论文标题
驱动具有局部相似性内核的分子，晶体和复杂系统的原子结构
Driving atomic structures of molecules, crystals, and complex systems with local similarity kernels
论文作者
论文摘要
访问具有原子水平细节的分子，晶体和复杂界面的结构对于对材料，化学反应和生化过程的理解和工程至关重要。当前，确定准确的原子位置在很大程度上依赖于难以访问的先进实验技术或计算强度的量子化学计算。我们描述了一种有效的数据驱动的局部相似性内核优化（Losiko）方法，通过将嵌入式局部原子环境与数据库中的嵌入式局部原子环境匹配，然后在数据库中最大化其相似性度量，从而获得原子结构。我们表明，Losiko仅利用了几何数据，并且可以包含在不同近似值下构建的量子化学数据库。通过包括已知的稳定条目，与最先进的量子化学方法相比，可以获得有机分子，无机固体，缺陷和复杂界面的化学知情原子结构。此外，我们表明，通过仔细策划数据库，可以获得对目标材料特征的偏置结构的逆设计。
Accessing structures of molecules, crystals, and complex interfaces with atomic level details is vital to the understanding and engineering of materials, chemical reactions, and biochemical processes. Currently, determination of accurate atomic positions heavily relies on advanced experimental techniques that are difficult to access or quantum chemical calculations that are computationally intensive. We describe an efficient data-driven LOcal SImilarity Kernel Optimization (LOSIKO) approach to obtain atomic structures by matching embedded local atomic environments with that in databases followed by maximizing their similarity measures. We show that LOSIKO solely leverages on geometric data and can incorporate quantum chemical databases constructed under different approximations. By including known stable entries, chemically informed atomic structures of organic molecules, inorganic solids, defects, and complex interfaces can be obtained, with similar accuracy compared to the state-of-the-art quantum chemical approaches. In addition, we show that by carefully curating the databases, it is possible to obtain structures with bias towards target material features for inverse design.
