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MIDDMI program support

Mines-affiliated support

Branden Kappes

Branden Kappes

Research Assistant Professor of Mechanical Engineering, Co-director MIDDMI

Aaron Stebner

Aaron Stebner

Rowlinson Assistant Professor of Mechanical Engineering, Materials Science

Amy Brice

Administrative support

Citrine-affiliated support

Chris Borg

Chris Borg

Citrine Data Engineer, Co-director MIDDMI

Josh Tappan

Josh Tappan

Citrine Community Manager

Bryce Meredig

Bryce Meredig

Citrine Chief Science Officier

MIDDMI projects

Phase Fraction Prediction in Additively Manufactured Titanium Alloys

The additive manufacturing (AM) division of the Stebner group develops machine learning algorithms to optimize processing parameters for new AM materials. Nathan is studying the impact of heat treatment methods on the microstructure and subsequent mechanical properties of Ti-6Al-4V.

Nathan Johnson

Nathan Johnson

PhD Candidate

Aaron Stebner

Aaron Stebner

Rowlinson Assistant Professor of Mechanical Engineering, Materials Science

Branden Kappes

Branden Kappes

Research Assistant Professor of Mechanical Engineering, Co-director MIDDMI

High-Throughput Prediction of Thermal Conductivity of Materials Using Machine Learning

The Toberer group operates at the interface between chemistry, solid-state physics, and material science, and targets new materials for energy applications. Kiarash is using machine learning to optimize the transport properties of Cu-based ternary and quaternary compounds.

Kiarash Gordiz

Kiarash Gordiz

Postdoctoral Fellow

Eric Toberer

Eric Toberer

Assistant Professor, Department of Physics

Integration of machine learning and combinatorial synthesis methods for triple conducting oxide discovery

The Advanced Energy Materials Laboratory is dedicated to the design, fabrication, and characterization of energy related materials and devices. Jake, Meagan, and Makenzie are focused in two areas: developing robust data workflows to investigate protonic ceramic ammonia fuel cells and using machine leaning models to sequentially design new triple conducting oxides.

Jake Huang

Jake Huang

PhD Candidate

Meagan Papac

Meagan Papac

PhD Candidate

Ryan O'Hayre

Ryan O'Hayre

Professor, Metallurgical and Materials Engineering

DESIGN OF SOFT MAGNETS USING DFT AND MACHINE LEARNING

The Ciobanu and Stebner groups are focused on materials modeling for energy applications. Rajesh and Sukriti aim to design soft magnetic crystalline materials by spanning compositional and structural space using a combination between DFT and machine learning techniques.

Rajesh Jha

Rajesh Jha

Postdoctoral Research Fellow

Sukriti Manna

Sukriti Manna

PhD Candidate

Cristian Ciobanu

Cristian Ciobanu

Professor, Mechanical Engineering

Aaron Stebner

Aaron Stebner

Rowlinson Assistant Professor of Mechanical Engineering, Materials Science

MACHINE LEARNING APPLIED TO PHASE EQUILIBRIA AND MECHANICAL PROPERTIES PREDICTION OF HIGH ENTROPY ALLOYS (HEAS)

As part of The Center for Advanced Non-Ferrous Structural Alloys (CANFSA) the Clarke group is focused on combining computational modeling (various length and time scales) and experimental approaches (alloying, processing and microstructure/property characterization) in order to advance industrially-relevant projects in an efficient and effective manner. Francisco, Alec, and Nick are employing a machine learning approach to obtain new empirical parameters to classify the phase equilibria and strength of HEAs.

Francisco Coury

Francisco Coury

PhD Candidate

 

Kester Clarke

Kester Clarke

Assistant Professor, Metallurgical and Materials Engineering

Alec Saville

Alec Saville

Undergraduate researcher

Nicholas Lipski

Nicholas Lipski

Undergraduate researcher

Amy Clarke

Amy Clarke

Associate Professor, Metallurgical and Materials Engineering

Developing a Data Format and Repository for Sharing First-principles Defect Calculations

The Stevanovic lab sits at the intersection between solid-state physics, materials science, large-scale (high-throughput) computations and big data. Prashun and Anuj are creating a data format for defect diagrams so that researchers have a reliable way to quantitatively read and compare the defect structure of materials.

Prashun Gorai

Prashun Gorai

Research Assistant Professor

Anuj Goyal

Anuj Goyal

Postdoctoral Fellow

Vladan Stevanovic

Vladan Stevanovic

Assistant Professor, Metallurgical and Materials Engineering

Classifying Material Interfaces: Linking Structural Descriptors

This project will merge atomistic modeling, advanced structural descriptors, and data analysis methods to reduce intrinsic loss within nanocrystalline thin films to help guide inverse design methodologies for functional ceramic materials.

Jacob Tavenner

Jacob Tavenner

PhD candidate

Garritt Tucker

Garritt Tucker

Assistant Professor, Mechanical Engineering

The effect of fatigue life on Austenite Steels

This semester Josh is conducting a literature review of the factors controlling the fatigue life of Austenite steels. The extracted parameters are input into a machine learning model to elicit non-obvious correlations between synthetic conditions and mechanical properties.

Josh Stackhouse

Josh Stackhouse

undergraduate

Kip Findley

Kip Findley

Associate Professor, Metallurgical and Materials Engineering