DigBat: An AI-Ready Digital Platform for Solid-State Battery Research
DigBat is the Digital Battery Platform from Tohoku University’s Advanced Institute for Materials Research, and the paper describing it landed in Nano Materials Science on 27 July 2026. It now holds 3,816 experimental solid-state electrolytes, 27,645 ionic conductivity entries and 852 computational materials across three modules — inorganic, solid polymer and gel polymer — up from just over 600 records in the 2023 DDSE release it grew out of. What makes it unusual is not the size but the plumbing: a common identifier joining experimental and computational records, explicit temperatures from 132.4 to 1261.6 K, embedded ion migration models, and a large language model assistant on the front. This breakdown covers what is inside, why activation energy matters more than headline conductivity, how the curation was done, where DigBat sits against the Materials Project and OQMD, and the limits a curated literature database cannot escape.
