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 …
Recent computational insights into hydrogen storage by MXene-based materials and shedding light on the storage mechanism
T Kopac - Journal of Energy Storage, 2024 - Elsevier
MXenes, a novel class of low-dimensional materials, have garnered increasing interest due
to their potential use in solid-state hydrogen storage. The hydrogen storage performance of …
to their potential use in solid-state hydrogen storage. The hydrogen storage performance of …
Recovery of brine resources through crown-passivated graphene, silicene, and boron nitride nanosheets based on machine-learning structural predictions
The rising global demand for brine resources necessitates the exploration of alternative
sources to complement existing natural sources. It is imperative to explore innovative …
sources to complement existing natural sources. It is imperative to explore innovative …
Enhancing Li+ recovery in brine mining: integrating next-gen emotional AI and explainable ML to predict adsorption energy in crown ether-based hierarchical …
Artificial intelligence (AI) is being employed in brine mining to enhance the extraction of
lithium, vital for the manufacturing of lithium-ion batteries, through improved recovery …
lithium, vital for the manufacturing of lithium-ion batteries, through improved recovery …
When Machine Learning Meets 2D Materials: A Review
The availability of an ever‐expanding portfolio of 2D materials with rich internal degrees of
freedom (spin, excitonic, valley, sublattice, and layer pseudospin) together with the unique …
freedom (spin, excitonic, valley, sublattice, and layer pseudospin) together with the unique …
2D MXenes as Electrode Materials for Metal-Sulfur Batteries: A Review
Metal-sulfur batteries (MSBs) have garnered significant interest as upcoming energy storage
options on account of their higher theoretical energy density. Nevertheless, these entities …
options on account of their higher theoretical energy density. Nevertheless, these entities …
A universal similarity based approach for predictive uncertainty quantification in materials science
Immense effort has been exerted in the materials informatics community towards enhancing
the accuracy of machine learning (ML) models; however, the uncertainty quantification (UQ) …
the accuracy of machine learning (ML) models; however, the uncertainty quantification (UQ) …
Predicting the work function of 2D MXenes using machine-learning methods
MXenes, which are graphene-like two-dimensional transition metal carbides and nitrides,
have tunable compositions and exhibit rich surface chemistry. This compositional flexibility …
have tunable compositions and exhibit rich surface chemistry. This compositional flexibility …
[HTML][HTML] Data science and material informatics in physical metallurgy and material science: An overview of milestones and limitations
Data science and material informatics are gaining traction in alloy design. This is due to
increasing infrastructure, computational capabilities and established open-source …
increasing infrastructure, computational capabilities and established open-source …
Study of the novel boron nitride polymorphs: First-principles calculations and machine learning
Q Fan, W Li, N Wu, Y Zhao, Y Song, X Yu… - Chinese Journal of …, 2024 - Elsevier
This study explores two new boron nitride polymorphs, namely C222 1 BN and I-4 BN,
characterized by sp 2 hybridization. The investigation is conducted through first-principles …
characterized by sp 2 hybridization. The investigation is conducted through first-principles …