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.

中文翻译：

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自然有意义且有效的描述符：基于稳健的一次性从头算描述符的材料特性机器学习

为新材料的发现建立数据驱动的管道需要对材料特征进行工程设计，这些特征可以通过计算得到，并可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次从头计算 (ROSA) 描述符。ROSA 的计算成本低廉，并被证明可以准确预测一系列材料特性。这些简单直观的描述符类是使用不完整的从头算计算从低理论水平的材料能量学产生的。我们展示了在基于 ML 的属性预测中结合 ROSA 描述符如何导致对各种晶体、非晶晶体、金属有机框架和分子的准确预测。