Work

Fast-Tracking Solar Cell Materials Discovery Using Automation, Physics Modeling, and Bayesian Machine Learning

Year: 2025

Type: conference-paper

Source: HAL (Le Centre pour la Communication Scientifique Directe)

Authors: Chakar, Joseph, Bonnassieux, Yvan, Cacovich, Stefania, P. Connolly, James, Kleider, Jean-Paul, Guillemoles, Jean-François, Puel, Jean-Baptiste, Schulz, Philip

Institutions: Université Paris-Saclay, Laboratoire de Génie Électrique et Électronique de Paris, Institut Photovoltaïque d’Île-de-France, École Polytechnique, Laboratoire de Physique des Interfaces et des Couches Minces, Institut Polytechnique de Paris, Électricité de France (France)

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: closed

OpenAlex ID: W4416790846

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