Software / AI

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.