# Knowledge Discovery from Atomic Structures using Feature Importances

arXiv: https://arxiv.org/abs/2303.09453 · HTML: https://mlsanity.com/paper/2303.09453/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 61 references that are in the map.
- First posted on arXiv 28 Feb 2023.

## Builds on (most cited first, 8 of 8)

- [Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps](https://mlsanity.com/paper/1312.6034/) (2013)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [A Survey on the Explainability of Supervised Machine Learning](https://mlsanity.com/paper/2011.07876/) (2020)
- [Explainable Machine Learning for Scientific Insights and Discoveries](https://mlsanity.com/paper/1905.08883/) (2019)
- [DScribe: Library of Descriptors for Machine Learning in Materials Science](https://mlsanity.com/paper/1904.08875/) (2019)
- [Feature Importance Ranking for Deep Learning](https://mlsanity.com/paper/2010.08973/) (2020)
- [Minimal Learning Machine: Theoretical Results and Clustering-Based Reference Point Selection](https://mlsanity.com/paper/1909.09978/) (2019)
- [Machine learning and AI-based approaches for bioactive ligand discovery and GPCR-ligand recognition](https://mlsanity.com/paper/2001.06545/) (2020)

## Abstract (arXiv)

Molecular-level understanding of the interactions between the constituents of an atomic structure is essential for designing novel materials in various applications. This need goes beyond the basic knowledge of the number and types of atoms, their chemical composition, and the character of the chemical interactions. The bigger picture takes place on the quantum level which can be addressed by using the Density-functional theory (DFT). Use of DFT, however, is a computationally taxing process, and its results do not readily provide easily interpretable insight into the atomic interactions which would be useful information in material design. An alternative way to address atomic interactions is to use an interpretable machine learning approach, where a predictive DFT surrogate is constructed and analyzed. The purpose of this paper is to propose such a procedure using a modification of the recently published interpretable distance-based regression method. Our tests with a representative benchmark set of molecules and a complex hybrid nanoparticle confirm the viability and usefulness of the proposed approach.