Massachusetts-based solid-state battery technology company Factorial announced the launch of Gammatron, a proprietary AI-driven simulation platform designed to accelerate the development of next-generation batteries by improving how battery performance is predicted, validated, and optimized.
Born out of Factorial’s own R&D experience, Gammatron was built as a necessity-driven tool to address critical delays in battery development. Unlike traditional AI platforms focused solely on system-level modeling, Gammatron fuses electrochemistry, thermodynamics, and high-fidelity lab data – to simulate and optimize battery behavior at both the material and cell-system level.
“Validating a new cell design can take years, but with Gammatron, we’ve demonstrated that we can dramatically shorten that timeline—forecasting long-term performance from just two weeks of early testing, instead of the typical three to six months,” said Siyu Huang, CEO of Factorial. “By combining automation with data-driven insights, we’re accelerating development with greater speed and control.”
Key capabilities of Gammatron include:
– AI-driven digital twin for battery cells that accurately delivers cell state of health predictions, and accelerates fast charging optimization that maximizes capacity, minimizes degradation and internal stress, while ensuring battery safety and longevity.
– Accelerating electrolyte formulation using molecular modeling and machine learning to engineer compositions for specific performance targets based on a deep understanding of molecular interactions.
– Advanced physics-based modeling to simulate internal battery behavior, including stress, heat, and degradation, that can’t be directly observed in testing.
Used in Factorial’s joint development with Stellantis, Gammatron helped forecast battery performance before full test completion – a key factor in advancing the validation program ahead of its original schedule. In some cases, Gammatron-enabled protocol tuning has doubled cycle life without altering cell chemistry.
“Batteries are complex dynamic chemical systems. Gammatron combines machine learning with scientific feature engineering. Where most platforms hit a wall with shallow machine learning, Gammatron™ goes deeper and shows engineers which material and design changes will unlock longer life and higher performance,” said Raimund Koerver, VP of Business Development at Factorial. “It’s not just about predicting outcomes – it’s about enabling better ones.”
Developed to strengthen Factorial’s core focus on next-generation battery cells, this technology plays a critical supporting role in streamlining customer qualification, enhancing overall cell performance, and accelerating internal development. By integrating advanced capabilities such as material screening, battery cell digital twins, and cell manufacturing traceability, it enables more efficient, data-driven innovation across the battery lifecycle.
Initially launching as a tech-enabled service, Gammatron will be operated in-house for co-development with select partners. The platform is available for solid-state battery development and legacy lithium-ion programs.
Gammatron will be showcased at the upcoming MOVE London conference as part of Factorial’s collaboration with Stellantis.
