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PROJECTS/PROGRAMS

UQ

A photo of attendees attending MLMR Bootcamp 2018.

Machine Learning: Educating the Next Generation Materials Workforce

Ongoing
Annual Bootcamp: Machine Learning for Materials Research (MLMR) The fifth annual MLMR met in the summer of 2020 with 180 attendees from 12 countries, 30% of whom were from industry. Over the 5 years of the bootcamp, we have had attendees from a total of 19 countries. We also run tutorials at MRS
schematic of the scanning droplet cell apparatus

Autonomous Scanning Droplet Cell

Ongoing
Corrosion impacts a broad spectrum of application areas including infrastructure, transportation, and the military. The annual price tag for corrosion mitigation and remediation is 3.4 % of the US GDP. The team is particularly interested in discovering new metallic glasses (metals without long range

Stochastic Network Growth Simulation for Photopolymerization

Ongoing
We have developed a computational method to simulate the complex interactions of the stochastic processes during polymerization. Specifically, the firing rates of these stochastic events are determined based on monomer information ( e.g. functionalities, rate constants, diffusivities, interaction

High-Precision Structural Measurements

Ongoing
On the instrumental and data processing side, we focus on improving the precision of structural information derived using transmission electron microscopy (TEM). Aberration-corrected scanning TEM enables direct imaging of atomic columns but the precision of column positions extracted from the images

Statistical methods for MGI metrology

Ongoing
We focus on research and development of statistical methods to enable new measurement science and metrology relevant to MGI. In particular, we illustrate our methods for examples from near-field scanning probe microwave microscopy (NSSM) for characterization, development, and modeling of novel

Statistical modeling of images of electronic materials

Ongoing
Correcting NSSM images of materials for topographic backgrounds is a major challenge. Based on the Atomic Force Microscopy (AFM) topography images, we predict the signature of topographic variations to any NSSM image. The difference between the observed NSSM image and this prediction is our estimate

Assessment of Uncertainty in CALPHAD Descriptions

Ongoing
Knowledge of phase equilibria and phase transformations is absolutely essential for the development of new materials and processing methods. The CALPHAD (CALculation of PHAse Diagrams) method is a proven, indispensable tool in this endeavor. It combines thermodynamic descriptions of binary and

NIST Materials-focused Empirical Potentials Repositories and Efforts

Ongoing
SOFT MATTER WebFF: Force-field repository for organic and soft materials Frederick R. Phelan Jr., 1 and Huai Sun 2 1Materials Science and Engineering Division, NIST, Gaithersburg, MD 20899 2Aeon Technology Inc. and School of Chemistry and Chemical Engineering, Shanghai Jiao Tong University

Integrated Nanoscale Experimentation, Prediction, and Validation

Ongoing
A central tenet of the Materials Genome Initiative is that advanced computational methods will enable materials with new or superior properties to be discovered so as to improve the performance of components in applications ranging from communications to energy to health care. Such methods will take
Molecular dynamics simulation

Uncertainty Quantification in Computational Materials Science

Ongoing
Industry and other stakeholders increasingly rely upon simulations to inform their decisions. To make this strategy routine and reliable these simulation results must be accompanied by quantitative statements of their quality. Uncertainty quantification refers to the growing suite of tools situated
Lattice constant for AI using various exchange-correlation functionals

DFT Benchmarking

Ongoing
Since its inception exactly 50 years ago this year, density-functional theory (DFT) has evolved from an exotic idea of physicists into the most-widely used tool for the computational prediction of materials’ structures and electronic, optical, magnetic, and mechanical properties. While DFT presents