一种自动生成用于材料相空间探索的最小结构模板基组的方法
A method for the automatic generation of a minimal basis set of structural templates for material phase-space exploration
AI总结：
提出融合数据驱动与从头算晶体结构预测的最小模板基组自动构建方法，可快速准确预测二元相图，并扩展到多元材料空间的高通量探索。
摘要：
我们提出了一种通过自动构建具有代表性模板的最小基组来预测二元相图的新方法。其核心假设是，任何材料空间都可以划分为少数具有相似化学倾向和成键性质的区域，并且一个最小模板集能够有效表示不同区域间的关键化学趋势。通过将数据驱动技术与从头算晶体结构预测相结合，我们可以高效地划分材料空间，并构建反映化学行为变化的模板。初步结果表明，我们的方法预测二元凸包的精度可与资源密集型EA搜索相媲美，同时显著减少了计算时间（减少至1/25）。该方法可扩展至三元和多元体系，从而实现复杂材料空间的高效高通量探索与制图。通过为高通量材料发现提供一种变革性解决方案，我们的方法为发现先进量子材料和加速计算机模拟设计铺平了道路。
英文摘要：
We present a novel method for predicting binary phase diagrams through the automatic construction of a minimal basis set of representative templates. The core assumption is that any materials space can be divided into a small number of regions with similar chemical tendencies and bonding properties, and that a minimal set of templates can efficiently represent the key chemical trends across the different regions. By combining data-driven techniques with ab-initio crystal structure prediction, we can efficiently partition the materials space and construct templates reflecting variations in chemical behavior. Preliminary results demonstrate that our method predicts binary convex hulls with accuracy comparable to resource-intensive EA searches, while achieving a significant reduction in computational time (by a factor of 25). The method can be extended to ternary and multinary systems, enabling efficient high-throughput exploration and mapping of complex material spaces. By providing a transformative solution for high-throughput materials discovery, our approach paves the way for uncovering advanced quantum materials and accelerating in silico design.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
