Calculates bound states for square, parabolic, triangular and V-shaped potential energy profile

Computes the electronic and phonon structure of various materials in the spatial configuration of bulk , quantum wells, and wires

Device Simulation

A Hands-on Introduction to Physics-Informed Neural Networks

Nanoscience Energy

Maching Learning

Thermoelectric Generator

diffusion problems using comsol

Codigo PV organico

Links Jeff Neaton

Curso Grossman

Active learning via Bayesian optimization for materials discovery

This tool introduces users to filtered kriging used for surface interpolation.

This tutorial introduces unsupervised machine learning algorithms through dimensionality reduction via matrix decomposition techniques in the context of chemical decomposition of reactive materials in a Jupyter notebook on nanoHUB.org. The tool used in this demonstration…

Raman spectroscopy processing for Graphene growth characterization

Fast simulation of the DC current in a nanoscale double-gate MOSFET including thermionic emission and source-to-drain tunneling current.

Simulate density of states, carrier statistics, and carrier concentrations in two-dimensional semiconductors and graphene

Computer simulation is now an essential tool for the research and development of semiconductor processes and devices, but to use a simulation tool intelligently, one must know what\‘s \“under the hood.\” This talk is a tutorial introduction designed for someone using semiconductor device simulation for the first time. After reviewing the semiconductor equations, I will briefly describe how one solves them \“exactly\” on a computer. I\‘ll then discuss an example device simulation program and conclude with some thoughts about how to effectively use simulation in practice.

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This tutorial will introduce core concepts of machine learning through the lens of a basic workflow to predict material bandgaps from material compositions.

Tool to simulate thermoelectric power generation device/system with temperature-dependent material properties for waste heat recovery and wearable energy harvesting

This new course will give students hands-on experience with popular computational materials science and engineering software through a series of projects in: electronic structure calculation (e.g., VASP), molecular simulation (e.g., GROMACS), phase diagram modeling (e.g., Thermo-Calc), finite element modeling (e.g., OOF2), and materials selection. The course will familiarize students with a broad survey of software tools in computational materials science, scientific computing, and...

This course introduces fundamental concepts of artificial intelligence within the context of materials science and image segmentation. The two-week module was taught as part of a Computational Methods in Materials Science course at Northwestern University. The module is aimed at…