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Recent advances and applications of deep learning methods in materials science
Deep learning (DL) is one of the fastest-growing topics in materials data science, with
rapidly emerging applications spanning atomistic, image-based, spectral, and textual data …
rapidly emerging applications spanning atomistic, image-based, spectral, and textual data …
Machine learning aided design and optimization of thermal metamaterials
Artificial Intelligence (AI) has advanced material research that were previously intractable,
for example, the machine learning (ML) has been able to predict some unprecedented …
for example, the machine learning (ML) has been able to predict some unprecedented …
[HTML][HTML] A review of artificial neural networks in the constitutive modeling of composite materials
Abstract Machine learning models are increasingly used in many engineering fields thanks
to the widespread digital data, growing computing power, and advanced algorithms. The …
to the widespread digital data, growing computing power, and advanced algorithms. The …
Applied machine learning as a driver for polymeric biomaterials design
Polymers are ubiquitous to almost every aspect of modern society and their use in medical
products is similarly pervasive. Despite this, the diversity in commercial polymers used in …
products is similarly pervasive. Despite this, the diversity in commercial polymers used in …
Artificial intelligence and machine learning in design of mechanical materials
Artificial intelligence, especially machine learning (ML) and deep learning (DL) algorithms,
is becoming an important tool in the fields of materials and mechanical engineering …
is becoming an important tool in the fields of materials and mechanical engineering …
Predicting stress, strain and deformation fields in materials and structures with graph neural networks
Develo** accurate yet fast computational tools to simulate complex physical phenomena
is a long-standing problem. Recent advances in machine learning have revolutionized the …
is a long-standing problem. Recent advances in machine learning have revolutionized the …
[HTML][HTML] Data-driven compressive strength prediction of steel fiber reinforced concrete (SFRC) subjected to elevated temperatures using stacked machine learning …
Experimental studies using a substantial number of datasets can be avoided by employing
efficient methods to predict the mechanical properties of construction materials. The …
efficient methods to predict the mechanical properties of construction materials. The …
Integrating machine learning and multiscale modeling—perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences
Fueled by breakthrough technology developments, the biological, biomedical, and
behavioral sciences are now collecting more data than ever before. There is a critical need …
behavioral sciences are now collecting more data than ever before. There is a critical need …
Machine learning‐driven biomaterials evolution
Biomaterials is an exciting and dynamic field, which uses a collection of diverse materials to
achieve desired biological responses. While there is constant evolution and innovation in …
achieve desired biological responses. While there is constant evolution and innovation in …
Emerging materials intelligence ecosystems propelled by machine learning
The age of cognitive computing and artificial intelligence (AI) is just dawning. Inspired by its
successes and promises, several AI ecosystems are blossoming, many of them within the …
successes and promises, several AI ecosystems are blossoming, many of them within the …