Recent advances and applications of surrogate models for finite element method computations: a review
The utilization of surrogate models to approximate complex systems has recently gained
increased popularity. Because of their capability to deal with black-box problems and lower …
increased popularity. Because of their capability to deal with black-box problems and lower …
Advances in computational intelligence of polymer composite materials: machine learning assisted modeling, analysis and design
The superior multi-functional properties of polymer composites have made them an ideal
choice for aerospace, automobile, marine, civil, and many other technologically demanding …
choice for aerospace, automobile, marine, civil, and many other technologically demanding …
Active learning in materials science with emphasis on adaptive sampling using uncertainties for targeted design
One of the main challenges in materials discovery is efficiently exploring the vast search
space for targeted properties as approaches that rely on trial-and-error are impractical. We …
space for targeted properties as approaches that rely on trial-and-error are impractical. We …
A transfer learning approach for microstructure reconstruction and structure-property predictions
Stochastic microstructure reconstruction has become an indispensable part of computational
materials science, but ongoing developments are specific to particular material systems. In …
materials science, but ongoing developments are specific to particular material systems. In …
Microstructural materials design via deep adversarial learning methodology
Identifying the key microstructure representations is crucial for computational materials
design (CMD). However, existing microstructure characterization and reconstruction (MCR) …
design (CMD). However, existing microstructure characterization and reconstruction (MCR) …
Machine learning in materials design and discovery: Examples from the present and suggestions for the future
JE Gubernatis, T Lookman - Physical Review Materials, 2018 - APS
We provide a brief discussion of “What is machine learning?” and then give a number of
examples of how these methods have recently aided the design and discovery of new …
examples of how these methods have recently aided the design and discovery of new …
Machine-learning-assisted de novo design of organic molecules and polymers: opportunities and challenges
Organic molecules and polymers have a broad range of applications in biomedical,
chemical, and materials science fields. Traditional design approaches for organic molecules …
chemical, and materials science fields. Traditional design approaches for organic molecules …
Materials informatics: From the atomic-level to the continuum
In recent years materials informatics, which is the application of data science to problems in
materials science and engineering, has emerged as a powerful tool for materials discovery …
materials science and engineering, has emerged as a powerful tool for materials discovery …
Machine-learning-based predictions of polymer and postconsumer recycled polymer properties: a comprehensive review
There has been a tremendous increase in demand for virgin and postconsumer recycled
(PCR) polymers due to their wide range of chemical and physical characteristics. Despite …
(PCR) polymers due to their wide range of chemical and physical characteristics. Despite …
Prediction of tensile strength of polymer carbon nanotube composites using practical machine learning method
TT Le - Journal of Composite Materials, 2021 - journals.sagepub.com
This paper is devoted to the development and construction of a practical Machine Learning
(ML)-based model for the prediction of tensile strength of polymer carbon nanotube (CNTs) …
(ML)-based model for the prediction of tensile strength of polymer carbon nanotube (CNTs) …