A critical review of machine learning of energy materials
Abstract Machine learning (ML) is rapidly revolutionizing many fields and is starting to
change landscapes for physics and chemistry. With its ability to solve complex tasks …
change landscapes for physics and chemistry. With its ability to solve complex tasks …
Toward excellence of electrocatalyst design by emerging descriptor‐oriented machine learning
Abstract Machine learning (ML) is emerging as a powerful tool for identifying quantitative
structure–activity relationships to accelerate electrocatalyst design by learning from historic …
structure–activity relationships to accelerate electrocatalyst design by learning from historic …
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 …
components to maintaining an ecological balance in the future. Recent revolutions made in …
[HTML][HTML] Perspective on integrating machine learning into computational chemistry and materials science
Machine learning (ML) methods are being used in almost every conceivable area of
electronic structure theory and molecular simulation. In particular, ML has become firmly …
electronic structure theory and molecular simulation. In particular, ML has become firmly …
Machine learning in electron microscopy for advanced nanocharacterization: current developments, available tools and future outlook
In the last few years, electron microscopy has experienced a new methodological paradigm
aimed to fix the bottlenecks and overcome the challenges of its analytical workflow. Machine …
aimed to fix the bottlenecks and overcome the challenges of its analytical workflow. Machine …
Artificial intelligence to power the future of materials science and engineering
Artificial intelligence (AI) has received widespread attention over the last few decades due to
its potential to increase automation and accelerate productivity. In recent years, a large …
its potential to increase automation and accelerate productivity. In recent years, a large …
“Inverting” X-ray absorption spectra of catalysts by machine learning in search for activity descriptors
J Timoshenko, AI Frenkel - Acs Catalysis, 2019 - ACS Publications
The rapid growth of methods emerging in the past decade for synthesis of “designer”
catalysts—ranging from the size and shape-selected nanoparticles to mass-selected …
catalysts—ranging from the size and shape-selected nanoparticles to mass-selected …
Ceramic science of crystal defect cores
Ceramic materials are polycrystalline solids that are made up of metal and non-metal
elements, and inorganic crystal grains with specific crystal structures are fundamental …
elements, and inorganic crystal grains with specific crystal structures are fundamental …
Interpretable catalysis models using machine learning with spectroscopic descriptors
The complexity and dynamics of catalytic systems make it challenging to study the catalysts
and catalytic reactions. Fortunately, the advance of machine learning (ML) has made …
and catalytic reactions. Fortunately, the advance of machine learning (ML) has made …
From Characterization to Discovery: Artificial Intelligence, Machine Learning and High-Throughput Experiments for Heterogeneous Catalyst Design
J Benavides-Hernández, F Dumeignil - ACS Catalysis, 2024 - ACS Publications
This review paper delves into synergistic integration of artificial intelligence (AI) and
machine learning (ML) with high-throughput experimentation (HTE) in the field of …
machine learning (ML) with high-throughput experimentation (HTE) in the field of …