Multi-Scale Quantum Simulations of Conductive Bridging RAM
02 Nov 2015 | Online Presentations | Contributor(s): Michael Povolotskyi, nicolas onofrio, David M Guzman, Alejandro Strachan, Gerhard Klimeck
IWCE 2015 presentation.
MSE 597G: An Introduction to Molecular Dynamics
0.0 out of 5 stars
13 Nov 2008 | Online Presentations | Contributor(s): Alejandro Strachan
MSE 597G Lecture 7: Advanced Techniques for Molecular Dynamics Simulations
12 Nov 2008 | Online Presentations | Contributor(s): Alejandro Strachan
Thermostats and barostats,Linear methods for energy and force calculations,Coarse graining or mesodynamics,Validation and Verification.
MSE 597G Lecture 6: Interatomic potentials III
Reactive force fields,Parameterization of interatomic potentials
MSE 597G Lecture 5: Interatomic potentials II
Embedded atom model for metals,Three body terms for semiconductors: Stillinger-Weber,Electrostatics and Covalent interactions.
MSE 597G Lecture 4: Interatomic potentials I
14 Nov 2008 | Online Presentations | Contributor(s): Alejandro Strachan
Interatomic potentials: pairwise potentials.
MSE 597G Lecture 3: Statistical Mechanics II
Basic physics: statistical mechanics, Algorithms: Integrating the equations of motion.
MSE 597G Lecture 2: Statistical Mechanics I
11 Nov 2008 | Online Presentations | Contributor(s): Alejandro Strachan
Basic physics: statistical mechanics.
MSE 597G Lecture 1: Classical Mechanics
Basic physics: classical mechanics
MSE 597G An Introduction to Molecular Dynamics
4.5 out of 5 stars
13 Nov 2008 | Courses | Contributor(s): Alejandro Strachan
The goal of this short course is to provide an introduction to the theory and algorithms behind MD simulations, describe some of the most exciting recent developments in the field and exemplify with a few applications applications. The series also includes a tutorial on the nanoMATERIALS...
Molecular Exploration Tool
01 Aug 2014 | Tools | Contributor(s): Xueying Wang, nicolas onofrio, Alejandro Strachan, David M Guzman
The tool can display the molecule structures and run Lammps simulations.
Molecular Dynamics Simulation of Displacement Cascade in Molybdenum
06 Dec 2018 | Presentation Materials | Contributor(s): Gyuchul Park, Alejandro Strachan
Displacement cascade in molybdenum was conducted by using Molecular Dynamics (MD) Simulation method. LAMMPS tool was used to run the simulation at nanoHUB. Three primary questions were answered from the simulation:1. The number of displaced atoms/interstitials with respect to time when the...
Module 7: Active Learning for Design of Experiments
30 Sep 2020 | Online Presentations | Contributor(s): Alejandro Strachan, Juan Carlos Verduzco Gastelum
This module introduces active learning in the context of materials discovery with hands-on online simulations. Active learning is a subset of machine learning where the information available at a given time is used to decide what areas of space to explore next. In this module, we will explore...
Module 5: Neural Networks for Regression and Classification
01 Oct 2020 | Online Presentations | Contributor(s): Saaketh Desai, Alejandro Strachan
This module introduces neural networks for material science and engineering with hands-on online simulations. Neural networks are a subset of machine learning models used to learn mappings between inputs and outputs for a given dataset. Neural networks offer great flexibility and have shown...
Module 4: Linear Regression Models
01 Oct 2020 | Online Presentations | Contributor(s): Michael N Sakano, Saaketh Desai, Alejandro Strachan
This module introduces linear regression in the context of materials science and engineering. We will apply liner regression to predict materials properties and to explore correlations between materials properties via hands-on online simulations. Linear regression is a supervised machine...
Module 3: Materials Descriptors for Data Science
27 Jan 2021 | Online Presentations | Contributor(s): Alejandro Strachan, Juan Carlos Verduzco Gastelum, Zachary D McClure
This module focuses on the use of descriptors to improve the description of materials in machine learning. Augmenting input parameters with appropriate descriptors (a process sometimes called featurization) can often significantly improve the accuracy of predictive models. Ideal descriptors are...
Module 2: Querying Materials Data Repositories
30 Sep 2020 | Online Presentations | Contributor(s): Zachary D McClure, Alejandro Strachan
This module introduces modern tools for data acquisition, including performing large queries using application programming interfaces (APIs), with hands-on online workflows. Cyber-infrastructure platforms for data offer unparalleled access to data, this module will introduce tools to manage,...
Module 1: Making Data Accessible, Discoverable and Useful
27 Jan 2021 | Online Presentations | Contributor(s): Alejandro Strachan, Juan Carlos Verduzco Gastelum
This module focuses on the importance of make materials data discoverable, interoperable, and available and best practices to doing so. Data generation is both time consuming and costly, thus, making the available, as appropriate, with the community is critical to accelerate innovation. This is...
Microstructure Informed Shock-induced Temperature Network
21 Feb 2023 | Tools | Contributor(s): Juan Carlos Verduzco Gastelum, Robert Joseph Appleton, Alejandro Strachan
Melting with Molecular Dynamics
21 Feb 2019 | Teaching Materials | Contributor(s): Sam Reeve, Alejandro Strachan
In this computational lab you will perform online molecular dynamics (MD) simulations through nanoHUB to melt nickel samples and analyze the results in order to: Understand the process of melting at atomic scales Identify effects of surfaces and specimen size Describe differences...
Melting via molecular dynamics simulations
10 Mar 2015 | Teaching Materials | Contributor(s): Alejandro Strachan
In this assignment you will use MD simulations to study melting in metals using the nanoMATERIALS simulation tool in nanoHUB. You will build a supercell and heat it up to study its melting. You can visualize the atomic configuration as the temperature is increased and after melting. From the...
Melting point simulation using OpenKIM
22 Mar 2019 | Tools | Contributor(s): Martin Hunt, Alejandro Strachan, Saaketh Desai
Computes melting point using a coexistence technique using interatomic potentials from OpenKIM
Melting of RCCAs using Neural Network Reactive Forcefield
18 Apr 2022 | Tools | Contributor(s): Zachary Bastian, Saswat Mishra, Alejandro Strachan
Melting of Refractory Complex Concentrated Alloys using a Neural Network Reactive Forcefield
Mechanical response of materials using Jupyter
31 Jan 2023 | Tools | Contributor(s): Alejandro Strachan
This tool provides mathematical tools using python in Jupyter to explore and calculate mechanical properties of materials
ME 597A Lecture 13: Uncertainty Quantification of Molecular Dynamics Simulations
31 Jan 2011 | Online Presentations | Contributor(s): Alejandro Strachan
Guest lecturer: Alejandro Strachan.