Research Highlights

SrNbO3 as Transparent Conductor in the Visible and UV

June 1, 2020 | R. Engel-Herbert, V. Gopalan

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.

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

July 1, 2022 | E. Reichmanis, C. Meredith, M. Grover

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.

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

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.

Machine-learning Spectral Indicators of Topology

October 1, 2022

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.

Tuning Organic Solar Cell Domain Properties

Despite having achieved the long sought-after performance of 10% power conversion efficiency, high performance organic solar cells are still constrained to small devices fabricated by spin coating. Efforts to scale up via printing lag considerably behind, revealing an extreme sensitivity to different fabrication methods.

Crystallographic Distribution of Curvatures in Steel

How is the motion of an interface between two solid crystals related to its shape? This is a question that was impossible to address in the past because we were not able to see within solids

Grain Boundary Velocity Distributions

Polycrystalline metals and ceramics are opaque to most forms of radiation. Because of this, it has not been possible to observe the motion of grain boundaries within a polycrystalline network.

Grain Boundary Velocity and Curvature are Not Correlated

To the eye, most common metals and ceramics used in commercial products appear to be uniformly solid. But at the microscopic level, they are polycrystalline, made up of aggregates of grains that have different sizes, shapes, and crystal orientations.

Machine Learning on a Robotic Platform for the Design of Polymer-Protein Hybrids

Polymer–protein hybrids are intriguing materials that can bolster protein stability in non-native environments, thereby enhancing their utility in diverse medicinal, commercial, and industrial applications

Discovering Rare-earth-free Magnetic Materials

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

Creating Novel Magnetic Compounds with Complementary
Experimental and Computational Methods

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.

Emergent Network of Narrow Transport Channels

(a) Crystal structure of 1T-TiSe2 with Pt substituting for Ti; (b) temperature-dependent transport, STM showing topography (c) and spectroscopy (e), (d) schematic of domain walls, ARPES showing pseudogap formation at the L point.

Materials from Mathematics

Austenite/martensite interface in Cu69.5Al27Ni3.5. Zero elastic energy austenite/martensite interfaces possible under the co-factor conditions. Red is austenite and blue/green are two variants of martensite. These pictures exhibit large deformations, zero elastic energy, and perfect fitting of the phases under continuous variation of the volume fraction f.

Super-slick Coatings: Data Driven Design to Application

(Top left) Slices of a machine learning model designed to capture self-assembly of molecular monolayers on perovskite surfaces. (Top right) Photograph of a superslick surface comprising ZnO tetrapods functionalized with a molecular monolayer. (Lower) Lacy Douglas, Bill Tolar, Erick Braham, and Sarbajit Banerjee at I-corps Kick-off in Chicago.

Flexible Crystalline b-Ga2O3 Solar-blind Photodetectors

Wide bandgap (WBG) semiconductor-based solar-blind photodetectors (PDs) have attracted considerable attention as an emerging technology due to their unique spectral working region, which covers the deep ultra-violet (UV), and significant potential in many applications, such as secure data communication.

Enhancing the Electrocatalytic Activity of Molybdenum Disulfide

A) Illustration of the step-wise synthetic process comprising drop-casting, thermal annealing, and hydrothermal sulfidation/selenization used to grow MoS2-xSex/MoO3 nanosheets on carbon fiber paper. (B) SEM image of sample where Se:S=0.48 showing the homogeneous distribution of the nanosheets on carbon filter paper. (C) Raman spectra (514.5 nm excitation) acquired for MoS2-xSex/MoO3 samples with increasing concentration of Se. (D) Low-magnification and HRTEM images of a sample with a Se:S ratio of 0.62 illustrating the layered structure of MoS2/MoSe2. The left inset shows a SAED pattern of the chalcogenide layers.

Closed-loop Design of Heterostructures for Solar Energy Conversion

Schematic illustration of CdSe/β-SnxV2O5 heterostructures that enable photocatalytic water splitting; Right: illustration of design of MxV2O5 compounds with p-block cations that yield mid-gap states.

Predicting Anisotropic Performance in Thermoelectrics

Anisotropic properties yield anisotropic thermoelectric performance. Heat maps show lattice thermal conductivity (kL), mobility (uH) and ultimate thermoelectric performance (b)

Designing Materials to Revolutionize Our Engineering FutureIntellectual MeritCombining Experiment and Computation to Control Doping in Thermoelectric Materials

The structural evolution along the alloy between the ordered vacancy compound Hg2GeTe4 and Cu2HgGeTe4 was quantified as this alloy space is correlated with major changes in the carrier concentration. The alloy shows strong local ordering despite four distinct cationic species (Cu, Ge, Hg, vacancies) at high concentration. Further, the results indicated that the CuHg defects are driving the carrier concentration in the Cu2xHg2−xGeTe4 alloy

MOVCD of Complex Nitride Semiconductors

A unique, custom-designed research instrument for metal-organic chemical vapor deposition (MOCVD) is enabling the synthesis and study of complex nitride semiconductors, such as ZnGeN21, and alloys and heterostructures of these materials, alone and in combination with the binary nitrides (Al,Ga,In)

Nitride Semiconductor’s Family Expanded

The group III-nitrides (Al,Ga,In)N form the basis for the white LED lighting revolution, honored with the Nobel Prize in Physics in 2014.

Machine-assisted Discovery of Polymer-enzyme Complexes for Sustained Neural Regeneration

Among the many molecules that contribute to glial scarring, chondroitin sulfate proteoglycans (CSPGs) are known to be potent inhibitors of neuronal regeneration. Chondroitinase ABC (ChABC) degrades the glycosaminoglycan (GAG) side chains of CSPGs and promotes tissue regeneration. However, ChABC is thermally unstable and loses all activity within a few hours at 37 °C.

Common Perovskite Superfluoresces at High Temperatures

F. So, K. Gundogdu

Results of this study show that the creation and manipulation of collective coherent states in hybrid perovskites can be used as the basic building blocks for quantum applications.

An AI-driven, Cloud-based, Materials Discovery Platform for Nanomaterial Structure: PDFitc

We have developed a cloud-based, AI driven, platform for nanomaterial structure determination: “PDF in the cloud” (PDFitc.org), which consists of various applications for nanostructure determination, including a ML-based classifier for discovering material symmetry from a measured dataset, a high-throughput structure screening tool for predicting the structure of a measured signal, and a data-similarity visualization tool for finding changes in a signal in a time or temperature series.

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.

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.

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.

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.

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.

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

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.

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’.

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.

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.

Data-mining our Way to Better Nanoparticle Structures

Q. Du, S. Billinge, 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.

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.

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.

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.

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.

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.

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

R. Klie, A. Salehi-Khojin, 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.

Hydroxide Diffusion in Anion Exchange Membranes

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

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

SrNbO3 as Transparent Conductor in the Visible and UV

June 1, 2020 | 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.

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

J. Tovar, A. Ferguson

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.

Designing the World’s Brightest Fluorescent Materials

A. Flood, K. Raghavachari

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.

A Neural Network Approach for Catalysis

A. Heyden, G. Terejanu

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.