Machine learning models trained on molecular quadrupole moments predict electrostatic potentials rapidly, enabling faster ...

Lithium-ion batteries have become the quiet workhorses of the energy transition, but the way they are designed and tested has long been slow, expensive, and heavily empirical. Machine learning is now ...

Alfred University’s Inamori School of Engineering recently hosted a short course on battery machine learning, which was attended by a group of students and representatives of a Binghamton-area company ...

An agentic AI tool for battery researchers harnesses data from previous battery designs to predict the cycle life of new battery concepts. With information from just 50 cycles, the tool—developed at ...