Composites are ubiquitous throughout nature and often display both high strength and toughness, despite the use of simple base constituents. In the hopes of recreating the high-performance of natural composites, numerical methods such as finite element method (FEM) are often used to calculate the mechanical properties of composites. However, the vast design space of composites and computational cost of numerical methods limit the application of high-throughput computing for optimizing composite design, especially when considering the entire failure path. In this work, the authors leverage deep learning (DL) to predict material properties (stiffness, strength, and toughness) calculated by FEM, motivated by DL's significantly faster inference speed. Results of this study demonstrate potential for DL to accelerate composite design optimization.

To identify new face centered cubic high entropy alloys (HEAs), MnFeCoNiCu thin film samples were prepared by simultaneous magnetron sputtering of elements onto Si wafers. This sputtering arrangement yielded compositional gradients in the samples. The films exhibited regions with different phases, some of which were single-phase and non-equiatomic. To screen the crystal structure and composition across film samples, multiple characterization techniques were used: scanning electron microscopy, focused ion beam, energy-dispersive x-ray spectroscopy, x-ray diffraction, and electron backscattered diffraction analysis. Using this combinatorial method, candidate single-phase HEAs were identified and then successfully arc-melted in bulk form, followed by thermomechanical processing.

The shape memory properties of Ni–Al alloy are investigated using molecular dynamics simulation. The phase transformation behaviors for various Ni composition ratios are studied under quasistatic cooling and heating process. Various loadings, i.e., uniaxial, shear, and biaxial, are applied on a 68% Ni–Al alloy till plasticity takes place. The atomic configurations are inspected and analyzed using a common neighbor parameter. The shape recovery capability of the plastically deformed alloy is examined after heating above the phase transformation temperature. It is found that there would be shape recovery if the twinning plane reorientation or moving was the major yielding mechanism. For those loadings in which stacking faults or dislocations nucleate, the deformed model would not restore to its original shape after heating and the corresponding maximum shear stress is noticeably higher. There is no direct dependence between the yield strain and the shape recovery capability. Our findings could provide a possible explanation for the functional fatigue of the polycrystalline shape memory alloy.

This work is based on a recent theoretical study of how the hydrostatic pressure and core/shell sizes affect the optical properties associated with the transition from the ground state to first excited state (1s–1p), of an exciton confined in spherical core/shell quantum dots (SCSQDs). We have computed under an effective mass framework, linear, third-order nonlinear, and total absorption coefficients (AC) and refractive index (RI) as functions of photon energy for different sizes of SCSQDs with varying hydrostatic pressure. Our results show that the optical absorption is deeply dependent on the incident light intensity. Both AC and RI significantly influenced by the confinement and pressure effects.

We have successfully employed a charge transfer mechanism to convert carbon nanotube (CNT) powder into CNT flexible membrane with no binder. We have demonstrated the use of the CNT membranes as electrode in a stacked bipolar solid-state capacitor using grafoil as current collector that showed 80% capacitance retention over 10,000 cycles at 70 °C. The CNT membranes could have potential application in catalysis, photovoltaic, thermoelectric, and many others.

Advancements in temporal and spatial resolutions of microscopes promise to expand the frontiers of understanding in materials science. Imaging techniques produce images at a high-frame rate, streaming out a tremendous amount of data. Analysis of all these images is time-consuming and labor intensive, creating a bottleneck in material discovery that needs to be overcome. This paper summarizes recent progresses in machine learning and data science for expediting and automating material image analysis. The discussion covers both static image and dynamic image analyses, followed by remarks concerning ongoing efforts and future needs in automated image analysis that accelerates material discovery.

The development of more sustainable construction materials is a crucial step toward the reduction of CO2 emissions to mitigate climate change issues and minimize environmental impacts of the associated industries. Therefore, there is a growing demand for bio-based binders which are not only safer toward human and environmental health but also facilitate cleaner disposal of the construction materials and enable their compostability. Here, we summarize the most relevant bio-based polymers and molecules with applications in the construction sector. Due to the biologic nature of these materials, the existing biotechnologic processes, including synthetic biology, for their development and production have been evaluated.

Unlike other data intensive domains, understanding distributions, trends, correlations, and relationships in materials data sets typically involves navigating high-dimensional spaces with only a limited number of observations. Under these conditions extracting structure/property relationships is not straightforward and considerable attention must be given to the reduction of feature space before predictions can be made. Here we have used Kohonen networks (self-organizing maps) to identify hidden structure/property relationships in computational sets of twinned and single-crystal diamond nanoparticles based on structural similarity in multiple dimensions, and confirmed the importance of a limited number of surface chemical features using regression.

We introduce CRYSTAL, a multi-agent AI system for crystal-structure phase mapping. CRYSTAL is the first system that can automatically generate a portfolio of physically meaningful phase diagrams for expert-user exploration and selection. CRYSTAL outperforms previous methods to solve the example Pd-Rh-Ta phase diagram, enabling the discovery of a mixed-intermetallic methanol oxidation electrocatalyst. The integration of multiple data-knowledge sources and learning and reasoning algorithms, combined with the exploitation of problem decompositions, relaxations, and parallelism, empowers AI to supersede human scientific data interpretation capabilities and enable otherwise inaccessible scientific discovery in materials science and beyond.

Synthetic biology has a huge potential to produce the next generation of advanced materials by accessing previously unreachable (bio)chemical space. In this prospective review, we take a snapshot of current activity in this rapidly developing area, focusing on prominent examples for high-performance applications such as those required for protective materials and the aerospace sector. The continued growth of this emerging field will be facilitated by the convergence of expertise from a range of diverse disciplines, including molecular biology, polymer chemistry, materials science, and process engineering. This review highlights the most significant recent advances and addresses the cross-disciplinary challenges currently being faced.

Microscale testing has enjoyed significant developments, with the majority of testing focused on tensile/compression type tests and little focus on shear testing. With the recent advances in macroscale shear testing, we developed a novel shear structure for evaluating shear properties of bulk materials and films at the microscale. The shear response in single-crystal copper oriented along the [111] direction was found to have a yield strength of ∼180 MPa. Nanocrystalline copper specimens with different orientations showed sensitivity to the film texture with a shear yield strength nearly three times that of single-crystal copper. Shear specimens were fabricated with Cu film–Si substrate interface near the middle of the shear region and compressed to fracture. The shear response showed a mixed behavior of the stiff Si substrate and softer nanocrystalline film and failed in a brittle manner, indicating a response unique to the interface.

Here, we report that a marine sandworm Nereis virens jaw protein, Nvjp1, nucleates hemozoin with similar activity as the native parasite hemozoin protein, HisRPII. X-ray diffraction and scanning electron microscopy confirm the identity of the hemozoin produced from Nvjp1-containing reactions. Finally, we observed that nAl assembled with hemozoin from Nvjp1 reactions has a substantially higher energetic output when compared to analogous thermite from the synthetic standard or HisRPII-nucleated hemozoin. Our results demonstrate that a marine sandworm protein can nucleate malaria pigment and set the stage for engineering recombinant hemozoin production for nanoenergetic applications.

There is a genuine need to shorten the development period for new materials with desired properties. In this work, machine learning (ML) was conducted on a dataset of the elastic moduli of 219 bulk-metallic glasses (BMGs) and another dataset of the critical casting diameters (Dmax) of 442 BMGs. The resulting ML model predicted the moduli and Dmax of BMGs in good agreement with most experimentally measured values, and the model even identified some errors reported in the literature. This work indicates the great potential of ML in design of advanced materials with target properties.

Scanning thermal microscopy allows thermal characterization with nanoscale resolution. However, quantitative usage has been met with skepticism, because no standard exists for calibrating probe–sample thermal exchange. In this paper, three published strategies for calibrating probe–sample thermal exchange are directly compared, then used to measure bulk and thin-film thermal conductivity. It is shown that with an appropriately calibrated probe and film-on-substrate heat conduction model, thermal conductivity values of ultrathin-film (2.9–202 nm) Al2O3 on silicon are within 20% deviation of independently measured values, while more commonly used methods yield values that may deviate by more a factor of two.