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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,171 records · Page 65
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Towards physics-informed explainable machine learning and causal models for materials research

From emergent material descriptions to estimation of properties stemming from structures to optimization of process parameters for achieving best performance – all key facets of materials science and related fields have experienced tremendous growth with the introduction of data-driven models. This gradual progression goes at par with developments of machine learning workflows, from purely data-driven shallow models to those that are well-capable in encoding more complex graphs, symbolic representations, invariances, and positional embeddings. Furthermore, this perspective aims at summarizing strategic aspects of such transitions while providing insights into the requirements of bringing in explainable, interpretable predictive models, and causal learning to aid in materials design and discovery. Although the focus remains on a variety of functional materials by providing a handful of case studies, the applications of such integrated methodologies are universal to facilitate fundamental understandings of materials physics while enabling autonomous experiments.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Reproducibility in materials informatics: lessons from ‘A general-purpose machine learning framework for predicting properties of inorganic materials’

The integration of machine learning techniques in materials discovery has become prominent in materials science research and has been accompanied by an increasing trend towards open data and open-source tools to propel the field. Despite the increasing usefulness and capabilities of these tools, developers neglecting to follow reproducible practices presents a significant barrier for other researchers looking to use or build upon their work. In this study, we investigate the challenges encountered while attempting to reproduce a section of the results presented in “A general-purpose machine learning framework for predicting properties of inorganic materials.” Our analysis identifies four major categories of challenges: (1) reporting software dependencies, (2) recording and sharing version logs, (3) sequential code organization, and (4) clarifying code references within the manuscript. The result is a proposed set of tangible action items for those aiming to make material informatics tools accessible to, and useful for the community.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Mesoscale informed parameter estimation through machine learning: A case-study in fracture modeling

Scale bridging is a critical need in computational sciences, where the modeling community has developed accurate physics models from first principles, of processes at lower length and time scales that influence the behavior at the higher scales of interest. However, it is not computationally feasible to incorporate all of the lower length scale physics directly into upscaled models. This is an area where machine learning has shown promise in building emulators of the lower length scale models, which incur a mere fraction of the computational cost of the original higher fidelity models. We demonstrate the use of machine learning using an example in materials science estimating continuum scale parameters by emulating, with uncertainties, complicated mesoscale physics. Additionally, we describe a new framework to emulate the fine scale physics, especially in the presence of microstructures, using machine learning, and showcase its usefulness by providing an example from modeling fracture propagation. Our approach can be thought of as a data-driven dimension reduction technique that yields probabilistic emulators. Our results show well-calibrated predictions for the quantities of interests in a low-strain simulation of fracture propagation at the mesoscale level. Furthermore, on average, we achieve ~10% relative errors on time-varying quantities like total damage and maximum stresses. Successfully replicating mesoscale scale physics within the continuum models is a crucial step towards predictive capability in multi-scale problems.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Synthesis of Ultra‐Incompressible and Recoverable Carbon Nitrides Featuring CN 4 Tetrahedra

Abstract Carbon nitrides featuring three‐dimensional frameworks of CN 4 tetrahedra are one of the great aspirations of materials science, expected to have a hardness greater than or comparable to diamond. After more than three decades of efforts to synthesize them, no unambiguous evidence of their existence has been delivered. Here, the high‐pressure high‐temperature synthesis of three carbon–nitrogen compounds,tI14‐C 3 N 4 ,hP126‐C 3 N 4 , andtI24‐CN 2 , in laser‐heated diamond anvil cells, is reported. Their structures are solved and refined using synchrotron single‐crystal X‐ray diffraction. Physical properties investigations show that these strongly covalently bonded materials, ultra‐incompressible and superhard, also possess high energy density, piezoelectric, and photoluminescence properties. The novel carbon nitrides are unique among high‐pressure materials, as being produced above 100 GPa they are recoverable in air at ambient conditions.

Chemistry↗
DOE OSTI2025

Atomically precise synthesis of oxides with hybrid molecular beam epitaxy

Advancements in synthesis science are revolutionizing the way we create atomically precise materials. Techniques like molecular beam epitaxy (MBE) have set the benchmark for addressing long-standing questions in materials science by leveraging improved control over the composition and structure of existing materials and enabling materials discovery. In this review, we discuss recent innovations in MBE that are redefining its capabilities, enabling the fabrication of ultra-pure, defect-engineered films and the stabilization of metastable phases that were previously unattainable. These advancements are unlocking new opportunities in electronic, magnetic, and quantum technologies, where the precise tuning of material properties is essential for advancing device functionality and performance.

complex oxides↗
DOE OSTI2025

Extracting Material Property Measurements from Scientific Literature with Limited Annotations

Extracting material property data from scientific text is pivotal for advancing data-driven research in chemistry and materials science; however, the extensive annotation effort required to produce training data for named entity recognition (NER) models for this task often makes it a barrier to extracting specialized data sets. Here, in this work, we present a comparative study of the conventional, supervised NER methodology to alternative few-shot learning architectures and large language model (LLM)-based approaches that mitigate the need to label large training data sets. We find that the best-performing LLM (GPT-4o) not only excels in directly extracting relevant material properties based on limited examples but also enhances supervised learning through data augmentation. We supplement our findings with error and data quality assessments to provide a nuanced understanding of factors that impact property measurement extraction.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Sulfurization Engineering of One–Step Low–Temperature MoS 2 and WS 2 Thin Films for Memristor Device Applications

2D materials have been of considerable interest as new materials for device applications. Non-volatile resistive switching applications of MoS 2 and WS 2 have been previously demonstrated; however, these applications are dramatically limited by high temperatures and extended times needed for the large-area synthesis of 2D materials on crystalline substrates. The experimental results demonstrate a one-step sulfurization method to synthesize MoS 2 and WS 2 at 550 °C in 15 min on sapphire wafers. Furthermore, a large area transfer of the synthesized thin films to SiO 2 /Si substrates is achieved. Following this, MoS 2 and WS 2 memristors are fabricated that exhibit stable non-volatile switching and a satisfactory large on/off current ratio (10 3 –10 5 ) with good uniformity. Tuning the sulfurization parameters (temperature and metal precursor thickness) is found to be a straightforward and effective strategy to improve the performance of the memristors. Furthermore, the demonstration of large-scale MoS 2 and WS 2 memristors with a one-step low-temperature sulfurization method with simple strategy to tuning can lead to potential applications such as flexible memory and neuromorphic computing.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Defect engineering of porous aromatic frameworks via end capping improves dioxane removal from water

Amorphous porous organic polymers show promise for energy-efficient adsorptive separations, but it is difficult to understand or improve their performance through intentional structural modification. Herein, we report the synthesis of porous aromatic frameworks (PAFs) with pore structures tailored by the incorporation of a monofunctionalized end-capping monomer that disrupts framework topology. Combining experimental characterization with molecular simulations, we show that this defect engineering strategy yields less densely crosslinked networks, which leads to pore collapse and the presence of unique adsorption sites. These defect-engineered PAFs exhibit enhanced removal of 1,4-dioxane, an important environmental pollutant, from water. Increasing the concentration of end-capping monomers produces PAFs with narrower pore size distributions and improved 1,4-dioxane uptake. These results illustrate that defect engineering can effectively modulate polymer connectivity and porosity for applications in selective adsorptive separations. In conclusion, this technique avoids post-synthetic treatments and presents another approach to tailor amorphous polymeric adsorbents.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Site-controlled telecom-wavelength single-photon emitters in atomically-thin MoTe2

Abstract Quantum emitters (QEs) in two-dimensional transition metal dichalcogenides (2D TMDCs) have advanced to the forefront of quantum communication and transduction research. To date, QEs capable of operating in O-C telecommunication bands have not been demonstrated in TMDCs. Here we report site-controlled creation of telecom QEs emitting over the 1080 to 1550 nm telecommunication wavelength range via coupling of 2D molybdenum ditelluride (MoTe 2 ) to strain inducing nano-pillar arrays. Hanbury Brown and Twiss experiments conducted at 10 K reveal clear photon antibunching with 90% single-photon purity. The photon antibunching can be observed up to liquid nitrogen temperature (77 K). Polarization analysis further reveals that while some QEs display cross-linearly polarized doublets with ~1 meV splitting resulting from the strain induced anisotropic exchange interaction, valley degeneracy is preserved in other QEs. Valley Zeeman splitting as well as restoring of valley symmetry in cross-polarized doublets are observed under 8 T magnetic field.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Optical Forces on an Oscillating Dipole Near VO2 Phase Transition

We investigate optical forces on oscillating dipoles close to a phase change vanadium dioxide (VO2) film, which exhibits a metal-insulator transition around 340 K and low thermal hysteresis. This configuration emulates the interaction between an illuminated nanosphere and an interface and we employ a classical description to capture its important aspects. We consider both electric and magnetic dipoles for two different configurations, namely with the dipole moments parallel and perpendicular to the VO2 film. By using Bruggeman theory to describe the effective optical response of the material, we show that the thermal hysteresis present in the VO2 transition clearly shows up in the behavior of optical forces. In the near-field regime, the force on both dipoles can change from attractive to repulsive just by heating (or cooling) the film for a selected frequency range. We also verified that the optical forces are comparable to the Casimir-Polder force in a similar system, revealing the possibility of modulating or even changing the sign of the resultant force on an illuminated nano-object due to the presence of a thermochromic material. We hope that this work contributes to set the grounds for alternative approaches to control light-matter interactions using phase-change materials.

36 MATERIALS SCIENCE↗
DOE OSTI2018

High strength films from oriented, hydrogen-bonded “graphamid” 2D polymer molecular ensembles

The linear polymer poly(p-phenylene terephthalamide), better known by its tradename Kevlar, is an icon of modern materials science due to its remarkable strength, stiffness, and environmental resistance. Here, we propose a new two-dimensional (2D) polymer, “graphamid”, that closely resembles Kevlar in chemical structure, but is mechanically advantaged by virtue of its 2D structure. Using atomistic calculations, we show that graphamid comprises covalently-bonded sheets bridged by a high population of strong intermolecular hydrogen bonds. Molecular and micromechanical calculations predict that these strong intermolecular interactions allow stiff, high strength (6–8GPa), and tough films from ensembles of finite graphamid molecules. In contrast, traditional 2D materials like graphene have weak intermolecular interactions, leading to ensembles of low strength (0.1–0.5GPa) and brittle fracture behavior. These results suggest that hydrogen-bonded 2D polymers like graphamid would be transformative in enabling scalable, lightweight, high performance polymer films of unprecedented mechanical performance.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Modeling Mesoscale Processes of Scalable Synthesis Final Scientific/Technical Report

This is the final scientific and technical report for this award. Molecular dynamics (MD) is a powerful tool to study biomolecules at the nanoscale. One of its main limitations however is that such simulations typically require a timestep of 1 femtosecond while reactions and conformational changes typically happen on the time scale of 1 microsecond or more. This discrepancy severely restricts the impact of this method. We have developed a series of algorithms to mitigate this problem and allow predicting long-time scale events using much shorter simulations. The method is based on the concurrent adaptive sampling method. It consists in using probabilistic weights for trajectories and biasing the sampling such that rare but important trajectories, e.g., those that go over energy barriers and lead to relevant conformational changes in the molecule, are observed with much higher frequency that in a standard MD simulation. We will demonstrate below that this approach has been very successful. In collaboration with Jay Grate at PNNL we have applied this novel method to the study the triazine polymers, which can be con gured using different types of side chains in order to adopt different 3-dimensional structures. We have found novel stable con- formations and have analyzed the impact of side chain modifcations on the native folded shape of the polymer. Direct MD simulations would have been untractable for this problem. The method of replica exchange was also used successfully to calculate free energy profiles, in particular for cis-trans torsional angle changes with high energy barriers. Studying molecules at the atomistic scale is essential to design new molecules in chemical engineering, such as the triazine polymers developed by Grate, in material science to study the strength of metals for examples (e.g., dislocation dynamics), or in biochemistry to understand how proteins fold and how they interact with ligands (a key step in designing drugs). Although new hardware makes these simulations increasingly faster (e.g., GPU, Anton from DE Shaw Research), new algorithms are key to making these methods more tractable and increase their impact. At this point, because of limitations in modeling, one of the main techniques for advancing science remains to a large extent numerous and costly experiments. For exam- ple, when designing drug, where multiple variants must be screened, the ability to run tests on a computer rather than experimentally testing with high throughput methods thousands of compounds would represent a signi cant advance with broad benefits for the public at large.

36 MATERIALS SCIENCE↗
DOE OSTI2019

An Algorithm to Generate Synthetic 3D Microstructures from 2D Exemplars

The inverse problem of constructing 3D microstructures from 2D data is an area of active research within the materials science community. This paper presents the implementation of a robust, computationally efficient algorithm, Hierarchical Algorithm for the Reconstruction of Exemplars (HARE), written in Python to reconstruct 3D features in a given microstructure from up to three orthogonal 2D exemplars using nearest neighbor matching to reproduce feature qualities, such as shape, size, and distribution. HARE’s feature sampling implements histogram reweighting to avoid both over- and undersampling. A neighborhood voting scheme allows each pixel to provisionally affect its neighbors according to its weight. Finally, the algorithm is presently configured for two-phase materials and is being extended to accommodate multiple phases. HARE is a convenient and robust base from which to generate statistically representative synthetic microstructures for use in multi-scale modeling or machine learning applications to support advanced manufacturing and materials discovery.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Nonlinear increase of X-ray intensities from thin foils irradiated with a 200 TW femtosecond laser

We report, for the first time, that the energy of femtosecond optical laser pulses, E, with relativistic intensities I > 10 21 W/cm 2 is efficiently converted to X-ray radiation, which is emitted by “hot” electron component in collision-less processes and heats the solid density plasma periphery. As shown by direct high-resolution spectroscopic measurements X-ray radiation from plasma periphery exhibits unusual non-linear growth ~E 4–5 of its power. The non-linear power growth occurs far earlier than the known regime when the radiation reaction dominates particle motion (RDR). Nevertheless, the radiation is shown to dominate the kinetics of the plasma periphery, changing in this regime (now labeled RDKR) the physical picture of the laser plasma interaction. Although in the experiments reported here we demonstrated by observation of KK hollow ions that X-ray intensities in the keV range exceeds ~10 17 W/cm 2 , there is no theoretical limit of the radiation power. Therefore, such powerful X-ray sources can produce and probe exotic material states with high densities and multiple inner-shell electron excitations even for higher Z elements. As a result, femtosecond laser-produced plasmas may thus provide unique ultra-bright X-ray sources, for future studies of matter in extreme conditions, material science studies, and radiography of biological systems.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Metamorphic Epitaxy for Multijunction Solar Cells

Multijunction solar cells have proven to be capable of extremely high efficiencies by combining multiple semiconductor materials with bandgaps tuned to the solar spectrum. Reaching the optimum set of semiconductors often requires combining high-quality materials with different lattice constants into a single device, a challenge particularly suited for metamorphic epitaxy. In this article, we describe different approaches to metamorphic multijunction solar cells, including traditional upright metamorphic, state-of-the-art inverted metamorphic, and forward-looking multijunction designs on silicon. We also describe the underlying materials science of graded buffers that enables metamorphic subcells with low dislocation densities. Following nearly two decades of research, recent efforts have demonstrated high-quality lattice-mismatched multijunction solar cells with very little performance loss related to the mismatch, enabling solar-to-electric conversion efficiencies over 45%.

14 SOLAR ENERGY↗
DOE OSTI2026

Development of PNNL’s Plutonium Metallography Capability

The goal of this project was to develop plutonium metallography capabilities and establish workflows for characterization at PNNL’s Radiological Processing Facility (RPL). Developing and expanding on this capability opens more opportunities for PNNL to better support science through collaborations with other labs, plants, and sites withing the Department of Energy Complex and through programs within the National Nuclear Security Administration. This project established metallography equipment in an air glovebox, polishing protocols, as well as radiological and facility controls and procedures so that gram quantities of plutonium metal could be analyzed on multiple characterization tools. Data was collected and analyzed on two different delta phase plutonium - Gallium samples and a calciothermically reduced alpha plutonium metal using a combination of optical and electron microscopy, powder X-ray diffraction, and atom probe tomography. This report highlights the metallography capabilities, some preliminary data collections, and plans at PNNL to support plutonium material science.

36 MATERIALS SCIENCE↗