DMREF Specific Highlights

Data-Driven Elucidation of Solution-to- Device Feature Transfer for π‐Conjugated Polymer Semiconductors

1/1/2022

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

4/1/2021 | Q. X. Ai, V. Bhat, S. M. Ryno, K. Jarolimek, P. Sornberger, A. Smith, M. M. Haley, J. E. Anthony, and C. Risko

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.

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.

Data Centric Nanocomposite Design via Mixed-variable Bayesian Optimization

L. C. Brinson (Duke U.), W. Chen (Northwestern U.), L. Schadler (U. VT)

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

Jana Shen

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.

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