Computational materials science is the application of computational methods alone or in conjunction with experimental techniques to discover new materials and investigate existing materials such as: metals, ceramics, composites, semiconductors, nanostructures, 2D materials, metamaterials, polymers, liquid crystals, surfactants, emulsions, polymer nanocomposites, nanocrystal superlattices and nanoparticles.
Machine Learning in Physics
04 Nov 2021 | Contributor(s):: Nicolas Onofrio
Lectures and tutorials to learn how to write machine learning programs with Python
Andrés Felipe Sierra
Materials Simulation Toolkit for Machine Learning (MAST-ML) tutorial
07 May 2021 | Contributor(s):: Ryan Jacobs, BENJAMIN AFFLERBACH
Tutorial showing the many use cases for the MAST-ML package to build, evaluate and analyze machine learning models for materials applications.
MIT Atomic-Scale Modeling Toolkit
15 Jan 2008 | | Contributor(s):: daniel richards, Elif Ertekin, Jeffrey C Grossman, David Strubbe, Justin Riley, Enrique Guerrero
Tools for Atomic-Scale Modeling
Krishna Sai Kaligotla
Machine Learning Defect Behavior in Semiconductors
09 Nov 2020 | | Contributor(s):: Arun Kumar Mannodi Kanakkithodi
Develop machine learning models to predict defect formation energies in chalcogenides
Jeffrey W Bullard
Data Analysis of Normal Data Sets in Engineering
04 Jun 2020 | | Contributor(s):: Joseph Joshua Williams, Nancy Ruzycki
Statistical and data analysis concepts in engineering
Maria Salvacion Esmalla
Joseph Joshua Williams
Angelo Giovanni Oñate Soto
3 min. Research Talk: Identifying the Dimensionality of Crystal Structures
10 Feb 2020 | | Contributor(s):: Franco Vera
Today, researchers worldwide have identified over 100,000 distinct bulk materials. The underlying dimensionality of these materials is not always clear however, and as such researchers have sought to identify stable, lower dimensional materials derived from the bulk parent structures. A team of...
Citrine Tools for Materials Informatics
02 Dec 2019 | | Contributor(s):: Juan Carlos Verduzco Gastelum, Alejandro Strachan
Jupyter notebooks for sequential learning in the context of materials design. Run your own models, explore various methods and adapt the notebooks to your needs.
3 min Research Talk: Hierarchical Material Optimization using Neural Networks
29 Oct 2019 | | Contributor(s):: Miguel Arcilla Cuaycong
In this presentation, we sought to use a neural network (NN) to identify optimal arrangements of four different constituents in a tape spring to be used as snapping mechanisms in phase transforming cellular material that can dissipate energy.
Henry Coefficient Simulator
21 Oct 2019 | | Contributor(s):: Julian C Umeh, Thomas A Manz
Calculate Henry's constant of several sites on a nanoporous material
Gibbs Adsorption Simulator
23 Sep 2019 | | Contributor(s):: Julian C Umeh, Thomas A Manz
Simulates the adsorption of gases using Gibbs ensemble