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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

Navigating Large Chemical Spaces Using Graph Theory and Integer Programming

Navigating and analyzing large chemical spaces are necessary to accelerate the design and discovery of new molecules and chemical processes. In this work, we introduce a computational framework that integrates graph theory and integer programming to enable the efficient navigation of large chemical spaces. Our framework represents the chemical space as a graph, wherein nodes represent molecules and edges represent the degree of similarity or connectivity based on domain-specific information. Using the graph representation, we identify representative molecules by computing the so-called minimum dominating set (MDS), which in our context is the minimum set of molecules that is connected to all other molecules. We present a suite of solution strategies for the MDS problem including heuristic and rigorous integer programming (IP) approaches. We show that these approaches allow us to capture physicochemical properties and domain-specific logic and constraints, facilitating the identification of molecules with the target properties. We demonstrate the effectiveness of the proposed approach by navigating the chemical space of per- and polyfluoroalkyl substances (PFAS); this comprises approximately 15,000 molecular structures. We compare our framework against traditional dimensionality reduction and clustering methods such as t-SNE and K-means clustering.

Chemical structure↗
DOE OSTI2025

Charting the chemical space of Zintl phases with graph neural networks and bonding insights

A large number of Zintl phases have been discovered by solid-state chemists driven by empirical knowledge, chemical intuition and in some cases, through serendipitous accidents. These discoveries have only scratched the surface, given the vast compositional and structural diversity that Zintl phases can accommodate. The large chemical space of Zintl phases, as well as intermetallic compounds in general, remain under-explored. Here, we use graph neural networks and the upper bound energy minimization approach to efficiently scan a large chemical space of >90 000 hypothetical Zintl phases and accurately discover 1810 new thermodynamically stable phases with 90% precision, as validated with first-principles calculations. We show that our approach is more than 2× more accurate in predicting DFT stability than M3GNet (40% precision) on the same dataset. Using a random forest model and SHAP analysis, we demonstrate the critical role of ionic bonding in the thermodynamic stability of Zintl phases. Our results not only expand the known chemical landscape of Zintl phases but also highlight the efficacy of machine learning frameworks combined with domain knowledge in uncovering chemically meaningful insights across complex intermetallics.

36 MATERIALS SCIENCE↗
NASA NTRS2004

In-Space Chemical Propulsion System Model

Multiple, new technologies for chemical systems are becoming available and include high temperature rockets, very light propellant tanks and structures, new bipropellant and monopropellant options, lower mass propellant control components, and zero boil off subsystems. Such technologies offer promise of increasing the performance of in-space chemical propulsion for energetic space missions. A mass model for pressure-fed, Earth and space-storable, advanced chemical propulsion systems (ACPS) was developed in support of the NASA MSFC In-Space Propulsion Program. Data from flight systems and studies defined baseline system architectures and subsystems and analyses were formulated for parametric scaling relationships for all ACPS subsystems. The paper will first provide summary descriptions of the approaches used for the systems and the subsystems and then present selected analyses to illustrate use of the model for missions with characteristics of current interest.

Byers, David C.↗
NASA NTRS2004

In-Space Chemical Propulsion System Model

Multiple, new technologies for chemical systems are becoming available and include high temperature rockets, very light propellant tanks and structures, new bipropellant and monopropellant options, lower mass propellant control components, and zero boil off subsystems. Such technologies offer promise of increasing the performance of in-space chemical propulsion for energetic space missions. A mass model for pressure-fed, Earth and space-storable, advanced chemical propulsion systems (ACPS) was developed in support of the NASA MSFC In-Space Propulsion Program. Data from flight systems and studies defined baseline system architectures and subsystems and analyses were formulated for parametric scaling relationships for all ACPS subsystem. The paper will first provide summary descriptions of the approaches used for the systems and the subsystems and then present selected analyses to illustrate use of the model for missions with characteristics of current interest.

Byers, David C.↗
NASA NTRS1967

Non-chemical space propulsion.

Replacement of chemical launching rockets by advanced propulsion system, with particular reference to nuclear rocket

NUCLEAR ROCKET↗
NASA NTRS1993

Space Chemical Propulsion Test Facilities at NASA Lewis Research Center

The NASA Lewis Research Center, located in Cleveland, Ohio, has a number of space chemical propulsion test facilities which constitute a significant national space testing resource. The purpose of this paper is to make more users aware of these test facilities and to encourage their use through cooperative agreements between the government, industry, and universities. Research which is of interest to the government is especially encouraged and often can be done in a cooperative manner that best uses the resources of all parties. An overview of the Lewis test facilities is presented.

Urasek, Donald C.↗
NASA NTRS1993

Space chemical propulsion test facilities at NASA Lewis Research Center

The NASA Lewis Research Center, located in Cleveland, Ohio has a number of space chemical propulsion test facilities which constitute a significant national space testing resource. The purpose of this paper is to make more users aware of these test facilities and to encourage their use through cooperative agreements between the government, industry, and universities. Research which is of interest to the government is especiallly encouraged and often can be done in a cooperative manner that best uses the resources of all parties. This paper presents an overview of the Lewis test facilities. These facilities are clustered into three test areas: the Rocket Engine Test Facilities (RETF), the Rocket Laboratory (RL), and the Cryogenic Components Laboratory (CCL).

Urasek, Donald C.↗
DOE OSTI2026

SmileyLlama: modifying large language models for directed chemical space exploration

Here we show that large language models (LLMs) can be transformed via supervised fine-tuning of engineered prompts into SmileyLlama for exploring the chemical space of drug molecules. We benchmark SmileyLlama against pretrained LLMs and chemical language models trained from scratch for generating valid and novel drug-like molecules, and use direct preference optimization to both improve SmileyLlama’s adherence to a prompt and as part of the iMiner reinforcement learning framework to predict molecules with optimized three-dimensional conformations and high binding affinity to drug targets. By training an LLM to speak directly as a chemical language model, while retaining most of its natural language capabilities, we show that SmileyLlama can reliably generate molecules with user-specified properties rather than acting only as a chatbot with knowledge of chemistry or as a virtual assistant. While SmileyLlama is geared toward drug discovery, the supervised fine-tuning/direct preference optimization/LLM framework can be extended to other chemical, biological and materials applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2017

In-Space Chemical Propulsion Systems Roadmap

In-space propulsion begins where the launch vehicle upper stage leaves off, performing the functions of primary propulsion, reaction control, station keeping, precision pointing, and orbital maneuvering. The main engines used in space provide the primary propulsive force for orbit transfer, planetary trajectories and extra planetary landing and ascent. The reaction control and orbital maneuvering systems provide the propulsive force for orbit maintenance, position control, station keeping, and spacecraft attitude control. Advanced in-space propulsion technologies will enable much more effective exploration of our Solar System and will permit mission designers to plan missions to "fly anytime, anywhere, and complete a host of science objectives at the destinations" with greater reliability and safety. With wide range of possible missions and candidate propulsion technologies, the question of which technologies are "best" for future missions is a difficult one. A portfolio of propulsion technologies should be developed to provide optimum solutions for a diverse set of missions and destinations. A large fraction of the rocket engines in use today are chemical rockets; that is, they obtain the energy needed to generate thrust by chemical reactions to create a hot gas that is expanded to produce thrust. A significant limitation of chemical propulsion is that it has a relatively low specific impulse (Is, or thrust per mass flow rate of propellant). A significant improvement (>30%) in Is can be obtained by using cryogenic propellants, such as liquid oxygen and liquid hydrogen, for example. Historically, these propellants have not been applied beyond upper stages.

Oxidizers↗
DOE OSTI2025

Nuclear quantum effects in molecular liquids across chemical space

Abstract Nuclear quantum effects (NQEs) influence many physical and chemical phenomena, particularly those involving light atoms or occurring at low temperatures. However, their impact has been carefully quantified in few systems-like water-and is rarely considered more broadly. Here we use path-integral molecular dynamics to systematically investigate NQEs on thermophysical properties of 92 organic liquids at ambient conditions. Depending on chemical constitution, we find substantial impact across thermal expansivity, compressibility, dielectric constant, enthalpy of vaporization, and notably molar volume, which shows consistent, positive quantum-classical differences up to 5%; similar, less pronounced trends manifest as isotope effects from deuteration. Using data-driven analysis, we identify three features-molar mass, classical hydrogen density, and classical thermal expansivity-that accurately predict NQEs and facilitate understanding of how characteristics like branching and heteroatom content influence behavior. This work highlights the broad relevance of NQEs in molecular liquids, while also providing a conceptual and practical framework to anticipate their impact.

Science & Technology - Other Topics↗
DOE OSTI2024

Stochastic machine learning via sigma profiles to build a digital chemical space

This work establishes a different paradigm on digital molecular spaces and their efficient navigation by exploiting sigma profiles. To do so, the remarkable capability of Gaussian processes (GPs), a type of stochastic machine learning model, to correlate and predict physicochemical properties from sigma profiles is demonstrated, outperforming state-of-the-art neural networks previously published. The amount of chemical information encoded in sigma profiles eases the learning burden of machine learning models, permitting the training of GPs on small datasets which, due to their negligible computational cost and ease of implementation, are ideal models to be combined with optimization tools such as gradient search or Bayesian optimization (BO). Gradient search is used to efficiently navigate the sigma profile digital space, quickly converging to local extrema of target physicochemical properties. While this requires the availability of pretrained GP models on existing datasets, such limitations are eliminated with the implementation of BO, which can find global extrema with a limited number of iterations. A remarkable example of this is that of BO toward boiling temperature optimization. Holding no knowledge of chemistry except for the sigma profile and boiling temperature of carbon monoxide (the worst possible initial guess), BO finds the global maximum of the available boiling temperature dataset (over 1,000 molecules encompassing more than 40 families of organic and inorganic compounds) in just 15 iterations (i.e., 15 property measurements), cementing sigma profiles as a powerful digital chemical space for molecular optimization and discovery, particularly when little to no experimental data is initially available.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1991

Space chemical engines technology

The topics presented are covered in viewgraph form. The programmatic objective is to provide the technology necessary to proceed in the late 1990's with development of moderate-thrust LOX/LH2 expander cycle engines for various space transportation applications.

Berkopec, Frank D.↗