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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

A Case Study of Multimodal, Multi-institutional Data Management for the Combinatorial Materials Science Community

Although the convergence of high-performance computing, automation, and machine learning has significantly altered the materials design timeline, transformative advances in functional materials and acceleration of their design will require addressing the deficiencies that currently exist in materials informatics, particularly a lack of standardized experimental data management. The challenges associated with experimental data management are especially true for combinatorial materials science, where advancements in automation of experimental workflows have produced datasets that are often too large and too complex for human reasoning. The data management challenge is further compounded by the multimodal and multi-institutional nature of these datasets, as they tend to be distributed across multiple institutions and can vary substantially in format, size, and content. Furthermore, modern materials engineering requires the tuning of not only composition but also of phase and microstructure to elucidate processing–structure–property–performance relationships. To adequately map a materials design space from such datasets, an ideal materials data infrastructure would contain data and metadata describing (i) synthesis and processing conditions, (ii) characterization results, and (iii) property and performance measurements. In this work, we present a case study for the low-barrier development of such a dashboard that enables standardized organization, analysis, and visualization of a large data lake consisting of combinatorial datasets of synthesis and processing conditions, X-ray diffraction patterns, and materials property measurements generated at several different institutions. While this dashboard was developed specifically for data-driven thermoelectric materials discovery, we envision the adaptation of this prototype to other materials applications, and, more ambitiously, future integration into an all-encompassing materials data management infrastructure.

36 MATERIALS SCIENCE↗
DOE OSTI2026

A multimodal large language model for materials science

Understanding and predicting the properties of inorganic materials is crucial for accelerating advancements in materials science and driving applications in energy, electronics and beyond. Integrating material structure data with language-based information through multimodal large language models (LLMs) offers great potential to support these efforts by enhancing human–artificial intelligence interaction. However, a key challenge lies in integrating atomic structures at full resolution into LLMs. In this work, we introduce MatterChat, a versatile structure-aware multimodal LLM that unifies material structural data and textual inputs into a single cohesive model. MatterChat uses a bridging module to effectively align a pretrained universal machine learning interatomic potential with a pretrained LLM, reducing training costs and enhancing flexibility. Our results demonstrate that MatterChat greatly improves performance in material property prediction and human–artificial intelligence interaction, surpassing general-purpose LLMs such as GPT-4. We also demonstrate its usefulness in applications such as more advanced scientific reasoning and step-by-step material synthesis.

Tang, Yingheng [Lawrence Berkeley National Laborat↗
NASA NTRS1988

Ground based materials science experiments

The facilities at the Microgravity Materials Science Laboratory (MMSL) at the Lewis Research Center, created to offer immediate and low-cost access to ground-based testing facilities for industrial, academic, and government researchers, are described. The equipment in the MMSL falls into three categories: (1) devices which emulate some aspect of low gravitational forces, (2) specialized capabilities for 1-g development and refinement of microgravity experiments, and (3) functional duplicates of flight hardware. Equipment diagrams are included.

Meyer, M. B.↗
DOE OSTI2022

Rethinking radiation effects in materials science using the plasma-focused ion beam

This work demonstrates the viability of using a plasma-focused ion beam (PFIB) as a new platform to carry out radiation effects studies in solids. While materials subjected to irradiation in either nuclear reactors or energetic particle accelerators experience limitations with respect to inaccuracies in both flux measurements and lack of precise control of irradiation areas, we demonstrate a new irradiation technique that allows the exposure of a single specimen in multiple areas, at multiple doses, and in site-specific dependencies - all with flux variations on the order of only 1%. This versatile technique also allows materials to be exposed to significantly higher irradiation dose rates than conventional accelerators. To validate the new methodology, we selected the classical example of ion-beam-induced amorphization of pure single-crystal Si. By pioneering the use of the PFIB for radiation effects studies in materials science, we were able to exploit, in both micro- and nanometer-sized detail, the differences in electron image contrast arising from crystalline versus amorphous solid-state phases in classical semiconductors subjected to irradiation, thus providing new insights on amorphization mechanisms. This methodology opens new research frontiers at the fringe of materials science with promising applications beyond the scope of materials at extremes such as in nanopatterning, nanodevices and nanoarchitectonics.

36 MATERIALS SCIENCE↗
DOE OSTI2020

EFRC Materials Science of Actinides (Final Report)

The Energy Frontier Research Center Materials Science of Actinides (MSA) conducted basic research from 2009-2019. The scientific personnel and participating institutes changed several times during the evolution of the Center, and in total included 24 senior investigators, four national laboratories, and ten universities. Workforce development was a motivating goal of the university-led Center, which included many undergraduate, graduate, and post-doctoral researchers. The center focused on the actinide elements, which arise from the sequential filling of the 5f electron orbitals. Although the specific themes evolved during the Center’s history, the final research themes were: (1) Nanoscale Cage Clusters, (2) Complex Ceramic and Metallic Materials, and (3) Materials Under Extreme Environments. To date, the Center has produced 254 peer-reviewed journal publications and book chapters that acknowledged only the EFRC funding, and an additional 161 journal publications and book chapters that acknowledged funding from the EFRC and additional sources (a few manuscripts are still in preparation). Broad highlights include: (1) Development of an extensive family of actinide nanomaterials that self-assemble in aqueous solution, an understanding of their formation mechanisms and stabilities, and possible uses in nuclear fuel cycles; (2) Establishment of facilities and methodologies to conduct high-temperature calorimetry on transuranium materials, and extensive thermodynamic-based studies of uranium and thorium compounds important to the nuclear fuel cycle; (3) A vastly improved understanding of the behavior of actinide materials in extreme conditions, including high radiation fields, temperatures, and pressures, as well coupled effects of two or more of these; (4) Development of resonance ultrasound spectroscopy and direct measurement of aging of plutonium alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2017

The journey from forensic to predictive materials science using density functional theory

Approximate methods for electronic structure, implemented in sophisticated computer codes and married to ever-more powerful computing platforms, have become invaluable in chemistry and materials science. The maturing and consolidation of quantum chemistry codes since the 1980s, based upon explicitly correlated electronic wave functions, has made them a staple of modern molecular chemistry. Here, the impact of first principles electronic structure in physics and materials science had lagged owing to the extra formal and computational demands of bulk calculations.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Advances and opportunities in materials science for scalable quantum computing

We report that by harnessing unique quantum mechanical phenomena, such as superposition and entanglement, quantum computers offer the possibility to drastically outperform classical computers for certain classes of problems. The realization of this potential, however, presents a substantial challenge, because noise and imperfections associated with the materials used to fabricate devices can obscure the delicate quantum mechanical effects that enable quantum computing. Hence, progress in synthesis, characterization, and modeling of materials for quantum computing have driven many exciting advances in recent years and will become increasingly important in the years to come. As progressively more complex, multi-qubit systems come online, and as significant government and industrial investment drives research forward, new challenges and opportunities for materials science continue to emerge. The articles in this issue survey the current state of materials science progress and obstacles for some leading quantum computing platforms; opportunities for deeper involvement by materials scientists abound. Ultimate realization of the full potential of quantum computers will require a multidisciplinary effort spanning many traditional areas of expertise.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Commutative Algebra Modeling in Materials Science – A Case Study on Metal–Organic Frameworks (MOFs)

Metal-organic frameworks (MOFs) are a class of important crystalline and highly porous materials whose hierarchical geometry and chemistry hinder interpretable predictions in materials properties. Commutative algebra is a branch of abstract algebra that has been rarely applied in data and material sciences. We introduce the first ever commutative algebra modeling and prediction in materials science. Specifically, category-specific commutative algebra (CSCA) is proposed as a new framework for MOF representation and learning. It integrates element-based categorization with multiscale algebraic invariants to encode both local coordination motifs and global network organization of MOFs. These algebraically consistent, chemically aware representations enable compact, interpretable, and data efficient modeling of MOF properties such as Henry’s constants and uptake capacities for common gases. Compared to traditional geometric and graph-based approaches, CSCA achieves comparable or superior predictive accuracy while substantially improving interpretability and stability across data sets. By aligning commutative algebra with the chemical hierarchy, the CSCA establishes a rigorous and generalizable paradigm for understanding structure and property relationships in porous materials and provides a nonlinear algebra-based framework for data-driven material discovery.

Khaemba, Caleb S.↗
DOE OSTI2019

Unsupervised word embeddings capture latent knowledge from materials science literature

The overwhelming majority of scientific knowledge is published as text, which is difficult to analyse by either traditional statistical analysis or modern machine learning methods. By contrast, the main source of machine-interpretable data for the materials research community has come from structured property databases, which encompass only a small fraction of the knowledge present in the research literature. Beyond property values, publications contain valuable knowledge regarding the connections and relationships between data items as interpreted by the authors. To improve the identification and use of this knowledge, several studies have focused on the retrieval of information from scientific literature using supervised natural language processing, which requires large hand-labelled datasets for training. Here we show that materials science knowledge present in the published literature can be efficiently encoded as information-dense word embeddings (vector representations of words) without human labelling or supervision. Without any explicit insertion of chemical knowledge, these embeddings capture complex materials science concepts such as the underlying structure of the periodic table and structure-property relationships in materials. Furthermore, we demonstrate that an unsupervised method can recommend materials for functional applications several years before their discovery. This suggests that latent knowledge regarding future discoveries is to a large extent embedded in past publications. Lastly, our findings highlight the possibility of extracting knowledge and relationships from the massive body of scientific literature in a collective manner, and point towards a generalized approach to the mining of scientific literature.

36 MATERIALS SCIENCE↗
NASA NTRS1988

Applications of synchrotron radiation to materials science: Diffraction imaging (topography) and microradiography

Synchrotron radiation sources are now available throughout the world. The use of hard X-ray radiation from these sources for materials science is described with emphasis on diffraction imaging for material characterization. With the availability of synchrotron radiation, real-time in situ measurements of dynamic microstructural phenomena have been started. This is a new area where traditional application of X-rays has been superseded. Examples are chosen from limited areas and are by no means exhaustive. The new emerging information will, no doubt, have great impact on materials science and engineering.

Kuriyama, Masao↗
DOE OSTI2016

A Statistical Learning Framework for Materials Science: Application to Elastic Moduli of k -nary Inorganic Polycrystalline Compounds

Materials scientists increasingly employ machine or statistical learning (SL) techniques to accelerate materials discovery and design. Such pursuits benefit from pooling training data across, and thus being able to generalize predictions over, k -nary compounds of diverse chemistries and structures. This work presents a SL framework that addresses challenges in materials science applications, where datasets are diverse but of modest size, and extreme values are often of interest. Our advances include the application of power or Hölder means to construct descriptors that generalize over chemistry and crystal structure, and the incorporation of multivariate local regression within a gradient boosting framework. The approach is demonstrated by developing SL models to predict bulk and shear moduli (K and G, respectively) for polycrystalline inorganic compounds, using 1,940 compounds from a growing database of calculated elastic moduli for metals, semiconductors and insulators. The usefulness of the models is illustrated by screening for superhard materials.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Quantitative Scanning Transmission Electron Microscopy for Materials Science: Imaging, Diffraction, Spectroscopy, and Tomography

Scanning transmission electron microscopy (STEM) is one of the most powerful characterization tools in materials science research. Due to instrumentation developments such as highly coherent electron sources, aberration correctors, and direct electron detectors, STEM experiments can examine the structure and properties of materials at length scales of functional devices and materials down to single atoms. STEM encompasses a wide array of flexible operating modes, including imaging, diffraction, spectroscopy, and 3D tomography experiments. This review outlines many common STEM experimental methods with a focus on quantitative data analysis and simulation methods, especially those enabled by open source software. The hope is to introduce both classic and new experimental methods to materials scientists and summarize recent progress in STEM characterization. The review also discusses the strengths and weaknesses of the various STEM methodologies and briefly considers promising future directions for quantitative STEM research.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Application of nuclear-physics methods in space materials science

The brief history of the development of investigations at the Skobeltsyn Institute of Nuclear Physics, Moscow State University (SINP MSU) in the field of space materials science is outlined. A generalized scheme of a numerical simulation of the radiation impact on spacecraft materials and elements of spacecraft equipment is examined. The results obtained by solving some of the most important problems that modern space materials science should address in studying nuclear processes, the interaction of charged particles with matter, particle detection, the protection from ionizing radiation, and the impact of particles on nanostructures and nanomaterials are presented.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1993

Materials science on parabolic aircraft: The FY 1987-1989 KC-135 microgravity test program

This document covers research results from the KC-135 Materials Science Program managed by MSFC for the period FY87 through FY89. It follows the previous NASA Technical Memorandum for FY84-86 published in August 1988. This volume contains over 30 reports grouped into eight subject areas covering acceleration levels, space flight hardware, transport and interfacial studies, thermodynamics, containerless processing, welding, melt/crucible interactions, and directional solidification. The KC-135 materials science experiments during FY87-89 accomplished direct science, preparation for space flight experiments, and justification for new experiments in orbit.

Curreri, Peter A.↗
NASA NTRS1988

Preparation for microgravity: The role of the microgravity materials science laboratory

A laboratory dedicated to ground based materials processing in preparation for space flight was established at the NASA Lewis Research Center. Experiments are performed to delineate the effects of gravity on processes of both scientific and commercial interest. Processes are modeled physically and mathematically. Transport model systems are used where possible to visually track convection, settling, crystal growth, phase separation, agglomeration, vapor transport, diffusive flow, and polymers reactions. The laboratory contains apparatus which functionally duplicates apparatus available for flight experiments and other pieces instrumented specifically to allow process characterization. Materials addressed include metals, alloys, salts, glasses, ceramics, and polymers. The Microgravity Materials Science Laboratory is staffed by engineers and technicians from a variety of disciplines and is open to users from industry and academia as well as the government. Examples will be given of the laboratory apparatus typical experiments and results.

Johnston, J. Christopher↗
DOE OSTI2025

Seeing is Believing: Autonomous Microscopy and the Data Revolution in Materials Science

This presentation explores the transformative potential of autonomous electron microscopy and artificial intelligence (AI) in accelerating materials science discovery, particularly for energy applications and materials operating in extreme environments. We discuss pioneering self-driving laboratories at NREL designed to intelligently probe material synthesis and degradation across multiple scales, aiming to rapidly bridge the gap between atomic-level understanding and the development of high-performance, reliable materials. Utilizing advanced machine learning techniques, such as few-shot learning and multimodal analysis integrating imaging and spectroscopy, we demonstrate methods to extract actionable descriptors for material behavior, quantify complex microstructural evolution, and statistically link synthesis parameters to defect populations. This AI-driven approach promises to accelerate the creation of predictive materials tailored for specific missions, enabling faster development cycles and enhanced material assurance.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Fault-tolerant operation and materials science with neutral atom logical qubits

We report on the fault-tolerant operation of logical qubits on a neutral atom quantum computer, with logical performance surpassing physical performance for multiple circuits including Bell state preparation (12x error reduction), random circuits (15x), and a prototype Anderson Impurity Model ground state solver for materials science applications (up to 6x, non-fault-tolerantly). The logical qubits are implemented via the [[4, 2, 2]] code (C 4 ). Our work constitutes the first complete realization of the benchmarking protocol proposed by Gottesman 2016 demonstrating results consistent with fault tolerance. In light of recent advances on applying concatenated C 4 /C 6 detection codes to achieve error correction with high code rates and thresholds, our work can be regarded as a building block towards a practical scheme for fault tolerant quantum computation. Our demonstration of a materials science application with logical qubits particularly demonstrates the immediate value of these techniques on current experiments.

36 MATERIALS SCIENCE↗