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) 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 NMMLS. 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.
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)
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)
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)
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)
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)
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)
March. 9th (10 - 11 AM, Team Link)
Cosmin Safta (Sandia National Labs, CA) “Unsupervised learning techniques for hypersonic trajectory generation”David Montes de Oca Zapiain (Sandia National Labs, NM) "Calibration of Thermal Spray Microstructure Simulations to Experimental Data using Bayesian Optimization"
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.
Todd M. Alam (Sandia National Labs, NM) “Symbolic Regression using Multigene Genetic Programing to Extract the Underlying Physical Equations for Diffusion”
We look forward to the event and we hope you will be able to join us (from a distance).
Dr. Todd M. Alam