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At least 829 records · Page 46

Quantifications of growth rate and particle size of magnesium silicate hydrate cement using small‐angle X‐ray scattering

The focus of recent research on cement binders has shifted to alternatives such as magnesium silicate hydrate (M-S-H) to mitigate the environmental impact of conventional concrete production. However, a detailed understanding of the morphology and growth rate of M-S-H, which informs its applicability as a cementing agent, is lacking. These properties are important as they influence cement hydration and consequently the development of macroscopic properties. Here, in this study, the particle size distribution and growth rate of M-S-H synthesized by mixing aqueous stock solutions were analyzed using synchrotron-based small-angle X-ray scattering. The reaction solutions contained dissolved magnesium and silicon with concentrations ranging from 2 to 500 mM, corresponding to saturation indices with respect to M-S-H endmembers, M 0.75 -S-H and M 1.50 -S-H, between 5.8 and 10.3. The precipitation rates of M-S-H align with affinity-based kinetic models and can be described by the following equations: For M 0.75 -S-H: 𝑟 M 0.75 −S−H =10 −9.54±0.70 ⁢(Ω$^{0.59±0.08}_{M_{0.75}⁢\textrm{SH}}$−1), and for M 1.50 -S-H: 𝑟 M 1.50 −S−H =10 −7.56±0.37 ⁢(Ω$^{0.34±0.04}_{M_{1.50}⁢\textrm{SH}}$−1), where 𝑟 is the rate in mol/L/s, and Ω represents the saturation ratio with respect to M-S-H. Moreover, we observed that changing the [Mg]/[Si] ratio affects particle size, a trend that cannot be fully explained by changes in the saturation index alone. These insights are critical for improving the predictability and control of cementation in concrete that utilizes M-S-H-based binders.

M-S-H↗

Hydrogen and Electric Charging Infrastructure for Heavy-Duty Trucks: A Nationally Scalable Megaregion Assessment

Decarbonizing regional and long-haul freight is challenging due to the limitations of battery-electric commercial vehicles and infrastructure constraints. Hydrogen fuel cell medium- and heavy-duty vehicles (MHDVs) offer a viable alternative, aligning with the decarbonization goals of the Department of Energy and commercial entities. Historically, alternative fuels like compressed natural gas and liquefied propane gas have faced slow adoption due to barriers like infrastructure availability. To avoid similar issues, effective planning and deploying zero-emission hydrogen fueling infrastructure is crucial. This research develops deployment plans for affordable, accessible, and sustainable hydrogen refueling stations, supporting stakeholders in the decarbonized commercial vehicle freight system. It aims to benefit underserved and rural energy-stressed communities by improving air quality, reducing noise pollution, and enhancing energy resiliency. This research also provides a blueprint for replacing diesel in over-the-road Class 8 freight truck applications with hydrogen fueling solutions. The study focuses on the Texas Triangle Megaregion (I-45, I-35, and I-10), the I-10 corridor between San Antonio, TX, and Los Angeles, CA, and the I-5/CA-99 corridors between Los Angeles, CA, and San Francisco, CA. This area represents a significant portion of U.S. heavy-duty freight movement, carrying ~8.5% of the national freight volume. Using the OR-AGENT (Optimal Regional Architecture Generation for Efficient National Transport) modeling framework, the study conducts an advanced assessment of commercial vehicles, road and freight networks, and energy systems. The framework integrates data on freight mobility, traffic, weather, and energy pathways to deliver a region-specific, optimized vehicles powertrain architectures, infrastructure deployment solutions, operational logistics, and energy pathways. By considering all vehicle origin-destination pairs utilizing these corridors and all feasible fueling station location options, the framework's genetic algorithm identifies the minimum number and optimal locations of hydrogen refueling stations, ensuring no vehicle is stranded. It also determines fuel schedules and quantities at each station. A roadmap for station deployment based on multiple adoption trajectories ensures a strategic rollout of hydrogen refueling infrastructure.

Sujan, Vivek [ORNL] (ORCID:0000000269882342)↗

Extratropical Large-Scale Traveling Weather Systems in the Southern Hemisphere on Mars

From late-autumn through early-spring, the middle- and high-latitudes of both hemispheres of Mars and its predominantly carbon-dioxide atmosphere support mean equator-to-pole thermal contrasts, and then, support a strong mean westerly polar vortex. Observations from orbiting spacecraft indicate that this intense mean baroclinicity-barotropicity supports large-scale eastward traveling weather systems (i.e., transient, traveling synoptic-period waves, on the order of the Rossby deformation scale). On Earth, extratropical weather disturbances arise from wind-shear instabilities, and these are critical components of the terrestrial global circulation. So it is the case for Mars. Large-scale traveling weather systems on Mars serve as agents in the transport of heat, momentum and scalar and tracer quantities (e.g., atmospheric dust, watervapor, ice clouds, chemical species, etc). Such weather systems interact with other large-scale atmospheric circulation components, namely, quasi-stationary (i.e., forced Rossby) modes; global thermal tidal modes; and then, upon large-/continental- geographical scales, upslope/ down-slope flows amongst high relief, low relief, impact basins, and volcanic rises, and more. The character of Mars' traveling extratropical weather disturbances in its southern hemisphere during late winter through early spring is investigated using a high-resolution Mars global climate model (i.e., Mars GCM), and one from the Agency's Mars Climate Modeling Center (MCMC) based at the NASA Ames Research Center. The climate model includes several complex atmospheric physical packages. With such physics modules, our global climate simulations present comparatively well with observations of the planet's current water cycle (Haberle et al.,2019). The climate model is "forced" with an annual dust cycle (i.e., nudged based on MGS/TES observations). Compared to the northern-hemisphere counterparts, the southern synoptic-period weather disturbances and accompanying frontal waves have smaller meridional and zonal scales, and are less intense. Influences of the zonally asymmetric (i.e., east-west varying) topography on southern large-scale weather are investigated, in addition to large-scale up-slope/down-slope flows and the diurnal cycle. A southern storm zone in late winter and early spring presents in the western hemisphere via orographic influences from the Tharsis highlands, and the Argyre and Hellas impact basins. Geographically localized transient-wave activity diagnostics are constructed that illuminate dynamical differences amongst the simulations and these are presented.

Hollingsworth, Jeffery L.↗

Agentic Diagrammatica: Towards Autonomous Symbolic Computation in High Energy Physics

We present Diagrammatica, a symbolic computation extension to the HEPTAPOD agentic framework, which enables LLM agents to plan and execute multi-step theoretical calculations. Symbolic computation poses a distinctive reliability challenge for LLM agents, as correctness is governed by implicit mathematical conventions that are not encoded in a form that can be easily checked in the computational backend. We identify two complementary remedies, tool-constrained computation and targeted knowledge grounding, and pursue the first as the primary architecture. Concretely, we concentrate the agent's action distribution onto tool calls with convention-fixing semantics, in which the agent specifies a compact, human-auditable diagram specification and a trusted backend performs the symbolic or numerical manipulations exactly. The toolkit provides two complementary calculation paths consuming a shared diagram specification: Naive Dimensional Analysis (NDA) for order-of-magnitude rate estimates and Exact Diagrammatic Analysis (EDA) for tree-level symbolic calculations via automatic FeynCalc code generation, both supplemented by automatic Feynman diagram enumeration and a navigable theory knowledge base. The architecture is validated on two benchmarks: (1) an exhaustive catalog of all tree-level, single-vertex $1\to 2$ partial decay widths across scalar, fermion, and vector parents, with complete massless and threshold limits and Standard Model validation; and (2) an NDA sensitivity study of the muon decay multiplicity $μ^+ \to ν_μ\barν_e + n(e^+e^-) + e^-$, determining the maximum observable $n$ at current and planned muon experiments.

Menzo, Tony [Alabama U.; Fermilab] (ORCID:00000002↗

Data-driven Mori–Zwanzig modeling of Lagrangian particle dynamics in turbulent flows

The dynamics of Lagrangian particles in turbulence play a crucial role in mixing, transport, and dispersion in complex flows. Their trajectories exhibit highly nontrivial statistical behavior, motivating the development of surrogate models that can reproduce these trajectories without incurring the high computational cost of direct numerical simulations of the full Eulerian field. This task is particularly challenging because reduced-order models typically lack access to the full set of interactions with the underlying turbulent field. Novel data-driven machine learning techniques can be powerful in capturing and reproducing complex statistics of the reduced-order/surrogate dynamics. In this work, we show how one can learn a surrogate dynamical system that is able to evolve a turbulent Lagrangian trajectory in a way that is point-wise accurate for short-time predictions (with respect to Kolmogorov time) and stable and statistically accurate at long times. This approach is based on the Mori–Zwanzig formalism, which prescribes a mathematical decomposition of the full dynamical system into resolved dynamics that depend on the current state and the past history of a reduced set of observables, and the unresolved orthogonal dynamics due to unresolved degrees of freedom of the initial state. We show how by training this reduced order model on a point-wise error metric on short time-prediction, we are able to correctly learn the dynamics of Lagrangian turbulence, such that also the long-time statistical behavior is stably recovered at test time. This opens up a range of applications, for example, for the control of active Lagrangian agents in turbulence.

97 MATHEMATICS AND COMPUTING↗

Modeling Distributed Situation Awareness in Resilience-based Design of Complex Systems

Human operators play a major role in the resilience of complex systems–while human error is one of the biggest contributors to hazardous events, operators additionally play a critical role in mitigating hazardous events. A key factor underlying this operator resilience is situation awareness–the ability of operators to understand their environment and each other to achieve desired system functions. In contrast to situation awareness-related accident models in the literature, which are largely conceptual in nature, this work proposes the use of a dynamic simulation framework to concretely model both the effects of situation awareness-related human errors and situation awareness-related hazard-mitigating properties using the distributed situation awareness theory. This work then presents specialized model constructs to enable agents’ individual perceptions of the system state and transactions with other agents (and thus distributed situation awareness) to be represented in simulation. To demonstrate this framework, it is then adapted to an aircraft taxiway case study, where it is used to model aircraft conflicts due to lack of vision and poor communications from the air traffic controller. This demonstration shows the potential of using simulation models to rigorously understand situation awareness-related human errors and thus inform the design of resilience.

Resilience Modeling↗

Distributed and communication-efficient solutions to linear equations with special sparse structure

In this paper we report two distributed and communication-efficient algorithms based on the multi-agent system are proposed to solve a system of linear equations with the Laplacian sparse system matrix. One algorithm is based on the gradient descent method in optimization. In this algorithm, the agents only share partial information instead of all of their collective state vectors to save significant communication. The other algorithm is obtained by approximating Newton’s method for a faster convergence rate. Although it requires twice as much communication as the first one, it is still communication-efficient given the low dimension of the information shared among agents. The convergence at a linear rate is proved for both algorithms, and a comprehensive comparison of their convergence rate, communication burden, and computation costs is also performed. The proposed algorithms can be applied to various systems to solve those problems that can be modeled as a system of linear equations with a Laplacian sparse system matrix. Simulation results with the electric power system illustrate their effectiveness.

42 ENGINEERING↗

Deep reinforcement learning for complex evaluation of one-loop diagrams in quantum field theory

In this paper we present a technique based on deep reinforcement learning that allows for numerical analytic continuation of integrals that are often encountered in one-loop diagrams in quantum field theory. To extract certain quantities of two-point functions, such as spectral densities, mass poles or multiparticle thresholds, it is necessary to perform an analytic continuation of the correlator in question. At one-loop level in Euclidean space, this results in the necessity to deform the integration contour of the loop integral in the complex plane of the square of the loop momentum, to avoid nonanalyticities in the integration plane. Using a toy model for which an exact solution is known, we train a reinforcement learning agent to perform the required contour deformations. We report our study shows great promise for an agent to be deployed in iterative numerical approaches used to compute nonperturbative two-point functions, such as the quark propagator Dyson-Schwinger equation, or more generally, Fredholm equations of the second kind, in the complex domain.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Towards Immobilized Proton-Coupled Electron Transfer Agents for Electrochemical Carbon Capture from Air and Seawater

Electrochemical CO 2 separation has drawn attention as a promising strategy for using renewable energy to mitigate climate change. Redox-active compounds that undergo proton-coupled electron transfer (PCET) are an impetus for pH-swing-driven CO 2 capture at low energetic costs. However, multiple barriers hinder this technology from maturing, including sensitivity to oxygen and the slow kinetics of CO 2 capture. Here, we use vapor phase chemistry to construct a textile electrode comprising an immobilized PCET agent, poly(1-aminoanthraquinone) (PAAQ), and incorporate it into redox flow cells. This design contrasts with others that use dissolved PCET agents by confining proton-storage to the surface of an electrode kept separate from an aqueous, CO 2 -capturing phase. This system facilitates carbon capture from gaseous sources (a 1% CO 2 feed and air), as well as seawater, with the latter at an energetic cost of 202 kJ/mol CO2 , and we find that quinone moieties embedded within the electrode are more stable to oxygen than dissolved counterparts. Simulations using a 1D reaction-transport model show that moderate energetic costs should be possible for air capture of CO 2 with higher loadings of polymer-bound PCET moieties. The remarkable stability of this system sets the stage for producing textile-based electrodes that facilitate pH-swing-driven carbon capture in practical situations.

Ali, Fawaz↗

Radiological Recovery Logistics Tool - 20161

Argonne is building and testing a tool, the Radiological Recovery Logistics Tool (RRLT), that can be used during the response and recovery from a radiological or nuclear incident to effectively allocate appropriate commercial and public works equipment to mitigate, remove, and contain radiological contamination. The requirements for this tool - as well as development of the resulting software - is overseen by a steering committee of stakeholders from DHS's National Urban Security Technology Laboratory (NUSTL), the Federal Emergency Management Agency (FEMA), and the Environmental Protection Agency (EPA). One essential requirement is for RRLT to support the efficient and appropriate allocation of resources for a radiological response. Subsequent discussions between ANL and stakeholders have solidified the nature of this support to include identification of the types of resources to be allocated. The study reported in this paper has both factored fundamental concepts and connections out of this identification process and created a Knowledge Base detailing support goals, response scenarios, and efficacy information on dozens of equipment types. In short, RRLT will dynamically apply these findings to situational conditions surrounding contamination incidents. RRLT's Domain, the model of elements, ideas and relationships with which the tool will work, draws concepts from technical reports and stakeholder vocabularies to connect response goals and scenarios to types of equipment that offer utility towards those goals in those scenarios. RRLT's Knowledge Base will contain details on dozens of equipment types and facilitate the operator's discovery and consumption of these details most pertinent to a dynamically selected subset of goals. The core of its Domain Model is based on a report authored by this team. This report [1] contains a comprehensive list of proposed equipment to accomplish various missions or scenarios that might arise after a large-scale radiological contamination incident in an urban environment or critical infrastructure. The report divides potential response and recovery efforts into five support goals: Survey and monitoring of the contaminated area; Mitigation of received dose to first responders: Decontamination (gross and final) of buildings, vehicles, roadways, parks, and other surfaces: Waste management of solid waste generated during recovery operations: and Containment of wastewater and other waste generated during the response and recovery phases. RRLT's development is driven by use cases. A use case is an intention with which a user approaches the software. Use cases are grouped into delivery increments to schedule development, testing, and presentation to stakeholders. This model partitions the system into seven increments: User Arrival and Authentication, Search and Navigation, Equipment Recommendation, Plan Management, Content Management, and Expanded Access. Once a user 15 authenticated, RRLT will present the user with a dashboard that allows them to explore or search RRLT's content. The dashboard will also include a 'Plan' panel for collecting decisions and relevant observations about an incident at hand to facilitate development of an equipment list. RRLT will offer three general modes of access to items in the knowledge base: - Keyword search for direct discovery of items, - Navigation along predetermined paths from recovery goal towards equipment types, and - Interactive guidance towards equipment types by an autonomous software agent: the Equipment Recommendation Wizard. This presentation will detail progress in the development of the RRLT and also discuss opportunities for those interested in providing feedback on its content and functionality. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Modeling Distributed Situation Awareness in Resilience-Based Design of Complex Engineered Systems

Human operators play a major role in the resilience of complex systems–while human error is one of the biggest contributors to hazardous events, operators additionally play a critical role in mitigating hazardous events. A key factor underlying this operator resilience is situation awareness–the ability of operators to understand their environment and each other to achieve desired system functions. In contrast to situation awareness-related accident models in the literature, which are largely conceptual in nature, this work proposes the use of a dynamic simulation framework to concretely model both the effects of situation awareness-related human errors and situation awareness-related hazard-mitigating properties using the distributed situation awareness theory. This work then presents specialized model constructs to enable agents’ individual perceptions of the system state and transactions with other agents (and thus distributed situation awareness) to be represented in simulation. To demonstrate this framework, it is then adapted to an aircraft taxiway case study, where it is used to model aircraft conflicts due to lack of vision and poor communications from the air traffic controller. This demonstration shows the potential of using simulation models to rigorously understand situation awareness-related human errors and thus inform the design of resilience.

Resilience Modeling↗

Dynamic Ride-Matching for Large-Scale Transportation Systems

Efficient dynamic ride-matching (DRM) in large-scale transportation systems is a key driver in transport simulations to yield answers to challenging problems. Although the DRM problem is simple to solve, it quickly becomes a computationally challenging problem in large-scale transportation system simulations. Therefore, this study thoroughly examines the DRM problem dynamics and proposes an optimization-based solution framework to solve the problem efficiently. To benefit from parallel computing and reduce computational times, the problem’s network is divided into clusters utilizing a commonly used unsupervised machine learning algorithm along with a linear programming model. Then, these sub-problems are solved using another linear program to finalize the ride-matching. At the clustering level, the framework allows users adjusting cluster sizes to balance the trade-off between the computational time savings and the solution quality deviation. A case study in the Chicago Metropolitan Area, U.S., illustrates that the framework can reduce the average computational time by 58% at the cost of increasing the average pick up time by 26% compared with a system optimum, that is, non-clustered, approach. Another case study in a relatively small city, Bloomington, Illinois, U.S., shows that the framework provides quite similar results to the system-optimum approach in approximately 62% less computational time.

33 ADVANCED PROPULSION SYSTEMS↗

Discovering mechanisms for materials microstructure optimization via reinforcement learning of a generative model

Abstract The design of materials structure for optimizing functional properties and potentially, the discovery of novel behaviors is a keystone problem in materials science. In many cases microstructural models underpinning materials functionality are available and well understood. However, optimization of average properties via microstructural engineering often leads to combinatorically intractable problems. Here, we explore the use of the reinforcement learning (RL) for microstructure optimization targeting the discovery of the physical mechanisms behind enhanced functionalities. We illustrate that RL can provide insights into the mechanisms driving properties of interest in a 2D discrete Landau ferroelectrics simulator. Intriguingly, we find that non-trivial phenomena emerge if the rewards are assigned to favor physically impossible tasks, which we illustrate through rewarding RL agents to rotate polarization vectors to energetically unfavorable positions. We further find that strategies to induce polarization curl can be non-intuitive, based on analysis of learned agent policies. This study suggests that RL is a promising machine learning method for material design optimization tasks, and for better understanding the dynamics of microstructural simulations.

36 MATERIALS SCIENCE↗

ATOS-1: Designing the infrastructure for an advanced spacecraft operations system

The space industry has identified the need to use artificial intelligence and knowledge based system techniques as integrated, central, symbolic processing components of future mission design, support and operations systems. Various practical and commercial constraints require that off-the-shelf applications, and their knowledge bases, are reused where appropriate and that different mission contractors, potentially using different KBS technologies, can provide application and knowledge sub-modules of an overall integrated system. In order to achieve this integration, which we call knowledge sharing and distributed reasoning, there needs to be agreement on knowledge representations, knowledge interchange-formats, knowledge level communications protocols, and ontology. Research indicates that the latter is most important, providing the applications with a common conceptualization of the domain, in our case spacecraft operations, mission design, and planning. Agreement on ontology permits applications that employ different knowledge representations to interwork through mediators which we refer to as knowledge agents. This creates the illusion of a shared model without the constraints, both technical and commercial, that occur in centralized or uniform architectures. This paper explains how these matters are being addressed within the ATOS program at ESOC, using techniques which draw upon ideas and standards emerging from the DARPA Knowledge Sharing Effort. In particular, we explain how the project is developing an electronic Ontology of Spacecraft Operations and how this can be used as an enabling component within space support systems that employ advanced software engineering. We indicate our hope and expectation that the core ontology developed in ATOS, will permit the full development of standards for such systems throughout the space industry.

Poulter, K. J.↗

Xenicanes attenuate pro-inflammatory 5-lipoxygenase: Prospective natural anti-inflammatory leads from intertidal brown seaweed Padina tetrastromatica

Two previously unreported xenicane class of novaxenicin-type xenicin diterpenoids (1–2) bearing cyclonona[d]furo [2,3-b]pyrandiol and three xeniolide-type diterpenoids with unprecedented octahydrocyclonona[c]pyran-3(1H)-one backbones (3–5) were separated from the organic extract of the intertidal seaweed Padina tetrastomatica (family Dictyotaceae), collected from southern India. The compounds were deduced to bear a xenicane moiety with a 2-oxabicyclo[7.4.0]tridecane cyclic system. The structures of these specialized metabolites were attributed based on the extensive nuclear magnetic resonance spectral analyses, and comparison of related compounds. Xeniolide-type diterpenes (3–5) registered significantly greater attenuation potential against pro-inflammatory 5-lipoxygenase (IC{sub 50} ~ 2.04 mM) than that exhibited by non-steroidal anti-inflammatory drug ibuprofen (IC{sub 50} 4.50 mM, P< 0.05). The xeniolide derivative octahydro-1,7-dihydroxy-4-(4{sup 1}-hydroxy-4{sup 2}-methylpropyl)-6-(6{sup 1}-hydroxy-6{sup 2}-propenyl)-10-methyl-cyclonona[c]pyran-3(1H)-one (5) exhibited comparable antioxidant activity (DPPH IC{sub 50} 1.73 mM) along with standard antioxidative agent α-tocopherol (IC{sub 50} < 2 mM). In silico molecular modelling studies were performed to designate the 5-lipoxygenase inhibitory mechanism of the xenicanes, and the comparison of docking parameters suggested that the xeniolide derivative 5 exhibited least binding energy of −11.56 kcal mol{sup −1}, and that was corroborated with its greater inhibition potential against the pro-inflammatory enzyme. These results demonstrated that the xeniolide-type diterpenoids with previously unreported δ-lactone cyclononane framework might constitute promising anti-inflammatory leads with pro-inflammatory 5-lipoxygenase enzyme inhibitory activities.

60 APPLIED LIFE SCIENCES↗

A biomimetic approach to fast selection of optimal controlled variables using multiagent algorithms and a decomposition approach

Selection of primary controlled variables (CVs) with due consideration of economic loss and ease of control is an important step for achieving optimal plant operation under design and off-design conditions. However, the computational expense of the combinatorial optimization step that is undertaken for CV selection renders the existing approaches unaffordable for fast selection of optimal CVs. Two approaches are proposed in this paper for improving the computational efficiency of the optimization step. Typically, the CV selection process is typically executed by considering the entire plant together. Opposed to that, the first approach proposed here seeks to decompose the process plant into multiple partitions based on the connectivity strength. Then the CV selection algorithm can be executed on each partition independently. The decomposition algorithm is biomimetic and has been developed, in analogy to neuroscience, mimicking how the information about the connectivity information is extracted using a dynamic casual model. This approach can significantly decrease the number of combinatorial optimization problems that needs to be solved and facilitates parallelization of the solution. In the second approach, a multiagent optimization framework is proposed that utilizes metaheuristic optimization strategies such as the efficient ant colony algorithm, simulated annealing and the genetic algorithm. These two approaches can be used by themselves or in combination. This framework enables cooperative search by a group of algorithmic agents facilitated through an information sharing protocol. These two approaches are applied to a toy problem and an acid gas removal unit as part of an integrated gasification combined cycle. This study shows how the computational time and economic and control performance of the optimal CV sets vary as the number of partitions are changed.

42 ENGINEERING↗

Defect and Linker Effects on the Binding of Organophosphorous Compounds in UiO-66 and Rare-Earth MOFs

The adsorption of chemical warfare agents and their simulants by Zr (UiO-66) and rare-earth (Y, UiO-66-DOBDC analog)-based metal–organic frameworks (MOFs) is explored here using density functional theory. In particular, we investigate the role of linker functional group (OH, H) and metal atom identity on the binding energies of organophosphorous compounds. Commonly used cluster approximations for MOF secondary building units and various optimization constraints are compared with three-dimensional periodic results. An in-depth scan of potential binding sites and orientations reveals little effect due to metal identity, whereas the effect of linker functionalization depends on the substrate. This finding strongly suggests that full linkers and functional groups should be included in cluster models. Importantly, defect sites show considerably improved binding of organophosphorous compounds as compared to ideal clusters. Favorable binding is also demonstrated at two additional adsorption sites, ZrOH and μ 3 -OH, that likely play a role in the initial adsorption process. Finally, the results presented here portray the importance of including full three-dimensional pore structures in the adsorption process of organophosphorous compounds in MOFs; a critical first step in the degradation of these harmful chemicals.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

AdenPredictor: accurate prediction of the adenylation domain specificity of nonribosomal peptide biosynthetic gene clusters in microbial genomes

Microbial natural products represent a major source of bioactive compounds for drug discovery. Among these molecules, nonribosomal peptides (NRPs) represent a diverse class that include antibiotics, immunosuppressants, anticancer agents, toxins, siderophores, pigments, and cytostatics. The discovery of novel NRPs remains a laborious process because many NRPs consist of nonstandard amino acids that are assembled by nonribosomal peptide synthetases (NRPSs). Adenylation domains (A-domains) in NRPSs are responsible for selection and activation of monomers appearing in NRPs. During the past decade, several support vector machine-based algorithms have been developed for predicting the specificity of the monomers present in NRPs. These algorithms utilize physiochemical features of the amino acids present in the A-domains of NRPSs. In this article, we benchmarked the performance of various machine learning algorithms and features for predicting specificities of NRPSs and we showed that the extra trees model paired with one-hot encoding features outperforms the existing approaches. Moreover, we show that unsupervised clustering of 453560 A-domains reveals many clusters that correspond to potentially novel amino acids. While it is challenging to predict the chemical structure of these amino acids, we developed novel techniques to predict their various properties, including polarity, hydrophobicity, charge, and presence of aromatic rings, carboxyl, and hydroxyl groups.

59 BASIC BIOLOGICAL SCIENCES↗