基于进化算法的机器学习辅助采样法构建非晶LixSi的第一性原理phase diagrams
Constructing first-principles phase diagrams of amorphous LixSi using machine-learning-assisted sampling with an evolutionary algorithm
摘要：
非晶材料的原子模型需要结构尺寸和采样统计，这是具有挑战性的第一原理方法实现。在这里，我们提出了一种方法来加快采样的无定形和无序的材料使用的遗传算法和专门的机器学习的基础上人工神经网络（ANN）的潜力相结合。我们表明，约1,000第一性原理计算的非晶LiSi合金的例子是足够的人工神经网络电位辅助采样的低能量原子配置在整个非晶LixSi相空间。通过与使用分子动力学模拟和训练到~ 45，000个第一性原理计算的一般ANN势的LixSi配置的广泛采样的结果进行比较来验证所获得的相图。这表明了实用的第一性原理建模的非晶材料的方法。
英文摘要：
The atomistic modeling of amorphous materials requires structure sizes and sampling statistics that are challenging to achieve with first-principles methods. Here, we propose a methodology to speed up the sampling of amorphous and disordered materials using a combination of a genetic algorithm and a specialized machine-learning potential based on artificial neural networks (ANN). We show for the example of the amorphous LiSi alloy that around 1,000 first-principles calculations are sufficient for the ANN potential assisted sampling of low-energy atomic configurations in the entire amorphous LixSi phase space. The obtained phase diagram is validated by comparison with the results from an extensive sampling of LixSi configurations using molecular dynamics simulations and a general ANN potential trained to ~45,000 first-principles calculations. This demonstrates the utility of the approach for the first-principles modeling of amorphous materials.
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