Physics - Materials Science

SimXRD offers millions of simulated XRD patterns for improved crystal classification.

Examining nickel-vacancy centers and their impact on future technologies.

Explore the types and applications of magnetism and ferrites in modern technology.

This study examines hafnia's behavior under varying temperatures and pressures using machine learning.

Researchers leverage AI to improve materials modeling through a new deep learning approach.

Research on light-controlled magnetic materials aims to improve data storage technologies.

Learn about 2D magnets and their potential in technology.

Examining excitons reveals new opportunities for improving organic semiconductors.

A new interface enhances molecular dynamics simulations under realistic conditions.

A study on improving vibrational property predictions for materials using machine learning.

New materials improve light management in diverse technologies.

Researchers enhance material property prediction accuracy using transfer learning and machine learning techniques.

Examining how NiAl coating brittleness affects nickel-based superalloy durability.

Exploring how light changes the mechanical properties of zinc sulfide.

A deep look at how structure affects electron behavior in strontium ruthenate.

Researchers manipulate magnetization in GaMnN using electric fields for advanced electronics.

This article discusses methods to analyze square-triangle-rhombus tilings in higher dimensions.

A look at chirality's role in inorganic materials and its potential applications.

Exploring how excitons influence photocurrent in two-dimensional materials.

New method enhances detection of crystal tilt using thermal diffuse scattering.

This article discusses shear localization in metals and its influencing factors.

Researchers design hybrid materials that enhance electronics and energy applications.

New machine learning method improves efficiency of Koopmans spectral functional calculations.

This study examines how germanium alters the properties of magnetic topological insulators.

A novel approach to analyze dislocation networks under stress conditions.

Examining TMDCs' properties and their role in future electronic devices.

Study highlights how electric fields affect dipolar colloids and their clustering behavior.

Investigating dark count rate and degradation in Single-Photon Avalanche Diodes.

GNNOpt streamlines predicting optical properties from crystal structures, enhancing material discovery.

Exploring the electronic behaviors and potential applications of Kagome metals.

Machine learning techniques improve predictions of crystal structures from electron diffraction patterns.

Studying how computational model size affects inorganic mixed-halide perovskite alloys.

LiMgMnO offers exciting magnetic and thermal features for future technologies.

New methods improve long-range predictions in materials science using machine learning.

Researchers use FlowMM to predict and create new crystalline materials efficiently.

This article explores the challenges and opportunities of language models in materials science.

New software enhances efficiency and accuracy in analyzing surface structures.

Researchers study charge movement in organic semiconductors for better thermoelectric materials.

1T-TaS2 showcases remarkable electronic properties, potentially useful in advanced technology.

Research reveals unique properties of water in carbon nanotubes, impacting technology.