论文标题
基于遗传编程的碳间潜力的材料发现的学习
Genetic programming-based learning of carbon interatomic potential for materials discovery
论文作者
论文摘要
在寻找具有所需特性的结构的同时，有效，准确的原子间潜在功能对于材料的计算研究至关重要。传统上，潜在的功能或能源景观是由基于理论或启发式知识的专家设计的。在这里，我们提出了一种新方法，以利用强烈键入的平行遗传编程（GP）进行潜在功能发现。我们使用具有NSGA-III选择的多目标进化算法来通过符号回归来优化个体，适应性和复杂性。通过从858个碳结构绘制的863个独特的碳同素构型的DFT数据集，生成的电势能够以低计算成本预测$ \ pm 7.70 $ EV以内的总能量，同时跨多个碳结构概括。我们的代码是开源的，可在\ url {http://www.github.com/usccolumbia/mlpotential获取
Efficient and accurate interatomic potential functions are critical to computational study of materials while searching for structures with desired properties. Traditionally, potential functions or energy landscapes are designed by experts based on theoretical or heuristic knowledge. Here, we propose a new approach to leverage strongly typed parallel genetic programming (GP) for potential function discovery. We use a multi-objective evolutionary algorithm with NSGA-III selection to optimize individual age, fitness, and complexity through symbolic regression. With a DFT dataset of 863 unique carbon allotrope configurations drawn from 858 carbon structures, the generated potentials are able to predict total energies within $\pm 7.70$ eV at low computational cost while generalizing well across multiple carbon structures. Our code is open source and available at \url{http://www.github.com/usccolumbia/mlpotential
