Topic: Machine Learning for Material Applications
Machine Learning is seeing rapid development and applications to a wide range of scientific research areas. This bi-weekly symposium (virtual) will focus on Machine Learning as applied to materials, including research into the identification and optimization of new and novel materials, prediction of material properties and aging, improvements to characterization analysis methods, and optimization of material synthesis and processes. The organizers feel there is this growing nucleus of groups working on Machine Learning materials questions (SNL, LANL, UNM, GT, NMT, NMSU, UT and others) that we want to bring together for possible future collaborations and discussions. If you would like to present your recent results, we encourage you to consider presenting at MMLS. Every two weeks the one-hour session will be composed of two presentation (25 min. + 5 min live questions). The organizers strongly encourage presentations from students, student interns, postdocs and other researchers that are looking for an avenue to present their ongoing efforts.
July 20th (10 AM-11 AM, Team Link)
Prof. Michael Howard (Auburn University) "Understanding diffusion processes in soft materials using machine learning"
June 22nd (10 AM-11 AM, Team Link)
Beth Lindquist (Los Alamos National Laboratory) "Using Inverse Design to Facilitate Colloidal Self-Assembly"
May 18th (10 AM-11 AM, Team Link)
Rishi Gurnani (Georgia Tech) “Polymer Genome: accelerating materials discovery via data-driven approaches"
April 21st (11:30 AM-12:30 PM, Team Link)
Prof. Kaan Inal (University of Waterloo, Ontario, Canada) "A Machine Learning based Framework to Predict Local Strain Distribution, the Evolution of Plastic Anisotropy & Fracture in Additively Manufactured Alloys"
Saaketh Desai (Sandia National Labs, NM) “Parsimonious neural networks learn interpretable physical laws"
March. 23rd (10 - 11 AM, Team Link)
Jeff Greathouse (Sandia National Labs, NM) “Prediction of MD Simulated Self-Diffusion Coefficients in a Diverse Set of Pure Liquids"
Robert Malakhov (University of New Mexico, NM) "Combining image detection and ML to determine defect formation in gravure printing"
March. 9th (10 - 11 AM, Team Link)
David Montes de Oca Zapiain (Sandia National Labs, NM) "Calibration of Thermal Spray Microstructure Simulations to Experimental Data using Bayesian Optimization"
Cosmin Safta (Sandia National Labs, CA) “Low-Rank Tensor Network Approximations for Large Scale Models"
Feb. 23rd (10 - 11 AM, Team Link)
Marta D-Elia, (Sandia National Laboratories) "Data driven learning of robust nonlocal models: from molecular dynamics to nonlocal models for continuum mechanics".
Using machine learning to estimate ideal/non ideal mixing in binary ionic mixtures, David Rosenberger, (Los Alamos National Laboratory)
Feb. 9th (10 - 11 AM, Team Link)
"Data-driven discovery of high entropy alloy hydrides with targeted thermodynamic stability", Matt Witman (Sandia National Labs, CA)
Predicting self-diffusion in model and real systems using artificial neural networks, Joshua Allers (Sandia National Labs, NM, University of NM, CBE Grad Student)
Jan. 26th (10 - 11 AM, Team Link)“Accelerating phase-field based predictions via surrogate models trained by machine learning methods”, Remi Dingreville (Sandia National Labs, NM)
Jan. 12th (10 - 11 AM, Team Link)"Accelerated Novel Lattice Unit Cell Discovery with Deep Convolutional Neural Networks", Anthony Garland (Sandia National Labs, NM)
"Upscaling Finite-Size Diffusion Simulations to the Thermodynamic Limit", Calen Leverant (Sandia National Labs, NM & Univ. Florida, FL, Graduate student)
Dec. 15th (10 - 11 AM, Team Link)"Machine Learned SNAP Potentials for Materials Modeling", Mary Alice Cusentino (Sandia National Labs, NM)
"Leveraging SNAP Potentials for Accurate Magneto-Elastic Simulations of Materials", Julian Tranchida (Sandia National Labs, NM)
Dec. 1st (10 - 11 AM, Team Link)"Machine Learning Approaches for Identification of Thermally Activated Ionic Switch Solid Electrolytes", Christine C. Roberts (Sandia National Labs, NM)
"Artificial Neural Networks for Gas Sensor Array Response Prediction", Sleight Halley (University of New Mexico, CBE Grad Student)
We look forward to the event and we hope you will be able to join us (from a distance).
Dr. Todd M. Alam