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 …
Rechargeable batteries of the future—the state of the art from a BATTERY 2030+ perspective
M Fichtner, K Edström, E Ayerbe… - Advanced Energy …, 2022 - Wiley Online Library
The development of new batteries has historically been achieved through discovery and
development cycles based on the intuition of the researcher, followed by experimental trial …
development cycles based on the intuition of the researcher, followed by experimental trial …
Unleashing the power of artificial intelligence in materials design
The integration of artificial intelligence (AI) algorithms in materials design is revolutionizing
the field of materials engineering thanks to their power to predict material properties, design …
the field of materials engineering thanks to their power to predict material properties, design …
Overview: Computer vision and machine learning for microstructural characterization and analysis
Microstructural characterization and analysis is the foundation of microstructural science,
connecting materials structure to composition, process history, and properties …
connecting materials structure to composition, process history, and properties …
Biomaterialomics: Data science-driven pathways to develop fourth-generation biomaterials
Conventional approaches to developing biomaterials and implants require intuitive tailoring
of manufacturing protocols and biocompatibility assessment. This leads to longer …
of manufacturing protocols and biocompatibility assessment. This leads to longer …
Denoising diffusion algorithm for inverse design of microstructures with fine-tuned nonlinear material properties
NN Vlassis, WC Sun - Computer Methods in Applied Mechanics and …, 2023 - Elsevier
We introduce a denoising diffusion algorithm to discover microstructures with nonlinear fine-
tuned properties. Denoising diffusion probabilistic models are generative models that use …
tuned properties. Denoising diffusion probabilistic models are generative models that use …
Synthesizing controlled microstructures of porous media using generative adversarial networks and reinforcement learning
For material modeling and discovery, synthetic microstructures play a critical role as digital
twins. They provide stochastic samples upon which direct numerical simulations can be …
twins. They provide stochastic samples upon which direct numerical simulations can be …
Deep learning modeling in microscopy imaging: A review of materials science applications
The accurate analysis of microscopy images representing various materials obtained in
scanning probe microscopy, scanning tunneling microscopy, and transmission electron …
scanning probe microscopy, scanning tunneling microscopy, and transmission electron …
Modeling the solid electrolyte interphase: Machine learning as a game changer?
The solid electrolyte interphase (SEI) is a complex passivation layer that forms in situ on
many battery electrodes such as lithium‐intercalated graphite or lithium metal anodes. Its …
many battery electrodes such as lithium‐intercalated graphite or lithium metal anodes. Its …
Artificial intelligence in predicting mechanical properties of composite materials
The determination of mechanical properties plays a crucial role in utilizing composite
materials across multiple engineering disciplines. Recently, there has been substantial …
materials across multiple engineering disciplines. Recently, there has been substantial …