Research Highlights

RESEARCH

Computationally Driven Genetically Engineered Materials

Development of protein biomaterials that are capable of self-assembly into hydrogels has potential in biomedical applications including drug delivery and tissue engineering. A two-stage architecture, called DeepFRI, has been recently developed where functional salinity is established by training its algorithm on annotated structures from PDB and SWISS-MODEL and applying weighted class activation mapping of residues that are critical to function.

RESEARCH

New Topology & Tunable Superconductivity in a-Bi4I4

Given that 𝜷-Bi4I4 is the first weak topological insulator (TI) (identified by us, but not shown here), that 𝜶-Bi4I4 is a prototype higher-order TI (highlighted here), and that there is a room-temperature transition between the two structural phases (also identified by us, but not shown here), we have established a new (quasi-1D) TI paradigm that unifies the first and second order topological insulators.

RESEARCH

A New Pathway to Stable, Low-cost, Flexible Electronics

C. Risko, J. Anthony, O. Jurchescu

While progress in material and device design has been astonishing, low environmental and operational stabilities remain longstanding problems obstructing their immediate deployment in real world applications.

RESEARCH

Predicting the Rubbery Response of Polymer Liquids

S. Milner

Our work describes how different kinds of polymer entangle. The key insight is simple to state intuitively: polymer chains entangle as often as they can, limited only by how often they can closely approach each other. We turn this deceptively simple statement into a unified scaling theory for different kinds of polymer fluids.

RESEARCH

Enhanced Room Temperature Infrared LEDs

V. Podolskiy, D. Wasserman

Plasmonic-enhanced emission has been moved toward practical application by the demonstration of an electrically pumped light emitting diode (LED) whose emission properties far exceed state-of-the-art.

RESEARCH

Color, Structure, and Rheology of a Bottlebrush Copolymer Solution

D. Guironnet, S. Rogers

A combination of high-end microscopy, rheology, and neutron scattering was used to show how the shear rate alters the structure and color of these bottlebrush polymer

RESEARCH

Excitons and Polarons in Organic Materials

F. Spano

In this effort, the DMREF team has developed a new theory for deciphering the features of the optical absorption spectra of conjugated polymers and organic aggregates.

RESEARCH

Photonic Funnels

V. Podolskiy, D. Wasserman

Efficient optical coupling between nano- and macroscale areas is strongly suppressed by the diffraction limit. This work presents a possible solution to this fundamental problem via the experimental fabrication, characterization, and comprehensive theoretical analysis of structures referred to as ‘photonic funnels’.

RESEARCH

Recovery of Crude Oil from Aqueous Environments

K. Wooley

A nanoscopic sugar-based magnetic hybrid material was designed that is capable of tackling environmental pollution posed by marine oil spills while minimizing potential secondary problems that may occur from microplastic contamination.

RESEARCH

Designing Exceptional Gas-separation Membranes with Machine Learning

S. Kumar, B. Benicewicz

The field of polymer membrane design is primarily based on empirical observation, which limits discovery of new materials optimized for separating a given gas pair. Instead of relying on exhaustive experimental investigations, this team has trained a machine learning algorithm, through use of a topological, path-based hash of the polymer repeating unit.

RESEARCH

Data-mining our Way to Better Nanoparticle Structures

S. Billinge, Q. Du, D. Hsu

Taking inspiration from genomics based data-mining approaches, we showed how large databases of candidate structures can be generated algorithmically and then mined efficiently and robustly to screen for candidate nanoparticle structures.

RESEARCH

Interpretable Molecular Models for MoS2

J. Miao, Y. Huang, H. Heinz

MoS2 is a layered material with outstanding electrical and optical properties. Here, parameters for MoS2 with record accuracy are introduced, including validation and application to explain specific binding of peptides and amino acid residues.

RESEARCH

Informing Zeolite Synthesis Enabled by Natural Language Processing

E. Olivetti

We have built an automated way to extract and combine body text and table information from published literature on the synthesis of zeolites, an industrially significant catalyst material. These tools are important as they move the field closer to the ability to predict and design synthesis routes for zeolites.

RESEARCH

Resolving Order in Ternary Semiconductors via Resonant X-ray Diffraction

M. Toney, E. Toberer

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.

RESEARCH

Glass Sponges Inspire Mechanically Robust Lattice

K. Bertoldi, J. Aizenberg

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.

RESEARCH

Glass Transition Temperature from the Chemical Structure of Conjugated Polymers

E. Gomez, R. Colby

The Tg demarks the transition into a brittle glassy state, making its accurate prediction for conjugated polymers crucial for the design of soft, stretchable, or flexible electronics.

RESEARCH

Theory-guided Discovery of New Two-dimensional Metal-Chalcogenide Alloys with Exceptional Electrocatalytic Activity

A. Salehi-Khojin, R. Klie, R. Mishra

We report the synthesis of new two-dimensional binary alloys of transition-metal dichalcogenides. Some of these alloys show outstanding performance as electrocatalyst in Li-air batteries and for the reduction of CO2.

RESEARCH

Hydroxide Diffusion in Anion Exchange Membranes

M. Tuckerman, M. Hickner, S. Paddison, C. Bae

The development of reliable, cost-effective polymer architectures for use as anion exchange membranes (AEMs) is an important challenge facing emerging electrochemical device technologies.

RESEARCH

SrNbO3 as Transparent Conductor in the Visible and UV

R. Engel-Herbert

It has been a long-standing challenge to find a material that combines the mutually exclusive properties of high electrical conductivity and high optical transmission in the visible, and even harder, in the ultraviolet spectrum. A new class of transparent conductors – correlated metals – was recently discovered and found suitable.

RESEARCH

Machine Learning-enabled Computational Discovery of Self-assembling Biocompatible Nanoaggregates

A. Ferguson, J. Tovar

This work establishes new understanding of oligopeptide assembly, identifies promising new candidates for experimental testing, and presents a computational design platform that can be generically extended to other peptide-based and peptide-like systems.

RESEARCH

Designing the World’s Brightest Fluorescent Materials

K. Raghavachari, A. Flood

The brightest fluorescent material has been created, solving a problem that’s persisted in the field for more than a century. While fluorescent dyes are potential key components of materials needed for applications including efficient solar cells, medical diagnostics, and organic light emitting diodes (LEDs), electronic coupling between them in the solid state quenches their emission. Small-molecule Ionic Isolation Lattices (SMILES) provide a solution to this long-standing problem.

RESEARCH

A Neural Network Approach for Catalysis

G. Terejanu, A. Heyden

A neural network predictive model has been developed that combines an established additive atomic contribution-based model with the concepts of a convolutional neural network that, when extrapolating, achieves a statistically significant improvement over the previous models.