Combining machine learning and computational chemistry for predictive insights into chemical systems
Machine learning models are poised to make a transformative impact on chemical sciences
by dramatically accelerating computational algorithms and amplifying insights available from …
by dramatically accelerating computational algorithms and amplifying insights available from …
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 …
Analytical transmission electron microscopy for emerging advanced materials
With remarkable achievements in spatial resolution and its derived analytical techniques,
transmission electron microscopy (TEM) has been capable of probing enriched information …
transmission electron microscopy (TEM) has been capable of probing enriched information …
Directly probing the local coordination, charge state, and stability of single atom catalysts by advanced electron microscopy: A review
The drive for atom efficient catalysts with carefully controlled properties has motivated the
development of single atom catalysts (SACs), aided by a variety of synthetic methods …
development of single atom catalysts (SACs), aided by a variety of synthetic methods …
Solving the structure of “single-atom” catalysts using machine learning–assisted XANES analysis
“Single-atom” catalysts (SACs) have demonstrated excellent activity and selectivity in
challenging chemical transformations such as photocatalytic CO2 reduction. For …
challenging chemical transformations such as photocatalytic CO2 reduction. For …
Transmission x-ray microscopy and its applications in battery material research—a short review
Transmission x-ray microscopy (TXM), which can provide morphological and chemical
structural information inside of battery component materials at tens of nanometer scale, has …
structural information inside of battery component materials at tens of nanometer scale, has …
Latent representation learning for structural characterization of catalysts
Supervised machine learning-enabled mapping of the X-ray absorption near edge structure
(XANES) spectra to local structural descriptors offers new methods for understanding the …
(XANES) spectra to local structural descriptors offers new methods for understanding the …
Unsupervised machine learning for unbiased chemical classification in X-ray absorption spectroscopy and X-ray emission spectroscopy
We report a comprehensive computational study of unsupervised machine learning for
extraction of chemically relevant information in X-ray absorption near edge structure …
extraction of chemically relevant information in X-ray absorption near edge structure …
Materials characterization: Can artificial intelligence be used to address reproducibility challenges?
Material characterization techniques are widely used to characterize the physical and
chemical properties of materials at the nanoscale and, thus, play central roles in material …
chemical properties of materials at the nanoscale and, thus, play central roles in material …
Convolutional neural network prediction of the photocurrent–voltage curve directly from scanning electron microscopy images
Y Hayashi, Y Nagai, Z Pan, K Katayama - Journal of Materials …, 2023 - pubs.rsc.org
In the pursuit of efficient and sustainable energy conversion, high-performance
photocatalytic devices show promise. A key characteristic of these devices is the …
photocatalytic devices show promise. A key characteristic of these devices is the …