Machine learning in energy chemistry: introduction, challenges and perspectives-Reference-Cited by-同舟云学术

Machine learning in energy chemistry: introduction, challenges and perspectives

Published:2023 Issue:7 Volume:2 Page:896-921
ISSN:2753-1457
Container-title:Energy Advances
language:en
Short-container-title:Energy Adv.

Author:

Xu Yuzhi¹²,Ge Jiankai³^ORCID,Ju Cheng-Wei⁴^ORCID

Affiliation:

1. Department of Chemistry, New York University, New York, New York 10003, USA

2. NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai 200062, China

3. Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA

4. Pritzker School of Molecular Engineering, The University of Chicago, Chicago, Illinois 60637, USA

Abstract

This review explores machine learning's role in energy chemistry, spanning organic photovoltaics, perovskites, catalysis, and batteries, highlighting its potential to accelerate eco-friendly, sustainable energy development.

Publisher

Royal Society of Chemistry (RSC)

Subject

Energy (miscellaneous),Energy Engineering and Power Technology,Fuel Technology,Renewable Energy, Sustainability and the Environment

Link

http://pubs.rsc.org/en/content/articlepdf/2023/YA/D3YA00057E

Reference303 articles.

1. Impacts of urbanization and industrialization on energy consumption/CO2 emissions: Does the level of development matter?

2. Progress on perovskite materials for energy application

3. Modular and intensified — Reimagining manufacturing at the energy-chemistry nexus and beyond

4. Recycling of carbon dioxide to methanol and derived products – closing the loop

5. Adsorptive separation of carbon dioxide: From conventional porous materials to metal–organic frameworks

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