MatCreatioNN: Machine learning-guided computational discovery of photocatalysts for environmental applications
The rational design of photocatalysts for environmental remediation and CO2 conversion remains limited by the high computational cost and sparse experimental data describing multi-parameter photocatalytic behavior. This work presents an integrated machine-learning framework that couples reinforcement learning-based metal-organic framework (MOF) generation with a multi-stage Crystal Graph Convolutional Neural Network (CGCNN) prediction funnel to identify photocatalysts optimized across multiple electronic and structural features. 120,000 MOF candidates were generated and screened using 13 key descriptors, including band-gap suitability, CO2/H2O selectivity, adsorption energy, and structura...
| Authors | Satya Kokonda |
|---|---|
| Primary category | cond-mat.mtrl-sci |
| Published | 29 July 2026 |
| Last updated at source | 29 July 2026 |
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