[HTML][HTML] Where artificial intelligence stands in the development of electrochemical sensors for healthcare applications-A review
The electrochemical sensor (E-sensors) market trends have identified the biomedical
applications as a significant market growth with impact on personalized therapy. Given the …
applications as a significant market growth with impact on personalized therapy. Given the …
Efficient structure-informed featurization and property prediction of ordered, dilute, and random atomic structures
Abstract Structure-informed materials informatics is a rapidly evolving discipline of materials
science relying on the featurization of atomic structures or configurations to construct vector …
science relying on the featurization of atomic structures or configurations to construct vector …
[HTML][HTML] Jupyter widgets and extensions for education and research in computational physics and chemistry
Interactive notebooks are a precious tool for creating graphical user interfaces and teaching
materials. Python and Jupyter are becoming increasingly popular in this context, with …
materials. Python and Jupyter are becoming increasingly popular in this context, with …
Optical materials discovery and design with federated databases and machine learning
Combinatorial and guided screening of materials space with density-functional theory and
related approaches has provided a wealth of hypothetical inorganic materials, which are …
related approaches has provided a wealth of hypothetical inorganic materials, which are …
Data-driven design of high pressure hydride superconductors using DFT and deep learning
The observation of superconductivity in hydride-based materials under ultrahigh pressures
(for example, H 3 S and LaH 10) has fueled the interest in a more data-driven approach to …
(for example, H 3 S and LaH 10) has fueled the interest in a more data-driven approach to …
Machine learning prediction of materials properties from chemical composition: Status and prospects
In materials science, machine learning (ML) has become an essential and indispensable
tool. ML has emerged as a powerful tool in materials science, particularly for predicting …
tool. ML has emerged as a powerful tool in materials science, particularly for predicting …
Datatractor: Metadata, automation, and registries for extractor interoperability in the chemical and materials sciences
ML Evans, GM Rignanese, D Elbert, P Kraus - ar** machine learning models for crystal property predictions has been hampered by
the need for labeled data from costly experiments or Density Functional Theory (DFT) …
the need for labeled data from costly experiments or Density Functional Theory (DFT) …