A5009191395- Advancements in Battery Materials
- Advanced Battery Materials and Technologies
- Machine Learning in Materials Science
- Advanced battery technologies research
- Polymer composites and self-healing
- MXene and MAX Phase Materials
- Semiconductor materials and interfaces
- Advanced Materials Characterization Techniques
- Fuel Cells and Related Materials
- X-ray Diffraction in Crystallography
- Block Copolymer Self-Assembly
Shanghai Institute of Ceramics
2024
Chinese Academy of Sciences
2024
University of Chinese Academy of Sciences
2024
China Academy of Engineering Physics
2022
Southwest University of Science and Technology
2020-2022
Large Language Models (LLMs), such as GPT-4, are precipitating a new "industrial revolution" by significantly enhancing productivity across various domains. These models encode an extensive corpus of scientific knowledge from vast textual datasets, functioning near-universal generalists with the ability to engage in natural language communication and exhibit advanced reasoning capabilities. Notably, agents derived LLMs can comprehend user intent autonomously design, plan, utilize tools...
Deep-learning (DL) methods, in consideration of their excellence dealing with highly complex structure-performance relationships for materials, are expected to become a new design paradigm breakthroughs material performance. However, most cases, it is impractical collect massive-scale experimental data or open-source theoretical databases support training DL models sufficient prediction accuracy. In dataset consisting 483 porous silicone rubber observations generated via ink-writing additive...