npj: 功能材料中原子结构的贝叶斯预测

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订制先进有机/无机异质器件使其符合预期技术应用的功能特性，需要了解器件内部的微观结构并能对其调控。原子尺度量子力学模拟方法可以针对具体材料结构给出精确预测的能量和性质，然而，通过计算的结构仍然比较困难。

由芬兰阿尔托大学Milica Todorović等领导的团队，将人工智能采样策略、自然“构建块”表示与精确的量子力学计算相结合，提出了一种新颖的结构搜索方案。他们以C60团簇在二氧化钛（101）表面的吸附结构研究为例证明了该方法的准确性。其预测的吸附结构与实验观测很好的吻合。不仅如此他们还通过上述方法得到分子与表面的作用机理，理解了稳定吸附结构的成因。该研究提出的方法可以进一步推广用于分子聚集体和薄膜等大尺度表面吸附结构的研究。

该文近期发表于npj Computational Materials 5: 35 (2019)，英文标题与摘要如下，点击左下角“阅读原文”可以自由获取论文PDF。

Bayesian inference of atomistic structure in functional materials

Milica Todorović, Michael U. Gutmann, Jukka Corander & Patrick Rinke 

Tailoring the functional properties of advanced organic/inorganic heterogeneous devices to their intended technological applications requires knowledge and control of the microscopic structure inside the device. Atomistic quantum mechanical simulation methods deliver accurate energies and properties for individual configurations, however, finding the most favourable configurations remains computationally prohibitive. We propose a ‘building block’-based Bayesian Optimisation Structure Search (BOSS) approach for addressing extended organic/inorganic interface problems and demonstrate its feasibility in a molecular surface adsorption study. In BOSS, a Bayesian model identifies material energy landscapes in an accelerated fashion from atomistic configurations sampled during active learning. This allowed us to identify several most favourable molecular adsorption configurations for C₆₀  on the (101) surface of TiO₂  anatase and clarify the key molecule-surface interactions governing structural assembly. Inferred structures were in good agreement with detailed experimental images of this surface adsorbate, demonstrating good predictive power of BOSS and opening the route towards large-scale surface adsorption studies of molecular aggregates and films.

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