Work

Overcoming data scarcity challenges in AI-driven energy chemistry research

Year: 2025

Type: article

Abstract: Artificial Intelligence (AI) has become an increasingly important propellant for energy materials and energy chemistry research, such as accelerating advanced energy materials discovery, analyzing vast amounts of data from both experiments and computations, process optimization for materials syntheses, management and monitoring of energy storage devices such as lithium batteries, and algorithm-optimized grid load forecasting. Looking back at recent pioneering works of AI-driven energy chemistry research, constructing a dataset with both high quality and large quantity is almost the first step and largely determines the following success of training AI models and figuring out corresponding scientific issues.

Source: National Science Open

DOI: 10.1360/nso/20250039

Authors: Yu-Hang Yuan, Yu-Chen Gao, Xiang Chen, Qiang Zhang

Institution: Tsinghua University

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

SDG: Affordable and clean energy

Open Access status: diamond

Funders: National Natural Science Foundation of China, National Key Research and Development Program of China