Machine Learning Guided Discovery of Non‐Linear Optical Materials
Problem
Discovering new nonlinear optical (NLO) materials for solid-state lasers has relied on slow high-throughput calculations or chemical intuition.
Why it matters
NLO materials are crucial for laser frequency conversion and photonic technologies.
What the AI did
Two machine learning models predict refractive index and Vickers hardness, screening the OQMD database for NLO candidates by non-centrosymmetry, bandgap, and hardness.
Results
ML screening identified potential NLO candidates validated by DFT calculations; the approach recovered several already-established NLO materials.
Impact
The evidence supports a validated ML screening pipeline, but no newly synthesized material is reported.
Record
| Venue | Advanced Theory and Simulations |
|---|---|
| Published | 2024 |
| Work type | article |
| Authors | Sownyak Mondal, Raheel Hammad |
| Citations | 14 (OpenAlex, at import time) |
| References | 50 |
| Model or method | not stated in source |
| Organization | not stated in source |
| Primary source | https://doi.org/10.1002/adts.202400463 |
| Open access | hybrid |
| Keywords | nonlinear optical materials, machine learning, materials screening, DFT, refractive index |
Importance rating
real but narrower advance
Abstract states NLO materials are crucial for achieving desired frequencies in solid-state lasers; ML screening accelerates discovery, though no newly synthesized material is reported.
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