Computational Linker Design for Highly Crystalline Metal–Organic Framework NU-1000
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catalysis (heterogeneous), materials and chemistry↗
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Computational Investigation of Amine–Oxygen Exciplex Formation
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catalysis (homogeneous), catalysis (heterogeneous)↗
Non-redox Oxy-Insertion via Organometallic Baeyer–Villiger Transformations: A Computational Hammett Study of Platinum(II) Complexes
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catalysis (homogeneous), catalysis (heterogeneous)↗
Site heterogeneity and broad surface-binding isotherms in modern catalysis: Building intuition beyond the Sabatier principle
Learning the science of heterogeneous catalysis and electrocatalysis always starts with the simple case of a flat, uniform surface with an ideal adsorbate. It has of course been recognized for a century that real catalysts are more complicated. For the increasingly complex catalysts of the 21st century, this Perspective argues that surface heterogeneity and non-ideal binding isotherms are central features, and their implications need to be incorporated in current thinking. A variety of systems are described herein where catalyst complexity leads to broad, non-Langmuirian surface isotherms for the binding of hydrogen atoms – and this occurs even for ideal, flat Pt(111) surfaces. Modern catalysis employs nanoscale materials whose surfaces have substantial step, edge, corner, impurity, and other defect sites, and they increasingly have both metallic and non-metallic elements M n X m , including metal oxides, chalcogenides, pnictides, carbides, doped carbons, etc. The surfaces of such catalysts are often not crystal facets of the bulk phase underneath, and they typically have a variety of potential active sites. Catalytic surfaces in operando are often non-stoichiometric, amorphous, dynamic, and impure, and often vary from one part of the surface to another. Understanding of the issues that arise at such nanoscale, multi-element catalysts is just beginning to emerge. Yet these catalysts are widely discussed using Brønsted/Bell-Evans-Polanyi (BEP) relations, volcano plots, Tafel slopes, the Butler-Volmer equation, and other linear free energy relations (LFERs), which all depend on the implicit assumption that the active sites are “similar” and that surface adsorption is close to ideal. These assumptions underly the ubiquitous intuition based on the Sabatier Principle, that the fastest catalysis will occur when key intermediates have free energies of adsorption that are not too strong nor too weak. Current catalysis research often aims to minimize the complexity of non-ideal isotherms through experimental and computational design (e.g., the use of single crystal surfaces), and these studies are the foundation of the field. In contrast, this Perspective argues that the heterogeneity of binding sites and binding energies is an inherent strength of these catalysts. Here, this diversity makes many nanoscale catalysts inherently a high-throughput screen wrapped in a tiny package. Only by making the heterogeneity part of the foundation of catalysis models, sorting the types of active sites and dissecting non-ideal binding isotherms, will modern catalysis learn to harness the inherent diversity of real catalysts. Controlling and exploiting diversity rather than avoiding it will help to optimize complex modern catalysts and catalytic conditions.
Mayer, James M.↗
Establishing a geochemical heterogeneity model for a contaminated vadose zone – Aquifer system
A large set of sediment samples from a 1600 m2 experimental plot within a 2.2 km2 vadose zone and groundwater uranium (VI) plume were subject to physical, chemical, and mineralogic characterization. The plot is being used for field experimentation on U(VI) recharge and transport processes within a persistent groundwater plume that exists in the groundwater-river interaction zone of the Columbia River at the U.S. DOE Hanford site. The samples were obtained during the installation of 35 tightly spaced (10 m separation) groundwater monitoring wells. The characterization measurements for each sample included total contaminant concentrations (U and Cu primarily), bicarbonate extractable U(VI), sequential 238U(VI) contaminant desorption Kd, 233U(VI) adsorption Kd, grain size distribution, surface area, extractable poorly crystalline Fe(III) oxides, and mineralogy. The characterization objective was to inform a conceptual model of coupled processes controlling the anomalous longevity of the plume, and to quantify the spatial heterogeneity of the contaminant inventory and the primary properties effecting reactive transport. Correlations were drawn between chemical, physical, and reaction properties, and Gaussian simulation was used to compute multiple 3-D realizations of extractable U(VI), the 233U(VI) adsorption Kd, and the distribution of the reactive < 2 mm fraction. Adsorbed contaminant U(VI) was highest in the vadose zone and the zone of seasonal water table fluctuation lying at its base. Adsorbed U(VI) was measureable, but low, in the groundwater plume region where very high hydraulic conductivities existed. The distribution of adsorbed U(VI) displayed no apparent correlation with sediment physical or chemical properties. Desorption [238U(IV)] and adsorption [233U(VI)] Kd values showed appreciable differences due to mass transfer controlled surface complexation and the effects of long subsurface residence times. The 233U(VI) adsorption Kd, a combined measure of surface complexation strength and site concentration, was relatively uniform throughout the domain, displaying correlation with fines distribution and surface area. The characterization results revealed U(VI) supplied to the groundwater plume through spatially heterogeneous recharge from residual contamination in the zone of seasonal water table fluctuation, and transport of U(VI) controlled by weak, kinetically-controlled surface complexation in the coarse-textured saturated zone. Geostatistical relationships for the adsorbed contaminant U distribution in the characterization domain allow an extrapolation to inventory at the plume scale, a critical unknown for remedial action.
contaminant heterogeniety, uranium groundwater plu↗
Solution and Interface Structure and Dynamics in Geochemistry: Gateway to Link Elementary Processes to Mineral Nucleation and Growth
Our understanding of the process of mineral nucleation is starting to evolve from the solely thermodynamical aspects in classical nucleation theory toward a mechanistic study of reactions influenced by solvation structures of ions and their dynamics. A knowledge of how these atomic and molecular interactions respond to changing solution composition and/or the nature of an interface will form a fundamental foundation to understand rate-limiting reactions and processes for nucleation and growth. Here, we review recent advances from multidisciplinary experimental and computational approaches of the structure and dynamics of aqueous solutions and mineral–liquid interfaces that will influence rates of nucleation (and growth behavior) both homogeneously in aqueous solutions and heterogeneously at mineral–liquid interfaces. Furthermore, these processes include structure (and extent) of solute clustering, solvent degrees of freedom, and the presence of noninnocent charge-balancing ions, all of which can drive the selectivity and exert control over nucleation mechanisms. Similar controlling reactions can also occur at interfaces with the added complexities of how solute ions interact with and assemble on surface sites and use different mechanisms from bulk solutes, such as lateral solvent exchanges between distinct surface sites. To gain a predictive understanding of nucleation chemistry, we emphasize several key knowledge gaps that will need to be addressed in this field and highlight the importance of combining computations and experiments.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Shaping the Future of Self-Driving Autonomous Laboratories Workshop
The "Shaping the Future of Self-Driving Autonomous Laboratories" workshop, held in Denver on November 7-8, 2024, brought together leading experts from materials science and computing to address the growing need to revolutionize scientific research through AI-driven autonomous laboratories. The workshop identified critical challenges, including the integration of heterogeneous data, development of AI systems that understand fundamental physical principles, and comprehensive safety protocols. Key recommendations emerged around developing universal laboratory equipment interfaces, implementing automated metadata collection systems, and creating hybrid AI approaches that combine data-driven learning with scientific principles. The workshop emphasized maintaining human oversight while leveraging automation, transforming scientific education to prepare the next generation of researchers, and establishing a national consortium leveraging DOE facilities as anchors for broader collaboration with academia and industry. Participants stressed the urgency of addressing the growing disconnect between human decision-making timescales and modern instrumentation capabilities, highlighting the need for strategic automation while preserving essential human insight and oversight in the research process.
36 MATERIALS SCIENCE↗
On data-driven energy flexibility quantification: A framework and case study
Building energy flexibility is an important resource for a sustainable and resilient power grid, and an important measure to reduce utility costs for building owners. Quantifying energy flexibility for existing buildings can provide critical insights in optimizing their operation. Data-driven methods for building energy modeling and analytics are gaining popularity due to the increasingly available sensor and meter infrastructure, affordable computational resources, and advanced modeling algorithms. However, their application in quantifying the energy flexibility of real buildings is still limited due to the heterogeneous data types and limited data availability. Here, this study proposes a framework for building-level data-driven energy flexibility quantification that considers different levels of data availability and use cases. Two case studies with real building data collected at different scales were conducted to demonstrate the proposed framework for different purposes.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Privacy-Preserving Federated Learning for Science: Challenges and Research Directions
This paper discusses the key challenges and future research directions for privacy-preserving federated learning (PPFL), with a focus on its application to large-scale scientific AI models, in particular, foundation models~(FMs). PPFL enables collaborative model training across distributed datasets while preserving privacy-- an important collaborative approach for science. We discuss the need for efficient and scalable algorithms to address the increasing complexity of FMs, particularly when dealing with heterogeneous clients. In addition, we underscore the need for developing advance privacy-preserving techniques, such as differential privacy, to balance privacy and utility in large FMs emphasizing fairness and incentive mechanisms to ensure equitable participation among heterogeneous clients. Finally, we emphasize the need for a robust software stack supporting scalable and secure PPFL deployments across multiple high-performance computing facilities. We envision that PPFL would play a crucial role to advance scientific discovery and enable large-scale, privacy-aware collaborations across science domains.
Kim, Kibaek [Argonne National Laboratory (ANL)]↗
Integrating LEO and GEO Observations: Toward Optimal Summertime Satellite Precipitation Retrieval
Abstract Reliable quantitative precipitation estimation with a rich spatiotemporal resolution is vital for understanding the Earth’s hydrological cycle. Precipitation estimation over land and coastal regions is necessary for addressing the high degree of spatial heterogeneity of water availability and demand, and for resolving the extremes that modulate and amplify hazards such as flooding and landslides. Advancements in computation power along with unique high spatiotemporal and spectral resolution data streams from passive meteorological sensors aboard geosynchronous Earth-orbiting (GEO) and low Earth-orbiting (LEO) satellites offer exciting opportunities to retrieve information about surface precipitation phenomena using data-driven machine learning techniques. In this study, the capabilities of U-Net–like architecture are investigated to map instantaneous, summertime surface precipitation intensity at the spatial resolution of 2 km. The calibrated brightness temperature products from the Global Precipitation Measurement (GPM) Microwave Imager (GMI) radiometer are combined with multispectral images (visible, near-infrared, and infrared bands) from the Advanced Baseline Imager (ABI) aboard the GOES-R satellites as main inputs to the U-Net–like precipitation algorithm. Total precipitable water and 2-m temperature from the Global Forecast System (GFS) model are also used as auxiliary inputs to the model. The results show that the U-Net–like algorithm can capture fine-scale patterns and intensity of surface precipitation at high spatial resolution over stratiform and convective precipitation regimes. The evaluations reveal the potential of extracting relevant, high spatial features over complex surface types such as mountainous regions and coastlines. The algorithm allows users to interpret the inputs’ importance and can serve as a starting point for further exploration of precipitation systems within the field of hydrometeorology.
Meteorology & Atmospheric Sciences↗
Shape and Stress Sensing of Multilayered Composite and Sandwich Structures Using an Inverse Finite Element Method
The marked increase in the use of composite and sandwich material systems in aerospace, civil, and marine structures leads to the need for integrated Structural Health Management systems. A key capability to enable such systems is the real-time reconstruction of structural deformations, stresses, and failure criteria that are inferred from in-situ, discrete-location strain measurements. This technology is commonly referred to as shape- and stress-sensing. Presented herein is a computationally efficient shape- and stress-sensing methodology that is ideally suited for applications to laminated composite and sandwich structures. The new approach employs the inverse Finite Element Method (iFEM) as a general framework and the Refined Zigzag Theory (RZT) as the underlying plate theory. A three-node inverse plate finite element is formulated. The element formulation enables robust and efficient modeling of plate structures instrumented with strain sensors that have arbitrary positions. The methodology leads to a set of linear algebraic equations that are solved efficiently for the unknown nodal displacements. These displacements are then used at the finite element level to compute full-field strains, stresses, and failure criteria that are in turn used to assess structural integrity. Numerical results for multilayered, highly heterogeneous laminates demonstrate the unique capability of this new formulation for shape- and stress-sensing.
Cerracchio, Priscilla↗
Bragg Coherent Diffraction Imaging for In Situ Studies in Electrocatalysis
Electrocatalysis is at the heart of a broad range of physicochemical applications that play an important role in the present and future of a sustainable economy. Among the myriad of different electrocatalysts used in this field, nanomaterials are of ubiquitous importance. An increased surface area/volume ratio compared to bulk makes nanoscale catalysts the preferred choice to perform electrocatalytic reactions. Bragg coherent diffraction imaging (BCDI) was introduced in 2006 and since has been applied to obtain 3D images of crystalline nanomaterials. BCDI provides information about the displacement field, which is directly related to strain. Lattice strain in the catalysts impacts their electronic configuration and, consequently, their binding energy with reaction intermediates. Even though there have been significant improvements since its birth, the fact that the experiments can only be performed at synchrotron facilities and its relatively low resolution to date (~10 nm spatial resolution) have prevented the popularization of this technique. Herein, we will briefly describe the fundamentals of the technique, including the electrocatalysis relevant information that we can extract from it. Subsequently, we review some of the computational experiments that complement the BCDI data for enhanced information extraction and improved understanding of the underlying nanoscale electrocatalytic processes. We next highlight success stories of BCDI applied to different electrochemical systems and in heterogeneous catalysis to show how the technique can contribute to future studies in electrocatalysis. Finally, we outline current challenges in spatiotemporal resolution limits of BCDI and provide our perspectives on recent developments in synchrotron facilities as well as the role of machine learning and artificial intelligence in addressing them.
bragg coherent diffraction imaging↗
Nanocluster Active Sites Formed on Heterogeneous Thermal Catalysts and Electrocatalysts by Operando Reactive Environments
Here, in this Viewpoint, we summarize our recent studies in this endeavor, ending with a forward-looking perspective into the remaining challenges and open questions for consideration by the catalysis community. Through a computationally efficient framework of systematically investigating metal atom ejection from and migration onto metal surfaces, leading to the formation of metal atom clusters, we show that the surface of a metal catalyst is dynamic and much less rigid than previously thought. We suggest that the operando formed metal atom clusters are novel active sites, which can dominate the activity of steps or kinks (let aside terraces) from where their constituent atoms were ejected. Through more readily responding to reaction conditions by forming catalytically active clusters, solid metal catalysts approach not only homogeneous catalyst motifs, thus establishing a bridge between homogeneous and heterogeneous catalysis, but also liquid metals which have been suggested to be better than traditional heterogeneous catalysts.
adsorption↗
Reassessment of uncertainties for the PU-MET-INTER-002 benchmark - 14432
Uncertainty for the PU-MET-INTER-002 benchmark of ZPR-6/10 was unusually large for a ZPR assembly and was dominated by the uncertainties in matrix tube pitch and composition uncertainties for plutonium, graphite and stainless steel plates. The original uncertainty analysis for PU-MET-INTER-002 was limited by computer resources, software and the nuclear data available in 2002, all of which have been improved since. An as-built Monte Carlo model of ZPR-6/10 was used to quantify components of the transformation from the detailed model to a simplified RZ benchmark in 2002. Compositional uncertainties were estimated based upon TWODANT calculations of the RZ benchmark model (20 group, S{sub 8} - P{sub 1}). MC{sup 2}-2 was used with END/B-V.2 data to generate homogenized cross section data for TWODANT. The MC{sup 2}-2 calculations did not account for lattice cell spatial heterogeneity which was driven by the manual process required to generate ∼80 modifications to the deterministic model and by the software limitations of MC{sup 2}-2. Improvements to DIF3D-VARIANT, development of the MC{sup 2}-3 and BuildZPRModel codes, and better computer resources make accurate 3D transport calculations of reactivity changes practical today. BuildZPRModel imports the as-built geometry and creates a 'drawer' homogenized DIF3D model where each radial DIF3D mesh is the exact size of a single ZPR matrix tube and drawer. A MC{sup 2}-3 input is constructed for each drawer to produce homogenized cross section data where the spatial cell heterogeneity is captured by solving a 1D MOC transport calculation on the ultrafine energy group level. Collapsing 3D drawers into 1D MC{sup 2}-3 drawer models follows conventional procedures and is done by BuildZPRModel. MCNP6 was used to re-compute relevant geometry components of the uncertainty using ENDF/B-VII.0 data with the as-built model. Changes in the geometry components compared with earlier results are insignificant. DIF3D calculations of the compositional uncertainties were performed using 33, 70, 116, 230, 425, and 703 group energy structures and selectively combined with P{sub 3}-P{sub 7} transport approximations and P{sub 1} and P{sub 3} scattering kernels. 3D diffusion theory calculations were also performed for comparison. Many composition uncertainty components were essentially unchanged with the updated methodology. However, the dominant experimental uncertainty for PU-MET-INTER-002 is the composition of stainless steel. The new calculations reduce the stainless steel uncertainty from ∼115 pcm to ∼70 pcm which significantly reduces the total uncertainty for PU-MET-INTER-002. (authors)
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
Structural characterization of ligand binding and pH-specific enzymatic activity of mouse Acidic Mammalian Chitinase
Chitin is an abundant biopolymer and pathogen-associated molecular pattern that stimulates a host innate immune response. Mammals express chitin-binding and chitin-degrading proteins to remove chitin from the body. One of these proteins, Acidic Mammalian Chitinase (AMCase), is an enzyme known for its ability to function under acidic conditions in the stomach but is also active in tissues with more neutral pHs, such as the lung. Here, we used a combination of biochemical, structural, and computational modeling approaches to examine how the mouse homolog (mAMCase) can act in both acidic and neutral environments. We measured kinetic properties of mAMCase activity across a broad pH range, quantifying its unusual dual activity optima at pH 2 and 7. We also solved high-resolution crystal structures of mAMCase in complex with oligomeric GlcNAcn, the building block of chitin, where we identified extensive conformational ligand heterogeneity. Leveraging these data, we conducted molecular dynamics simulations that suggest how a key catalytic residue could be protonated via distinct mechanisms in each of the two environmental pH ranges. These results integrate structural, biochemical, and computational approaches to deliver a more complete understanding of the catalytic mechanism governing mAMCase activity at different pH. Engineering proteins with tunable pH optima may provide new opportunities to develop improved enzyme variants, including AMCase, for therapeutic purposes in chitin degradation.
59 BASIC BIOLOGICAL SCIENCES↗
Unified Nusselt- and Sherwood-number correlations in axisymmetric finite-gap stagnation and rotating-disk flows
This paper develops a unified analysis of stagnation flow heat and mass transport, considering both semi-infinite domains and finite gaps, with and without rotation of the stagnation surface. An important objective is to derive Nusselt- and Sherwood-number correlations that represent heat and mass transport at the stagnation surface. The approach is based on computationally solving the governing conservation equations in similarity form as a boundary-value problem. The formulation considers ideal gases and incompressible fluids. The correlated results depend on fluid properties in terms of Prandtl, Schmidt, and Damkohler numbers. Heterogeneous chemistry at the stagnation surface is represented as a single first-order reaction. A composite Reynolds number represents the combination of stagnation flows with and without stagnation-surface rotation.
97 MATHEMATICS AND COMPUTING↗
Dynamic behavior of molecular Pd-acetate trimers and dimers in heterogeneous vinyl acetate synthesis
Vinyl acetate monomer (VAM) is a crucial intermediate in the production of various polymers. While molecular Pd-acetate trimers and dimers, such as Pd 3 (OAc) 6 and K 2 Pd 2 (OAc) 6 , are known to form on potassium acetate (KOAc)-promoted PdAu catalysts during heterogeneous VAM synthesis, their mechanistic role remains unclear. Here, we study the dynamics of different Pd-acetate species by utilizing in situ and operando crystallographic and spectroscopic characterizations combined with computational modeling on monometallic Pd model catalysts. The promoter-free catalyst expectedly shows low catalytic activity and VAM selectivity, corresponding to the complete reduction of Pdn(OAc) 2n species to form Pd 0 and PdC x nanoparticles. Conversely, noticeable quantities of K n Pd 2 (OAc) n+4 species remain on the KOAc-promoted catalyst, leading to smaller nanoparticle formation with 10 times the activity and double the selectivity for VAM. This study reveals that molecular Pd-acetate trimers and dimers are significant indicators of catalytic performance and highlights their structurally dynamic nature in heterogeneous vinyl acetate chemistry.
Jacobs, Hunter P. [Rice Univ., Houston, TX (United↗
Exploring new topography-based subgrid spatial structures for improving land surface modeling
Topography plays an important role in land surface processes through its influence on atmospheric forcing, soil and vegetation properties, and river network topology and drainage area. Land surface models with a spatial structure that captures spatial heterogeneity, which is directly affected by topography, may improve the representation of land surface processes. Previous studies found that land surface modeling, using subbasins instead of structured grids as computational units, improves the scalability of simulated runoff and streamflow processes. In this study, new land surface spatial structures are explored by further dividing subbasins into subgrid structures based on topographic properties, including surface elevation, slope and aspect. Two methods (local and global) of watershed discretization are applied to derive two types of subgrid structures (geo-located and non-geo-located) over the topographically diverse Columbia River basin in the northwestern United States. In the global method, a fixed elevation classification scheme is used to discretize subbasins. The local method utilizes concepts of hypsometric analysis to discretize each subbasin, using different elevation ranges that also naturally account for slope variations. The relative merits of the two methods and subgrid structures are investigated for their ability to capture topographic heterogeneity and the implications of this on representations of atmospheric forcing and land cover spatial patterns. Results showed that the local method reduces the standard deviation (SD) of subgrid surface elevation in the study domain by 17 to 19 % compared to the global method, highlighting the relative advantages of the local method for capturing subgrid topographic variations. The comparison between the two types of subgrid structures showed that the non-geo-located subgrid structures are more consistent across different area threshold values than the geo-located subgrid structures. Altogether the local method and non-geo-located subgrid structures effectively and robustly capture topographic, climatic and vegetation variability, which is important for land surface modeling.
58 GEOSCIENCES↗