Machine learning in nanoscience: big data at small scales
Recent advances in machine learning (ML) offer new tools to extract new insights from large
data sets and to acquire small data sets more effectively. Researchers in nanoscience are …
data sets and to acquire small data sets more effectively. Researchers in nanoscience are …
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 …
Extracting knowledge from data through catalysis informatics
AJ Medford, MR Kunz, SM Ewing, T Borders… - Acs …, 2018 - ACS Publications
Catalysis informatics is a distinct subfield that lies at the intersection of cheminformatics and
materials informatics but with distinctive challenges arising from the dynamic, surface …
materials informatics but with distinctive challenges arising from the dynamic, surface …
Automating crystal-structure phase mapping by combining deep learning with constraint reasoning
Crystal-structure phase mapping is a core, long-standing challenge in materials science that
requires identifying crystal phases, or mixtures thereof, in X-ray diffraction measurements of …
requires identifying crystal phases, or mixtures thereof, in X-ray diffraction measurements of …
From tunable core-shell nanoparticles to plasmonic drawbridges: Active control of nanoparticle optical properties
The optical properties of metallic nanoparticles are highly sensitive to interparticle distance,
giving rise to dramatic but frequently irreversible color changes. By electrochemical …
giving rise to dramatic but frequently irreversible color changes. By electrochemical …
“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 …
Machine learning for predicting thermodynamic properties of pure fluids and their mixtures
Establishing a reliable equation of state for largely non-ideal or multi-component liquid
systems is challenging because the complex effects of molecular configurations and/or …
systems is challenging because the complex effects of molecular configurations and/or …
Characterising degradation of perovskite solar cells through in-situ and operando electron microscopy
FU Kosasih, C Ducati - Nano Energy, 2018 - Elsevier
Organic-inorganic hybrid perovskite solar cells have exhibited power conversion efficiencies
comparable to more established PV technologies thanks to their favourable optoelectronic …
comparable to more established PV technologies thanks to their favourable optoelectronic …
Bacterial nano-factories as a tool for the biosynthesis of TiO2 nanoparticles: characterization and potential application in wastewater treatment
ZL Azizi, S Daneshjou - Applied Biochemistry and Biotechnology, 2024 - Springer
The development of reliable and eco-conscious processes for nanoparticle synthesis
constitutes a significant element in nanotechnology. TiO2 nanoparticles (NPs) are becoming …
constitutes a significant element in nanotechnology. TiO2 nanoparticles (NPs) are becoming …