Explainable machine learning in materials science
Abstract Machine learning models are increasingly used in materials studies because of
their exceptional accuracy. However, the most accurate machine learning models are …
their exceptional accuracy. However, the most accurate machine learning models are …
Machine learning for high performance organic solar cells: current scenario and future prospects
A Mahmood, JL Wang - Energy & environmental science, 2021 - pubs.rsc.org
Machine learning (ML) is a field of computer science that uses algorithms and techniques for
automating solutions to complex problems that are hard to program using conventional …
automating solutions to complex problems that are hard to program using conventional …
Machine-learning and high-throughput studies for high-entropy materials
The combination of multiple-principal element materials, known as high-entropy materials
(HEMs), expands the multi-dimensional compositional space to gigantic stoichiometry. It is …
(HEMs), expands the multi-dimensional compositional space to gigantic stoichiometry. It is …
Machine learning-assisted ultrafast flash sintering of high-performance and flexible silver–selenide thermoelectric devices
Flexible thermoelectric generators (TEGs) have shown immense potential for serving as a
power source for wearable electronics and the Internet of Things. A key challenge …
power source for wearable electronics and the Internet of Things. A key challenge …
Machine learning approaches for thermoelectric materials research
Thermoelectric (TE) materials provide a solid‐state solution in waste heat recovery and
refrigeration. During the past few decades, considerable effort has been devoted towards …
refrigeration. During the past few decades, considerable effort has been devoted towards …
AI applications through the whole life cycle of material discovery
We provide a review of machine learning (ML) tools for material discovery and sophisticated
applications of different ML strategies. Although there have been a few published reviews on …
applications of different ML strategies. Although there have been a few published reviews on …
Using machine learning and feature engineering to characterize limited material datasets of high-entropy alloys
D Dai, T Xu, X Wei, G Ding, Y Xu, J Zhang… - Computational Materials …, 2020 - Elsevier
The prediction of the phase formation of high entropy alloys (HEAs) has attracted great
research interest recent years due to their superior structure and mechanical properties of …
research interest recent years due to their superior structure and mechanical properties of …
Machine learning in materials discovery: confirmed predictions and their underlying approaches
The rapidly growing interest in machine learning (ML) for materials discovery has resulted in
a large body of published work. However, only a small fraction of these publications includes …
a large body of published work. However, only a small fraction of these publications includes …
Machine learning boosts the design and discovery of nanomaterials
Y Jia, X Hou, Z Wang, X Hu - ACS Sustainable Chemistry & …, 2021 - ACS Publications
Nanomaterials (NMs) have developed quickly and cover various fields, but research on
nanotechnology and NMs largely relies on costly experiments or complex calculations (eg …
nanotechnology and NMs largely relies on costly experiments or complex calculations (eg …
Enabling catalyst discovery through machine learning and high-throughput experimentation
Machine learning is an avenue to unravel multidimensional relationships present in catalytic
systems. We describe a novel framework that incorporates machine learning algorithms with …
systems. We describe a novel framework that incorporates machine learning algorithms with …