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At least 649 records · Page 36

The Science of Detached Bridgman Growth and Solutocapillary Convection in Solid Solution Crystals

Bridgman and Float-zone crystal growth experiments are planned for NASA's First Materials Science Research Rack using the European Space Agency's Materials Science Laboratory with the Low Gradient Furnace (LGF) and Float Zone Furnace with Rotating Magnetic Field (FMF) inserts, respectively. Samples will include germanium and germanium-silicon alloys with up to 10 atomic percent silicon. The Bridgman part of the investigation includes detached growth samples and so there will be a solid-liquid-gas tri-junction in those experiments just as there will be in all float-zone experiments. There are other similarities as well as significant differences between the types of growth that will be discussed. The presentation will call attention to the reasons that experiments in microgravity will provide information unattainable from Earth-based experiments.

Szofran, F. R.↗

Microgravity Disturbance Characterization of the Quench Module Insert (QMI) Phase Change Device (PCD)

The Materials Science Research Facility (MSRF) is a multi-user, multi-purpose facility for materials science research. One experiment within the MSRF will be the Quench Module Insert (QMI), a high-temperature furnace with unique capabilities for processing different classes of materials. The primary functions of the QMI furnace are to melt, directionally solidify, and quench metallic samples, providing data to aid in understanding the effects of the microgravity environment on the characteristics of these processed metals. The QMI houses sealed individual sample ampoules containing material to be processed. Quenching of the samples in the QMI furnace is accomplished by releasing low-melting-point metallic shoes into contact with the outside of the sample ampoule, dissipating heat and cooling the sample inside. The impact from this method of quench will induce sample vibrations which could be large enough to adversely affect sample quality. Utilizing breadboard hardware, the sample quench sequence, releasing the shoes, was conducted. Data was collected from accelerometers located on the breadboard sample cartridge, indicating the maximum acceleration achieved by the sample. The primary objective of the test described in this presentation was to determine the acceleration imparted on the sample by the shoe contact. From this information, the science community can better assess whether this method of quench will allow them to obtain the data they need.

Gattis, Christy↗

Liquid-State Dewetting of Pulsed-Laser-Heated Nanoscale Metal Films and Other Geometries

Metal films of nanoscale thickness, deposited on substrates and exposed to laser heating, provide systems that involve several interesting multiphysics effects. In addition to fluid mechanical aspects associated with a free boundary setup, other relevant physical effects include phase change, thermal flow, and liquid–solid interactions. Such films are challenging to model, in particular because inertial effects may be relevant, and large contact angles require care when considering the long-wave formulation. Applications of nanoscale metal films are numerous, and the materials science community is actively pursuing more complex setups involving templated films and substrates, bimetallic films and alloys, and a variety of elemental film geometries. The goal of this review is to discuss our current understanding of thin metal film systems, while also providing an overview of the challenges in this research area, which stands at the intersection of fluid mechanics, materials science, and thermal physics.

36 MATERIALS SCIENCE↗

CDAC Student Report: Summary of LLNL Internship

Multiple objectives motivated me to apply for an internship at LLNL: I wanted to experience the work environment at a national lab, to learn about research and job opportunities at LLNL in particular, and to gain greater experience with code development, particularly within the realm of high performance computing (HPC). This summer I was selected to participate in LLNL's Computational Chemistry and Material Science Summer Institute (CCMS). CCMS is a 10 week program hosted by the Quantum Simulations group leader, Dr. Eric Schwegler. CCMS connects graduate students to mentors at LLNL involved in similar re- search and provides weekly seminars on a broad array of topics from within chemistry and materials science. Dr. Xavier Andrade and Dr. Erik Draeger served as my co-mentors over the summer, and Dr. Andrade continues to mentor me now that CCMS has concluded. Dr. Andrade is a member of the Quantum Simulations group within the Physical and Life Sciences at LLNL, and Dr. Draeger leads the HPC group within the Center for Applied Scientific Computing (CASC). The two have worked together to develop Qb@ll, an open-source first principles molecular dynamics code that was the platform for my summer research project.

36 MATERIALS SCIENCE↗

Machine learning in materials informatics: recent applications and prospects

Propelled partly by the Materials Genome Initiative, and partly by the algorithmic developments and the resounding successes of data-driven efforts in other domains, informatics strategies are beginning to take shape within materials science. These approaches lead to surrogate machine learning models that enable rapid predictions based purely on past data rather than by direct experimentation or by computations/simulations in which fundamental equations are explicitly solved. Data-centric informatics methods are becoming useful to determine material properties that are hard to measure or compute using traditional methods—due to the cost, time or effort involved—but for which reliable data either already exists or can be generated for at least a subset of the critical cases. Predictions are typically interpolative, involving fingerprinting a material numerically first, and then following a mapping (established via a learning algorithm) between the fingerprint and the property of interest. Fingerprints, also referred to as “descriptors”, may be of many types and scales, as dictated by the application domain and needs. Predictions may also be extrapolative—extending into new materials spaces—provided prediction uncertainties are properly taken into account. This article attempts to provide an overview of some of the recent successful data-driven “materials informatics” strategies undertaken in the last decade, with particular emphasis on the fingerprint or descriptor choices. The review also identifies some challenges the community is facing and those that should be overcome in the near future.

36 MATERIALS SCIENCE↗

Freeze It or Leave It? Evaluating the Role of Cryo-Electron Microscopy in Battery Research

Cryogenic electron microscopy (cryo-EM) continues to gain prominence in materials science, particularly in battery research where it has enabled high-resolution, multimodal characterization of electrode materials and interfaces that otherwise degrade quickly under electron beam irradiation. But as anyone who has attempted cryo-EM techniques knows, freezing comes at a cost; cryo-EM experiments are time-consuming, highly sensitive, and carry an increased risk of artifacts due to issues such as frost contamination. Thus, when planning new characterization of battery materials or other beam-sensitive samples, it is critical to consider whether (and which) cryo-EM techniques are appropriate, based on study goals and an understanding of electron beam-sample interactions. Here we review such considerations for battery materials to elucidate the questions of when, why, and how to freeze to achieve high-quality characterization.

25 ENERGY STORAGE↗

On the frontiers of coupled extreme environments

Coupled extreme environments pose among the highest demands on materials, stressing the limits of temperature, pressure, electric and magnetic fields, atomic displacement rates, and chemical potentials that can be simultaneously sustained. Here, this issue highlights exciting new materials science methods that will enable rapid progress in what has traditionally been the most difficult materials arena. New opportunities in artificial intelligence, multi-physics simulations capabilities, the use of extreme conditions to synthesize novel materials, and our ability to interrogate materials under relevant conditions provide new avenues to understand and design new materials.

36 MATERIALS SCIENCE↗

1L Mark-IV Target Design Review

This presentation includes General Design Considerations; Current (Mark-III) Lower Tier; Mark-III Upper Tier; Performance Metrics; General Improvements for Material Science; General Improvements for Nuclear Science; Improving FOM for Nuclear Science; General Design Considerations Summary; Design Optimization Studies; Expected Mark-IV Performance: Material Science; Expected Mark-IV Performance: Nuclear Science (Disk); Mark IV Enables Much Wider Range of Nuclear-Science FOM Gains than Mark III; Mark-IV Performance Summary; Rod or Disk? Center or Real FOV?; and Project Cost and Schedule.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Neutron Tomography at the Los Alamos Neutron Science Center

Neutron imaging is an incredibly powerful tool for non-destructive sample characterization and materials science. Neutron tomography is one technique that results in a three-dimensional model of the sample, representing the interaction of the neutrons with the sample. This relies both on reliable data acquisition and on image processing after acquisition. Over the course of the project, the focus has changed from the former to the latter, culminating in a large-scale reconstruction of a meter-long fossilized skull. The full reconstruction is not yet complete, though tools have been developed to improve the speed and accuracy of the reconstruction. This project helps to improve the capabilities of LANSCE and LANL with regards to imaging large or unwieldy objects.

36 MATERIALS SCIENCE↗

Moving closer to experimental level materials property prediction using AI

Abstract While experiments and DFT-computations have been the primary means for understanding the chemical and physical properties of crystalline materials, experiments are expensive and DFT-computations are time-consuming and have significant discrepancies against experiments. Currently, predictive modeling based on DFT-computations have provided a rapid screening method for materials candidates for further DFT-computations and experiments; however, such models inherit the large discrepancies from the DFT-based training data. Here, we demonstrate how AI can be leveraged together with DFT to compute materials properties more accurately than DFT itself by focusing on the critical materials science task of predicting “formation energy of a material given its structure and composition”. On an experimental hold-out test set containing 137 entries, AI can predict formation energy from materials structure and composition with a mean absolute error (MAE) of 0.064 eV/atom; comparing this against DFT-computations, we find that AI can significantly outperform DFT computations for the same task (discrepancies of $$>0.076$$ > 0.076 eV/atom) for the first time.

36 MATERIALS SCIENCE↗

Electrical control of magnetism by electric field and current-induced torques

The remanent magnetization of ferromagnets has long been studied and used to store binary information. While early magnetic memory designs relied on magnetization switching by locally generated magnetic fields, key insights in condensed matter physics later suggested the possibility of doing it by electrical means instead. In the 1990s, Slonczewski and Berger formulated the concept of current-induced spin torques in magnetic multilayers through which a spin-polarized current generated by a first ferromagnet may be used to switch the magnetization of a second one. This discovery drove the development of spin-transfer-torque magnetic random-access memories (MRAMs). More recent fundamental research revealed other types of current-induced torques named spin-orbit torques (SOTs) and will lead to a new generation of devices including SOT MRAMs and skyrmion-based devices. Parallel to these advances, multiferroics and their magnetoelectric coupling, first investigated experimentally in the 1960s, experienced a renaissance. Dozens of multiferroic compounds with new magnetoelectric coupling mechanisms were discovered and high-quality multiferroic films were synthesized (notably of BiFeO 3 ), also leading to novel device concepts for information and communication technology such as the magnetoelectric spin-orbit (MESO) transistor. The story of the electrical switching of magnetization, which is discussed in this review, is that of a dance between fundamental research (in spintronics, condensed matter physics, and materials science) and technology (MRAMs, MESO transistors, microwave emitters, spin diodes, skyrmion-based devices, components for neuromorphics, etc.). This pas de deux has led to major scientific and technological breakthroughs in recent decades (such as the conceptualization of pure spin currents, the observation of magnetic skyrmions, and the discovery of spin-charge interconversion effects). As a result, this field has not only propelled MRAMs into consumer electronics products but also fueled discoveries in adjacent research areas such as ferroelectrics or magnonics. Here, in this review, recent advances in the control of magnetism by electric fields and by current-induced torques are covered. Fundamental concepts in these two directions are reviewed first, their combination is then discussed, and finally current various families of devices harnessing the electrical control of magnetic properties for various application fields are addressed. The review concludes by giving perspectives in terms of both emerging fundamental physics concepts and new directions in materials science.

36 MATERIALS SCIENCE↗

Machine learning-based microstructure prediction during laser sintering of alumina

Abstract Predicting material’s microstructure under new processing conditions is essential in advanced manufacturing and materials science. This is because the material’s microstructure hugely influences the material’s properties. We demonstrate an elegant machine learning algorithm that faithfully predicts the microstructure under new conditions, without the need of knowing the governing laws. We name this algorithm, RCWGAN-GP, which is regression-based conditional generative adversarial networks with Wasserstein loss function and gradient penalty. This algorithm was trained with experimental SEM micrographs from laser-sintered alumina under various laser powers. The RCWGAN-GP realistically regenerates the SEM micrographs under the trained laser powers. Impressively, it also faithfully predicts the alumina’s microstructure under unexplored laser powers. The predicted microstructure features, including the morphology of the sintered particles and the pores, match the experimental SEM micrographs very well. We further quantitatively examined the prediction accuracy of the RCWGAN-GP. We trained the algorithm with computer-created micrograph datasets of secondary-phase growth governed by the well-known Johnson–Mehl–Avrami (JMA) equation. The RCWGAN-GP accurately regenerates the micrographs at the trained time series, in terms of the grains’ shapes, sizes, and spatial distributions. More importantly, the predicted secondary phase fraction accurately follows the JMA curve.

08 HYDROGEN↗

Developing quantitative capabilities for a scanning microwave impedance microscope

Currently no instrument exists to quantitatively measure electrical properties (dielectric constant and conductivity) on sub-micron regions, a capability of extreme importance to measure the functioning of sub-micron structures and devices. In particular, such electrical measurements could support the development and manufacture of advanced energy related materials used in technologies such as photovoltaic cells, fuel cells and advanced batteries, thereby increasing performance and/or lowering production costs. The goal of this project is to develop microwave imaging add-on modules and probes capable of quantitatively measuring the electrical properties of small regions of a material using microwaves. These modules for scanning probe microscopes, such as atomic force microscopes, make use of the near-field interaction between the exciting microwaves and a material and can resolve regions 10 nanometers in size (less than a million atoms) with little perturbation of the material. In Phase 1 commercial probes and modified hardware were used to establish the feasibility of using microwave imaging systems to quantify local permittivity and conductivity. Repeatable measurements that vary in a systematic way with electrical parameters were obtained from a variety of samples. In Phase 2 research prototypes of the needed hardware and probes were developed and their performance against test structures and real-world samples was demonstrated. Result of this research project include demonstration of quantification of both permittivity and doping level at precisions up to 10% using multiple technical approaches. The level of performance and ease of use meets market expectations and the technology and protocols researched under this grant are currently being commercialized. Quantitative capabilities of this sort will provide a key capability to energy related technologies, and to a wide variety of semiconductor, biological, and materials science applications. Applications include both materials research in academic and industrial labs, as well as manufacturing applications. In a manufacturing environment the quantitative measurement and imaging capabilities could be used both as metrology for process control and as a tool for failure analysis and yield improvement.

36 MATERIALS SCIENCE↗

Local Dynamics in Liquids and Glassy Materials

Atomic dynamics in strongly disordered matter, such as liquids and glasses, is characterized by correlated local dynamics. The conventional descriptions of atomic dynamics in condensed matter in terms of phonons and diffusion are inadequate for their dynamics, and instead we have to describe the dynamics in terms of spatial and temporal atomic correlations. Experimentally this means that it is insufficient to describe the dynamics through the dispersion of collective excitations in the dynamic structure factor, S(Q,E), where Q and E are the momentum and energy exchanges in scattering. As an alternative, herein we propose a real-space description in terms of the dynamic pair-density function (DyPDF) and the Van Hove function (VHF), and discuss recent results on relaxor ferroelectrics and superfluid 4 He measured by inelastic neutron scattering and water by inelastic x-ray scattering. As the subjects of research in condensed matter physics and materials science turn to more complex materials, the real-space approach is likely to become the mainstream method of research in atomic dynamics.

36 MATERIALS SCIENCE↗

Materials Research at Idaho National Laboratory

I am going to spent 8 mins to introduce materials science related research at INL to the student and early career researchers who attend MRS Falls 2025. I am one of 10 panelists, all of whom are from different national labs.

36 - MATERIALS SCIENCE↗

NASA-UVA light aerospace alloy and structures technology program (LA2ST)

The NASA-UVa Light Aerospace Alloy and Structure Technology (LAST) Program continues to maintain a high level of activity, with projects being conducted by graduate students and faculty advisors in the Departments of Materials Science and Engineering, Civil Engineering and Applied Mechanics, and Mechanical and Aerospace Engineering at the University of Virginia. This work is funded by the NASA-Langley Research Center under Grant NAG-1-745. Here, we report on progress achieved between January 1 and June 30, 1992. The objectives of the LA2ST Program is to conduct interdisciplinary graduate student research on the performance of the next generation, light weight aerospace alloys, composites and thermal gradient structures in collaboration with Langley researchers. Technical objectives are established for each research project. We aim to produce relevant data and basic understanding of material mechanical response, corrosion behavior, and microstructure; new monolithic and composite alloys; advanced processing methods; new solid and fluid mechanics analyses; measurement advances; and critically, a pool of educated graduate students for aerospace technologies. The accomplishments presented in this report cover topics including: (1) Mechanical and Environmental Degradation Mechanisms in Advance Light Metals and Composites; (2) Aerospace Materials Science; (3) Mechanics of Materials and Composites for Aerospace Structures; and (4) Thermal Gradient Structures.

Gangloff, Richard P.↗

Keeping LAMMPS cutting edge

Since its inception 30 years ago, LAMMPS has grown to be a world-class molecular dynamics code and a cornerstone of computational materials science research. This project aimed to keep LAMMPS at the forefront of molecular dynamics simulations by adapting LAMMPS to the latest developments in machine learning technology and hardware. Initially, the project set out to provide a unified implementation of active learning for efficient training data generation in LAMMPS, but the research trajectory pivoted to address more immediate and impactful opportunities. On the hardware side, recent record-breaking molecular dynamics simulations were developed on the Cerebras wafer-scale AI chip, and this project has developed an interface between LAMMPS and the hardware-specific molecular dynamics code to accelerate and simplify development and user adoption. On the software side, PyTorch’s Ahead-of-Time (AOT) compilation features promised increased performance for state-of-the-art equivariant neural network potentials, and this project laid the groundwork for their adoption in LAMMPS, resulting in a nearly 20x acceleration in extreme cases. Combined with a comprehensive benchmark study of LAMMPS across all current exascale systems, this project has reinforced LAMMPS’s role as a versatile, high-performance tool for current and future materials science applications.

36 MATERIALS SCIENCE↗

Uncertainty-Driven Rapid Thermodynamic Assessment of Nb-Ta-Zr System and Effects of C impurities (L25GF9298S): Annual Progress Report

An integrated computational materials engineering (ICME) method is in development for rapid thermodynamic experimental investigation and high-fidelity computational modeling of refractory multi-principal element alloys (RMPEAs). These ultra-high-temperature (UHT) alloys are of interest for structural applications in extreme environments, but deficiency of reliable data, especially melting temperatures, impedes the prediction of alloys with favorable properties. The method leverages UHT capabilities and computational expertise of LLNL’s Materials Science Division and the McCormack Lab’s UHT conical nozzle levitation (CNL) system to iteratively map the Nb-Ta-Zr phase space, with focus on the liquidus surface, through targeted experiments selected by quantifying uncertainty in the thermodynamic model fitting parameters. This method will reduce the time to map uncharted RMPEA phase space and thereby accelerate discovery and development of advanced materials for applications in extreme environments.

36 MATERIALS SCIENCE↗