Work

Express Analysis of the Structural and Electronic Properties of Nanomaterials Using Big Data, Large Language Models and Generative Artificial Intelligence

Year: 2025

Type: article

Abstract: The possibility of using large language models, generative machine learning and big data methods for predictive analysis of the electronic properties of nanostructures based on semiconductor materials is considered, without limiting the generality of this approach for other crystalline materials. The conceptual solution in the form of a cross-platform software application with an advanced search generation capability for applying this approach to working with scientific data arrays is described. Preliminary test results showed a positive result of using the developed solution to predict semiconductor properties (bandgap width, Fermi energy) of a reference material. Prospects for the development and implementation of big data methods, large language models, and generative artificial intelligence in the context of modern trends in materials science are discussed.

Source: Репозиторий БГУИР (BSUIR Repository)

DOI: 10.35596/1729-7648-2025-23-3-46-53

Authors: N. A. Shymanski, A. V. Baglov, Kh. B. Musaev, O. N. Ruzimuradov, L. S. Khoroshko

Institutions: Belarusian State University, Belarusian State University of Informatics and Radioelectronics, Tashkent Institute of Irrigation and Agricultural Mechanization Engineers, Turin Polytechnic University

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W4413062171

FWCI:

Cites: 7

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Related to: 10