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.

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.

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-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.

HybriD3 Materials Database

V. Blum

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

Polymer Foams for Oil Recovery

D. Adamson, A. Dobrynin