标题： 金属玻璃机器学习势的有效训练：CuZrAl 验证 显示英文标题

标题： Efficient training of machine learning potentials for metallic glasses: CuZrAl validation

摘要： 原子间势在揭示微观细节和结构-性质关系方面起着至关重要的作用，这是多尺度模拟以及辅助高通量实验的基础。 对于金属玻璃而言，由于其独特的无序结构的复杂性，开发这些势具有挑战性。 因此，这种重要材料类别的化学特异性相互作用势通常缺失。 在这里，我们通过实现一种高效的方法来设计金属玻璃的机器学习原子间势（MLIP），并对CuZrAl系统进行了基准测试。 通过结合Lennard-Jones代理模型与交换蒙特卡洛采样和密度泛函理论（DFT）校正，我们捕捉了从14个数量级过冷中得到的各种非晶态结构。 这些不同的结构为模型提供了稳健且高效的训练，并适用于更广泛的能量范围。 这种方法减少了对大量DFT和从头优化数据集的需求，同时保持了高精度。 我们的MLIP在预测结构性、能量性和机械性能方面显示出与CuZrAl的经典嵌入原子方法（EAM）相当的结果。 这项工作为开发复杂金属玻璃的新MLIP铺平了道路，包括新兴的多元和高熵金属玻璃。 显示英文摘要

摘要： Interatomic potentials play a vital role in revealing microscopic details and structure-property relations, which are fundamental for multiscale simulations and to assist high-throughput experiments. For metallic glasses, developing these potentials is challenging due to the complexity of their unique disordered structure. As a result, chemistry-specific interaction potentials for this important class of materials are often missing. Here, we solve this gap by implementing an efficient methodology for designing machine learning interatomic potentials (MLIPs) for metallic glasses, and we benchmark it with the widely studied CuZrAl system. By combining a Lennard-Jones surrogate model with swap-Monte Carlo sampling and Density Functional Theory (DFT) corrections, we capture diverse amorphous structures from 14 decades of supercooling. These distinct structures provide robust and efficient training of the model and applicability to the wider spectrum of energies. This approach reduces the need for extensive DFT and ab initio optimization datasets, while maintaining high accuracy. Our MLIP shows results comparable to the classical Embedded Atom Method (EAM) available for CuZrAl, in predicting structural, energetic, and mechanical properties. This work paves the way for the development of new MLIPs for complex metallic glasses, including emerging multicomponent and high entropy metallic glasses.