[Illinois] CSE Seminar Series: Advances in First-principles Computational Materials Science
20 Nov 2012 | Online Presentations | Contributor(s): Elif Ertekin
Title: Advances in first-principles computational materials science Subtitle: Things we can calculate now, that we couldn't when I was in grad school. The capability to rationally design new materials with tailored properties and functionality on a computer remains a grand challenge whose...
SEM Image Segmentation Workshop
12 Jan 2021 | Tools | Contributor(s): Aagam Rajeev Shah, Darren K Adams, Mitisha Surana, Ricardo Toro, Sameh H Tawfick, Elif Ertekin
This tool introduces users to machine learning used to segment microscopy images
SEM Image Processing Tool
02 Oct 2018 | Tools | Contributor(s): Joshua A Schiller, Matthew Glen Robertson, Kristina M Miller, Kevin James Cruse, Kevin Liu, Darren K Adams, Benjamin Galewsky, Elif Ertekin, Sameh H Tawfick
Analysis and feature detection in SEM images of Graphene.
Resources and Cyberinfrastructure for Laser Powder Bed Fusion – Tools to enable 3D Additive Metals Manufacturing
11 Jan 2024 | Online Presentations | Contributor(s): Elif Ertekin, The Micro Nano Technology - Education Center
We will describe laser powder bed fusion, how machine learning and modeling/simulation tools can help optimize the process, and opportunities to engage students in the work.
Overview of Network for Computational Nanotechnology Nanomanufacturing Node and Gr-ResQ
27 Sep 2022 | Online Presentations | Contributor(s): Elif Ertekin, The Micro Nano Technology - Education Center
This presentation highlights the node’s tools and cyberinfrastructure, available on the NCN Cyberplatform (nanoHUB), that connect nanomanufacturing researchers from academia and industry to share resources, data, and knowledge.
Overview of Computational Nanoscience: a UC Berkeley Course
5.0 out of 5 stars
01 Feb 2008 | Courses | Contributor(s): Jeffrey C Grossman, Elif Ertekin
This course will provide students with the fundamentals of computational problem-solving techniques that are used to understand and predict properties of nanoscale systems. Emphasis will be placed on how to use simulations effectively, intelligently, and cohesively to predict properties that...
Nanodiamond Raman Analysis Tool
0.0 out of 5 stars
03 Jun 2022 | Tools | Contributor(s): Adrian Manjarrez, Aagam Rajeev Shah, Darren K Adams, Elif Ertekin, Lili Cai
Analyze the Raman spectra of diamond nanoparticles and films.
MIT Atomic-Scale Modeling Toolkit
4.5 out of 5 stars
15 Jan 2008 | Tools | Contributor(s): David Strubbe, Enrique Guerrero, daniel richards, Elif Ertekin, Jeffrey C Grossman, Justin Riley
Tools for Atomic-Scale Modeling
Graphene Raman Fitting Tool
09 May 2018 | Tools | Contributor(s): Matthew Glen Robertson, Aagam Rajeev Shah, Darren K Adams, Elif Ertekin
Analyze Raman spectra from graphene
Gr-ResQ
05 Nov 2018 | Tools | Contributor(s): Joshua A Schiller, Kaihao Zhang, Kevin James Cruse, Darren K Adams, Elif Ertekin, Sameh H Tawfick, Mitisha Surana, Aagam Rajeev Shah, Ricardo Toro
Query submit and analyse graphene sythesis data.
FDNS21: Future Directions in Nanomaterial Synthesis: From Rational Design to Data-Driven Manufacturing
21 Apr 2021 | Workshops | Contributor(s): Sameh H Tawfick (organizer), Elif Ertekin (organizer), Lili Cai (organizer), Arend van der Zande (organizer)
Bringing together the leaders in nanomaterials synthesis from around the world.
Computational Nanoscience, Pop-Quiz Solutions
15 May 2008 | Teaching Materials | Contributor(s): Elif Ertekin, Jeffrey C Grossman
The solutions to the pop-quiz are given in this handout.University of California, Berkeley
Computational Nanoscience, Pop-Quiz
This quiz summarizes the most important concepts which have covered in class so far related to Molecular Dynamics, Classical Monte Carlo Methods, and Quantum Mechanical Methods.University of California, Berkeley
Computational Nanoscience, Lecture 9: Hard-Sphere Monte Carlo In-Class Simulation
19 Feb 2008 | Teaching Materials | Contributor(s): Elif Ertekin, Jeffrey C Grossman
In this lecture we carry out simulations in-class, with guidance from the instructors. We use the HSMC tool (within the nanoHUB simulation toolkit for this course). The hard sphere system is one of the simplest systems which exhibits an order-disorder phase transition, which we will explore with...
Computational Nanoscience, Lecture 8: Monte Carlo Simulation Part II
14 Feb 2008 | Teaching Materials | Contributor(s): Elif Ertekin, Jeffrey C Grossman
In this lecture, we continue our discussion of Monte Carlo simulation. Examples from Hard Sphere Monte Carlo simulations based on the Metropolis algorithm and from Grand Canonical Monte Carlo simulations of fullerene growth on spherical surfaces are presented. A discussion of meaningful...
Computational Nanoscience, Lecture 7: Monte Carlo Simulation Part I
15 Feb 2008 | Teaching Materials | Contributor(s): Jeffrey C Grossman, Elif Ertekin
The purpose of this lecture is to introduce Monte Carlo methods as a form of stochastic simulation. Some introductory examples of Monte Carlo methods are given, and a basic introduction to relevant concepts in statistical mechanics is presented. Students will be introduced to the Metropolis...
Computational Nanoscience, Lecture 6: Pair Distribution Function and More on Potentials
13 Feb 2008 | Teaching Materials | Contributor(s): Jeffrey C Grossman, Elif Ertekin
In this lecture we remind ourselves what a pair distribution function is, how to compute it, and why it is so important in simulations. Then, we revisit potentials and go into more detail including examples of typical functional forms, relative energy scales, and what to keep in mind when...
Computational Nanoscience, Lecture 5: A Day of In-Class Simulation: MD of Carbon Nanostructures
In this lecture we carry out simulations in-class, with guidance from the instructors. We use the LAMMPS tool (within the nanoHUB simulation toolkit for this course). Examples include calculating the energy per atom of different fullerenes and nantubes, computing the Young's modulus of a nanotube...
Computational Nanoscience, Lecture 4: Geometry Optimization and Seeing What You're Doing
In this lecture, we discuss various methods for finding the ground state structure of a given system by minimizing its energy. Derivative and non-derivative methods are discussed, as well as the importance of the starting guess and how to find or generate good initial structures. We also briefly...
Computational Nanoscience, Lecture 3: Computing Physical Properties
2.5 out of 5 stars
11 Feb 2008 | Teaching Materials | Contributor(s): Jeffrey C Grossman, Elif Ertekin
In this lecture, we'll cover how to choose initial conditions, and how to compute a number of important physical observables from the MD simulation. For example, temperature, pressure, diffusion coefficient, and pair distribution function will be highlighted. We will also discuss briefly the...
Computational Nanoscience, Lecture 2: Introduction to Molecular Dynamics
30 Jan 2008 | Teaching Materials | Contributor(s): Jeffrey C Grossman, Elif Ertekin
In this lecture, we present and introduction to classical molecular dynamics. Approaches to integrating the equations of motion (Verlet and other) are discussed, along with practical considerations such as choice of timestep. A brief discussion of interatomic potentials (the pair potential and...
Computational Nanoscience, Lecture 29: Verification, Validation, and Some Examples
16 May 2008 | Teaching Materials | Contributor(s): Jeffrey C Grossman, Elif Ertekin
We conclude our course with a lecture of verification, and validation. We describe what each of these terms means, and provide a few recent examples of nanoscale simulation in terms of these concepts.University of California, Berkeley
Computational Nanoscience, Lecture 28: Wish-List, Reactions, and X-Rays.
After a brief interlude for class feedback on the course content and suggestions for next semester, we turn to modeling chemical reactions. We describe chain-of-state methods such as the Nudged Elastic Band for determining energy barriers. The use of empirical, QM/MM methods are described. We...
Computational Nanoscience, Lecture 27: Simulating Water and Examples in Computational Biology
16 May 2008 | Teaching Materials | Contributor(s): Elif Ertekin, Jeffrey C Grossman
In this lecture, we describe the challenges in simulating water and introduce both explicit and implicit approaches. We also briefly describe protein structure, the Levinthal paradox, and simulations of proteins and protein structure using First Principles approaches and Monte Carlo...
Computational Nanoscience, Lecture 23: Modeling Morphological Evolution
In this lecture, we present an introduction to modeling the morphological evolution of materials systems. We introduce concepts of coarsening, particle-size distributions, the Lifshitz-Slyozov-Wagner model, thin film growth modes (Layer-by-Layer, Island growth, and Stranski-Krastanov), and...