Machine learning for electrocatalyst and photocatalyst design and discovery

H Mai, TC Le, D Chen, DA Winkler… - Chemical …, 2022 - ACS Publications
Electrocatalysts and photocatalysts are key to a sustainable future, generating clean fuels,
reducing the impact of global warming, and providing solutions to environmental pollution …

Developing sustainable, high-performance perovskites in photocatalysis: design strategies and applications

H Mai, D Chen, Y Tachibana, H Suzuki, R Abe… - Chemical Society …, 2021 - pubs.rsc.org
Solar energy is attractive because it is free, renewable, abundant and sustainable.
Photocatalysis is one of the feasible routes to utilize solar energy for the degradation of …

Machine learning for perovskite solar cells and component materials: key technologies and prospects

Y Liu, X Tan, J Liang, H Han, P Xiang… - Advanced Functional …, 2023 - Wiley Online Library
Data‐driven epoch, the development of machine learning (ML) in materials and device
design is an irreversible trend. Its ability and efficiency to handle nonlinear and game …

Converting nanotoxicity data to information using artificial intelligence and simulation

X Yan, T Yue, DA Winkler, Y Yin, H Zhu… - Chemical …, 2023 - ACS Publications
Decades of nanotoxicology research have generated extensive and diverse data sets.
However, data is not equal to information. The question is how to extract critical information …

Enhanced visible light photocatalytic performance of Sr0. 3 (Ba, Mn) 0.7 ZrO3 perovskites anchored on graphene oxide

W Shahzad, AK Badawi, ZA Rehan, AM Khan… - Ceramics …, 2022 - Elsevier
In search of better materials for visible light photocatalytic performance, perovskite Sr 0.3
(Ba/Mn) 0.7 ZrO 3 nanopowders anchored on graphene oxide were synthesized for the …

Recent development of organic–inorganic hybrid photocatalysts for biomass conversion into hydrogen production

A Augustin, C Chuaicham, M Shanmugam… - Nanoscale …, 2022 - pubs.rsc.org
Over the last few years, photocatalysis using solar radiation has been explored extensively
to investigate the possibilities of producing fuels. The production and systematic usage of …

Navigating challenges and opportunities of machine learning in hydrogen catalysis and production processes: Beyond algorithm development

MNI Salehmin, TS Kiong, H Mohamed, DA Umar… - Journal of Energy …, 2024 - Elsevier
With the projected global surge in hydrogen demand, driven by increasing applications and
the imperative for low-emission hydrogen, the integration of machine learning (ML) across …

Machine learning in the development of adsorbents for clean energy application and greenhouse gas capture

H Mai, TC Le, D Chen, DA Winkler… - Advanced …, 2022 - Wiley Online Library
Addressing climate change challenges by reducing greenhouse gas levels requires
innovative adsorbent materials for clean energy applications. Recent progress in machine …

One-pot microwave-assisted synthesis of In2S3/In2O3 nanosheets as highly active visible light photocatalysts for seawater splitting

YR Lin, YC Chang, FH Ko - International Journal of Hydrogen Energy, 2024 - Elsevier
A facile one-pot microwave-assisted synthesis prepared a new noble metal-free
heterostructure of In 2 S 3/In 2 O 3 nanosheets for blue LED-light-induced photocatalytic …

Enhancing prediction accuracy of physical band gaps in semiconductor materials

H Masood, T Sirojan, CY Toe, PV Kumar… - Cell Reports Physical …, 2023 - cell.com
Accurate band-gap prediction is essential for designing and discovering new materials with
desired properties. However, current methods for calculating band gaps based on local and …