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 …

Combustion machine learning: Principles, progress and prospects

M Ihme, WT Chung, AA Mishra - Progress in Energy and Combustion …, 2022 - Elsevier
Progress in combustion science and engineering has led to the generation of large amounts
of data from large-scale simulations, high-resolution experiments, and sensors. This corpus …

Machine learning for catalysis informatics: recent applications and prospects

T Toyao, Z Maeno, S Takakusagi, T Kamachi… - Acs …, 2019 - ACS Publications
The discovery and development of catalysts and catalytic processes are essential
components to maintaining an ecological balance in the future. Recent revolutions made in …

Efficient removal of greenhouse gases: machine learning-assisted exploration of metal–organic framework space

R Xin, C Wang, Y Zhang, R Peng, R Li, J Wang… - ACS …, 2024 - ACS Publications
Global warming is a crisis that humanity must face together. With greenhouse gases (GHGs)
as the main factor causing global warming, the adoption of relevant processes to eliminate …

Accelerated discovery of stable lead-free hybrid organic-inorganic perovskites via machine learning

S Lu, Q Zhou, Y Ouyang, Y Guo, Q Li, J Wang - Nature communications, 2018 - nature.com
Rapidly discovering functional materials remains an open challenge because the traditional
trial-and-error methods are usually inefficient especially when thousands of candidates are …

Polymer informatics: Current status and critical next steps

L Chen, G Pilania, R Batra, TD Huan, C Kim… - Materials Science and …, 2021 - Elsevier
Artificial intelligence (AI) based approaches are beginning to impact several domains of
human life, science and technology. Polymer informatics is one such domain where AI and …

Autonomous discovery in the chemical sciences part I: Progress

CW Coley, NS Eyke, KF Jensen - … Chemie International Edition, 2020 - Wiley Online Library
This two‐part Review examines how automation has contributed to different aspects of
discovery in the chemical sciences. In this first part, we describe a classification for …

Deep learning for computational chemistry

GB Goh, NO Hodas, A Vishnu - Journal of computational …, 2017 - Wiley Online Library
The rise and fall of artificial neural networks is well documented in the scientific literature of
both computer science and computational chemistry. Yet almost two decades later, we are …

Multiscale studies on ionic liquids

K Dong, X Liu, H Dong, X Zhang, S Zhang - Chemical reviews, 2017 - ACS Publications
Ionic liquids (ILs) offer a wide range of promising applications because of their much
enhanced properties. However, further development of such materials depends on the …

Machine learning in analytical chemistry: From synthesis of nanostructures to their applications in luminescence sensing

M Mousavizadegan, A Firoozbakhtian… - TrAC Trends in …, 2023 - Elsevier
Over the past decade, the wide-scale adoption of artificial intelligence (AI) and machine
learning (ML) has transformed the landscape of scientific research and development, which …