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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

A Comparative Multi-System Approach to Characterizing Bioactivity of Commonly Occurring Chemicals

A 2019 retrospective study analyzed wristband personal samplers from fourteen different communities across three different continents for over 1530 organic chemicals. Investigators identified fourteen chemicals (G14) detected in over 50% of personal samplers. The G14 represent a group of chemicals that individuals are commonly exposed to, and are mainly associated with consumer products including plasticizers, fragrances, flame retardants, and pesticides. The high frequency of exposure to these chemicals raises questions of their potential adverse human health effects. Additionally, the possibility of exposure to mixtures of these chemicals is likely due to their co-occurrence; thus, the potential for mixtures to induce differential bioactivity warrants further investigation. This study describes a novel approach to broadly evaluate the hazards of personal chemical exposures by coupling data from personal sampling devices with high-throughput bioactivity screenings using in vitro and non-mammalian in vivo models. To account for species and sensitivity differences, screening was conducted using primary normal human bronchial epithelial (NHBE) cells and early life-stage zebrafish. Mixtures of the G14 and most potent G14 chemicals were created to assess potential mixture effects. Chemical bioactivity was dependent on the model system, with five and eleven chemicals deemed bioactive in NHBE and zebrafish, respectively, supporting the use of a multi-system approach for bioactivity testing and highlighting sensitivity differences between the models. In both NHBE and zebrafish, mixture effects were observed when screening mixtures of the most potent chemicals. Observations of BMC-based mixtures in NHBE (NHBE BMC Mix) and zebrafish (ZF BMC Mix) suggested antagonistic effects. In this study, consumer product-related chemicals were prioritized for bioactivity screening using personal exposure data. High-throughput high-content screening was utilized to assess the chemical bioactivity and mixture effects of the most potent chemicals.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Chemical Safety and Security Education in ACS-Approved Chemistry Programs

To date, chemical security education practices in postsecondary institutions are poorly understood. The purpose of this study is to provide an initial understanding of the practices, attitudes, and barriers toward chemical security education for undergraduate and graduate programs in the United States (US) by surveying representatives of American Chemical Society (ACS)-approved programs. All programs with ACS-approved undergraduate chemistry programs (n = 691) were contacted for participation: 21% (n = 148) fully completed and 6% (n = 41) partially completed the survey for a combined total of 27% complete and/or partially complete surveys (n = 189). We observed that most programs currently teach chemical safety (undergraduate >99%, graduate 73%); however, only about one-third of programs teach chemical security at any education level (undergraduate 32%, graduate 34%). We also observed that safety education is provided more frequently than security education. Further, ACS-approved programs reported that their chemical safety culture was stronger than chemical security culture and felt that safety should be taught differently than security. The overwhelming majority of respondents (96%) indicated that chemical safety should be mandatory at some level, while only about half of respondents (57%) indicated that chemical security should be mandatory at some level. More efforts are needed by the chemistry community to raise awareness of the importance of chemical security education so that more institutions commit to training their faculty and students on the topic. The authors suggest that adoption of chemical security education could be increased if ACS were to advocate for chemical security by including it in its guidelines for educational program approval.

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

An expert-driven literature review of “negative” chemicals for developmental neurotoxicity (DNT) in vitro assay evaluation

To date, approximately 200 chemicals have been tested in US Environmental Protection Agency (EPA) or Organization for Economic Co-operation and Development (OECD) developmental neurotoxicity (DNT) guideline studies, leaving thousands of chemicals without traditional animal information on DNT hazard potential. To address this data gap, a battery of in vitro DNT new approach methodologies (NAMs) has been proposed. Evaluation of the performance of this battery will increase the confidence in its use to determine DNT chemical hazards. One approach to evaluate DNT NAM performance is to use a set of chemicals to evaluate sensitivity and specificity. Since a list of chemicals with potential evidence of in vivo DNT has been established, this study aims to develop a curated list of “negative” chemicals for inclusion in a “DNT NAM evaluation set”. A workflow, including a literature search followed by an expert-driven literature review, was used to systematically screen 39 chemicals for lack of DNT effect. Expert panel members evaluated the scientific robustness of relevant studies to inform chemical categorizations. Following review, the panel discussed each chemical and made categorical determinations of “Favorable”, “Not Favorable”, or “Indeterminate” reflecting acceptance, lack of suitability, or uncertainty given specific limitations and considerations, respectively. Further, the panel determined that 10, 22, and 7 chemicals met the criteria for “Favorable”, “Not Favorable”, and “Indeterminate”, for use as negatives in a DNT NAM evaluation set. Ultimately, this approach not only supports DNT NAM performance evaluation but also highlights challenges in identifying large numbers of negative DNT chemicals.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Key environmental and production factors for understanding variation in switchgrass chemical attributes

Abstract Switchgrass ( Panicum virgatum L.) is a promising feedstock for bioenergy and bioproducts; however, its inherent variability in chemical attributes creates challenges for uniform conversion efficiencies and product quality. It is necessary to understand the range of variation and factors (i.e., field management, environmental) influencing chemical attributes for process improvement and risk assessment. The objectives of this study were to (1) examine the impact of nitrogen fertilizer application rate, year, and location on switchgrass chemical attributes, (2) examine the relationships among chemical attributes, weather and soil data, and (3) develop models to predict chemical attributes using environmental factors. Switchgrass samples from a field study spanning four locations including upland cultivars, one location including a lowland cultivar, and between three and six harvest years were assessed for glucan, xylan, lignin, volatiles, carbon, nitrogen, and ash concentrations. Using variance estimation, location/cultivar, nitrogen application rate, and year explained 65%–96% of the variation for switchgrass chemical attributes. Location/cultivar × year interaction was a significant factor for all chemical attributes indicating environmental‐based influences. Nitrogen rate was less influential. Production variables and environmental conditions occurring during the switchgrass field trials were used to successfully predict chemical attributes using linear regression models. Upland switchgrass results highlight the complexity in plant responses to growing conditions because all production and environmental variables had strong relationships with one or more chemical attributes. Lowland switchgrass was limited to observations of year‐to‐year environmental variability and nitrogen application rate. All explanatory variable categories were important for lowland switchgrass models but stand age and precipitation relationships were particularly strong. The relationships found in this study can be used to understand spatial and temporal variation in switchgrass chemical attributes. The ability to predict chemical attributes critical for conversion processes in a geospatial/temporal manner would provide state‐of‐the‐art knowledge for risk assessment in the bioenergy and bioproducts industry.

09 BIOMASS FUELS↗
DOE OSTI2020

Tools and Incentives for Implementing Codes of Ethics to Help Prevent the Reemergence of Chemical Weapons

Since the entry into force of the Chemical Weapons Convention (CWC) and the establishment of the Organisation for Prohibition of Chemical Weapons (OPCW) in 1997, 96% of the world’s declared stockpile, comprising 72,305 metric tons of chemical warfare agents have been destroyed. Despite this notable progress, the convergence of chemistry and biology brings new challenges to preventing chemical weapons production, as the technical capability to manipulate the chemistry of microorganisms has diffused beyond the chemical industry and academic research communities. The development and voluntary adoption by chemical practitioners around the world of ethical codes or guidelines, such as The Hague Ethical Guidelines and the Global Chemists’ Code of Ethics (GCCE) , are important steps in strengthening international nonproliferation efforts through their potential to impact the intent of the individual by normalizing ethical behavior. However, the simple act of adopting a code does not guarantee ethical behavior. Care must be taken to ensure those adopting the code understand specifically the behavior being asked of them, and they must be given ample opportunities to practice exercising those behaviors in low-risk situations and encouragement to keep trying when they fail. Furthermore, in small-to-medium chemical companies, concerns that adopting such measures could negatively impact profit margins may dissuade some business owners from voluntarily adopting ethical codes, while a country’s decision makers may not prioritize establishment of new regulatory mechanisms that could address some of these concerns. In this paper, factors that may positively influence a chemical company’s decision to adopt ethical guidelines, such as integration of social sustainability elements into frameworks and approaches to maintain social legitimacy in the eyes of the broader community, are explored. Voluntary adoption of ethical codes gives teeth to arms control regimes. By encouraging chemical companies to become engaged in the community around them and providing easy-to-use tools like scenario-based E-learning to enable remote engagement and facilitate understanding of the principles undergirding the GCCE, they actively participate in making the world a better place and minimizing the potential for reemergence of chemical weapons.

Small Chemical Companies, Codes of Ethics, E-learn↗
DOE OSTI2024

Chemical Doppelgangers in GALAH DR3: The Distinguishing Power of Neutron-capture Elements among Milky Way Disk Stars

The observed chemical diversity of Milky Way stars places important constraints on Galactic chemical evolution and the mixing processes that operate within the interstellar medium. Recent works have found that the chemical diversity of disk stars is low. For example, the Apache Point Observatory Galactic Evolution Experiment (APOGEE) "chemical doppelganger rate," or the rate at which random pairs of field stars appear as chemically similar as stars born together, is high, and the chemical distributions of APOGEE stars in some Galactic populations are well-described by two-dimensional models. However, limited attention has been paid to the heavy elements (Z > 30) in this context. In this work, we probe the potential for neutron-capture elements to enhance the chemical diversity of stars by determining their effect on the chemical doppelganger rate. We measure the doppelganger rate in GALactic Archaeology with HERMES DR3, with abundances rederived using The Cannon, and find that considering the neutron-capture elements decreases the doppelganger rate from ~2.2% to 0.4%, nearly a factor of 6, for stars with –0.1 < [Fe/H] < 0.1. While chemical similarity correlates with similarity in age and dynamics, including neutron-capture elements does not appear to select stars that are more similar in these characteristics. Our results highlight that the neutron-capture elements contain information that is distinct from that of the lighter elements and thus add at least one dimension to Milky Way abundance space. This work illustrates the importance of considering the neutron-capture elements when chemically characterizing stars and motivates ongoing work to improve their atomic data and measurements in spectroscopic surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

A data engineering approach for sustainable chemical end-of-life management

The presence of chemicals causing significant adverse human health and environmental effects during end-of-life (EoL) stages is a challenge for implementing sustainable management efforts and transitioning towards a safer circular life cycle. Conducting chemical risk evaluation and exposure assessment of potential EoL scenarios can help understand the chemical EoL management chain for its safer utilization in a circular life-cycle environment. However, the first step is to track the chemical flows, estimate releases, and potential exposure pathways. Hence, this work proposes an EoL data engineering approach to perform chemical flow analysis and screening to support risk evaluation and exposure assessment for designing a safer circular life cycle of chemicals. This work uses publicly-available data to identify potential post-recycling scenarios (e.g., industrial processing/use operations), estimate inter-industry chemical transfers, and exposure pathways to chemicals of interest. Furthermore, a case study demonstration shows how the data engineering framework identifies, estimates, and tracks chemical flow transfers from EoL stage facilities (e.g., recycling and recovery) to upstream chemical life cycle stage facilities (e.g., manufacturing). Also, the proposed framework considers current regulatory constraints on closing the recycling loop operations and provides a range of values for the flow allocated to post-recycling uses associated with occupational exposure and fugitive air releases from EoL operations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

High-throughput screening tools facilitate calculation of a combined exposure-bioactivity index for chemicals with endocrine activity

High-throughput and computational tools provide a new opportunity to calculate combined bioactivity of exposure to diverse chemicals acting through a common mechanism. We used high throughput in vitro bioactivity data and exposure predictions from the U.S. EPA’s Toxicity and Exposure Forecaster (ToxCast and ExpoCast) to estimate combined estrogen receptor (ER) agonist activity of non-pharmaceutical chemical exposures for the general U.S. population. High-throughput toxicokinetic (HTTK) data provide conversion factors that relate bioactive concentrations measured in vitro (µM), to predicted population geometric mean exposure rates (mg/kg/day). These data were available for 22 chemicals with ER agonist activity and were estimated for other ER bioactive chemicals based on the geometric mean of HTTK values across chemicals. For each chemical, ER bioactivity across ToxCast assays was compared to predicted population geometric mean exposure at different levels of in vitro potency and model certainty. Dose additivity was assumed in calculating a Combined Exposure-Bioactivity Index (CEBI), the sum of exposure/bioactivity ratios. Combined estrogen bioactivity was also calculated in terms of the percent maximum bioactivity of chemical mixtures in human plasma using a concentration-addition model. Estimated CEBIs vary greatly depending on assumptions used for exposure and bioactivity. In general, CEBI values were <1 when using median of the estimated general population chemical intake rates, while CEBI were ≥1 when using the upper 95th confidence bound for those same intake rates for all chemicals. Concentration-addition model predictions of mixture bioactivity yield comparable results. Based on current in vitro bioactivity data, HTTK methods, and exposure models, combined exposure scenarios sufficient to influence estrogen bioactivity in the general population cannot be ruled out. Future improvements in screening methods and computational models could reduce uncertainty and better inform the potential combined effects of estrogenic chemicals.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Life-cycle analysis of lithium chemical production in the United States

To achieve its ambitious national decarbonization goals, the United States has incentivized the domestic production of materials critical to decarbonization technologies, including lithium-ion batteries (LIBs). These materials include battery-grade lithium chemicals (Li-chemicals), for which the U.S. is encouraging domestic production from resources (sedimentary clays and low Li-content brines (LLCBs)) that differ substantially from conventional sources (Salar brines and spodumene ores). Here, we conduct the first-ever comparative life-cycle analysis of Li-chemical production from all alternative resources (in the U.S.) and conventional sources based on data from company literature for U.S.-related production efforts. Two energy sources (electricity and natural gas), four material inputs (HCl, NaOH, Na 2 CO 3 , and CaO), and process carbon emissions dominate the life-cycle impacts (≥90% share) of U.S.-based Li-chemical production. Comparatively, the life-cycle impacts of alternative sources-based Li-chemicals lie between those for Li-chemical production from Salar brines and from spodumene ores. At the battery level, the shift in Li-chemical sourcing causes a notable change in LIB's life-cycle impacts (by ~5–15%), independent of the cathode chemistry employed. Our study highlights the relevance of a decarbonized electric grid and the capture and sequestration of process carbon emissions generated during Li-chemical and upstream material production in decarbonizing Li-chemical production from alternative sources. Further decarbonization would necessitate using decarbonized material inputs and a shift away from natural gas towards renewable energy for alternative resource-based Li-chemical production processes.

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

Chemical Process Safety at TRISO-Based, Metal-Based, and Salt-Based Fuel Fabrication Facilities: Technical Assessment and Guidance Assessment

As part of efforts to prepare for potential and ongoing safety reviews for licensing of advanced non-light-water reactor fuel cycles, the U.S. Nuclear Regulatory Commission (NRC) tasked Pacific Northwest National Laboratory to prepare an assessment on the state of knowledge of potential chemical processes at fuel cycle facilities supporting the front end of these fuel cycles, and to assess the associated regulatory guidance. This report provides a technical assessment of chemical process safety considerations to support NRC licensing reviews of fabrication processes for tri-structural isotropic (TRISO) based, metallic-based, and salt-based fuels. The assessments involved collecting publicly available information on the fuel fabrication processes to (i) identify the operational process steps, characteristics and chemicals involved, (ii) identify the physical safety considerations and health safety considerations during licensing reviews of the various process steps, and (iii) collect information to support assessments of severity of accidents and potential mitigative measures to be implemented. The assessment provides a foundational basis on chemical process safety considerations for advanced fuel fabrication activities, although it is recognized that licensing reviews may necessitate design-specific considerations. The specific conditions under which chemical hazards emerge will require process-specific considerations, highlighting the importance of process-informed interpretation. The assessment also determined that exposure guidelines and limits to assess the consequences of acute exposures are limited for some chemicals, although alternative limits and supplementary information from databases or safety data sheets provide sufficient information to evaluate consequences of acute exposures. In addition, it was identified that metallic and salt fuel fabrication processes may involve beryllium, which is an exposure hazard. The regulatory framework for the licensing of advanced fuel cycle facilities, per 10 CFR Part 70 Domestic Licensing of Special Nuclear Material, is deemed robust and flexible to address the chemical safety considerations in this report. A review was conducted on various regulatory guidance and technical basis documents. This included reviewing NUREG-1520, Revision 2, Standard Review Plan for Fuel Cycle Facilities License Applications – Final Report and the process descriptions in Appendix A of NUREG/CR-6410, Nuclear Fuel Cycle Facility Accident Analysis Handbook, to address advanced fuel types. As new fuels will involve process-specific chemical uses, process-specific considerations are provided in this report. Additionally, it is noted that the U.S. Department of Energy protective action criteria database includes Temporary Emergency Exposure Limits (TEELs) for process-specific chemicals. This report provides technical information to support chemical safety assessments of new advanced fuel cycle facilities and identifies technical and safety information to support licensing reviews. No regulatory barriers were identified for the licensing of advanced fuel cycle facilities.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Rotational Velocities of Am and Non-chemical-peculiar Stars Based on Kepler and LAMOST DR5

Rotation is a critical physical process operating in the formation of Am stars. There is a strong correlation between low-velocity rotation and chemical peculiarity. However, the existence of many non-chemical-peculiar slow rotators challenges the understanding of Am stars. The purpose of our work is to search for low-velocity rotating non-chemical-peculiar A-type stars and Am stars and to make a comparative analysis. In this paper, we pick out a sample from the LAMOST–Kepler project, including 21 Am stars, 125 non-chemical-peculiar slow rotators, and 53 non-chemical-peculiar fast rotators. We calculate the rotational frequencies through a periodic change of light curves caused by inhomogeneous stellar surfaces and then obtain the rotational velocities. For slow rotators, the age of Am stars is statistically younger than that of non-chemical-peculiar stars in the same temperature bin. In the comparison of the period, the average amplitude, and stellar mass of Am and non-chemical-peculiar stars, we discover that there is no difference in the photometric variability between Am and non-chemical-peculiar stars, which implies similar inhomogeneities on the surfaces. The average amplitude of non-chemical-peculiar stars has a downward trend with the increase of effective temperature and stellar mass, which is consistent with the theoretical prediction caused by weak dynamo-generated magnetic fields in A-type stars. In addition, we confirm four non-chemical-peculiar stars that have flares by checking field-of-view images, pixel images, and pixel-level light curves.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Chemical Heterointerface Engineering on Hybrid Electrode Materials for Electrochemical Energy Storage

Abstract The chemical heterointerfaces in hybrid electrode materials play an important role in overcoming the intrinsic drawbacks of individual materials and thus expedite the in‐depth development of electrochemical energy storage. Benefiting from the three enhancement effects of accelerating charge transport, increasing the number of storage sites, and reinforcing structural stability, the chemical heterointerfaces have attracted extensive interest and the electrochemical performances of hybrid electrode materials have been significantly optimized. In this review, recent advances regarding chemical heterointerface engineering in hybrid electrode materials are systematically summarized. Especially, the intrinsic behaviors of chemical heterointerfaces on hybrid electrode materials are refined based on built‐in electric field, van der Waals interaction, lattice mismatch and connection, electron cloud bias and chemical bond, and their combination. The strategies for introducing chemical heterointerfaces are classified into in situ local transformation, in situ growth, cosynthesis, and other strategy. The recent progress about the chemical heterointerfaces engineering specially focusing on metal‐ion batteries, supercapacitors, and Li–S batteries are introduced in detail. Furthermore, the classification and characterization of chemical heterointerfaces are briefly described. Finally, the emerging challenges and perspectives about future directions of chemical heterointerface engineering are proposed.

Li, Wenbin↗
DOE OSTI2021

Navigating Transition-Metal Chemical Space: Artificial Intelligence for First-Principles Design

Conspectus The variability of chemical bonding in open-shell transition-metal complexes not only motivates their study as functional materials and catalysts but also challenges conventional computational modeling tools. Here, tailoring ligand chemistry can alter preferred spin or oxidation states as well as electronic structure properties and reactivity, creating vast regions of chemical space to explore when designing new materials atom by atom. Although first-principles density functional theory (DFT) remains the workhorse of computational chemistry in mechanism deduction and property prediction, it is of limited use here. DFT is both far too computationally costly for widespread exploration of transition-metal chemical space and also prone to inaccuracies that limit its predictive performance for localized d electrons in transition-metal complexes. These challenges starkly contrast with the well-trodden regions of small-organic-molecule chemical space, where the analytical forms of molecular mechanics force fields and semiempirical theories have for decades accelerated the discovery of new molecules, accurate DFT functional performance has been demonstrated, and gold-standard methods from correlated wavefunction theory can predict experimental results to chemical accuracy. The combined promise of transition-metal chemical space exploration and lack of established tools has mandated a distinct approach. In this Account, we outline the path we charted in exploration of transition-metal chemical space starting from the first machine learning (ML) models (i.e., artificial neural network and kernel ridge regression) and representations for the prediction of open-shell transition-metal complex properties. The distinct importance of the immediate coordination environment of the metal center as well as the lack of low-level methods to accurately predict structural properties in this coordination environment first motivated and then benefited from these ML models and representations. Once developed, the recipe for prediction of geometric, spin state, and redox potential properties was straightforwardly extended to a diverse range of other properties, including in catalysis, computational “feasibility”, and the gas separation properties of periodic metal–organic frameworks. Interpretation of selected features most important for model prediction revealed new ways to encapsulate design rules and confirmed that models were robustly mapping essential structure–property relationships. Encountering the special challenge of ensuring that good model performance could generalize to new discovery targets motivated investigation of how to best carry out model uncertainty quantification. Distance-based approaches, whether in model latent space or in carefully engineered feature space, provided intuitive measures of the domain of applicability. With all of these pieces together, ML can be harnessed as an engine to tackle the large-scale exploration of transition-metal chemical space needed to satisfy multiple objectives using efficient global optimization methods. In practical terms, bringing these artificial intelligence tools to bear on the problems of transition-metal chemical space exploration has resulted in ML-model assessments of large, multimillion compound spaces in minutes and validated new design leads in weeks instead of decades.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Chespa: Streamlining Expansive Chemical Space Evaluation of Molecular Sets

Thousands of chemical properties can be calculated for small molecules, which can be used to place the molecules within the context of a broader “chemical space.” These definitions vary based on compounds of interest and the goals for the given chemical space definition. Here, we introduce a customizable (i.e., modular) Python module, chespa, built to easily assess different chemical space definitions through cluster-ing of compounds in these spaces and visualize trends of these clusters. To demonstrate this, chespa currently streamlines prediction of vari-ous molecule descriptors (predicted chemical properties, molecular substructures, AI-based chemical space, and chemical class ontology) in order to test 6 different chemical space definitions. Furthermore, we investigated how these varying definitions trend with mass spectrometry (MS)-based observability, i.e., the ability of a molecule to be observed with MS (e.g., as a function of the molecule ionizability), using an example data set from the U.S. EPA's Non-Targeted Analysis Collaborative Trial (ENTACT), where blinded samples had been analyzed previously, providing 1,398 data points. Improved understanding of observability would offer many advantages in small molecule identifica-tion, such as (i) a priori selection of experimental conditions based on suspected sample composition, (ii) the ability to reduce the number of candidate structures during compound identification by removing those less likely to ionize, and, in turn, (iii) a reduced false discovery rate and increased confidence in identifications. Factors controlling observability are not fully understood, making prediction of this property non-trivial and a prime candidate for chemical space analysis. Chespa is available at github.com/pnnl/chespa.

Nunez, Jamie↗
DOE OSTI2025

Chemical timescale effects on detonation convergence

Numerical simulations of detonation-containing flows have emerged as crucial tools for designing next- generation power and propulsion devices. As these tools mature, it is important for the combustion community to properly understand and isolate grid resolution effects when simulating detonations. To this end, the objective of this work is to provide a comprehensive analysis of the numerical convergence of unsteady detonation simulations, with focus on isolating the impacts of chemical timescale modifications on convergence characteristics in the context of operator splitting. With the aid of an AMReX-based adaptive mesh refinement flow solver-which enables resolutions up to ($\mathcal{O}$ (1000) cells-per-induction length-the convergence analysis is conducted using two kinetics configurations: (1) the simplified three-step Arrhenius-based model mechanism of Short and Quirk (1997), where chemical timescales in the detonation are modified by adjusting activation energies in the initiation and branching reactions, and (2) a detailed hydrogen- air mechanism, where the chemical timescales are adjusted by varying the ambient pressure. The convergence of unsteady self-sustained detonations in one-dimensional channels is then analyzed with reference to steady-state theoretical baseline solutions using these mechanisms. The goal of the analysis is to provide a detailed comparison of the effects of grid resolution on both macroscopic (peak pressures and wave speeds) and microscopic (wave structure) quantities of interest, drawing connections between the deviations from steady-state baselines and minimum chemical timescales. In particular, chemical timescale reductions were found to have minimal impact on the convergence of macroscopic properties. Furthermore, analyses of microscopic convergence trends, particularly in the reaction front location, revealed a key insight: maintaining the induction time while eliminating prohibitive chemical timescales through mechanism simplifications and combustion modeling can significantly enhance detonation convergence properties. Ultimately, this work uncovers resolution-dependent unsteady detonation convergence regimes and highlights the important role played by not only the chemical timescales, but also the ratio between the chemical timescale and induction time on the numerical convergence of the detonation wave structure.

Adaptive mesh refinement↗
DOE OSTI2021

Importance of Engineered and Learned Molecular Representations in Predicting Organic Reactivity, Selectivity, and Chemical Properties

Machine-readable chemical structure representations are foundational in all attempts to harness machine learning for the prediction of reactivities, selectivities, and chemical properties directly from molecular structure. The featurization of discrete chemical structures into a continuous vector space is a critical phase undertaken before model selection, and the development of new ways to quantitatively encode molecules is an active area of research. Here, we highlight the application and suitability of different representations, from expert-guided “engineered” descriptors to automatically “learned” features, in different prediction tasks relevant to organic and organometallic chemistry, where differing amounts of training data are available. These tasks include statistical models of stereo- and enantioselectivity, thermochemistry, and kinetics developed using experimental and quantum chemical data. The use of expert-guided molecular descriptors provides an opportunity to incorporate chemical knowledge, domain expertise, and physical constraints into statistical modeling. In applications to stereoselective organic and organometallic catalysis, where data sets may be relatively small and 3D-geometries and conformations play an important role, mechanistically informed features can be used successfully to obtain predictive statistical models that are also chemically interpretable. We provide an overview of several recent applications of this approach to obtain quantitative models for reactivity and selectivity, where topological descriptors, quantum mechanical calculations of electronic and steric properties, along with conformational ensembles, all feature as essential ingredients of the molecular representations used. Alternatively, more flexible, general-purpose molecular representations such as attributed molecular graphs can be used with machine learning approaches to learn the complex relationship between a structure and prediction target. This approach has the potential to out-perform more traditional representation methods such as “hand-crafted” molecular descriptors, particularly as data set sizes grow. One area where this is particularly relevant is in the use of large sets of quantum mechanical data to train quantitative structure–property relationships. A general approach toward curating useful data sets and training highly accurate graph neural network models is discussed in the context of organic bond dissociation enthalpies, where this strategy outperforms regression using precomputed descriptors. Finally, we describe how graph neural network predictions can be incorporated into mechanistically informed statistical models of chemical reactivity and selectivity. Once trained, this approach avoids the expensive computational overhead associated with quantum mechanical calculations, while maintaining chemical interpretability. We illustrate examples for which fast predictions of bond dissociation enthalpy and of the identities of radicals formed through cleavage of a molecule’s weakest bond are used in simple physical models of site-selectivity and reactivity.

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

Identifying University Chemicals That Pose Security Risks: A Simple Qualitative Approach

Various laboratory-focused tools and methodologies for completing a safety risk assessment have been published, yet few similar resources to address chemical security exist. Herein, we describe a chemical security risk assessment case study at a university in a developing country. In this case study, we demonstrate a chemical security risk assessment for a university chemistry department, using an original inventory of 645 entries which was condensed to 295 chemicals after removing duplicates and erroneous entries. We then prioritized to highlight 83 chemicals of interest based on hazardous or dual-use properties that could lead to unacceptable consequences. We further refined to a list of 34 high-risk chemicals that required action, 48 chemicals that may need further justification and consideration for additional protection, and 1 chemical that did not need further consideration for additional protection.

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

Modeling Equilibrium Solid–Liquid Interfaces under Effective Constant Chemical Potential Using Machine Learning Interatomic Potentials

The chemical potential (μ) of species in solution is essential for understanding various chemical processes at interfaces. Molecular dynamics (MD) simulations, constrained by fixed compositions, cannot maintain constant chemical potential with reference to a targeted concentration or chemical potential under nonequilibrium or dynamic conditions, as solute species can migrate to the interface and deplete (or enrich) the bulk due to solute-interface interactions. In this study, we introduce a simple and computationally efficient approach named iterative quasi-constant chemical potential molecular dynamics (iqCμMD) simulation, which helps simulate targeted molar concentrations of species in solution. iqCμMD overcomes the limitations of conventional MD by adjusting the number of species in the solution to reach a target bulk concentration (chemical potential), which allows simulation of the interface under the bulk conditions comparable to experiment. We demonstrate our approach using machine learning interatomic potential (MLIP)-based MD simulations of the Na 2 SO 4,aq –graphene interface, and to show the transferability of our approach, we also perform classical force field-based MD simulations of NaCl aq –air and NaCl aq –graphite interfaces, which produce comparable results to previous CμMD simulations. Our results also show that the iqCμMD approach efficiently achieves the desired bulk ion concentration within two iterations, and by utilizing MLIPs, we can achieve converged results using relatively small-scale simulations compared to previous CμMD simulations. By combining iqCμMD with MLIP-driven simulations, solid–liquid interfaces can be modeled under an effective constant chemical potential with DFT-level accuracy. Here, we show that iqCμMD offers a robust and simple computational framework for constant chemical potential simulations, as its only requirement is to be able to converge interfacial simulations with a measurable bulk region.

Chemical structure↗