论文标题
自然意义，高效的描述符：基于强大的一声从头算法的机器学习材料属性
Naturally-meaningful and efficient descriptors: machine learning of material properties based on robust one-shot ab initio descriptors
论文作者
论文摘要
建立一个数据驱动的管道来发现新型材料，需要可以进行可行计算的材料特征的工程，并可以应用于预测材料的目标特性。在这里，我们提出了一类新的描述符来描述晶体结构，我们将其称为稳健的单发（ROSA）描述符。 Rosa在计算上很便宜，并且显示出可以准确预测一系列材料特性。这些简单而直观的描述符类别是从材料的能量学以低水平的材料的能量学使用不完整的从头开始计算而产生的。我们证明了ROSA描述符在基于ML的性质预测中的掺入如何导致在广泛的晶体，非晶晶体，金属有机框架和分子上进行准确的预测。我们认为，这些描述符的计算成本较低和易用性将显着改善基于ML的预测。
Establishing a data-driven pipeline for the discovery of novel materials requires the engineering of material features that can be feasibly calculated and can be applied to predict a material's target properties. Here we propose a new class of descriptors for describing crystal structures, which we term Robust One-Shot Ab initio (ROSA) descriptors. ROSA is computationally cheap and is shown to accurately predict a range of material properties. These simple and intuitive class of descriptors are generated from the energetics of a material at a low level of theory using an incomplete ab initio calculation. We demonstrate how the incorporation of ROSA descriptors in ML-based property prediction leads to accurate predictions over a wide range of crystals, amorphized crystals, metal-organic frameworks and molecules. We believe that the low computational cost and ease of use of these descriptors will significantly improve ML-based predictions.
