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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Predicting Band-Gap of Inorganic Materials Using Neuromorphic Graph Learning

Predicting properties of inorganic materials is a heavily researched topic, with several new prediction approaches emerging as competitors. One such competitor is graph neural networks, which leverage the structure of the graph to aid in the prediction process. In this work, we propose integration of neuromorphic computation into the graph neural network pipeline. We call this approach Neuromorphic Graph Learning (NGL). We utilize the NGL approach to leverage evolutionary algorithms and a novel Spike Pipeline for Raster Analysis (SPIRE) for the prediction of band gap in inorganic materials.

Mulet, Ian [University of Tennessee (UT)]↗
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 OSTI2023

Predicting the synthesizability of crystalline inorganic materials from the data of known material compositions

Abstract Reliably identifying synthesizable inorganic crystalline materials is an unsolved challenge required for realizing autonomous materials discovery. In this work, we develop a deep learning synthesizability model ( SynthNN ) that leverages the entire space of synthesized inorganic chemical compositions. By reformulating material discovery as a synthesizability classification task, SynthNN identifies synthesizable materials with 7× higher precision than with DFT-calculated formation energies. In a head-to-head material discovery comparison against 20 expert material scientists, SynthNN outperforms all experts, achieves 1.5× higher precision and completes the task five orders of magnitude faster than the best human expert. Remarkably, without any prior chemical knowledge, our experiments indicate that SynthNN learns the chemical principles of charge-balancing, chemical family relationships and ionicity, and utilizes these principles to generate synthesizability predictions. The development of SynthNN will allow for synthesizability constraints to be seamlessly integrated into computational material screening workflows to increase their reliability for identifying synthetically accessible materials.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Dataset of solution-based inorganic materials synthesis procedures extracted from the scientific literature

The development of a materials synthesis route is usually based on heuristics and experience. A possible new approach would be to apply data-driven approaches to learn the patterns of synthesis from past experience and use them to predict the syntheses of novel materials. However, this route is impeded by the lack of a large-scale database of synthesis formulations. In this work, we applied advanced machine learning and natural language processing techniques to construct a dataset of 35,675 solution-based synthesis procedures extracted from the scientific literature. Each procedure contains essential synthesis information including the precursors and target materials, their quantities, and the synthesis actions and corresponding attributes. Every procedure is also augmented with the reaction formula. Through this work, we are making freely available the first large dataset of solution-based inorganic materials synthesis procedures.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Navigating phase diagram complexity to guide robotic inorganic materials synthesis

Abstract Efficient synthesis recipes are needed to streamline the manufacturing of complex materials and to accelerate the realization of theoretically predicted materials. Often, the solid-state synthesis of multicomponent oxides is impeded by undesired by-product phases, which can kinetically trap reactions in an incomplete non-equilibrium state. Here we report a thermodynamic strategy to navigate high-dimensional phase diagrams in search of precursors that circumvent low-energy, competing by-products, while maximizing the reaction energy to drive fast phase transformation kinetics. Using a robotic inorganic materials synthesis laboratory, we perform a large-scale experimental validation of our precursor selection principles. For a set of 35 target quaternary oxides, with chemistries representative of intercalation battery cathodes and solid-state electrolytes, our robot performs 224 reactions spanning 27 elements with 28 unique precursors, operated by 1 human experimentalist. Our predicted precursors frequently yield target materials with higher phase purity than traditional precursors. Robotic laboratories offer an exciting platform for data-driven experimental synthesis science, from which we can develop fundamental insights to guide both human and robotic chemists.

Chen, Jiadong (ORCID:0009000476038838)↗
DOE OSTI2026

Author Correction: An autonomous laboratory for the accelerated synthesis of inorganic materials

Following publication of this article, concerns were raised about the unambiguous identification of the compound structures using diffraction as well as the original claims of material novelty. We acknowledge that the original claims of material novelty were subject to misinterpretation—their intention was to indicate that the materials were new to the prediction platform, not necessarily new to science. The article text has been updated to reflect this in the HTML and PDF versions of the article.

Szymanski, Nathan J. [University of California, Be↗
DOE OSTI2022

Oil skimmer with oleophilic coating

A method of fabricating an coating includes providing a coating comprising a base material. The base material is coated with an inorganic material using at least one of an atomic layer deposition (ALD), a molecular layer deposition (MLD), or sequential infiltration synthesis (SIS) process. The SIS process includes at least one cycle of exposing the coating to a first metal precursor for a first predetermined time and a first partial pressure. The first metal precursor infiltrates at least a portion of the base material and binds with the base material. The coating is exposed to a second co-reactant precursor for a second predetermined time and a second partial pressure. The second co-reactant precursor reacts with the first metal precursor, thereby forming the inorganic material on the base material. The inorganic material infiltrating at least the portion of the base material. The inorganic material is functionalized with a material.

Darling, Seth B.↗
DOE OSTI2022

Strong, thermo-reversible salogels with boronate ester bonds as thermal energy storage materials

Inorganic salt hydrates are promising phase change materials (PCMs) but suffer from low viscosity at temperatures above their melting point resulting in leakage problems during thermal storage applications. To achieve shape stabilization of one type of molten inorganic PCM – calcium nitrate tetrahydrate (CNH) – this work explored gelation of polyvinyl alcohol (PVA) in this solvent and the effect of dynamic boronate ester bonds on salogel strength. The occurrence of gelation of PVA in molten CNH but not in water is rationalized by the extremely high salt content and scarcity of hydration water in CNH, enabling intermolecular hydrogen bonding between PVA chains. While neat PVA salogels in CNH were weak, with a gel-to-sol transition temperature (T gel ) below room temperature, the addition of small amounts of borax (<~0.3 wt%) introduced dynamic covalent crosslinks and yielded salogels with T gel tunable over a wide temperature range from 7 to 70 °C. Here, the PVA/borax salogels were about one order of magnitude stronger than their well-known PVA/borax hydrogel counterparts, and, unlike PVA/borax hydrogels, were capable of retaining their shape and preventing leakage of molten CNH. Moreover, the salogels exhibited reversible and repeatable temperature-triggered gel-to-sol transitions and the ability to self-heal. The low polymer and crosslinker concentration also ensured that more than 95% of the heat of fusion of neat CNH was maintained in the salogels and was retained after twenty cycles of melting and crystallization, demonstrating the robust nature of these energy storage materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Mechanical Properties of 2D LiInP 2 Se 6 : Implication for Semiconductor Applications

Metal phosphorus trichalcogenides (MPTCs) are emerging 2D semiconductor materials with unique functional properties that set them apart from other 2D systems. Despite the importance of their mechanical properties for improving the semiconductor device’s durability and performance, as well as for utilizing strain effects to customize material properties and create new functionality, our current understanding of MPTCs’ mechanical behavior is lacking and lags behind our knowledge of their other properties. Here, we use LiInP 2 Se 6 as a model example of MPTCs and report the first experimental measurements of the elastic and plastic (fracture) properties along both in-plane and out-of-plane directions by atomic force microscopy and nanoindentation. Being a 2D material that is entirely inorganic, LiInP 2 Se 6 surprisingly exhibits mechanical properties that resemble those of hybrid organic–inorganic materials rather than pure inorganic 2D materials. It has a soft crystal structure with low elastic moduli, a low difference in in-plane vs out-of-plane mechanical properties, and a combination of elastic and plastic characteristics of hybrid organic–inorganic materials. Furthermore, our work provides the mechanical information critically needed to mitigate and/or harness the strain effects in LiInP 2 Se 6 -based semiconductor devices and sheds light on the mechanical behaviors of MPTCs with indispensable insights.

2D semiconductor↗
DOE OSTI2025

Energy Exchange in Dynamic DNA-Metal Hybrid Materials (Final Technical Report)

This report details research results from the 1 year renewal of DE-SC0017270, Energy Exchange in Dynamic DNA-Metal Hybrid Materials, including the primary findings associated with this research. This work investigated interactions between dynamic DNA origami energy harvesting materials and inorganic materials responsive to electromagnetic fields with two primary areas of inquiry: cyclic energy conversion and mechanical energy storage and transfer. This work resulted in 5 publications, including 1 in revision, and 1 issued patent.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Filtration membranes

Polymeric membranes are modified via SIS to promote membrane resilience, prolong membrane lifetime, and mitigate fouling. Modified membranes include an inorganic material within an outer portion of the modified membrane and a polymeric core that remains unmodified by the inorganic material. The polymer may be removed leaving an inorganic material patterned from an initial unmodified polymeric membrane.

Source record↗
DOE OSTI2026

Aluminum Based Solvent-Free Organic–Inorganic Hybrid Materials

In emerging materials, molecular hybrids are especially promising, as they have molecular level mixing of the organic and inorganic components, producing homogeneous materials without interfaces that can deteriorate properties. However, the current methods of manufacturing molecular hybrids are based on solution processing, which is impractical for bulk materials such as may be used for optically clear radiation and electromagnetic shielding components or photonics. Here we examine molecular hybrids composed of aluminum isopropoxide (AIP) and epoxy resins aiming to understand the molecular scale chemistry and manufacturability of these hybrid materials. DSCmonitored cure revealed the ideal cure temperature for these materials is 160−170 °C and demonstrated that an AIP concentration of 16.7 wt % maximizes the extent of reaction. Kinetic analysis of the curing reaction showed the Sestak−Berggren autocatalytic model is effective at temperatures over 140 °C but the reaction has diffusion limitations at a temperature of 120 °C. Mechanical testing with custom resin molds revealed a decrease in properties of the bulk samples with increasing AIP content due to an increase in defects but further testing with nanoindentation demonstrated comparable or improved mechanical properties of AIP-epoxy hybrids compared to epoxy resin with a standard hardener. Ultimately, this work lays the foundation for hardener-free epoxy-aluminum inorganic/organic hybrids and presents opportunities to expand on properties for specific applications such as thermal conductivity, optical clarity, and dielectric constant.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Chapter 6: Thermoelectric Energy Harvesters and Applications

With recent progress in the field of wearable and bio-integrated devices, which are widely used for various applications, such as real-time health monitoring, point-of-care diagnostics, and biological actuators, the need for a continuous and reliable power source is increasing. As solid-state devices with no moving parts, thermoelectric energy generators provide a reliable solution for harnessing body heat and converting it into electricity on demand. However, traditional thermoelectric generators, comprising inorganic materials, are rigid and bulky, limiting their wide deployment. Nonconventional thermoelectric generators, composed of organic and hybrid thermoelectric materials, are flexible and lightweight. In this chapter, we will discuss the working principle of thermoelectric energy harvesting, including materials, devices, and applications. While we briefly touch upon the inorganic materials, we will focus primarily on the organic and hybrid materials, which are nontoxic, flexible, easy to manufacture, easy to scale, and readily available at low cost, providing a promising solution for lightweight and conformal thermoelectric power generation.

body heat harvesting↗