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DOE OSTI2021

Resilience-based Recovery Scheduling of Transportation Network in Mixed Traffic Environment: A Deep-Ensemble-Assisted Active Learning Approach

Devising effective post-hazard recovery strategies is critical in enhancing the resilience of transportation networks (TNs). However, existing work does not consider the multiclass users’ travel behavior in network functionality quantification and the metaheuristic solution procedures often suffer from extensive computational burden due to the exploration need in large solution space and the expensive functionality quantification. Here, this study develops a bilevel decision-making framework for the resilience-based recovery scheduling of the TN in a mixed traffic environment with connected and autonomous vehicles (CAVs) and human-driven vehicles (HDVs). The lower level quantifies the TN's functionality over time considering different travel behavior of CAV and HDV users arisen from their different levels of information perception. The upper level presents a novel deep-ensemble-assisted active learning approach to balance optimization performance and computational cost. This framework can help decision makers better quantify the TN's functionality to support effective recovery scheduling of TN with different mixed traffic scenarios ranging from HDV-only to future CAV-dominant traffic. The optimization approach bears the potential to be extended to solving general large-scale network recovery scheduling problems effectively and efficiently. The proposed methodology is demonstrated using a real-world traffic network in Southern California under earthquake considering deterministic and stochastic repair durations.

42 ENGINEERING↗

DOE OSTI2023

Reliability modeling in a predictive maintenance context: A margin-based approach

Current system reliability methods (typically based on fault trees or reliability block diagrams) can effectively propagate reliability data from the asset to the system level in order to identify system critical points. However, employed asset reliability data are an approximated integral representation of the past industrywide operational experience, and they neglect the present asset health status (available, for example, from online monitoring data and diagnostic assessments) and forecasted health projection (when available from prognostic models). Asset health should be informed solely by that specific asset’s current and historical performance data and should not be an approximated integral representation of the past industrywide operational experience (as currently performed by system reliability models through Bayesian updating processes). Sensor data, diagnostic assessments, and prognostic assessments are in fact not considered in plant reliability models used to inform system engineers on the most critical assets. In addition, the propagation of quantitative health data from the asset to the system level is a challenge given the diverse nature and structure of health data elements (e.g., vibration spectra, temperature readings, expected failure time). Ideally, in a predictive maintenance context, system reliability models should support decision making by propagating available health information from the asset to the system level in order to provide a quantitative snapshot of system health and identify the most critical assets. Here, this paper is directly addressing these two goals by proposing a different approach for reliability modeling that relies on asset diagnostic and prognostic assessments, along with monitoring data to measure asset health. The propagation of health data from the asset to the system level is performed through fault tree models not in probability terms, but in terms of margin where margin is the “distance” between the present status and an undesired event (e.g., failure or unacceptable performance). Through a cause-effect lens, while classical reliability models target the effect associated with asset performance, a margin-based approach focuses on the cause of an undesired asset performance (i.e., its health). Hence, thinking of reliability in terms of margins implies decision-making based on causal reasoning. We will show how fault tree models can be solved using a margin language and how this process can effectively assist system engineers to identify the most critical assets.

97 - MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Projecting Life-Cycle Environmental Impacts of Corn Production in the U.S. Midwest under Future Climate Scenarios using machine learning approach

Emission factors and process-based mechanism models are popular approaches used to estimate life-cycle environmental impacts. However, emission factors are often incapable of describing spatial and temporal heterogeneity of agricultural emissions, whereas process-based mechanism models, capable of capturing the heterogeneity, tend to be very complicated, and time-consuming. Efficient predictions of life-cycle environmental impacts from agricultural production are lacking. This study develops a rapid predictive model to quantify life-cycle global warming (GW), eutrophication (EU) and acidification (AD) impacts of corn production using a novel machine learning approach. We used the boosted regression tree (BRT) model to estimate current and future life-cycle environmental impacts of corn production in 12 U.S. Midwest at county scale under four Intergovernmental Panel on Climate Change (IPCC)’s climate scenarios (RCP2.6. 4.5, 6.0, and 8.5) for years 2009-2100. Results show that future life-cycle GW, EU and AD impacts of corn production will increase under all four scenarios, with the worst environmental impacts shown under the RCP 8.5 scenario. Significant spatial variation were estimated to be 3.6, 29.9 and 1.8-folds under the RCP4.5 scenario, and 6.6, 152.9 and 1.6-folds under the RCP8.5 scenario, respectively. Findings from this study demonstrate the importance of considering county-level life-cycle environmental impacts and the influences of climate change in future years to aid in developing potential adaptation and mitigation programs and policies.

Machine learning, climate change, projections, env↗

DOE OSTI2025

Accelerating the discovery of low-energy structure configurations: A computational approach that integrates first-principles calculations, Monte Carlo sampling, and Machine Learning

Finding Minimum Energy Configurations (MECs) is essential in fields such as physics, chemistry, and materials science, as they represent the most stable states of the systems. In particular, identifying such MECs in multi-component alloys considered candidate PFMs is key because it determines the most stable arrangement of atoms within the alloy, directly influencing its phase stability, structural integrity, and thermo-mechanical properties. However, since the search space grows exponentially with the number of atoms considered, obtaining such MECs using computationally expensive first-principles DFT calculations often results in a cumbersome task. To escape the above compromise between physical fidelity and computational efficiency, we have developed a novel physics-based data-driven approach that combines Monte Carlo sampling, first-principles DFT calculations, and Machine Learning to accelerate the discovery of MECs in multi-component alloys. More specifically, we have leveraged well-established Cluster Expansion (CE) techniques with Local Outlier Factor models to establish strategies that enhance the reliability of the CE method. In this work, we demonstrated the capabilities of the proposed approach for the particular case of a tungsten-based quaternary high-entropy alloy. However, the method is applicable to other types of alloys and enables a wide range of applications.

36 MATERIALS SCIENCE↗

DOE OSTI2026

A first principles approach for determining solute effect on electrical resistivity of Aluminum solid solution

There are no general approaches for predicting the effect of solutes on electrical resistivity changes in aluminum (Al) solid solutions. Here, we introduce a first principles-based approach for predicting temperature-dependent electrical resistivity of Al solid solutions. A combination of electronic structure methods was applied to calculate electronic scattering effects on chemical disorder and temperature induced atomic vibrations or phonons. Our calculations are validated by contrasting experimental resistivity measurements for two elements in Al solid solution: at 0.3 at. % concentration, Sn (an sp-valence solute) increases the residual resistivity by 0.2 μΩ·m, whereas Zr (a d-valence solute) causes a 2.5 μΩ·m increase. This trend may deviate for solute elements in Al that form semiconductors in pure state, like Si. Our findings provide an understanding of the role of valence electron character of solute atoms in determining electrical resistivity of Al alloys.

Samolyuk, German [ORNL] (ORCID:0000000168778255)↗

DOE OSTI2023

Graph-based Compact Modeling (GCM) of CMOS transistors for efficient parameter extraction: A machine learning approach

Parameter extraction of compact transistor models is an expensive process, heavily relying on engineering knowledge and experience. To automate such a process, we propose a novel approach, Graph-based Compact Model (GCM), that integrates physical modeling and data-driven learning. GCM utilizes Graph Neural Networks (GNNs) to establish the model structure, while retaining the physicality in compact models. Here, we implement our GCM in Verilog-A to support circuit simulations. As demonstrated with an academic 7 nm FinFET PDK, the new approach automatically generates a GCM model within a minute, and achieves excellent accuracy and efficiency in SPICE.

42 ENGINEERING↗

DOE OSTI2025

A consumer-centric approach to quantify efficiency of receiving goods purchased via online

Virtual participation in shopping activities has increased exponentially in the past four years compared to the last couple of decades. E-tailing or online shopping offers the convenience of goods reaching a consumer instead of a consumer traveling to a store, but it has downsides like geographical service variability and negative social externalities such as increased energy consumption and emissions. This study proposes a novel approach to quantify e-tailing efficiency from the consumers’ viewpoint. The methodology is innovative in its integration of accessibility theory with energy and cost impedance factors and consideration of delivering and picking up goods purchased via online. The methodology is implemented for the San Francisco Bay Area and is subject to scenarios that see enhancements to various facets of online shopping delivery. Results demonstrate that increasing the frequency of e-commerce deliveries helps improve e-tailing efficiency in rural locations, while improvements in energy and cost aspects of delivery modes are seen to improve e-tailing efficiencies in the central parts of the region. The approach proposed can provide valuable insights on where people have limited benefits from online shopping and how emerging delivery mechanisms can change the quality of the e-commerce experience within a city. This research offers a replicable framework for assessing e-commerce systems in diverse geographic contexts, contributing to the development of equitable and environmentally sustainable urban freight systems.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2021

Dual-solver hybrid computational approaches for design and analysis of vertical lift vehicles

Abstract The cost of Reynolds-Averaged Navier-Stokes simulations can be restrictive to implement in aeromechanics design and analysis of vertical lift configurations given the cost to resolve the flow on a mesh sufficient to provide accurate aerodynamic and structural loads. Dual-solver hybrid methods have been developed that resolve the configuration and the near field with the Reynolds-Averaged Navier-Stokes solvers, while the wake is resolved with vorticity-preserving methods that are more cost-effective. These dual-solver approaches can be integrated into an organisation’s workflow to bridge the gap between lower-fidelity methods and the expensive Reynolds-Averaged Navier-Stokes when there are complex physics present. This paper provides an overview of different dual-solver hybrid methods, coupling approaches, and future efforts to expand their capabilities in the areas of novel configurations and operations in constrained and turbulent environments.

Engineering↗

DOE OSTI2023

Nontraditional Approaches To Enable High-Energy and Long-Life Lithium–Sulfur Batteries

In this study, lithium-sulfur (Li-S) batteries are promising for automotive applications due to their high theoretical energy density (2600 Wh/kg). In addition, the natural abundance of sulfur could mitigate the global raw material supply chain challenge of commercial lithium-ion batteries that use critical elements, such as nickel and cobalt. However, due to persistent polysulfide shuttling and uncontrolled lithium dendrite growth, Li-S batteries using nonencapsulated sulfur cathodes and conventional ether-based electrolytes suffer from rapid cell degradation upon cycling. Despite significant improvements in recent decades, there is still a big gap between lab research and commercialization of the technology. To date, the reported cell energy densities and cycling life of practical Li-S pouch cells remain largely unsatisfactory. Traditional approaches to improving Li-S performance are primarily focused on confining polysulfides using electronically conductive hosts. However, these micro- and mesoporous hosts suffer from limited pore volume to accommodate high sulfur loading and the associated volume change during cycling. Moreover, they fail to balance adsorption-conversion of polysulfides during charge-discharge, leading to the formation of massive dead sulfur. Such hosts are themselves electrochemically inactive, which decreases the practical energy density. In contrast, a series of nontraditional approaches, paired with advances in multiscale mechanistic understanding, have recently demonstrated exciting performance outcomes not only in conventional coin cells but also in practical pouch cells. In this Account, we first introduce our novel cathode design strategies to overcome polysulfide shuttling and sluggish redox kinetics in thick S cathodes via selenium-sulfur chemistry and cathode host engineering. Next, we gain a mechanistic understanding of Li-S batteries in various types of electrolytes via a series of spectroscopic, nuclear magnetic resonance, and electrochemical methods. Meanwhile, a novel cathode solid electrolyte interphase encapsulation strategy via nonviscous highly fluorinated ether-based electrolyte is introduced. The established selection rule by investigating how solvating power retards the shuttle effect and induces robust cathode/solid-electrolyte interphase formation is also included. We then discuss how the synergistic interactions between rational cathode structures and electrolytes can be exploited to tailor the reaction pathways and kinetics of S cathodes under high mass loading and lean electrolyte conditions. In addition, a novel interlayer design to simultaneously overcome degradation processes (polysulfide shuttling and lithium dendrite formation) and accelerate redox reaction kinetics is presented. Finally, this Account concludes with an overview of the challenges and strategies to develop Li-S pouch cells with high practical energy density, long cycle life, and fast-charging capability.

25 ENERGY STORAGE↗

DOE OSTI2026

Development of a Flow-through Cell for Ultrasonic Extraction (UE)─Single Particle (SP)─ICP-MS─an Approach for Nano/Microparticle Elemental and Isotopic Analysis

Nanotechnology is a salient part of the scientific landscape, and analytical approaches are rapidly evolving to enable small-scale characterization of nano- and microparticle compositions and impurities. Here, a flow-through sonicating cell was developed for direct particle extraction from a silicon wafer and integrated with an inductively coupled plasma–mass spectrometer (ICP-MS) for subsequent elemental and isotopic characterization of the released particles. Ultrasonic extraction (UE)─single particle (SP)─ICP-MS offers controlled particle mobilization from solid substrates and allows for increased sample throughput by eliminating the need for pre-extraction of particles and decreasing sample preparation steps. Coupling this device to an ICP-MS with a time-of-flight (TOF) mass analyzer, it is possible to distinguish unique isotopic compositions of the particles. Both tungsten and nickel isotopically tagged particles, which were deposited on Si wafers, are presented here with analysis via UE─SP─ICP-MS. This approach could support efforts in the fields of particle synthesis, nuclear safeguards and forensics, environmental monitoring, and semiconductor industries in which the detection of particles from substrates and wafers is critical.

Paul, Molly [ORNL] (ORCID:0009000009672055)↗

DOE OSTI2024

Exploring the Influence of Linker Substitution and Ratios on Cooperative Framework Flexibility through the Mixed-Linker Approach

Cooperatively flexible metal–organic frameworks that exhibit step-shaped, or Type V-like, adsorption–desorption profiles can lower requisite pressure–temperature swings and thus energy input, necessary for an array of gas storage, delivery, and separations applications. However, such benefits are lost if the pressure threshold of the adsorption and desorption steps at a given temperature does not match the conditions dictated by the application, such as H 2 storage and delivery or olefin–paraffin separations. Unfortunately, the discovery of cooperatively flexible frameworks remains wholly serendipitous and de novo design remains impossible. Accordingly, there is a great need to further our understanding of flexibility such that we can intuitively derivatize known frameworks and, ultimately, design entirely new ones to meet the requisite conditions of energy-consumptive processes. In this work, we demonstrate that the mixed-linker, or multivariate, approach is a powerful tool for the derivation of a known flexible framework, with variances in linker substitution and ratio giving rise to a family wherein significant changes to the step-shaped adsorption–desorption profiles for multiple adsorbates are observed. Specifically, we report 12 isostructural mixed-linker derivatives of CdIF-13 (sod–Cd(benzimidazolate) 2 ) with six point-modified benzimidazole linkers spontaneously synthesized through prototypical solvothermal conditions. Furthermore, each is shown by PXRD to exhibit similar reversible flexibility to CdIF-13 and by TGA and DSC to be similarly thermally stable. Isothermal gas adsorption measurements with N 2 at 77 K, CO 2 at 195 K, and propane at 298 K demonstrate the wide-ranging, adsorbate-dependent effects of linker substitution and ratio, including dramatic reductions in the adsorption threshold pressure, evolution of multiple steps, and complete absence of observable adsorption. Aided by prior crystallographic characterization of CdIF-13, the observed trends were analyzed in an attempt to resolve the influence of linker substitution and ratios on structural behavior. In total, this work illustrates how the mixed-linker approach enables the synthesis and study of a large catalog of functionally modified frameworks, which can help identify the variables that influence flexibility and sorption behavior.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Data-Driven Mapping of the Cesium Cadmium Bromide Phase Space Utilizing a Soft-Chemistry Approach

Soft-chemistry techniques provide a versatile approach to synthesizing inorganic materials under mild conditions, enabling access to compositions and structures that are challenging to achieve through traditional thermodynamically driven solid-state methods. However, these solution-based routes often result in phase competition, requiring precise control over reaction conditions to achieve selective product formation. While one-variable-at-a-time (OVAT) approaches have traditionally been used for phase selection, data-driven strategies are emerging as more efficient methods for navigating complex synthetic spaces. Ternary metal halides, such as cesium cadmium bromides (Cs–Cd–Br), are of growing interest due to their potential in wide and ultrawide band gap applications. Unlike the well-studied cesium lead halide phases, the compositional diversity and solution-based synthesis of ternary Cs–Cd–Br phases remain largely unexplored. This study systematically investigates the synthetic phase space of the Cs–Cd–Br system by constructing a data-driven phase map. Using a common set of precursors and a standardized experimental procedure, we successfully synthesize all four known Cs–Cd–Br phases—CsCdBr 3 , Cs 2 CdBr 4 , Cs 3 CdBr 5 , and Cs 7 Cd 3 Br 13 —each exhibiting distinct structures, morphologies, and optical properties. Our findings highlight the potential of soft-chemistry methods for expanding the library of ternary metal halides and provide key insights into the thermodynamic and kinetic factors governing phase formation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Particle Markov Chain Monte Carlo Approach to Inference in Transient Surface Kinetics

Here, in this work, we develop a novel Bayesian approach to study the adsorption and desorption of CO onto a Pd(111) surface, a process of great importance in natural sciences. The motivation for this work comes from the recent availability of time-resolved infrared spectroscopy data and the need for model interpretability and uncertainty quantification in chemical processes. The objective is to learn the relevant parameters that characterize the process: coverage with time, rate constants, activation energies, and pre-exponential factors. Our approach consists of three main schemes: (i) a problem design and probabilistic model for the whole system, (ii) a particle Markov chain Monte Carlo sampler to learn the hidden coverages and rate constant parameters, and (iii) two Bayesian formulations to infer the activation energies and pre-exponential factors. The flexibility of the Bayesian framework allows for uncertainty quantification where possible and integration of mathematical constraints in the model to reflect the system physically. We found that our results for the activation energies and pre-exponential factor are in agreement with those reported in the experimental literature, independently, and we provide discussions on the advantages and disadvantages as well as applicability to other systems.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Reduced-Cost Four-Component Relativistic Double Ionization Potential Equation-of-Motion Coupled-Cluster Approaches with 4-Hole–2-Particle Excitations and Three-Body Clusters

The double ionization potential (DIP) equation-ofmotion (EOM) coupled-cluster (CC) method with 4-hole−2- particle (4h-2p) excitations on top of the CC with singles, doubles, and triples calculation, abbreviated as DIP-EOMCCSDT(4h-2p), along with its perturbative DIP-EOMCCSD(T)(a)(4h-2p) approximation, are extended to a relativistic four-component (4c) framework. In addition, we introduce and test a new computationally practical DIP-EOMCC approach, which we call DIPEOMCCSD( T)(ã)(4h-2p), that approximates the treatment of 4h- 2p correlations within the DIP-EOMCCSD(T)(a)(4h-2p) method and reduces the $\mathcal{N}$ 8 scaling characterizing DIP-EOMCCSDT(4h- 2p) and DIP-EOMCCSD(T)(a)(4h-2p) to $\mathcal{N}$ 7 with the system size $\mathcal{N}$. Further improvements in computational efficiency are obtained using the frozen natural spinor (FNS) approximation to reduce the numbers of unoccupied spinors entering the correlated steps of the DIP-EOMCC calculations according to a well-defined occupation-number-based threshold. The resulting 4c-FNS-DIPEOMCC approaches are used to compute DIPs for the series of inert gas atoms from argon to radon as well as the vertical DIPs in Cl 2 , Br 2 , HBr, and HI, which have been experimentally examined in the past. We demonstrate that, when using complete basis set extrapolations and FNS truncation threshold of 10 −4.5 , the 4c-FNS-DIP-EOMCCSD(T)(ã)(4h-2p) calculations are capable of predicting DIPs in agreement with experimental data, improving upon their nonrelativistic and spin-free scalar-relativistic counterparts, particularly when examining DIPs characterized by stronger spin−orbit coupling effects.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Bond Dissociation Energies of the Actinide Halides AnX, An = Ac–Lr and X = F–I, Utilizing Relativistic Composite Coupled Cluster Approaches

Bond dissociation energies (BDEs) have been calculated for the set of actinide halides AnX with An=Ac, Pa, and Np-Lr and X=F-I. Two composite thermochemistry methods based on the Feller-Peterson-Dixon (FPD) approach have been utilized, one involving spinor-based relativistic CCSD(T) calculations where spin-orbit (SO) was included at the orbital level and another using scalar relativistic CCSD(T) with a posteriori SO contributions based on 2-component multireference configuration interaction calculations. The method that was chosen for a given actinide halide was based on which representation yielded the best single determinant reference determinant for the coupled cluster calculation. The spinor-based method was chosen for all cases except for AmX, CmX, and BkX. Both composite approaches included contributions accounting for basis set truncation, outer-core correlation, the Gaunt interaction, and QED. The scalar FPD results, as well as the spinor-based calculations for AcF, also included higher order electron correlation up through CCSDT(Q). In addition to BDEs, CCSD(T) equilibrium bond lengths, harmonic frequencies, and vibrational anharmonicity constants are reported for all species. Last, the FPD BDEs for the fluorides were used to confirm the trend across the actinide series previously predicted by Gibson using bonding models based atomic promotion energies that provide a single 6d electron for bonding. In particular the local minimum in the BDEs at AmF is confirmed in the present calculations. Furthermore, the BDEs for LrX are predicted to be slightly larger than those of AcX, making them the largest in the actinide halide series.

Actinides↗

DOE OSTI2020

Reduced Artifact Approach for Determining Diffusion Coefficients in Time-Resolved Microscopy

Ultrafast microscopy methods traditionally assume a Gaussian profile to extract excited state diffusivities from transport measurements. Although this fitting method recovers accurate diffusion coefficients when the point spread function is well-represented by a Gaussian, even minor spatial aberrations introduced by the imaging system cause significant errors in the determined value. To provide a more accurate measure of excited state transport in nano- and microscale materials systems, in this work an alternative analysis protocol is proposed that numerically convolves the Green’s function solution to the diffusion equation with the experimentally measured point spread function. In contrast to the Gaussian fitting approach, the numerical convolution is shown to be robust against artifacts caused by nonideal point spread functions. Furthermore, the numerical convolution approach is highly effective at resolving anisotropic diffusion in modeled data.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

A Metal-on-Metal Growth Approach to Metal–Metal Oxide Core–Shell Nanostructures with Plasmonic Properties

Hybrid core–shell nanoparticles integrate the material properties from individual components; however, the synthesis of core–shell nanoparticles with dissimilar materials remains challenging. Herein, we applied a metal-on-metal thin film growth approach to control the conformal deposition of non-precious metal shells on the Cu-based metal cores to form core–shell structures of metal–metal oxide hybrids (M–M'O x , where M = AuCu 3 or Cu, M' = Fe, Mn, and Ni). The deposition kinetics were controlled by a temperature-regulated thermal decomposition of zerovalent transition metal complexes. It is predicted that the conformal deposition can be promoted by keeping the initial deposition temperature close to the thermal decomposition temperature of the zerovalent precursors. The mechanisms of strain reduction and interdiffusion facilitate conformal deposition over island growth in the synthesis with slow reaction kinetics. This study provides insightful guidance to metal-on-metal growth in solution at the nanoscale and thus the seed-mediated approach to hybrid core–shell structures. The optical properties of these metal–metal oxide hybrids were investigated experimentally and interpreted by theoretical simulations. Despite damping effects, the plasmonic properties of these Cu-based core-metal oxide shell structures may have the potential to enable plasmon-enhanced applications.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2025

Understanding Differences in Water Adsorption Isotherms: Structural Variations, Force Fields, and Monte Carlo Simulation Approaches

Accurate prediction of water adsorption in micro- and mesoporous materials with hydrophobic pores is essential for the design and characterization of advanced adsorbent materials for separation and energy applications. Here, we assess the reproducibility and consistency of water adsorption isotherms in two microporous all-silica MFI zeolite structures (MFI-K and MFI-O) using two different zeolite force fields and three simulation approaches: grand canonical Monte Carlo (GCMC), Gibbs ensemble Monte Carlo (GEMC), and transition matrix Monte Carlo (TMMC). We demonstrate that consistent treatment of the bulk fluid phase in GCMC and TMMC simulations is critical for reconciling isotherms across methods, and we construct simulation-based equations of state for the TIP4P water model to enable rigorous fugacity-to-pressure conversions. Large shifts in the isotherms are observed for two zeolite force fields developed using different parametrization strategies, with the GCS force field representing implicitly a defect-containing all-silica zeolite, whereas the TraPPE-zeo force field accurately represents an essentially defect-free all-silica zeolite. While water in the van Koningsveld structure of MFI exhibits a first-order phase transition and condensation-like step for adsorption near room temperature, water in the Olson structure of MFI displays continuous adsorption, attributed to differences in the adsorption free energy landscapes. Structural analysis reveals that small geometric variations, particularly Si–O–Si bond angles near the strongest adsorption sites, lead to these substantial differences in adsorption behavior. Furthermore, our results highlight the sensitivity of simulated water adsorption isotherms in hydrophobic frameworks to seemingly small differences in the framework structures, force field parametrization, and simulation approaches.

36 MATERIALS SCIENCE↗