We study the thermal conductivity distribution of hypothetical graphene-like materials composed of carbon and heavy carbon atoms. These materials are representative of alloys and disordered materials, which are relatively unexplored for thermal properties owing to their large configuration spaces. Since the full thermal conductivity calculations using the Boltzmann transport equation based solutions are computationally prohibitive for each of the 232 considered configurations, we employ regularized autoencoders, a class of generative machine learning models that transform the configuration space to the latent space in which materials are clustered according to the target property. Such conditioning allows selective sampling of high thermal conductivity materials from the latent space. We find that the model is able to learn the underlying thermal transport physics of the system under study and is able to predict superlattice-like configurations with high thermal conductivity despite their higher mass.

中文翻译：

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机器学习辅助空间转换加速高导热合金的发现

我们研究了由碳和重碳原子组成的假设类石墨烯材料的热导率分布。这些材料是合金和无序材料的代表，由于其较大的配置空间，它们的热性能相对未被探索。由于使用基于玻尔兹曼传输方程的解决方案的完整热导率计算对于 232 种考虑的配置中的每一种而言在计算上都令人望而却步，我们采用正则化自动编码器，这是一类生成机器学习模型，可将配置空间转换为材料聚集的潜在空间根据目标属性。这种调节允许从潜在空间中选择性地采样高热导率材料。