refer for CMSE course project

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Illustrates mathematical concepts and their applications

Machine Learning for Materials Science: Part 1

Machine Learning Lab Module by Benjamin Afflerbach

Machine Learning Force Field for Materials

MSEML: Machine Learning for Materials Science Tool on

Simulate a 3D spheroid cancer tumor with PhysiCell

Vision The Network for Computational Nanotechnology seeks to bring a new perspective to engineering education to meet the challenges and opportunities of modern nanotechnology. Fifty years ago our field faced a similar challenge brought on by the advent of the transistor and it was met effectively by the Semiconductor Electronics Education Committee (SEEC), a group of 30 leaders in the field from both industry and academia who produced seven undergraduate textbooks and four films that reshaped the teaching of electronics and trained a generation of engineers ready to lead the modern electronics industry. Today we face the need for a comparable revolution in education. Ever since the birth of solid state physics, materials have been described in terms of average material parameters like the mobility or the optical absorption coefficient which are then used as inputs to macroscopic device models. This two-step approach is being widely used even for modern nanostructured materials, but we believe that it is no longer adequate to meet the challenges and opportunities of our day. An integrated approach is needed that embeds new ways of thinking, emerging from current research on nanoscience, directly into the models used for non-equilibrium problems like nanoscale transistors, energy conversion devices and bio-sensors. The objective of this initiative is to establish and disseminate the fundamentals of this novel viewpoint through a carefully coordinated collection of seminars, short courses and full-semester courses. “Electronics from the Bottom-up” is designed to be a resource for educators and self-learners and a model for a new way of teaching electronic devices that we hope will inspire students and prepare them to contribute to the development of nanoelectronic technology in the 21st Century. This project, launched in the fall of 2006, is producing a set of educational resources that are being disseminated at summer schools, lectures, and on nanoHUB.org.

This project is supported by the Intel Foundation and the NSF-funded Network for Computational Nanotechnology

1. 12 Summer School

July 16-20, Purdue University, West Lafayette, IN, USA 2012 Summer School

1. 11 Summer School

July 18-22, Purdue University, West Lafayette, IN, USA The 2011 Summer School featured a set of ten lectures on the topic “Near-Equilibrium Transport: Fundamentals and Applications” and a set of five lectures on “Solar Cell Fundamentals.” Five tutorials on selected topics in nanoscience and nanotechnology were also presented. Summer Schools

As part of the Network for Computational Nanotechnology (NCN), our research group, guided by Mark Lundstrom, specializes in the development of simulation tools that allow researchers to gain insight into critical problems facing today’s nanotechnology initiatives. Through this theoretical approach to nanoscience, we seek to gain fundamental understanding of the basics behind these increasingly complex systems. Our primary goal is understanding electronics from the “bottom-up,” by which we mean understanding electronic conduction at the atomistic level; then we work toward formulating new simulation techniques, developing a new generation of software tools, and bringing new understanding and approaches into the education of device engineers. In order to accomplish these goals, we develop and employ in-depth simulation tools that incorporate Monte-Carlo, Non-Equilibrium Greens Function, and Drift-Diffusion computational methods. Our work helps others through the nanoHUB, an online resource dedicated to pioneering the development of nanotechnology from science to manufacturing through innovative theory, exploratory simulation, and novel cyberinfrastructure. Our group’s current research includes development of simulation and modeling tools for nanowire transistor devices, III-V semiconductors, scattering, carbon nanotubes, ballistic transport, and thermoelectrics.

Data Archiving

Data Archiving Data Preservation via SVN

A five-week course on the basic physics that govern materials at atomic scales.

This tutorial gives an introductory demonstration of how to create and use Jupyter notebooks. It showcases the libraries Pandas to manipulate and organize data with functionalities similar to those of Excel on python, and Plotly, a library used to create interactive plots for enhanced…

This tool generates all necessary input files for LAMMPS simulations of molecular systems starting with an atomistic structure.

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