DMREF Specific Highlights
Data-Driven Elucidation of Solution-to- Device Feature Transfer for π‐Conjugated Polymer Semiconductors
January 1, 2022 | C. Meredith, C. Risko, E. Reichmanis, M. Grover
The DMREF team recently published a perspective article that discusses the need for additional fundamental insight into the solution behavior of donor-acceptor based organic semiconducting (OSC) conjugated polymers.
OCELOT: Toward Data-driven Discovery of Organic Semiconductors
April 1, 2021
While the synthetic chemist can fine tune the chemical structure and architecture of π-conjugated molecules, and in turn the electronic, redox, and optical properties, the performance of organic semiconductors (OSC) are dependent on how these molecular building blocks pack and interact in the solid state.
HybriD3 Materials Database
V. Blum
MatD3 is intended to provide a simple solution for making diverse datasets available individually and rapidly.
Data-driven Shape Memory Alloy Discovery using Artificial Intelligence Materials Selection (AIMS) Framework
Previous studies have focused on minimizing hysteresis under no stress, but not under applied stress.
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”.
Data Centric Nanocomposite Design via Mixed-variable Bayesian Optimization
With an unprecedented combination of mechanical and electrical properties, polymer nanocomposites have the potential to be widely used across multiple industries. Tailoring nanocomposites to meet application specific requirements remains a challenging task, owing to the vast, mixed-variable design space that includes composition and microstructures of the nanocomposite material.
Industrial Collaboration:pH-responsive Inhibitors
Targeting β -secretase (BACE1) with small-molecule inhibitors offers a promising route for the prevention and treatment of Alzheimer’s disease. However, the intricate pH dependence of BACE1 function and inhibitor efficacy has posed a major challenge for structure-based drug design.
Data Driven Discovery of Conjugated Polyelectrolytes for Neuromorphic Computing
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
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.
Tools for Block Polymer Materials Discovery
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
Broadening Participation in Electronic Materials Research Through Knowledge and Data Exchange
Enhancing Access to Data. In collaboration with DMR-1729489, we are working to deliver an open data/software ecosystem by disseminating broadly research data through the Metals and Insulators through Structural Tuning (MIST) website hosted on data.world (https://data.world/dmref-mist).
Broadening Participation in Electronic Materials Research
Enhancing Access to Machine-Learning Models. We packaged our electronic classifiers and made them publically available. They are easily accessible via an interactive Jupyter notebookhosted by Binder.
Integrating Physics-based Models with Data-driven Methods for Materials Discovery
Metal-insulator transition (MIT) compounds are materials that can undergo an electronic phase changes and are promising platforms to build next-generation low-power microelectronics. Accelerated discovery is challenging using high-throughput screening because high-fidelity quantum-mechanical simulations are computationally prohibitive to perform.
Evidence for Spin Swapping in an Antiferromagnet
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.
Polymer Foams for Oil Recovery
D. Adamson, A. Dobrynin
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.
