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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 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Performance of a Hybrid HVAC-Integrated Thermal Storage Device

Thermal equipment in buildings is a primary contributor to peak loads on the electrical grid. Thermal energy storage is a cost-effective strategy to decouple electric use from thermal loads, thus reducing grid peak costs for building owners. One method for storing thermal energy in a building is to integrate a phase change material (PCM) directly into the heating, ventilation, and air conditioning system. These systems often require additional glycol loops, pumps, valves, and heat exchangers to couple the storage to the cooling system and building space, which increases the complexity and cost. This work will discuss an alternate approach where the storage is added directly into the heat pump evaporator. A detailed two-dimensional finite difference heat transfer model of a PCM-refrigerant-glycol heat exchanger was developed to simulate the performance of this component. The fluid stream was discretized along the flow direction to capture changes in the fluid properties and local heat transfer rates, and the phase change material was discretized in both the x and y directions to capture the movement of the melt front. The model was used to understand the impact of different material and geometric properties on the charge and discharge characteristics of the device. Finally, a Ragone framework analogous to that used for electrochemical batteries was used to maximize the energy density and round-trip efficiency of the device while supplying loads appropriate for space cooling in buildings.

buildings↗
DOE OSTI2017

Aqueous and Template-Free Synthesis of Meso-Macroporous Polymers for Highly Selective Capture and Conversion of Carbon Dioxide

Meso–macroporous polymers possessing nitrogen functionality were innovatively synthesized through an aqueous and template-free route herein. In particular, the polymerization of 1-(4-vinylbenzyl)-1,3,5,7-tetraazaadamantan-1-ium chloride in aqueous solution under high temperatures induces the decomposition of the hexamethylenetetramine unit into ammonia and formaldehyde molecules, followed by the cross-linking of benzene rings through “resol chemistry”. During this process, extended meso–macroporous frameworks were constructed, and active nitrogen species were incorporated. Taking the advantage of the meso–macroporosity and nitrogen functionality, the synthesized polymers offer competitive CO 2 capacities (0.37–1.58 mmol g –1 at 0 °C and 0.15 bar) and outstanding CO 2 /N 2 selectivities (155–324 at 0 °C). Moreover, after complexed with metal ions, the synthesized polymers show excellent activity for catalyzing the cycloaddition of propylene oxide with CO2 (yield>98.5 %, turnover frequency: 612.9–761.1 h –1 ).

36 MATERIALS SCIENCE↗
DOE OSTI2016

Methodology for the structural design of single spoke accelerating cavities at Fermilab

Fermilab is planning to upgrade its accelerator complex to deliver a more powerful and intense proton-beam for neutrino experiments. In the framework of the so-called Proton Improvement Plan-II (PIP-II), we are designing and developing a cryomodule containing superconducting accelerating cavities, the Single Spoke Resonators of type 1 (SSR1). In this paper, we present the sequence of analysis and calculations performed for the structural de- sign of these cavities, using the rules of the American Society of Mechanical Engineers (ASME) Boiler and Pressure Vessel Code (BPVC). The lack of an accepted procedure for addressing the design, fabrication, and inspection of such unique pressure vessels makes the task demanding and challenging every time. Several factors such as exotic materials, unqualified brazing procedures, limited nondestructive examination, and the general R&D nature of these early generations of cavity design, conspire to make it impractical to obtain full compliance with all ASME BPVC requirements. However, the presented approach allowed us to validate the design of these new generation of single spoke cavities with values of maximum allowable working pressure that exceed the safety requirements. This set of rules could be used as a starting point for the structural design and development of similar objects.

42 ENGINEERING↗
DOE OSTI2022

Enhancing Risk Analysis of Accidental Release Using CFD Modeling and Machine Learning

This project report presents the results from the AU31 project that is entitled “Enhancing Risk Analysis of Accidental Release Using CFD Modeling and Machine Learning” performed between FY2021 and FY2022. The technical objective of this project is to enhance risk analysis of accidental release of radioactive materials and provide novel risk forecasting and capabilities for assessing unplanned hazardous chemical releases for the U.S. Department of Energy (DOE). The approach includes a cross-platform finite element analysis of airborne chemical release, machine learning (ML) for simulation and forecasting, and a big data approach to data management, transformation and analysis capable of handling petabyte-scale unstructured and structured data. The outcome of these findings were assembled and accessible as a final output via an intuitive web hosted geospatial-based user interface for demonstration and general dissemination. These new results also provide a mechanism of combining this knowledge in a risk analysis framework tool. The geospatial analysis and output can utilize 5 years of qualified meteorological data to illustrate projected plume footprints that are generated from computational aspects of this study. Specifically, we show the development of a geospatial risk analysis capabilities to respond to and plan for accidental release of radioactive materials and to provide a risk forecasting tool for assessing hazardous chemical releases for the DOE. The focus of this effort is germane to common practices in risk analysis; and it is highly relevant to the timely and transparent release of information related to materials at risk including hazardous materials and chemical vapors and rapid assessment of the potential impacts on the workforce at active sites across the DOE complex.

42 ENGINEERING↗
DOE OSTI2012

Monte Carlo Simulation of Massive Absorbers for Cryogenic Calorimeters

There is a growing interest in cryogenic calorimeters with macroscopic absorbers for applications such as dark matter direct detection and rare event search experiments. The physics of energy transport in calorimeters with absorber masses exceeding several grams is made complex by the anisotropic nature of the absorber crystals as well as the changing mean free paths as phonons decay to progressively lower energies. We present a Monte Carlo model capable of simulating anisotropic phonon transport in cryogenic crystals. We have initiated the validation process and discuss the level of agreement between our simulation and experimental results reported in the literature, focusing on heat pulse propagation in germanium. The simulation framework is implemented using Geant4, a toolkit originally developed for high-energy physics Monte Carlo simulations. Geant4 has also been used for nuclear and accelerator physics, and applications in medical and space sciences. We believe that our current work may open up new avenues for applications in material science and condensed matter physics.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Regional surrogates for predictive control of digital twins

Digital twins of complex systems must involve a model that is fast, generalizable, and usable for real-time control. For example, high-fidelity nonlinear multiphysics simulations can capture laser-material interactions, but are too slow for optimization or model predictive control (MPC). Reduced-order models, used to accelerate such computation, frequently fail to generalize to unseen inputs or control states. We show theoretically that this failure is intrinsic, i.e., that a learned model is non-unique outside the sampled subspace when its low-rank structure arises from limited excitation and clustered eigenvalues, rather than from a user-imposed truncation alone. Motivated by this result, we propose a control-ready regional surrogate-construction framework for both autonomous and nonautonomous dynamics; it employs Koopman lifting to represent nonlinearities, while preserving spatial locality. We illustrate our approach by constructing a control-ready surrogate for the digital twin of a thermal component of additive-manufacturing process. Our surrogate, localized in space through a von Neumann stencil, is learned from noisy high-fidelity simulations that emulate thermal-camera images collected during the manufacturing. It is linear in thermo-physically augmented states so that MPC reduces to a convex quadratic program. The surrogate requires no online correction, generalizes to unseen scan paths and power profiles of the laser, and is more than three orders of magnitude faster than a finite-difference solver. Furthermore, when the MPC sequence computed on the digital twin is applied to this solver, closed-loop temperature regulation is recovered, showing that the surrogate preserves control-relevant input-output behavior.

Data-driven model↗
NASA NTRSDate not supplied

Convolution-Based Numerical Solutions of Transient Temperature Fields during Powder Bed Fusion Additive Manufacturing: Theory, Accuracy, and Computational Cost

Powder bed fusion (PBF) additive manufacturing has found numerous applications in the aerospace domain. However, components fabricated via PBF have a complex time-temperature history that significantly impacts subsequent mechanical performance. This study examines convolution-based numerical solutions of transient temperature fields that support simulations involving arbitrary beam shapes and paths during PBF. The convolutional approach is verified through comparisons with analytical solutions of the temperature field. The computational speed and accuracy of the method are assessed through comparisons with other explicit and implicit numerical techniques. In addition, the straightforward translation of the approach from a CPU to a GPU implementation and the resultant performance improvement are presented. The role of the technique in predicting microstructure evolution during PBF (for a greater process-structure-property-performance framework) is also demonstrated. This work supports the development of computational materials methods for understanding and controlling the time-temperature history during PBF.

powder bed fusion↗
DOE OSTI2017

Indirect detection of infinite-speed MAS solid-state NMR spectra

Heavy spin-1/2 nuclides are known to possess very large chemical shift anisotropies that can challenge even the most advanced magic-angle-spinning (MAS) techniques. Wide manifolds of overlapping spinning sidebands and insufficient excitation bandwidths often obfuscate meaningful spectral information and force the use of static, low-resolution solid-state (SS)NMR methods for the characterization of materials. In order to address these issues, we have merged fast-magic-angle-turning (MAT) and dipolar heteronuclear multiple-quantum coherence (D-HMQC) experiments to obtain D-HMQC-MAT pulse sequences which enable the rapid acquisition of 2D SSNMR spectra that correlate isotropic 1 H chemical shifts to the indirectly detected isotropic “infinite-MAS” spectra of heavy spin-1/2 nuclides. Furthermore, for these nuclides, the combination of fast MAS and 1 H detection provides a high sensitivity, which rivals the DNP-enhanced ultra-wideline SSNMR. The new pulse sequences were used to determine the Pt coordination environments in a complex mixture of decomposition products of transplatin and in a metal-organic framework with Pt ions coordinated to the linker ligands.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Capability Building Progression of an Insider Threat Mitigation Program at an International Research Reactor

The nuclear industry recognizes the difficulties involved in developing effective managerial and leadership skills in a highly technical and proficient workforce such as that found in nuclear facilities. Implementing an insider threat mitigation program (ITMP) within the nuclear industry is a complex and ongoing process that demands a comprehensive understanding of human behavior, an organization’s security culture, and rigorous regulatory requirements yet also accounts for facility characteristics, physical security, material flow, and activities involving nuclear material. Given the high-consequence nature of research reactor operations, even minor lapses can lead to safety, security, and reputational risks. An effective ITMP requires a defense-in-depth approach that incorporates behavioral analysis, robust vetting procedures, continuous monitoring, and cross-disciplinary coordination. It must also promote a culture of vigilance and accountability at all levels up to and including executive leadership but be flexible enough to adapt to evolving global threats and technological advances. Insider threat mitigation is not a one-time effort but rather a sustained commitment to excellence in safety and security. Establishing a culture in which personnel proactively report incidents and issues that could affect nuclear safety and security is vital to maintaining a safe and secure operational environment. This document was developed to guide senior management and research reactor organizations in creating comprehensive programs to effectively manage and mitigate insider threat behaviors and actions. It focuses on the key pillars of an effective ITMP, including the national legal framework, security culture, preventive and protective measures, cyber security, and performance evaluation. By using a systematic approach during implementation, facilities can foster environments conducive to insider threat detection and support long-term program sustainability. The document also provides strategies for improving communication across all levels of an organization, helping to eliminate barriers that hinder the development of robust ITMPs and enhance overall security culture. In today’s organizations, the concept of leveraging safety and security culture lessons to facilitate knowledge transfer is rapidly evolving to expedite insider threat management and security culture improvements. This document outlines the rationale for evaluating an ITMP based on national customs, culture, and stakeholders. The elements are all germane to reliability and trustworthiness and relate to security concerns that states may encounter. The document focuses not only on individual perceptions regarding security issues and capability building but also on team building and how to resolve concerns. The implementers of a facility’s ITMP may zero in on indicators of insider threats within their enterprise. This material will benefit organizations when it is applied using a systematic and structured approach as demonstrated throughout the document.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2025

Computational toolkit for predicting thickness of 2D materials using machine learning and autogenerated dataset by large language model

The thickness of 2D materials not only plays a crucial role in determining the performance of nanoelectronic and optoelectronic devices but also introduces complexities in predicting volume-dependent properties, such as energy storage capacity, due to the intrinsic vacuum within these materials. Although a plethora of experimental techniques, including but not limited to optical contrast, Raman spectroscopy, nonlinear optical spectroscopy, near-field optical imaging, and hyperspectral imaging, facilitate the measurement of 2D material thickness, comprehensive data for many materials remain elusive. Over the past decade, the exponential proliferation of 2D materials and their heterostructures has outstripped the capabilities of conventional experimental and computational approaches. In this evolving landscape, machine learning (ML) has emerged as an indispensable tool, offering a scalable approach to augment these traditional methodologies. Addressing the critical gap, we introduce THICK2D—Thickness Hierarchy Inference and Calculation Kit for 2D Materials. This Python-based computational framework harnesses an autogenerated thickness database, developed using large language models, and advanced ML algorithms to facilitate the rapid and scalable estimation of material thickness, relying solely on crystallographic data. To demonstrate the utility and robustness of THICK2D, we successfully used the toolkit to predict the thickness of more than 8000 2D-based materials, sourced from two extensive 2D materials databases. THICK2D is disseminated as an open-source utility, accessible on GitHub at https://github.com/gmp007/THICK2D, and archived on Zenodo at https://10.5281/zenodo.11216648.

Ekuma, Chinedu E. (ORCID:0000000258527556)↗
DOE OSTI2016

Two actinide-organic frameworks constructed by a tripodal flexible ligand: Occurrence of infinite ((UO{sub 2})O{sub 2}(OH){sub 3}){sub 4n} and hexanuclear (Th{sub 6}O{sub 4}(OH){sub 4}) motifs

Two new actinide metal-organic frameworks were constructed by using a tripodal flexible ligand tris (2-carboxyethyl) isocyanurate (H{sub 3}tci) under hydrothermal condition. The combination of H{sub 3}tci and uranyl nitrate hexahydrate in aqueous solution leads to the isolation of [(UO{sub 2}){sub 2}(H{sub 2}O){sub 4}]{sub 0.5}(tci){sub 2}(UO{sub 2}){sub 4}(OH){sub 4}·18H{sub 2}O (1), which contains two distinct UO{sub 2}{sup 2+} coordination environments. Four uranyl cations, linked through μ{sub 3}-OH respectively, result in the edge-sharing ribbons. Then, the layer structure is constructed by U-O clusters linked through other eight-coordinated uranyl unions, giving rise to a porous structure in the space. Topological analysis reveals that complex 1 belongs to a (4, 8)-connected net with a schläfli symbol of (3{sup 4.}2{sup 6.}3){sub 2}(3{sup 4.}4{sup 6.}5{sup 6.}6{sup 8.}7{sup 3.}8). Th{sub 3}(tci){sub 2}O{sub 2}(OH){sub 2}(H{sub 2}O){sub 3}·12H{sub 2}O (2) generated by the reaction of H{sub 3}tci and thorium nitrate tetrahydrate, possesses nine-fold coodinated Th(IV) centers with a monocapped square antiprismatic geometry. The hexamers “Th{sub 6}O{sub 4}(OH){sub 4}” motifs are connected together by the carboxylate groups, showing a three-dimensional structures. Complex 2 takes on an 8-connected architecture and the point symbol is (4{sup 24.}6{sup 4}). - Graphical abstract: Two new 3D actinide metal-organic frameworks were constructed by using a tripodal flexible ligand tris (2-carboxyethyl) isocyanurate (H3tci) and their topological structures were displayed. The infinite ((UO{sub 2})O{sub 2}(OH){sub 3}){sub 4n} and hexanuclear (Th{sub 6}O{sub 4}(OH){sub 4}) motifs were found in the title actinides networks.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Insights into distorted lamellar phases with small-angle scattering and machine learning

Lamellar phases are essential in various soft matter systems, with topological defects significantly influencing their mechanical properties. In this report, we present a machine-learning approach for quantitatively analyzing the structure and dynamics of distorted lamellar phases using scattering techniques. By leveraging the mathematical framework of Kolmogorov–Arnold networks, we demonstrate that the conformations of these distorted phases – expressed as superpositions of complex waves – can be reconstructed from small-angle scattering intensities. Through the contour analysis of wave field phase singularities, we obtain the statistics of the spatial distribution of topological defects. Furthermore, we establish that the temporal evolution of these defects can be derived from the time-dependent traveling wave field, informed by the dispersion relation of spectral components. This method opens new avenues for investigating the dynamics of distorted lamellar phases using various dynamic scattering techniques such as neutron spin echo and X-ray photon correlation spectroscopy. These findings enhance our microscopic understanding of how defects influence the physical properties of lamellar materials, with implications for both equilibrium and non-equilibrium states in general lamellar systems.

36 MATERIALS SCIENCE↗
NASA NTRSDate not supplied

Computational Investigation of Oxidative Etch Pitting in FiberForm and Its Impact on Material Properties

Oxidation-driven carbon erosion does not occur uniformly but rather through the development of localized etch pits at active surface sites. These active sites form due to atomic defects on the carbon surface, making them significantly more reactive than the surrounding, non-defective areas. As a result, these sites are the first to react during ablation, leading to their removal. This process creates new defects in neighboring atoms, increasing their reactivity and causing localized carbon removal around these active sites. In this way, the highly reactive defective areas serve as nucleation points for the formation and growth of etch pits, which can have adverse effects on structural integrity of FiberForm. To better understand how these etch pits impact the material properties of carbon fiber microstructures, we have developed a new capability within the direct simulation Monte Carlo (DSMC) framework to capture the etch pit formation process. This capability, integrated into the DSMC code SPARTA (Stochastic Parallel Rarefied-gas Time-accurate Analyzer), models material removal in the presence of active sites, leading to the formation of etch pits. The current work focuses on studying the effects of these etch pits on the material properties of FiberForm, a widely used base material in thermal protection systems (TPS). The microstructure of virgin FiberForm, obtained via X-ray microtomography, is imported into SPARTA to generate the ablated geometries with etch pits. These modified microstructures are then analyzed using the Porous Microstructure Analysis (PuMA) software to compute various material properties, including elasticity, thermal conductivity, and permeability. We investigate the variation of these properties due to the complex surface topology changes caused by etch pit formation. Additionally, we compare the effects of pitting with the conventional model of shrinking fibers, traditionally used to simulate the ablation of carbon structures. Significant differences emerge between the two approaches. Consequently, this physically realistic model of material removal through etch pit formation offers improved accuracy in predicting the degradation of carbon-based TPS during oxidation. It also provides insights into other mechanisms, such as spallation, where chunks of material are removed into the flow due to etch pit growth. Ultimately, this model enhances our understanding of failure modes in these materials during ablation.

PuMA↗
DOE OSTI2022

Unstructured mesh-based multi-physics reactor transient simulation with iMC code

In this study, we present a time-dependent Monte Carlo simulation result about a complex fuel-loaded light water reactor system with thermal-hydraulic feedback using the KAIST iMC code. The intra-pin temperature distribution is calculated based on Monte Carlo-tallied detailed power distribution and the finite element heat transfer method. The temperature effect of fuel, absorber, and coolant are adequately reflected into the dynamic response by the on-the-fly Doppler broadened cross-section correction. For the time-dependent Monte Carlo transport simulation, the predictor-corrector quasi-static Monte Carlo method is used for this study. We tested the coupled analysis framework with a 17-by-17 fuel assembly loaded with the centrally-shielded burnable absorber (CSBA) fuel element. The temperature feedback effect on the material cross-section and the moderator density incurred negative reactivity in response to the external positive reactivity insertion, showing the inherent stability of the reactor system. The pin-wise power and temperature change during the system transient are presented and discussed. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2013

High temperature synthesis of two open-framework uranyl silicates with ten-ring channels: Cs{sub 2}(UO{sub 2}){sub 2}Si{sub 8}O{sub 19} and Rb{sub 2}(UO{sub 2}){sub 2}Si{sub 5}O{sub 13}

The uranyl silicates Cs{sub 2}(UO{sub 2}){sub 2}Si{sub 8}O{sub 19} and Rb{sub 2}(UO{sub 2}){sub 2}Si{sub 5}O{sub 13} were obtained by mixing stoichiometric amounts of uranium metal, tellurium dioxide, silicon dioxide, and an excess of correspondent alkali metal halide flux. These compounds crystallize in the orthorhombic space groups Pnma and C222 with eight and two units per unit cell, respectively. Their crystal structures are dominated by zippered pentagonal bipyramidal chains of UO{sub 7} and silicates layer that are further connected into 3D frameworks. The cesium compound has silicate double layers while rubidium has a single layer. Six-ring voids and ten-ring channels are found in both compounds. - Graphical abstract: A view of the three-dimensional network structure of Cs{sub 2}(UO{sub 2}){sub 2}Si{sub 8}O{sub 19}. Highlights: Black-Right-Pointing-Pointer Three-dimensional uranium silicates. Black-Right-Pointing-Pointer Analogs of natural uranyl silicate minerals. Black-Right-Pointing-Pointer Complexity and symmetry ambiguity of uranyl silicates.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Python Library for Monte Carlo Simulations with Ab Initio and Machine-Learned Interatomic Potentials

There is a growing need in the simulation community for software that provides a transparent, reproducible, usable, and extensible (TRUE) Monte Carlo (MC) simulation framework employing energies from ab initio methods and machine-learning interatomic potentials (MLIPs). We introduce a Python library (ASE-MC) that adds Monte Carlo functionality to the Atomic Simulation Environment (ASE) package. Now, we can combine the powerful tools used to build systems and perform ab initio and MLIP in ASE with MC simulation algorithms to sample the configurational space with a concise Python script. After presenting the design philosophy, we demonstrate the flexibility of our approach using selected examples. These example simulations include liquid water described with a message-passing MLIP in the canonical and isothermal–isobaric ensembles, sampling the characteristic dihedral angle of biphenyl and comparing an MLIP to first-principles calculations, and a grand canonical Monte Carlo simulation of ammonia adsorption on Pt(111). These examples showcase the main features of the software, which include flexibility in the choice of ab initio or MLIP engine, ab initio or MLIP grand canonical MC with cavity bias insertions and deletions, the ability to add custom MC moves to the move set, and how users can condense complex MC workflows into a single Python script. Finally, this library serves as a framework for reproducible Monte Carlo simulations, facilitating easy reproduction of the work and application to new systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

2016 High Performance Computing Annual Report

Sandia National Laboratories’ California site is celebrating its 60th anniversary (1956 to 2016), and high performance computing has been a key enabler for its scientists and engineers throughout much of its history. Since its founding, Sandia California has helped pioneer the use of HPC platforms including hosting Sandia’s first Cray-1 supercomputer in the 1970s and supporting development of scalable cluster computing platforms to create a new paradigm for cost-effective supercomputing in the 1990s. Recent decades of investment in creation of scalable application frameworks for scientific computing have also enabled new generations of modeling and simulation codes. These resources have facilitated computational analysis of complex phenomena in diverse applications spanning national defense, energy, and homeland security. Today, Sandia California researchers work with partners in academia, industry, and national labs to evolve the state-of-the-art in HPC, modeling, and data analysis (including foundational capabilities for exascale computing platforms) and apply them in transformational ways. Research efforts include mitigating the effects of silent hardware failures that can jeopardize the results of large-scale computations, developing exascale-capable asynchronous task-parallel programming models and runtime systems, formulating new techniques to better explore and analyze extreme-scale data sets, and increasing our understanding of materials and chemical sciences which has applications spanning nuclear weapons stockpile stewardship to more efficient automobile engines. The following section highlights some of these research and applications projects and further illustrates the breadth of our HPC capabilities.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Exploring the Effects of Node Topology, Connectivity, and Metal Identity on the Binding of Nerve Agents and Their Hydrolysis Products in Metal–Organic Frameworks

Recent studies have shown that metal–organic frameworks (MOFs) built from hexanuclear M(IV) oxide cluster nodes are effective catalysts for nerve agent hydrolysis, where the properties of the active sites on the nodes can strongly influence the reaction energetics. The connectivity and metal identity of these M6 nodes can be easily tuned, offering extensive opportunities for computational screening to predict promising new materials. Thus, we used density functional theory (DFT) to examine the effects of node topology, connectivity, and metal identity on the binding energies of multiple nerve agents and their corresponding hydrolysis products. By computing an optimization metric based on the relative binding strengths of key hydrolysis reaction species (water, agent, and bidentate-bound products), we predicted optimal M6 nodes for hydrolyzing specific nerve agent and simulant molecules, where our results are in qualitative agreement with observed experimental trends. This analysis highlighted the notion that no single metal or node topology is optimal for all possible organophosphates, suggesting that MOFs should be selected based on the agent of interest. Using the large amount of data generated from our DFT calculations, we then derived quantitative structure–activity relationship (QSAR) models to help explain the complex trends observed in the binding energies. Through linear regression, we identified the most important descriptors for describing the binding of nerve agents and their hydrolysis products to M6 nodes. These results suggested that both molecular and node properties, including both structural and chemical features, collectively contribute to the binding energetics. By performing a thorough statistical analysis, we showed that our QSAR models are capable of making quantitatively accurate binding energy predictions for nerve agents and their hydrolysis products in a wide variety of M(IV)-MOFs. In conclusion, the insights gained herein can be used to guide future experiments for the synthesis of MOFs with enhanced catalytic activity for organophosphate hydrolysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗