# Naturally-meaningful and efficient descriptors: machine learning of material properties based on robust one-shot ab initio descriptors

arXiv: https://arxiv.org/abs/2203.03392 · HTML: https://mlsanity.com/paper/2203.03392/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 32 references that are in the map.
- First posted on arXiv 2 Mar 2022.

## Built on by (most cited first, 2 of 2)

- [Extracting human interpretable structure-property relationships in chemistry using XAI and large language models](https://mlsanity.com/paper/2311.04047/) (2023)
- [Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability](https://mlsanity.com/paper/2509.03547/) (2025)

## Abstract (arXiv)

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.