Directory profiles · Battery Business Directory
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Sources and links on page 483 (10)
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- https://www.sandboxaq.com/solutions/large-quantitative-models
- https://molecular-universe.com/about
- https://www.microsoft.com/en-us/research/blog/mattergen-a-new-paradigm-of-materials-design-with-generative-ai/
- https://www.sdsc.edu/news/2025/PR20250826-battery-tech.html
- https://citrine.io/wp-content/uploads/2021/09/Better-Battery-Design-using-AI.pdf
- https://factorialenergy.com/technology/
- https://www.automotiveworld.com/articles/aionics-ai-to-revolutionise-ev-battery-development/
- https://www.noble.ai/resources/advancing-lithium-metal-battery-technology-through-ai-driven-insights
- https://arxiv.org/pdf/2507.16110v1
- https://pubs.acs.org/doi/10.1021/acsmaterialslett.4c02674
Text on page 483
LQMs and LLMs in Battery Research - Screening Battery Material Candidates
Key Takeaways: Impact on Battery Materials Discovery
Screen electrolyte, electrode, and SEI materials at scale
Reduce costly experiments and accelerate innovation
Enable generative design of next-gen battery chemistries
Examples of Public Efforts LQM-driven models
SandboxAQ – Large Quantitative Model (LQM) for solid-state electrolyte and cathode discovery.
SES – Uses generative AI to discover new battery electrolyte additives
Microsoft – MatterGen model for AI-driven materials design
SDSC – AI + HPC to screen next-gen battery chemistries
Morrow / Citrine – AI platform to optimize battery materials and manufacturing
Factorial – Uses AI to accelerate solid-state electrolyte development.
Aionics – ML discovery of new electrolytes, additives, and interphase materials
NobleAI – AI for lithium-metal battery materials and electrolyte prediction LLM-driven models
ChatBattery – LLM tools for electrode material design and analysis
ChatSSB – Large Language Models for Solid-state battery research
Source: SandboxAQ, SES, Microsoft, SDSC, Morrow/Citrine, Factorial, Avionics, NobleAI, ChatBattery, ChatSSB
Extracted from the PDF; charts and images may read imperfectly.
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