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AI Expands Mathematics: A New Frontier of Reverse Problem Generation for Computational AI, Q&A

September 17, 2026
AI Research
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Associate Professor Hiroshi Kera of Chiba University builds the answer first and constructs the problem around it — a reversal that turned an unglamorous data-generation chore into two new algebraic theorems. We set out what reverse problem generation is, the published runtimes behind it, how accurate the resulting Transformer models actually are, and why Kera expects a field he provisionally calls “AI Algebra” to grow out of the approach.

Physics Ph.D. Student Develops AI Tools to Tackle an Industry ‘Bottleneck’

August 26, 2026
Artificial Intelligence
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The University of Toledo published a profile on 26 August 2026 of Alex Bordovalos, a physics doctoral student building artificial intelligence tools to automate the slowest step in spectroscopic ellipsometry — turning raw polarisation spectra into thickness and optical constants. His advisor, Dr. Nik Podraza, calls it “a major bottleneck in industry and research”. This breakdown separates the university announcement from the peer-reviewed evidence behind it: what the technique actually measures and why its inverse problem needs a human, what Bordovalos and five co-authors published in the Journal of Applied Physics in September 2025, how two series of neural networks split the structural model from the parameter values, the 81-point maps and three amorphous silicon samples used to validate it, the half a million simulations behind the doctoral tool, and the simulation-to-reality gap that still has to be closed on real cadmium telluride cells at Toledo’s Wright Center. It closes with the procurement questions any buyer of automated measurement software should be asking.

DigBat: An AI-Ready Digital Platform for Solid-State Battery Research

August 25, 2026
Artificial Intelligence
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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.