Opportunities and challenges for machine learning in materials science
Advances in machine learning have impacted myriad areas of materials science, such as
the discovery of novel materials and the improvement of molecular simulations, with likely …
the discovery of novel materials and the improvement of molecular simulations, with likely …
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 …
A steel property optimization model based on the XGBoost algorithm and improved PSO
K Song, F Yan, T Ding, L Gao, S Lu - Computational Materials Science, 2020 - Elsevier
Exploring the relationships between the properties of steels and their compositions and
manufacturing parameters is extremely crucial and indispensable to understanding the …
manufacturing parameters is extremely crucial and indispensable to understanding the …
[HTML][HTML] Ensemble Machine Learning approach for evaluating the material characterization of carbon nanotube-reinforced cementitious composites
F Bagherzadeh, T Shafighfard - Case Studies in Construction Materials, 2022 - Elsevier
Time and cost-efficient techniques are essential to avoid extra conventional experimental
studies with large data-set for material characterization of composite materials. This study is …
studies with large data-set for material characterization of composite materials. This study is …
[HTML][HTML] Prediction of flyrock distance induced by mine blasting using a novel Harris Hawks optimization-based multi-layer perceptron neural network
In mining or construction projects, for exploitation of hard rock with high strength properties,
blasting is frequently applied to breaking or moving them using high explosive energy …
blasting is frequently applied to breaking or moving them using high explosive energy …
Deep learning object detection in materials science: Current state and future directions
R Jacobs - Computational Materials Science, 2022 - Elsevier
Deep learning-based object detection models have recently found widespread use in
materials science, with rapid progress made in just the past two years. Scanning and …
materials science, with rapid progress made in just the past two years. Scanning and …
Interpretable models for extrapolation in scientific machine learning
Data-driven models are central to scientific discovery. In efforts to achieve state-of-the-art
model accuracy, researchers are employing increasingly complex machine learning …
model accuracy, researchers are employing increasingly complex machine learning …
Modeling antiphase boundary energies of Ni3Al-based alloys using automated density functional theory and machine learning
Antiphase boundaries (APBs) are planar defects that play a critical role in strengthening Ni-
based superalloys, and their sensitivity to alloy composition offers a flexible tuning …
based superalloys, and their sensitivity to alloy composition offers a flexible tuning …
Accelerated design and discovery of perovskites with high conductivity for energy applications through machine learning
We use machine learning tools for the design and discovery of ABO3-type perovskite oxides
for various energy applications, using over 7000 data points from the literature. We …
for various energy applications, using over 7000 data points from the literature. We …
A strategic approach to machine learning for material science: how to tackle real-world challenges and avoid pitfalls
The exponential growth and success of machine learning (ML) has resulted in its application
in all scientific domains including material science. Advancement in experimental …
in all scientific domains including material science. Advancement in experimental …