Software / AI
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Organizations mentioned or cited on this 2025 report page. A historical mention does not establish current products, availability or endorsement.
Sources and links on page 481 (6)
Links embedded in the original report PDF, including citations and other references. These destinations have not been independently reviewed. Links open in a new tab.
- https://www.weforum.org/stories/2025/01/building-an-ecosystem-for-large-quantitative-ai-models/
- https://www.forbes.com/councils/forbestechcouncil/2024/09/26/the-future-of-large-quantitative-models-a-thoughtful-exploration/
- https://research.aimultiple.com/large-quantitative-models/
- https://talbotwest.com/ai-insights/what-are-large-quantitative-models-lqms
- https://www.itpro.com/technology/artificial-intelligence/what-are-large-quantitative-models-lqms
- https://www.aviso.com/blog/large-quantitative-models
Text on page 481
Modeling Paradigms in AI for Battery R&D: Large Language Models (LLMs) and Large Quantitative Models (LQMs) Different AI model classes address different layers of the battery R&D workflow, from information processing to physics-based prediction
LLMs
Marketing copy
Customer service
Software code
Media
LQMs
Cathodes
Anodes
Electrolytes
Binders
LQMs integrate physics- and chemistry-based models with large-scale scientific datasets to support quantitative prediction of material and system behavior.
LLMs learn from large-scale language and media data to run tasks such as knowledge synthesis, communication, and software-assisted workflows
Text, images, video Internet content
Source: The World Economic Forum, Forbes, AIMultiple, Talbot West, IT Pro, Aviso AI
Chemicals, materials, formulations
Math, biology, chemistry, physics
Extracted from the PDF; charts and images may read imperfectly.
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