Reorganization Energy Predictions with Graph Neural Networks

4/22/2024 | Daniel Tabor

These results demonstrate the feasibility of reorganization energy predictions on the benchmark QM9 data set without needing DFT-optimized geometries and demonstrate the types of features needed for robust models that work on diverse chemical spaces.

Curated Materials Data of Hybrid Perovskites

10/1/2023 | R. Chakraborty and V. Blum

Hybrid perovskites have emerged as a group of semiconductors that can solve key problems involving efficiency and production in optoelectronics and spintronics research. Over the past decade, this field has evolved to a point where the literature contains an enormous volume of chemical and physical information. The dispersed nature of the large, rapidly growing body of hybrid perovskite materials data poses a barrier to systematic discovery efforts, which can be solved by materials property databases, either by high-throughput or by systematic, accurate human-curated efforts.

Materials Simulation Toolkit

3/10/2023 | Dane Morgan (University of Wisconsin)

VTAnDeM: A Python Toolkit for Simultaneously Visualizing Phase Stability, Defect Energetics, and Carrier Concentration

1/1/2023 | M. Y. Toriyama, J. X. Qu, L. C. Gomes, and E. Ertekin

VTAnDeM offers a graphical interface that allows the user to interact directly with the chemical phase space of a given material and to visualize the defect formation energetics and ensuing carrier concentrations.

Polymer Foams for Oil Recovery

11/3/2022

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

4/1/2022 | 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.

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.

Hyperspectral Data Analytics and Image Analysis Toolsets Across Length Scales

1/1/2022 | D. A. Santos, J. L. Andrews, B. Lin, L. R. De Jesus, Y. Luo, S. Pas, M. A. Gross, L. Carillo, P. Stein, Y. Ding, B.-X. Xu, and S. Banerjee

We have curated a large database of X-ray absorption spectra for phase-pure lithiated transition metal oxides with well-defined lithiation stoichiometries.

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.

Data Reproducibility and Traceability forCommunity Materials Databases: Qresp for MatD3

1/16/2020 | 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.