Data Driven Discovery of Conjugated Polyelectrolytes for Neuromorphic Computing

2/6/2023 | Gang Lu & Xu Zhang (California State University Northridge), Thuc-Quyen Nguyen & Guillermo Bazan (UCSB)

Dual mode transistors, the type of transistors that work in both depletion mode and enhancement mode, were reported more than 50 years ago using inorganic semiconductors, such as Silicon, but have not been shown in organic electronics

Glass Sponges Inspire Mechanically Robust Lattice

2/5/2023

The predominantly deep-sea hexactinellid sponges are known for their ability to construct remarkably complex skeletons from amorphous hydrated silica. Here, using a combination of finite element simulations and mechanical tests on 3D-printed specimens of different lattice geometries, we show that the sponge’s diagonal reinforcement strategy achieves the highest buckling resistance for a given amount of material.

A New Paradigm for Accessing Chemical Information

2/3/2023 | Gregory Payne and William Bentley

In the 1960s work began toward the personal computer – a landmark in information processing. Since then, devices to access and analyze information have become smaller, faster, cheaper, easier to use and more powerful.

Hybrid Photocatalysts: Tuning Charge Transfer Dynamics and Redox Reactivity with Interfacial Chemistry & Electronic Structure

1/1/2023 | David Watson (SUNY-Buffalo) and Sarbajit Banerjee (Texas A&M University)

Photocatalysts that store the sun’s energy in chemical bonds are needed to combat global warming and reduce humanity’s reliance on fossil fuels.

Resolving Order in Ternary Semiconductors via Resonant X-ray Diffraction

12/12/2022 | Eric Toberer (Colorado School of Mines) and Michael Toney (Stanford U.)

By effectively characterizing the cation site order in ZnGeP2, we have demonstrated an example of the tunability of properties in II-IV-V2 materials at nearly fixed lattice parameters—making these materials promising for integration into current technologies. This could have a beneficial impact on devices such as LEDs and solar cells.

Discovering Rare-earth-free Magnetic Materials

10/14/2022 | J. Chelikowsky , K. Ho, C. Wang, D. Sellmyer, X. Xu

An open-access database is designed to facilitate machine learning.

Machine-learning Spectral Indicators of Topology

10/1/2022 | Mingda Li, Massachusetts Institute of Technology

Topological materials are promising for next-generation energy and information applications. However, the experimental determination of topology can be painstaking, with a few limitations such as limited sample types, high technical barriers, and limited sample environment.

Machine Learning Accelerated Design and Discovery of Rare-earth Phosphates as Next Generation Environmental Barrier Coatings

9/24/2022

Researchers from Rensselaer Polytechnic Institute synthesized single phase multiple component rare-earth phosphate as potential environmental barrier coatings of structure materials for space and aerospace application.

Composition Gradient High-Throughput Polymer Libraries Enabled by Passive Mixing and Elevated Temperature

7/1/2022

High-throughput experimentation (HTE) methods are key to enabling informatics-driven workflows to accelerate discovery of high-performance multicomponent materials. Here, we designed a solution coating system able to operate at temperatures over 110 °C to deposit composition gradient polymer libraries. The methodology provides an avenue for efficiently screening multiparameter spaces of a wide range of materials relevant to today’s applications.

Creating Novel Magnetic Compounds with Complementary Experimental and Computational Methods

6/14/2022 | J. Chelikowsky, K. Ho, C. Wang, D. Sellmyer, X. Xu

The search for new magnetic materials with high saturation magnetic polarization (Js), magnetocrystalline anisotropy (K1), and Curie temperature (Tc) is important for a wide range of applications including information and energy processing.

Research Highlights