Machine learning for electrocatalyst and photocatalyst design and discovery
Electrocatalysts and photocatalysts are key to a sustainable future, generating clean fuels,
reducing the impact of global warming, and providing solutions to environmental pollution …
reducing the impact of global warming, and providing solutions to environmental pollution …
Applying Classical, Ab Initio, and Machine-Learning Molecular Dynamics Simulations to the Liquid Electrolyte for Rechargeable Batteries
Rechargeable batteries have become indispensable implements in our daily life and are
considered a promising technology to construct sustainable energy systems in the future …
considered a promising technology to construct sustainable energy systems in the future …
Combustion, chemistry, and carbon neutrality
K Kohse-Höinghaus - Chemical Reviews, 2023 - ACS Publications
Combustion is a reactive oxidation process that releases energy bound in chemical
compounds used as fuels─ energy that is needed for power generation, transportation …
compounds used as fuels─ energy that is needed for power generation, transportation …
Reactant-induced dynamics of lithium imide surfaces during the ammonia decomposition process
Ammonia decomposition on lithium imide surfaces has been intensively investigated owing
to its potential role in a sustainable hydrogen-based economy. Here, through advanced …
to its potential role in a sustainable hydrogen-based economy. Here, through advanced …
Review of machine learning for hydrodynamics, transport, and reactions in multiphase flows and reactors
Artificial intelligence (AI), machine learning (ML), and data science are leading to a
promising transformative paradigm. ML, especially deep learning and physics-informed ML …
promising transformative paradigm. ML, especially deep learning and physics-informed ML …
[HTML][HTML] SELFIES and the future of molecular string representations
Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad
applications to challenging tasks in chemistry and materials science. Examples include the …
applications to challenging tasks in chemistry and materials science. Examples include the …
Overview on theoretical simulations of lithium‐ion batteries and their application to battery separators
For the proper design and evaluation of next‐generation lithium‐ion batteries, different
physical‐chemical scales have to be considered. Taking into account the electrochemical …
physical‐chemical scales have to be considered. Taking into account the electrochemical …
Exploiting machine learning for controlled synthesis of carbon dots-based corrosion inhibitors
Benefitting from their prominent corrosion inhibition properties, excellent water solubility and
benign environmental friendliness, carbon dots (CDs) have functioned as an ideal candidate …
benign environmental friendliness, carbon dots (CDs) have functioned as an ideal candidate …
Chemical reaction networks and opportunities for machine learning
Chemical reaction networks (CRNs), defined by sets of species and possible reactions
between them, are widely used to interrogate chemical systems. To capture increasingly …
between them, are widely used to interrogate chemical systems. To capture increasingly …
Toward autonomous laboratories: Convergence of artificial intelligence and experimental automation
The ever-increasing demand for novel materials with superior properties inspires retrofitting
traditional research paradigms in the era of artificial intelligence and automation. An …
traditional research paradigms in the era of artificial intelligence and automation. An …