Machine-learning-driven bottom-up design of atomically-layered heterostructures for green H2 production

© Marie-Paule PILENI/Nicolas GOUBET/ERC/CNRS Images
  • Dr. José Julio Gutiérre Moreno - Barcelona Supercomputing Center - Spain
  • Dr. Mohammad Hussein Naseef Al Assadi - RIKEN - Japan
  • Dr. Esmaeil Doust Khah Heragh - Koç University - Turkey
  • Prof. Marco Fronzi - Shibaura Institute of Technology - Japan
  • Prof. Paolo Mele - Shibaura Institute of Technology - Japan

 The field of catalytic green hydrogen production, although indispensable for the transition to a
renewable future, still suffers from widespread irreproducibility of results that limit its full
commercialisation. The largest obstacle lies in the methods widely employed in synthesising the
active catalysts, which impede unambiguously identifying the property-structure relationship. The
vast catalytic samples reported in the literature lack uniformity in shape, size, growth orientation of
particles, and composition. In an atomically imprecise material, the observed catalytic activity may
well be from a subset of the various particles in the sample, which bring about uncertainty in linking
the functional properties to a specific atomic level characteristic of the material. This piece of insight
is critical for any further improvement in the field.

This project aims at understanding, designing, and synthesising atomically controlled
heterostructure thin films for (electro)(photo)catalyst H2 production.

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