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Meeting 2027 TMS Annual Meeting & Exhibition
Symposium AI/ML/Data Informatics for Materials Discovery: Bridging Experiment, Theory, and Modeling
Presentation Title A Paired Benchmark of Tight-Binding and Graph Neural Networks for Electronic Bandgap Prediction
Author(s) Jaehyung Lee
On-Site Speaker (Planned) Jaehyung Lee
Abstract Scope Machine learning surrogates for the electronic bandgap have converged on two model families. Graph neural networks are trained on DFT databases, while tight-binding neural networks retain orbital resolved electronic structure. The community lacks a paired benchmark quantifying their accuracy and failure modes. We release SlakoNet DB, a paired benchmark on 31,211 hull-stable crystals. It carries predictions from a Slater-Koster tight-binding network and ALIGNN graph networks with PBE, TB-mBJ, and OptB88vdW labels. On non-metals, label matched ALIGNN reaches 0.27 eV mean absolute error versus SlakoNet’s 1.78 eV. SlakoNet’s residual is bimodal, with two failure modes: open-shell transition metal collapse and ionic fluoride collapse. A 4f coverage wall limits its scope. A cross domain stress test confirms the taxonomy is model specific. On the full multimillion entry set, the ranking reverses as it becomes metallic. A downstream funnel surfaces semiconductor candidates for photovoltaics and power electronics. A demo is available at atomgpt.org/slakonet.
Proceedings Inclusion? Planned:
Keywords Electronic Materials, Machine Learning,

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