DMREF Specific Highlights

Materials Simulation Toolkit

Dane Morgan (University of Wisconsin)

Data Driven Discovery of Topological Phononic Materials

This DMREF project has demonstrated an alternative avenue for the prediction of new topological materials from simple spectroscopic features, addressing the DMREF value of “significantly accelerate materials discovery and development”. In particular, the synergy of machine-learning modeling with the experimental validation addresses the DMREF concept to “work synergistically in a closed loop fashion.” The broadening of materials candidates further supports the DMREF mission to foster the “translation of materials research toward application”.

Tools for Block Polymer Materials Discovery

K. Dorfman (U. MN)

Implementing the Materials Genome Initiative-inspired approach for block polymer materials discovery employed by the PIs requires the availability of fast computational software for computing block polymer phase behavior

Data Driven Discovery of Conjugated Polyelectrolytes for Neuromorphic Computing

Gang Lu & Xu Zhang (California State University Northridge) Thuc-Quyen Nguyen & Guillermo Bazan (UCSB)

In this project, we have constructed a database on conjugated polyelectrolytes (CPEs) based on high-throughput first-principles calculations and machine learning modeling.

Data Reproducibility and Traceability forCommunity Materials Databases: Qresp for MatD3

Volker Blum

The discovery of new materials as well as the determination of a vast set of materials properties for science and technology is a fast-growing field of research, with contributions from many groups worldwide.

Polymer Foams for Oil Recovery

This unique combination of solvent selectivity and electro-mechanical response opens a path for the design of foam-like materials that could find applications in oil recovery, mechano-chemical sensors, flexible electronics, and energy storage.

Data-driven Shape Memory Alloy Discovery using Artificial Intelligence Materials Selection (AIMS) Framework

W. Trehern, R. Ortiz-Ayala, K. C. Atli, R. Arroyave, I. Karaman

Previous studies have focused on minimizing hysteresis under no stress, but not under applied stress.

HybriD3 Materials Database

MatD3 is intended to provide a simple solution for making diverse datasets available individually and rapidly.

Evidence for Spin Swapping in an Antiferromagnet

J. Zhou (U. Texas-Austin); G. Fiete (Northeastern U.); C.L. Chien (Johns Hopkins U.)

In accordance with the MGI mission to convert materials discovery to application as quickly as possible, the LaFeO3material we studied has now been incorporated into spin caloritronic devices.

High Throughput Design of Metallic Glasses with Physically Motivated Descriptors

Dane Morgan and Paul Voyles (University of Wisconsin)

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