High-entropy alloys show promising properties for novel catalytic designs, but their vast potential configurations make them challenging to study computationally. Additionally, the traditional methods for data acquisition required to train neural networks on these broad systems can be inefficient. To address this, we propose an active learning methodology that integrates genetic algorithms with deep convolutional neural networks trained on Density Functional Theory calculations via a simple closed feedback loop. This approach streamlines data acquisition and the exploration of large configurational spaces simultaneously. We illustrate its effectiveness on high-entropy clusters of variable sizes and compositions, the vast state spaces of which are automatically explored and trained on, so as to generate and predict the stability of any cluster within the latent space given minimal computational requirements. Importantly, this method is adaptable for use in a variety of other systems of different sizes, chemical compositions, and stoichiometry.

中文翻译：

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绘制高熵态空间以发现新材料


高熵合金在新颖的催化设计中显示出有前景的特性，但其巨大的潜在配置使它们难以进行计算研究。此外，在这些广泛的系统上训练神经网络所需的传统数据采集方法可能效率低下。为了解决这个问题，我们提出了一种主动学习方法，该方法将遗传算法与通过简单的闭反馈环进行密度泛函理论计算训练的深度卷积神经网络相结合。这种方法同时简化了数据采集和大型配置空间的探索。我们说明了它在可变大小和组成的高熵簇上的有效性，其巨大的状态空间被自动探索和训练，以便在给定最小计算要求的情况下生成和预测潜在空间内任何簇的稳定性。重要的是，该方法适用于不同尺寸、化学成分和化学计量的各种其他系统。