W2D2S2 - Accelerating Atomistic Simulations of Solid-Phase Processing with Neural Network Potentials
The Western Washington Data-driven Discovery Seminar Series (W2D2S2) is hosting a series of events in partnership with Pacific Northwest National Laboratory again this Fall on Thursdays from 3:00-4:00pm! Please keep an eye out and email firstname.lastname@example.org for the zoom invite link!
Keynote Speaker: Dr. Jenna Pope, Pacific Northwest National Laboratory
Title: Accelerating Atomistic Simulations of Solid-Phase Processing with Neural Network Potentials
Abstract: Neural network potentials (NNPs) can greatly accelerate atomistic simulations, allowing one to sample a broader range of structural outcomes than possible with ab initio methods. This talk presents an active sampling algorithm to train an NNP that shows DFT-level accuracy in dynamic shear simulations of a model Cu-Ni multilayer system. We then use the NNP in conjunction with a perturbation scheme to stochastically sample structural and energetic changes caused by shear-induced deformation, demonstrating the range of possible intermixing and vacancy migration pathways that can be obtained as a result of the speedups provided by the NNP.
Bio: Jenna Pope is a Data Scientist in the National Security Directorate at Pacific Northwest National Laboratory. Her research focuses on the application of data science and deep learning to chemistry and materials science. Her projects are highly interdisciplinary and involve close collaboration with both experimentalists and modelers/theoreticians. She is a member of the American Chemical Society and serves on review panels for NSF. She received a BS in chemistry from the University of West Florida and a PhD in computational chemistry from the University of Georgia. She publishes under the name Jenna A. Bilbrey.
Keynote Speaker: Loc Truong, Pacific Northwest National Laboratory
Title: Differential Property Prediction: A Machine Learning Approach to Experimental Design in Advanced Manufacturing
Abstract: Advanced manufacturing techniques have enabled the production of materials with state-of-the-art properties. In many cases however, the development of physics-based models of these techniques lags behind their development in the lab. This means that material and process development proceeds largely via trial and error. This is sub-optimal since experiments are cost-, time-, and labor-intensive. In this work we propose a machine learning framework, differential property classification (DPC), which enables an experimenter to leverage machine learning's unparalleled pattern matching capability to pursue data-driven experimental design. DPC takes two possible experiment parameter sets and outputs a prediction of which will produce a material with a more desirable property specified by the operator. We demonstrate the success of DPC on AA7075 tube manufacturing process and mechanical property data using shear assisted processing and extrusion (ShAPE), an emerging solid phase processing technology. We show that by focusing on the experimenter's need to choose between multiple candidate experimental parameters, we can reframe the challenging regression task of predicting material properties from processing parameters, into a classification task on which machine learning models can achieve good performance.
Bio: Loc Truong joined PNNL in 2021 as a data scientist in the Data Sciences and Analytics Group. Loc's research background is primarily focused on computer vision, and adversarial machine learning. He’s constantly looking for new ways to apply data science to solve real world problems.
See our W2D2S2 website for more information
Please email email@example.com for the zoom invite link!
The Fall 2022 W2D2S2 Organizing Committee
Sarah Akers, Ying Bao, Stefan Dernbach, Brian Hutchinson, Tim Kowalczyk, Kimihiro Noguchi, Mohammad Taufique