{"id":7491,"date":"2025-11-27T13:20:26","date_gmt":"2025-11-27T04:20:26","guid":{"rendered":"https:\/\/aizoth.com\/?post_type=use_case&p=7491"},"modified":"2025-12-05T14:00:39","modified_gmt":"2025-12-05T05:00:39","slug":"contents-e028","status":"publish","type":"use_case","link":"https:\/\/aizoth.com\/en\/use_case\/contents-e028\/","title":{"rendered":"Ionic Conductivity Prediction Using Multi-Sigma\u00ae"},"content":{"rendered":"\n

Multi-Sigma\u00ae<\/sup> enables fast, AI-driven prediction of ionic conductivity using only composition and basic crystal information. A two-stage surrogate model first predicts key structural and dynamic properties, then estimates ionic conductivity across 18 orders of magnitude. This approach accelerates early-stage screening of solid electrolytes without requiring DFT or molecular dynamics simulations.<\/strong><\/strong><\/p>\n\n\n\n

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1. AI Chain Analysis<\/strong><\/h2>\n\n\n\n

Advancing solid-state battery research requires rapid screening of Li-ion conductive materials.
However, experimental ionic conductivity measurements are limited, and DFT\/MD simulations are computationally expensive. Multi-Sigma\u00ae <\/sup>addresses this challenge using a two-stage surrogate model. The first stage predicts intermediate material properties from composition and crystal data, and the second stage uses these properties to estimate ionic conductivity. This enables large-scale screening of candidate materials without running ab-initio simulations.<\/p>\n\n\n\n

Stage 1 uses open material databases to predict key structural, thermodynamic, and ion-transport descriptors from composition and crystal data.
These learned descriptors capture how the lattice environment governs Li-ion mobility.
Stage 2 then uses them to accurately estimate experimental ionic conductivity across 18 orders of magnitude.<\/p>\n\n\n\n

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2. AI Prediction<\/strong><\/strong><\/h2>\n\n\n\n

The two-stage model predicts ionic conductivity accurately across 10\u207b\u00b9\u2077 to 10\u207b\u00b9 S\/cm. Stage 1 generates transport-relevant descriptors, and Stage 2 uses them to produce conductivity values that align well with experimental data (Log R\u00b2 = 0.80).<\/p>\n\n\n\n

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Stage 1: Intermediate property prediction<\/strong><\/strong><\/p>\n\n\n\n

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Stage 2: Ionic conductivity prediction<\/strong><\/strong><\/p>\n\n\n\n

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This usecase is based on results obtained from a project, JPNP23001, commissioned by the New Energy and Industrial Technology Development Organization (NEDO).
Data Source:  Rajapriya, N.; Yoshitake, M.; Nagata, T.; Kawajiri, K. Two-stage AI surrogate for predicting ionic conductivity from crystal structure using DFT and topological descriptors. Presented at the ACS Fall 2025 National Meeting & Exposition, Washington, DC, August 19, 2025; COMP Poster Session, Poster 817, Abstract 4308994.<\/p>\n\n\n\n

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