Tennessee - Oak Ridge Innovation Institute（布雷德森跨学科研究中心; 田纳西大学; 田纳西大学-橡树岭创新研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对X射线与中子联合晶体结构精修中非凸、噪声及多目标权衡难题，提出CrystalMO-TuRBO多目标信任域贝叶斯优化，通过两阶段全局探索与局部细化，在Ho2Ti2O7数据上提升收敛性、鲁棒性和参数精度。
AI 中文摘要
晶体结构精修是材料表征中的一个基本逆问题，其中结构参数被优化以重现实验衍射数据。传统方法，如最小二乘和基于似然的优化，依赖局部搜索，并且常常难以应对非凸、含噪声且高度相关的参数景观，尤其是在整合多种衍射模态时。X射线和中子数据的联合精修尤其具有挑战性，因为它们的灵敏度互补但相互竞争，通常通过需要手动加权的标量化目标进行组合，导致次优解。我们提出CrystalMO-TuRBO，一种用于联合晶体结构精修的多目标信任域贝叶斯优化架构。该方法将X射线和中子差异建模为独立目标，并将问题转化为归一化最大化设置。引入两阶段优化策略：第一阶段使用跨多个标量化的并行信任域贝叶斯优化进行全局探索，以识别参数空间中的有前景区域；第二阶段在收缩区域内进行局部细化，以实现高精度解。这种设计明确区分了全局搜索与细粒度优化，满足了精修任务独特的精度要求。我们在实验采集的单晶Ho2Ti2O7的X射线和中子衍射数据上评估了所提方法。结果表明，与经典精修方法和贝叶斯优化基线相比，在单晶烧绿石材料体系的精修中，所提方法在收敛性、鲁棒性和参数精度方面均有提升。
英文摘要
Crystal structure refinement is a fundamental inverse problem in materials characterization, where structural parameters are optimized to reproduce experimental diffraction data. Conventional approaches, such as least-squares and likelihood-based optimization, rely on local search and often struggle with non-convex, noisy, and highly correlated parameter landscapes, particularly when integrating multiple diffraction modalities. Joint refinement of X-ray and neutron data is especially challenging due to their complementary but competing sensitivities, which are typically combined through scalarized objectives requiring manual weighting and leading to suboptimal solutions. We propose CrystalMO-TuRBO, a multi-objective trust region Bayesian optimization architecture for joint crystal structure refinement. The method models X-ray and neutron discrepancies as separate objectives and transforms the problem into a normalized maximization setting. A two-phase optimization strategy is introduced: Phase 1 performs global exploration using parallel trust-region Bayesian optimization across multiple scalarizations to identify promising regions of the parameter space, while Phase 2 conducts localized refinement within a shrinking region to achieve high-precision solutions. This design explicitly separates global search from fine-grained optimization, addressing the unique accuracy requirements of refinement tasks. We evaluate the proposed method on experimentally collected X-ray and neutron diffraction data from single-crystal Ho2Ti2O7. Results demonstrate improved convergence, robustness, and parameter precision compared to classical refinement methods and Bayesian optimization baselines on refinement of a single-crystal pyrochlore material system.
