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Material Measurement Laboratory / Material Data Division

Data Science and AI Group

Projects/Programs

Displaying 1 - 21 of 21
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Annual Machine Learning for Materials Research Bootcamp

Ongoing
Four days of lectures and hands-on exercises covering a range of data analysis topics from introduction to python and data pre-processing to advanced machine learning analysis techniques. Example topics include: Identifying important features in complex/high dimensional data Visualizing high...
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...
Schematic image of automated MOF synthesis platform under construction.

Autonomous Synthesis of Metal-Organic Frameworks

Ongoing
Metal-organic frameworks (MOFs) show great promise for a variety of applications, from industrial gas separation, to catalysis, to PFAS absorption. One challenge to the adoption of these technologies is determining and optimizing the synthesis of these materials. There are large computational...
infographic explaining how a battery passport works

EV Battery Passport

Completed
End-of-life issues associated with EV batteries (EVBs) are estimated to have a steep rise in the coming decades. Given the finite nature of material resources worldwide, developing a circular EV batteries (EVB) infrastructure is needed to keep high value battery materials in the economy, therefore...
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Comparative Mammalian Proteome Aggregator Resource (CoMPARe) Program

Ongoing
Comparative proteomics strives to gain insight into key underlying molecular changes that result in unique phenotypes across related taxa. Proteomic analysis complements comparative genomics by providing evidence of protein abundance, orthogonal to gene copy number and amount of transcript. To...
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Consistency Analysis and Uncertainty in ‘omic Data

Ongoing
‘Omics is increasingly moving out of the laboratory and towards use in industrial and commercial applications. For instance, in biomanufacturing, there is a need for quality control when developing biotherapeutics, which will almost certainly require a machine-learning classifier to separate...
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High Throughput Thin-film Materials Science

Ongoing
There are many materials science problems that don’t have good modeling frameworks and exist in large design spaces. In these domains the best approach is to rapidly acquire data. To achieve this we can deposit metallic alloys as thin films with a range of composition (or less commonly processing...
A group of students in a laboratory with computers and equipment

A Low-Cost Robot Science Kit for Education

Ongoing
Despite its low cost, Legolas has been demonstrated for machine learning-driven hypothesis design, discovery, and validation. For the last four years, Legolas has been used in hands-on courses at the University of Maryland to teach next-generation workforce skills, including ML, control systems...
MLF_EquationofState1

Machine Learning Fluid Equations of State

Ongoing
Understanding the thermodynamic properties of fluids and fluid mixtures is of central importance in many fields of science and engineering ranging from medicine to consumer products. The nature of the particles in a fluid can vary greatly depending on the type of interactions present, e.g., dipole...
Illustration with logo style printing: “organic ingredients”, “100% natural”, “100% organic”; “natural ingredients”, and “authentic” centered in bold letters.

Machine Learning to Predict Food Provenance

Ongoing
Adulteration of food and food products is a pernicious problem which is difficult to solve as supply chains and international trade routes become increasingly complex; yet agriculture contributed over $1 trillion to the US GDP in 2017, [1] illustrating the importance of protecting this and related...
Multi_Colloidal_Crystals2

Machine Learning to Predict Multicomponent Colloidal Crystals

Ongoing
There is a direct link between a material’s macroscopic properties and its microscopic structure, which makes rational bottom-up self-assembly a powerful tool for engineering properties of materials. In general, colloids are facile material building blocks whose shape, charge, and surface...
Integrated multi-physics simulation and validation

Multiscale Modeling and Validation of Semiconductor Materials and Devices

Ongoing
The limitations of scaling traditional CMOS (complementary metal-oxide semiconductors) designs have necessitated that the semiconductor industry consider new materials and design concepts. For wide bandgap semiconductor devices, optimization of materials and fabrication processes is needed to...
Illustrated cube has crystal framework in gray with red balls interspersed in the open spaces.

Performance Metrics for Industrial Gas Separation and Purification

Ongoing
Sorbents in Direct Air Capture (DAC) are exposed to the atmosphere, where they adsorb CO2 then are isolated from atmosphere, the CO2 is driven off by heating or pulling vacuum, refreshing the sorbents. This adsorption-refresh cycle depends not only on the competitive adsorption of CO2 with the other...
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