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.
FDNS21: Future Directions in Nanomaterial Synthesis: From Rational Design to Data-Driven Manufacturing
27 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.
Nanodiamond Raman Analysis Tool
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.
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
1st nanoMFG Node Workshop on Data-Science ENabled Advances in Nanomanufacturing (DSEAN)
22 Apr 2019 | Workshops | Contributor(s): Kimani C Toussaint, ST H Tawfick, Chenhui Shao, Narayan Aluru, Placid M. Ferreira, Elif Ertekin, Taylor, Hayden, Jay R Roloff
This workshop brought together experts in nanomanufacturing, data science, and cyberinfrastructure.
Gr-ResQ
27 Aug 2019 | 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.
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.
Graphene Raman Fitting Tool
29 May 2018 | Tools | Contributor(s): Matthew Glen Robertson, Aagam Rajeev Shah, Darren K Adams, Elif Ertekin
Analyze Raman spectra from graphene
[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...
Computational Nanoscience, Lecture 29: Verification, Validation, and Some Examples
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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
15 May 2008 | Teaching Materials | Contributor(s): Elif Ertekin, Jeffrey C Grossman
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...
Computational Nanoscience, Pop-Quiz Solutions
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 21: Quantum Monte Carlo, part II
15 May 2008 | Teaching Materials | Contributor(s): Jeffrey C Grossman, Elif Ertekin
This is our second lecture in a series on Quantum Monte Carlo methods. We describe the Diffusion Monte Carlo approach here, in which the approximation to the solution is not restricted by choice of a functional form for the wavefunction. The DMC approach is explained, and the fixed node...
Computational Nanoscience, Lecture 20: Quantum Monte Carlo, part I
This lecture provides and introduction to Quantum Monte Carlo methods. We review the concept of electron correlation and introduce Variational Monte Carlo methods as an approach to going beyond the mean field approximation. We describe briefly the Slater-Jastrow expansion of the wavefunction, and...
Computational Nanoscience, Lecture 19: Band Structure and Some In-Class Simulation: DFT for Solids
30 Apr 2008 | Teaching Materials | Contributor(s): Jeffrey C Grossman, Elif Ertekin
In this class we briefly review band structures and then spend most of our class on in-class simulations. Here we use the DFT for molecules and solids (Siesta) course toolkit. We cover a variety of solids, optimizing structures, testing k-point convergence, computing cohesive energies, and...
Computational Nanoscience, Lecture 18.5: A Little More, and Lots of Repetition, on Solids
Here we go over again some of the basics that one needs to know and understand in order to carry out electronic structure, atomic-scale calculations of solids.
Computational Nanoscience, Lecture 16: More and Less than Hartree-Fock
In the lecture we discuss both techniques for going "beyond" Hartree-Fock in order to include correlation energy as well as techniques for capturing electronic structure effects while not having to solve the full Hartree-Fock equations (ie, semi-empirical methods). We also very briefly touch upon...
Computational Nanoscience, Lecture 15: In-Class Simulations: Hartree-Fock
Using a range of examples, we study the effect of basis set on convergence, the Hartree-Fock accuracy compared to experiment, and explore a little bit of molecular chemistry.
Computational Nanoscience, Lecture 14: Hartree-Fock Calculations
A description of the Hartree-Fock method and practical overview of its application. This lecture is to be used in conjunction with the course toolkit, with the Hartree-Fock simulation module.
Computational Nanoscience, Lecture 13: Introduction to Computational Quantum Mechanics
In this lecture we introduce the basic concepts that will be needed as we explore simulation approaches that describe the electronic structure of a system.
Computational Nanoscience, Lecture 18: Density Functional Theory and some Solid Modeling
21 Mar 2008 | Teaching Materials | Contributor(s): Elif Ertekin, Jeffrey C Grossman
We continue our discussion of Density Functional Theory, and describe the most-often used approaches to describing the exchange-correlation in the system (LDA, GGA, and hybrid functionals). We discuss as well the strengths and weaknesses of the LDA and present some examples of its use. Finally, a...