Tight-Binding Approximation-Enhanced Global Optimization

Solving and predicting atomic structures from first-principles methodologies is limited by the computational cost of exploring the search space, even when relatively inexpensive density functionals are used. Here, we present an efficient approach where the search is performed using density functional tight-binding, with an automatic adaptive parametrization scheme for the repulsive pair potentials. We successfully apply the method to the genetic algorithm optimization of bulk carbon, titanium dioxide, palladium oxide, and calcium hydroxide, and we assess the stability of the unknown crystal structure of palladium hydroxide.基于第一性原理方法（first-principles methodologies）求解与预测原子结构的过程，其核心瓶颈在于探索搜索空间所需的计算成本，即便使用相对轻量化的密度泛函（density functional）亦是如此。为此，本文提出一种高效求解方案，该方案采用密度泛函紧束缚（density functional tight-binding）方法开展结构搜索，并针对排斥对势（repulsive pair potentials）配置了自动自适应参数化机制。我们将该方法成功应用于块体碳、二氧化钛、氧化钯与氢氧化钙的遗传算法优化，并对尚未探明的氢氧化钯晶体结构的稳定性进行了评估。