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All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

Chapter 8: Materials for Exploration Systems

Materials science and processing research in space can be thought of as a field of study that began with the sounding rocket experiments in the 1950s. Material science studies of the lunar surface materials returned during the Apollo missions enabled the study of lunar resource utilization. The study of materials science and processing in space continued with over 30 years of microgravity materials processing research which continues today in the International Space Station. These studies are the technical foundation that could enable lower cost human exploration through the use of in-situ propellant production, the production of energy from space resources, and the eventual establishment of a substantial portion of humanity living self sufficiently off Earth.

Curreri, Peter A.↗
DOE OSTI2018

On the nexus between atom probe microscopy and density functional theory simulations

The capability of precisely determining the atomic identity and position, coupled with an understanding of the inter-atomic interactions is crucial to enabling microstructure-property relationships in modern materials science. Two approaches to the investigation of materials at the atomic-scale are atom probe microscopy (APM) and density functional theory (DFT). On the one hand, APM offers an ultimate in 3D elemental analysis by determining the chemical identity and atomic position in a material. On the other, first principles DFT simulations allow the prediction of atomic structure and, critically, the electronic structure which is the gateway to calculating the real-world properties of materials. Both approaches are undertaken at the atomic-scale, and so are intrinsically complementary. This paper provides a review of the opportunities and challenges in combining these approaches to do materials science. We first summarise the status of these approaches and set out a framework for the current main workflows by which APM experiments inform DFT simulations and vice-versa. We point out certain gaps between the approaches and ways that these may be bridged via a series of case-studies organised into the aforementioned workflow framework.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Automated generation and ensemble-learned matching of X-ray absorption spectra

X-ray absorption spectroscopy (XAS) is a widely used materials characterization technique to determine oxidation states, coordination environment, and other local atomic structure information. Analysis of XAS relies on comparison of measured spectra to reliable reference spectra. However, existing databases of XAS spectra are highly limited both in terms of the number of reference spectra available as well as the breadth of chemistry coverage. In this work, we report the development of XASdb, a large database of computed reference XAS, and an Ensemble-Learned Spectra IdEntification (ELSIE) algorithm for the matching of spectra. XASdb currently hosts more than 800,000 K-edge X-ray absorption near-edge spectra (XANES) for over 40,000 materials from the open-science Materials Project database. We discuss a high-throughput automation framework for FEFF calculations, built on robust, rigorously benchmarked parameters. FEFF is a computer program uses a real-space Green’s function approach to calculate X-ray absorption spectra. Here, we will demonstrate that the ELSIE algorithm, which combines 33 weak “learners” comprising a set of preprocessing steps and a similarity metric, can achieve up to 84.2% accuracy in identifying the correct oxidation state and coordination environment of a test set of 19 K-edge XANES spectra encompassing a diverse range of chemistries and crystal structures. The XASdb with the ELSIE algorithm has been integrated into a web application in the Materials Project, providing an important new public resource for the analysis of XAS to all materials researchers. Finally, the ELSIE algorithm itself has been made available as part of veidt, an open source machine-learning library for materials science.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRSDate not supplied

Vapor Growth of ZnSe-based Compound Semiconductors in Low Gravity environment on International Space Station

Six low-gravity experiments of crystal growth of ZnSe-based compound semiconductors by physical vapor transport (PVT) are scheduled to be processed inside Low Gradient Furnace (LGF) on Materials Science Research Rack (MSRR) in Material Science Laboratory (MSL) on International Space Station (ISS) starting from February 27th, 2023. The flight samples consist of three unseeded growths of ZnSe, Cr-doped ZnSe and ZnSeTe as well as growth of ZnSe on top of three single crystal ZnSe seeds with orientation of (100), (110) and (111). The main scientific objective of the flight experiments is to establish the effects of gravity-driven fluid flows on Earth to (i) the non-uniform incorporation of impurities and defects and (ii) the deviation from stoichiometry observed in the grown crystals caused by growth interface fluctuations and evolution as the results of buoyancy-driven convection and irregular fluid-flows. The investigation will also evaluate the effects of gravity on the PVT process by examining (i) the growth kinetics on various seed orientations, (ii) the dopant segregation and distribution in the Cr doped ZnSe, and (iii) the compositional segregation and distribution in the ternary ZnSeTe crystal grown by PVT. Additionally, from X-ray diffraction/topography and by measuring the density and distribution of various crystalline defects it will assess the self-induced strain effects developed during processing at elevated temperatures caused by the weight of the crystals. During the ground-based preparations, the following tasks have been accomplished: • Establish the thermodynamic properties of the ZnSe binary and related ternary systems by measuring the partial pressures of the vapor phase coexisting with the condensed phases and perform phase diagram calculations for comparison. • Evaluate the effects of gravity-driven convection in the growth process by performing experiments under various vapor transport orientations relative to gravity vector, i.e., horizontal, vertically stabilized and destabilized configurations. • Establish a quantitative correlation between growth parameters and the characteristics and properties of the grown crystals by characterizing the crystals using a variety of techniques. • Evaluate the fundamentals of the current theories on vapor transport and crystal growth kinetics by performing in-situ and real-time monitoring techniques during growth. • Develop a one-dimensional diffusion limited theory of mass transport and two- and three-dimensional numerical simulations of both mass transport and heat transfer and thus establish the microgravity requirements for the flight experiments and to predict the characteristics of the grown crystals such as the solid-vapor interface shapes… etc. and establish a fundamental understanding of the crystal growth process from the comparison with experimental results.

ZnSe↗
DOE OSTI2016

Materials Characterization using the Facilities of the Center for Synchrotron Radiation Research and Instrumentation (CSRRI), a NSUF Partner Facility

Advanced nuclear energy systems are envisioned to operate at high temperatures, high burn-up, harsh neutron irradiation, and corrosive environments. New materials and nuclear fuels with improved performance, safety and reliability are needed for the increasingly demanding operating conditions. The fundamental challenge in developing new materials with improved irradiation, temperature, and corrosion resistance is to understand and control chemical and physical phenomena in complex systems over multiple time and length scales. Solving these scientific and technical challenges will require an integrated approach of all available computational and experimental capabilities. This talk will focus on the application of synchrotron radiation techniques to characterize materials of interest to nuclear material science at the facilities operated by the Center For Synchrotron Radiation Research and Instrumentation (CSRRI) at the Illinois Institute of Technology. The CSRRI is a partner facility with the U. S. Department of Energy's Nuclear Science User Facility (NSUF). Using the CSRRI facilities, such as the Materials Research Collaborative Access Team (MRCAT) beamline, we can study the materials science of irradiated and model materials to improve the understanding of the performance of materials in nuclear reactors. (author)

36 MATERIALS SCIENCE↗
DOE OSTI2022

Unconventional excitonic states with phonon sidebands in layered silicon diphosphide

Complex correlated states emerging from many-body interactions between quasiparticles (electrons, excitons and phonons) are at the core of condensed matter physics and material science. In low-dimensional materials, quantum confinement affects the electronic, and subsequently, optical properties for these correlated states. Here, by combining photoluminescence, optical reflection measurements and ab initio theoretical calculations, we demonstrate an unconventional excitonic state and its bound phonon sideband in layered silicon diphosphide (SiP 2 ), where the bound electron–hole pair is composed of electrons confined within one-dimensional phosphorus–phosphorus chains and holes extended in two-dimensional SiP 2 layers. The excitonic state and emergent phonon sideband show linear dichroism and large energy redshifts with increasing temperature. Our ab initio many-body calculations confirm that the observed phonon sideband results from the correlated interaction between excitons and optical phonons. With these results, we propose layered SiP2 as a platform for the study of excitonic physics and many-particle effects.

36 MATERIALS SCIENCE↗
DOE OSTI2021

X-ray diffraction technique

This is a guest lecture for University of Idaho graduate-level course Chemical and Materials Analysis. I will be talking about the fundamentals of X-ray Diffraction technique and some examples for its application in material science research.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Analyzing machine learning models to accelerate generation of fundamental materials insights

Abstract Machine learning for materials science envisions the acceleration of basic science research through automated identification of key data relationships to augment human interpretation and gain scientific understanding. A primary role of scientists is extraction of fundamental knowledge from data, and we demonstrate that this extraction can be accelerated using neural networks via analysis of the trained data model itself rather than its application as a prediction tool. Convolutional neural networks excel at modeling complex data relationships in multi-dimensional parameter spaces, such as that mapped by a combinatorial materials science experiment. Measuring a performance metric in a given materials space provides direct information about (locally) optimal materials but not the underlying materials science that gives rise to the variation in performance. By building a model that predicts performance (in this case photoelectrochemical power generation of a solar fuels photoanode) from materials parameters (in this case composition and Raman signal), subsequent analysis of gradients in the trained model reveals key data relationships that are not readily identified by human inspection or traditional statistical analyses. Human interpretation of these key relationships produces the desired fundamental understanding, demonstrating a framework in which machine learning accelerates data interpretation by leveraging the expertize of the human scientist. We also demonstrate the use of neural network gradient analysis to automate prediction of the directions in parameter space, such as the addition of specific alloying elements, that may increase performance by moving beyond the confines of existing data.

36 MATERIALS SCIENCE↗
DOE OSTI2021

DMMSC Talk

The new monthly Dynamic Mesoscale Material Science Capability Seminar Series features Lab highlights from materials science areas, including cutting-edge research at national and international light sources, in-depth investigations of technical gaps, and advanced studies of accelerator technologies that might provide probes in this region.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Effects of Radiation-Induced Defects on Corrosion

The next generation of nuclear reactors will expose materials to conditions that, in some cases, are even more extreme than those in current fission reactors, inevitably leading to new materials science challenges. Radiation-induced damage and corrosion are two key phenomena that must be understood both independently and synergistically, but their interactions are often convoluted. In the light water reactor community, a tremendous amount of work has been done to illuminate irradiation-corrosion effects, and similar efforts are under way for heavy liquid metal and molten salt environments. While certain effects, such as radiolysis and irradiation-assisted stress corrosion cracking, are reasonably well established, the basic science of how irradiation-induced defects in the base material and the corrosion layer influence the corrosion process still presents many unanswered questions. In this review, we summarize the work that has been done to understand these coupled extremes, highlight the complex nature of this problem, and identify key knowledge gaps.

36 MATERIALS SCIENCE↗
DOE OSTI2023

C-band high gradient research and facilities at Los Alamos National Laboratory [Slides]

Achieving high-gradient performance (low breakdown rates, low field emission, new materials for HOM absorption, cathodes at high gradient etc.) is a materials science problem. Los Alamos is, at core, a materials science laboratory with particular expertise in metallurgy. Los Alamos also considers itself the steward of accelerator science for the NNSA part of the DOE complex. Thus, Los Alamos has both an institutional interest in, and capability to address, this problem space. High gradient C-band work directly aligns with future NNSA and LANL missions.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Generative Adversarial Networks and Mixture Density Networks-Based Inverse Modeling for Microstructural Materials Design

Abstract There are two broad modeling paradigms in scientific applications: forward and inverse. While forward modeling estimates the observations based on known causes, inverse modeling attempts to infer the causes given the observations. Inverse problems are usually more critical as well as difficult in scientific applications as they seek to explore the causes that cannot be directly observed. Inverse problems are used extensively in various scientific fields, such as geophysics, health care and materials science. Exploring the relationships from properties to microstructures is one of the inverse problems in material science. It is challenging to solve the microstructure discovery inverse problem, because it usually needs to learn a one-to-many nonlinear mapping. Given a target property, there are multiple different microstructures that exhibit the target property, and their discovery also requires significant computing time. Further, microstructure discovery becomes even more difficult because the dimension of properties (input) is much lower than that of microstructures (output). In this work, we propose a framework consisting of generative adversarial networks and mixture density networks for inverse modeling of structure–property linkages in materials, i.e., microstructure discovery for a given property. The results demonstrate that compared to baseline methods, the proposed framework can overcome the above-mentioned challenges and discover multiple promising solutions in an efficient manner.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Data readiness pipeline patterns for scientific AI at scale: Insights from climate, fusion, life sciences, and materials

This article examines how data readiness for AI principles apply to large scientific datasets used to train foundation models. We analyze archetypal workflows across four representative domains—climate, nuclear fusion, life sciences, and materials—to identify common preprocessing patterns and domain‐specific constraints. We introduce a two‐dimensional readiness model that combines canonical preprocessing patterns with a five‐level operational readiness scale, both tailored to high‐performance computing (HPC) environments. This construct helps outline key challenges in transforming large‐scale scientific data into formats suitable for scalable AI training. Together, these dimensions form a conceptual maturity matrix that characterizes scientific data readiness and guides infrastructure development toward standardized, cross‐domain support for scalable and reproducible AI for science. Finally, we evaluate this maturity matrix in the context of case studies including ClimaX (climate), AFLOW (materials), OpenFold (proteomics), and DIII‐D fusion disruption‐prediction workflows, from which we distill lessons learned and provide recommendations to guide practitioners in developing robust AI‐readiness pipelines. Finally, we discuss remaining cross‐cutting challenges that persist across scientific domains.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

An unusual type of polymorphism in a liquid crystal

Polymorphism is a remarkable concept in chemistry, materials science, computer science, and biology. Whether it is the ability of a material to exist in two or more crystal structures, a single interface connecting to two different entities, or alternative phenotypes of an organism, polymorphism determines function and properties. In materials science, polymorphism can be found in an impressively wide range of materials, including crystalline materials, minerals, metals, alloys, and polymers. Here in this paper we report on polymorphism in a liquid crystal. A bent-core liquid crystal with a single chiral side chain forms two structurally and morphologically significantly different liquid crystal phases solely depending on the cooling rate from the isotropic liquid state. On slow cooling, the thermodynamically more stable oblique columnar phase forms, and on rapid cooling, a not heretofore reported helical microfilament phase. Since structure determines function and properties, the structural color for these phases also differs.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Fusion Materials Research at Oak Ridge National Laboratory in Fiscal Year 2015

The realization of fusion energy is a formidable challenge with significant achievements resulting from close integration of the plasma physics and applied technology disciplines. Presently, the most significant technological challenge for the near-term experiments such as ITER, and next generation fusion power systems, is the inability of current materials and components to withstand the harsh fusion nuclear environment. The overarching goal of the Oak Ridge National Laboratory (ORNL) fusion materials program is to provide the applied materials science support and understanding to underpin the ongoing Department of Energy (DOE) Office of Science fusion energy program while developing materials for fusion power systems. In doing so the program continues to be integrated both with the larger United States (US) and international fusion materials communities, and with the international fusion design and technology communities.This document provides a summary of Fiscal Year (FY) 2015 activities supporting the Office of Science, Office of Fusion Energy Sciences Materials Research for Magnetic Fusion Energy (AT-60-20-10-0) carried out by ORNL. The organization of this report is mainly by material type, with sections on specific technical activities. Four projects selected in the Funding Opportunity Announcement (FOA) solicitation of late 2011 and funded in FY2012-FY2014 are identified by “FOA” in the titles. This report includes the final funded work of these projects, although ORNL plans to continue some of this work within the base program.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2019

Frontiers of Materials Research: A Decadal Survey

Modern materials science builds on knowledge from physics, chemistry, biology, mathematics, computer and data science, and engineering sciences to enable us to understand, control, and expand the material world. Although it is anchored in inquiry-based fundamental science, materials research is strongly focused on discovering and producing reliable and economically viable materials, from super alloys to polymer composites, that are used in a vast array of products essential to today’s societies and economies. Frontiers of Materials Research: A Decadal Survey is aimed at documenting the status and promising future directions of materials research in the United States in the context of similar efforts worldwide. This third decadal survey in materials research reviews the progress and achievements in materials research and changes in the materials research landscape over the last decade; research opportunities for investment for the period 2020-2030; impacts that materials research has had and is expected to have on emerging technologies, national needs, and science; and challenges the enterprise may face over the next decade.

36 MATERIALS SCIENCE↗
NASA NTRS2016

Thermochemistry of CaO-MgO-Al2O3-SiO2 (CMAS) and Advanced Thermal and Environmental Barrier Coating Systems

CaO-MgO-Al2O3-SiO2 (CMAS) oxides are constituents in a broad number of materials and minerals which have recently inferred to discussions in materials science, planetary science, geochemistry and cosmochemistry communities. In materials science, there is increasing interest in the degradation studies of thermal (TBC) and environmental (EBC) barrier coatings of gas turbines by molten CMAS. These coatings have been explored to be applied on silicon-based ceramics and composites which are lighter and more temperature capable hot-section materials of gas turbines than the current Ni-based superalloys. The degradation of the coatings occurs when CMAS minerals carried by the intake air into gas turbines, e.g. in aircraft engines, reacts at high temperatures (1000C) with the coating materials. This causes premature failure of the static and rotating components of the turbine engines. We discuss some preliminary results of the reactions between CMAS and Rare-Earth (RE Y, Yb and Gd) oxide stabilized ZrO2 systems, and stability of the resulting oxides and silicates.

CaO-MgO-Al2O3-SiO2 (CMAS)↗
NASA NTRS2017

Thermochemistry of CaO-MgO-Al2O3-SiO2 (CMAS) and Advanced Thermal and Environmental Barrier Coating Systems

CaO-MgO-Al2O3-SiO2 (CMAS) oxides are constituents in a broad number of materials and minerals which have recently inferred to discussions in materials science, planetary science, geochemistry and cosmochemistry communities. In materials science, there is increasing interest in the degradation studies of thermal (TBC) and environmental (EBC) barrier coatings of gas turbines by molten CMAS. CMAS minerals usually are carried by the intake air into gas turbines, e.g. in aircraft engines, and their deposits react at high temperatures (1000C) with the coating materials. This causes degradation and accelerated failure of the static and rotating components of the turbine engines. We discuss some preliminary results of the reactions between CMAS and Rare-Earth (RE Y, Yb, Dy, Gd, Nd and Sm) oxide stabilized ZrO2 or HfO2 systems, and the stability of the resulting oxides and silicates. Plasma sprayed hollow tube samples ( 2.2 mm and 26 mm height) were half filled with CMAS powder, wrapped and sealed with platinum foil, and heat treated at 1310 C for 5h. Samples were characterized by differential scanning calorimetry, X-ray diffraction and cross section electron microscopy analysis.

degradation↗