Work

Density-aware active learning for materials discovery: a case study on functionalized nanoporous materials

Year: 2025

Type: article

Abstract: , a scientist conducting experiments), have proven highly effective for such tasks. However, challenges remain, particularly for regression tasks, which are generally considered more complex in the AL framework. This complexity stems from the need for uncertainty estimation and the continuous nature of the output space. In this work, we introduce density-aware greedy sampling (DAGS), an active learning method for regression that integrates uncertainty estimation with data density, specifically designed for large design spaces (DS). We evaluate DAGS in both synthetic data and multiple real-world datasets of functionalized nanoporous materials, such as metal-organic frameworks (MOFs) and covalent-organic frameworks (COFs), for separation applications. Our results demonstrate that DAGS consistently outperforms both random sampling and state-of-the-art AL techniques in training regression models effectively with a limited number of data points, even in datasets with a high number of features.

Source: Physical Chemistry Chemical Physics

DOI: 10.1039/d5cp02908b

Authors: V. Gkatsis, P. Maratos, C. Rekatsinas, G. Giannakopoulos, P. Krokidas

Institutions: National and Kapodistrian University of Athens, National Centre of Scientific Research "Demokritos", Institute of Informatics & Telecommunications, National Technical University of Athens, SciFY

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: European Commission, HORIZON EUROPE Framework Programme, HORIZON EUROPE Digital, Industry and Space

Awards: G3576123142, A catalyst for EuropeaN ClOUd Services in the era of data spaces, high-performance and edge computing, A catalyst for EuropeaN ClOUd Services in the era of data spaces, high-performance and edge computing