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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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9 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Materials Data on TiSi by Materials Project

TiSi is Parent of FeAs superconductors-derived structured and crystallizes in the orthorhombic Amm2 space group. The structure is three-dimensional. Ti4+ is bonded in a 8-coordinate geometry to eight equivalent Si4- atoms. There are a spread of Ti–Si bond distances ranging from 2.63–2.88 Å. Si4- is bonded to eight equivalent Ti4+ and four equivalent Si4- atoms to form a mixture of distorted corner, edge, and face-sharing SiTi8Si4 cuboctahedra. All Si–Si bond lengths are 2.79 Å.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Materials Data on TiSi by Materials Project

TiSi crystallizes in the orthorhombic Pnma space group. The structure is three-dimensional. Ti4+ is bonded in a 7-coordinate geometry to seven equivalent Si4- atoms. There are a spread of Ti–Si bond distances ranging from 2.56–2.78 Å. Si4- is bonded in a 9-coordinate geometry to seven equivalent Ti4+ and two equivalent Si4- atoms. Both Si–Si bond lengths are 2.41 Å.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Materials Data on TiSi by Materials Project

TiSi crystallizes in the orthorhombic Amm2 space group. The structure is three-dimensional. Ti4+ is bonded in a 8-coordinate geometry to eight equivalent Si4- atoms. There are a spread of Ti–Si bond distances ranging from 2.62–2.90 Å. Si4- is bonded to eight equivalent Ti4+ and four equivalent Si4- atoms to form a mixture of distorted edge, face, and corner-sharing SiTi8Si4 cuboctahedra. All Si–Si bond lengths are 2.80 Å.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Evolution of intermetallic phases in an Al–Si–Ti alloy during solution treatment

A cast Al–Si–Ti alloy was solution treated at 540 °C for different periods between 0 and 72 h to understand the evolution of intermetallic phases. Only an (Al,Si) 3 Ti intermetallic phase with a low Si content was found in the as-cast alloy. The (Al,Si) 3 Ti particles were converted partly into a lamellar structure, a eutectoid phase, consisting of a Si-rich phase and an Al phase during solution treatment. The amount of the lamellar structure increased with the solution treatment time, but the composition of either constitute was kept almost unchanged regardless of the solution treatment times. The lamellar Si-rich phase is (Al,Si) 2 Ti (τ 2 ) with a TiSi 2 (C49-type) structure based on thermodynamic calculations and high-resolution TEM analyses. FCC Al phase is the product residing between the τ 2 lamellae after the completion of transformation from (Al,Si) 3 Ti to τ 2 phase. A near-rational orientation relationship (OR) between the Al and τ 2 phases is determined as Al [110]//τ 2 [100], Al ()//τ 2 [060]. The phase transformation from (Al,Si) 3 Ti to τ 2 being a result of the diffusion of Ti and Si within the original (Al,Si) 3 Ti particulates as well as the Si diffusion from the Al matrix during solution treatment is proposed. The formation of the lamellar structure in the microstructure is attributed mainly to the limited diffusivity of Ti element.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Viscosity in water from first-principles and deep-neural-network simulations

Abstract We report on an extensive study of the viscosity of liquid water at near-ambient conditions, performed within the Green-Kubo theory of linear response and equilibrium ab initio molecular dynamics (AIMD), based on density-functional theory (DFT). In order to cope with the long simulation times necessary to achieve an acceptable statistical accuracy, our ab initio approach is enhanced with deep-neural-network potentials (NNP). This approach is first validated against AIMD results, obtained by using the Perdew–Burke–Ernzerhof (PBE) exchange-correlation functional and paying careful attention to crucial, yet often overlooked, aspects of the statistical data analysis. Then, we train a second NNP to a dataset generated from the Strongly Constrained and Appropriately Normed (SCAN) functional. Once the error resulting from the imperfect prediction of the melting line is offset by referring the simulated temperature to the theoretical melting one, our SCAN predictions of the shear viscosity of water are in very good agreement with experiments.

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

DeePMD-kit v2: A software package for deep potential models

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features, such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, DP-range correction, DP long range, graphics processing unit support for customized operators, model compression, non-von Neumann molecular dynamics, and improved usability, including documentation, compiled binary packages, graphical user interfaces, and application programming interfaces. This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, this article presents a comprehensive procedure for conducting molecular dynamics as a representative application, benchmarks the accuracy and efficiency of different models, and discusses ongoing developments.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Heat transport in liquid water from first-principles and deep neural network simulations

In this work, we compute the thermal conductivity of water within linear response theory from equilibrium molecular dynamics simulations, by adopting two different approaches. In one, the potential energy surface (PES) is derived on the fly from the electronic ground state of density functional theory (DFT) and the corresponding analytical expression is used for the energy flux. In the other, the PES is represented by a deep neural network (DNN) trained on DFT data, whereby the PES has an explicit local decomposition and the energy flux takes a particularly simple expression. By virtue of a gauge invariance principle, established by Marcolongo, Umari, and Baroni, the two approaches should be equivalent if the PES were reproduced accurately by the DNN model. We test this hypothesis by calculating the thermal conductivity, at the GGA (PBE) level of theory, using the direct formulation and its DNN proxy, finding that both approaches yield the same conductivity, in excess of the experimental value by approximately 60%. Besides being numerically much more efficient than its direct DFT counterpart, the DNN scheme has the advantage of being easily applicable to more sophisticated DFT approximations, such as meta-GGA and hybrid functionals, for which it would be hard to derive analytically the expression of the energy flux. We find in this way that a DNN model, trained on meta-GGA (SCAN) data, reduces the deviation from experiment of the predicted thermal conductivity by about 50%, leaving the question open as to whether the residual error is due to deficiencies of the functional, to a neglect of nuclear quantum effects in the atomic dynamics, or, likely, to a combination of the two.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

SENTRA: A Modular Computational Graph Framework for Critical Mineral and Materials Supply Chains: Part I: Network Construction Latent-Quantity Estimation, and Temporal Graph Forecasting

Global supply chains for critical minerals and materials are complex, evolving networks of countries, products, production stages, and trade relationships. Existing analytical approaches are limited by fragmented data and static network representations that do not capture the dynamic production dependencies linking raw materials, intermediate products, and final goods across multiple countries. Trade and production statistics provide only a partial view of domestic production, inventories, and material flows, making it difficult to identify indirect sourcing pathways, hidden dependencies, and embedded foreign exposures. This paper introduces the Supply Chain Exposure Network Tracking and Risk Assessment (SENTRA) framework, a modular graph-based computational framework for constructing, analyzing, and forecasting dynamic supply chain networks. As the first paper in a three-part methodological series, it establishes the computational foundation of SENTRA by constructing a temporal attributed multi-relational graph whose nodes represent product–country pairs and whose edges encode observed trade and within-country value-chain relationships. Statistical estimation and constrained optimization recover latent production, final demand, and product input dependency coefficients while enforcing economic accounting constraints. Graph-derived exposure measures quantify direct, transshipment, value-chain, and multi-hop supply chain dependencies independently of the forecasting model. A temporal graph forecasting architecture based on a relational graph neural network then forecasts the evolution of the graph under mass-balance constraints with distribution-free conformal uncertainty quantification. Validation on the global aluminum supply chain shows that the learned graph representations recover economically meaningful supply chain structure, accurately forecast out-of-sample trade relationships, and produce well-calibrated prediction intervals. Subsequent papers apply this computational foundation to exposure assessment, disruption analysis, and scenario-based policy analysis, and extend the framework to multimaterial supply chain modeling and decision support.

36 MATERIALS SCIENCE↗
DOE OSTI2020

The Renewable Opportunity in Mining

Renewable energy can provide mine operators with solutions to several of the challenges they face today, including energy costs, fossil fuel volatility, social license to operate, and meeting environmental, societal, and governance goals. However, the integration of wind, solar, storage, and other renewable technologies presents several technical barriers that are generally not well understood by the mining industry. Many of these barriers will require further research, development, demonstration, and deployment (RDD&D) before we see higher levels of renewables adoption in the mining industry.

ENERGY PLANNING, POLICY, AND ECONOMY↗