77 datasets found

A dataset containing 5321 2D microstructure slices sampled from segmented X-ray tomography images of a composite specimen and its corresponding FE simulation.

The dataset used in this paper for testing the proposed modular machine learning-based elastoplasticity framework.

The dataset used in this paper for testing the proposed modular machine learning-based elastoplasticity framework.

The dataset used in this paper for training and testing the proposed modular machine learning-based elastoplasticity framework.

The Materials Project (MP) dataset is a collection of materials data, with a focus on bandgaps.

The AFLOW dataset is a collection of materials data, with a focus on bandgaps.

Big data has ushered in a new wave of predictive power using machine learning models. In this work, we assess what big means in the context of typical materials-science...

The Materials Project dataset contains theoretical crystal structure information using Density-Functional Theory based convex hull construction in composition, energy space that...

High-throughput materials discovery is an experimentation pipeline for rapidly synthesizing and identifying new materials. In this pipeline, a handful of elements are deposited...

High-throughput materials discovery involves the rapid synthesis, measurement, and characterization of many different but structurally-related materials. A key problem in...

The dataset is used for community detection in complex physical systems, including image segmentation and analysis of amorphous materials.

The AIMD dataset generated from a melt-quench ab initio molecular dynamics (AIMD) protocol for Hafnium dioxide (HfO2)

The Fe/N2 dataset consists of molecular dynamics simulations of a nitrogen molecule adsorbing on an iron (Fe) <111> surface at high temperature (T = 700 K) and breaking in...

The Cu/formate dataset is a collection of molecular dynamics simulations of the dehydrogenation reaction of formate on a copper (Cu) <110> surface.

The OC20 dataset is a large dataset of ab initio calculations aimed at estimating adsorption energies on catalytic surfaces.

The dataset used to train the CGCNN model to visualize the compositional and structural similarities between elemental boron.

The dataset used to train the CGCNN model to visualize the compositional and structural similarities between perovskite crystals.

The MatSci-NLP dataset is a collection of materials science text for NLP tasks.

The HoneyBee dataset is a collection of instructions for materials science tasks.

The dataset used in this paper is a collection of thin-film libraries with lateral-gradient laser spike annealing (lg-LSA) experiments.