A poster to illustrate both the depth and breadth of the MINED research group. MINED explores data-driven solutions to materials science and manufacturing problems. We generate and consume large spatiotemporal, multimodal datasets across the materials science domain (e.g. ceramics, metals, composites, natural materials). MINED develops software and protocols for data generation, data processing, and data analytics. We are working to develop new paradigms in information sharing to fuel the emerging Materials Genome Initiative.

1. The GT MINED research team is
focused on finding new pathways to
dramatically reducing the cost and time required
for the design, manufacture, and deployment of
new/improved materials in advanced
technologies. MINED uses revolutionary cross-
disciplinary research direction that merges
materials science and engineering, manufacturing
sciences, computational sciences and
engineering, applied mathematics, digital signal
processing, systems theory, data and information
sciences, machine learning, and advanced
statistics.
RESEARCH team 
CROSS CUTTING
interdisciplinary workflows 
MINED designs experiments that
produce the large statistical datasets
which are required by multiscale
models. The datasets are derived
from recently designed multi-modal
materials characterization methods
to probe novel sample and testing
geometries that contain gradients in
both structure and processing.
data GENERATION 
COLLABORATION 
data PROCESSING
multimodal spatiotemporal info
MINED is building a suite of open science tools to
segment, quantify, & visualize Big Data. There is no
dataset too complicated.
Al-Cu Solidification
Voorhees (16TB)
hierarchical fiber composites
Tailored analytics, visualization, and algorithms
designed weave & fiber scale evolving 3-D datasets.
nonlinear materials analytics
Complicated data requires complicated tools
that account for long-range order and non
Euclidean measures.
Fuel Cell Membranes
Kumbur (Drexel)
Ablator Panels
Mansour (NASA)
3-D OIM
Gumbsch (KIT)
Polymer MD
Jacobs (GT)
Brazed Wires
NRL
high-throughput experiments
With an ICME mindset, experimental information and
datasets are designed to feed directly into complicated
multiscale simulation routines.
compressed sensing
Sophisticated sampling patterns produce high pedigree
empirical information at a fraction of the time and cost.
19m 4.5s30s
nonlinear simulation
Novel high-throughput experimental designs are
validated homegrown numerical simulations on
Ductile metals
in-situ structure-response imaging
Extract in-situ SPP relationships using mulitple SEM
detectors coupled with an in-situ nanoindenter
Sample
Indenter tip
SEM pole piece
EBSD detector
data GENERATION
Double Cone Specimen
Jominy Bar Tests
FEM Crystal Plasticity
Spherical Nanoindentation
LOCALIZATION relationships
Machine learning techniques provide data
driven means to inverting simulation data to
flow top-down & predict extreme behaviors.
data ANALYTICS
STRUCTURE identification
MINED pushes the limits of statistical structure measures by
building algorithms, workflows, and ideas to take the fullest
advantage of large, expensive materials data.
α-β Titanium
Aluminum MD
4-D Organic
Solar Cells
Hazelnut CT
materials INFORMATICS
MINED combines extensive knowledge of materials & manufacturing
SPP relationships, advanced statistics, and modern data science to
rapidly provide high-value, objective knowledge of material systems.
Spatial Statistics
HOMOGENIZATION SPP
Improved models to extract
Bottom-up knowledge from
large materials datasets.
MINED has depth and breadth in
large multimodal spatiotemporal
materials dataset analysis. MINED
develops advanced statistical
algorithms, feature identification,
graph methods, and visualization
techniques to explore enormous
volumetric images.
data PROCESSING 
MINED combines extensive materials
knowledge with machine learning
algorithms that can effectively
explore the growing Materials Big
Data ecosystem. They provide insight
into structure comparison along with
objective top-down and bottom-up
scale bridging techniques.
data ANALYTICS 
Real-time collaboration tools
MINED has built a global collaboration network across metals, ceramics, and composite materials
domains. MINED realizes that “half the time, half the cost” starts with moving information at the speed
of the modern world wide web, and aims to do so by contributing software and datasets to the open
source community in real time. MINED’s “Lab to the Cloud” gives our collaborators immediate access to
experimental datasets using cloud storage. Similarly, in silico datasets and their underlying codes are
often provided, with consent, as accompaniments to research publications. As such, MINED promotes a
focused and transparent research process with limited time wasted in redundant explorations.