An integrated AI-driven framework for maximizing the efficiency of heterostructured nanomaterials in photocatalytic hydrogen production

An integrated AI-driven framework for maximizing the efficiency of heterostructured nanomaterials in photocatalytic hydrogen production

Abstract The urgency for sustainable and efficient hydrogen production has increased interest in heterostructured nanomaterials, known for their excellent photocatalytic properties. Traditional synthesis methods often rely on trial-and-error, resulting in inefficiencies in material discovery and opt...

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Main Authors: Pramod N. Belkhode, Shrikant M. Awatade, Chander Prakash, Sagar D. Shelare, Deepali Marghade, Sameer Sheshrao Gajghate, Muhamad M. Noor, Milon Selvam Dennison
Format: Article
Language:English
Published: Nature Portfolio 2025-07-01
Series:Scientific Reports
Subjects:
Hydrogen production
Heterostructured nanomaterials
Graph neural networks
Reinforcement learning
Physics-Informed neural networks
Online Access:https://doi.org/10.1038/s41598-025-10785-6
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https://doi.org/10.1038/s41598-025-10785-6

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