Computational Modeling Techniques Research Articles

(Invited) Electrocatalyst Development for Efficient CO2 Electrolysis

This talk will discuss recent efforts at the National Energy Technology Laboratory (NETL) to develop efficient electrocatalysts that convert CO2 and water into C1 products, such as carbon monoxide (CO) and formic acid (HCOOH). The first example combines advanced surface science techniques, computational modeling, and electrochemical evaluation to track the size-dependent CO2 reduction vs. HER selectivity of silver nanocatalysts with sub-nanometer resolution. Understanding size-dependent catalytic properties is important to maximize the utilization of costly, state-of-the-art catalyst materials. Experimental efforts synthesized and evaluated Ag nanocatalysts with controlled diameters between 2-6 nm, and we identified ~4 nm Ag particles as the optimum balance of activity, selectivity, and catalyst utilization. Computational modeling techniques, including DFT, transition state theory, and microkinetic modeling identified key catalytically active surface sites that were responsible for HER vs. CO2 reduction, determined the respective active sites’ population as a function of particle size, and then predicted HER vs. CO2 reduction rates and selectivity for Ag nanocatalyst with diameters between 1-10 nm. Our computational results predicted ~4 nm particle provided the highest combination of product selectivity, catalyst utilization, and catalytic activity based on a total metal basis, which was in near perfect agreement with experiential results. These experimental and computational results should guide future catalyst design efforts by placing a lower bound on the size of practical Ag catalysts. A second example will highlight efforts to develop 3D structured and chemically-doped SnO2 catalysts for the conversion of CO2 into formate and formic acid. We coupled novel synthetic techniques with advanced synchrotron characterization, computational modeling, and evaluation MEA-style electrolyzer devices. In the case of 3D structured SnO2 catalysts we found CO2 conversion activity was substantially improved by increasing the electrocatalytically active surface area, while doping SnO2 catalysts with heteroatoms created new active sites that lowered the CO2 conversion overpotential and boosted performance.

Folding Coarse-Grained Oligomer Models with PyRosetta.

Non-biological foldamers are a promising class of macromolecules that share similarities to classical biopolymers such as proteins and nucleic acids. Currently, designing novel foldamers is a non-trivial process, often involving many iterations of trial synthesis and characterization until folded structures are observed. In this work, we aim to tackle these foldamer design challenges using computational modeling techniques. We developed CG PyRosetta, an extension to the popular protein folding python package, PyRosetta, which introduces coarse-grained (CG) residues into PyRosetta, enabling the folding of toy CG foldamer models. Although these models are simplified, they can help explore overarching physical hypotheses about how oligomers can form. Through systematic variation of CG parameters in these models, we can investigate various folding hypotheses at the CG scale to inform the design process of new foldamer chemistries. In this study, we demonstrate CG PyRosetta's ability to identify minimum energy structures with a diverse structural search over a range of simple models, as well as two hypothesis-driven parameter scans investigating the effects of side-chain size and internal backbone angle on secondary structures. We are able to identify several types of secondary structures from single- and double-helices to sheet-like and knot-like structures. We show how side-chain size and backbone bond angle both play an important role in the structure and energetics of these toy models. Optimal side-chain sizes promote favorable packing of side chains, while specific backbone bond angles influence the specific helix type found in folded structures.

Optimal anti-amyloid-beta therapy for Alzheimer's disease via a personalized mathematical model.

With the recent approval by the FDA of the first disease-modifying drug for Alzheimer's Disease (AD), personalized medicine will be increasingly important for appropriate management and counseling of patients with AD and those at risk. The growing availability of clinical biomarker data and data-driven computational modeling techniques provide an opportunity for new approaches to individualized AD therapeutic planning. In this paper, we develop a new mathematical model, based on AD cognitive, cerebrospinal fluid (CSF) and MRI biomarkers, to provide a personalized optimal treatment plan for individuals. This model is parameterized by biomarker data from the AD Neuroimaging Initiative (ADNI) cohort, a large multi-institutional database monitoring the natural history of subjects with AD and mild cognitive impairment (MCI). Optimal control theory is used to incorporate time-varying treatment controls and side-effects into the model, based on recent clinical trial data, to provide a personalized treatment regimen with anti-amyloid-beta therapy. In-silico treatment studies were conducted on the approved treatment, aducanumab, as well as on another promising anti-amyloid-beta therapy under evaluation, donanemab. Clinical trial simulations were conducted over both short-term (78 weeks) and long-term (10 years) periods with low-dose (6 mg/kg) and high-dose (10 mg/kg) regimens for aducanumab, and a single-dose regimen (1400 mg) for donanemab. Results confirm those of actual clinical trials showing a large and sustained effect of both aducanumab and donanemab on amyloid beta clearance. The effect on slowing cognitive decline was modest for both treatments, but greater for donanemab. This optimal treatment computational modeling framework can be applied to other single and combination treatments for both prediction and optimization, as well as incorporate new clinical trial data as it becomes available.

Left Atrial Appendage Occlusion: Current Stroke Prevention Strategies and a Shift Toward Data-Driven, Patient-Specific Approaches.

The left atrial appendage (LAA) is a complex structure with unknown physiologic function protruding from the main body of the left atrium. In patients with atrial fibrillation, the left atrium does not contract effectively. Insufficient atrial and LAA contractility predisposes the LAA morphology to hemostasis and thrombus formation, leading to an increased risk of cardioembolic events. Oral anticoagulation therapies are the mainstay of stroke prevention options for patients; however, not all patients are candidates for long-term oral anticoagulation. Percutaneous occlusion devices are an attractive alternative to long-term anticoagulation therapy, although they are not without limitations, such as peri-implant leakage and device-related thrombosis. Although efforts have been made to reduce these risks, significant interpatient heterogeneity inevitably yields some degree of device-anatomy mismatch that is difficult to resolve using current devices and can ultimately lead to insufficient occlusion and poor patient outcomes. In this state-of-the-art review, we evaluated the anatomy of the LAA as well as the current pathophysiologic understanding and stroke prevention strategies used in the management of the risk of stroke associated with atrial fibrillation. We highlighted recent advances in computed tomography imaging, preprocedural planning, computational modeling, and novel additive manufacturing techniques, which represent the tools needed for a paradigm shift toward patient-centric LAA occlusion. Together, we envisage that these techniques will facilitate a pipeline from the imaging of patient anatomy to patient-specific computational and bench-top models that enable customized, data-driven approaches for LAA occlusion that are engineered specifically to meet each patient's unique needs.

Implicit finite-difference time-domain schemes for TDEM modeling in three dimensions

Geophysical exploration methods that use controlled electromagnetic sources in time domain are becoming ubiquitous because of their ease of deployment and data coverage capabilities. Although these prospecting methodologies have evolved significantly, most computational numerical modeling techniques have been developed in the frequency domain. Given this evolution, it is pertinent to ask whether some known advantages of implicit finite-difference (FD) modeling techniques, which are common among other disciplines, apply to the time-domain electromagnetic (TDEM) problem in geophysics. To explore the potential advantages of 3D time-domain modeling for TDEM, we have analyzed the differences between two implicit FD formulations: a single-order backward Euler scheme and a second-order Crank-Nicolson scheme. To validate our algorithms, we tested them with existing analytical solutions for simple geometries and performed detectability tests for various electromagnetic sources. The results find an acceptable match of both numerical schemes to the analytical solutions and higher accuracy of the second-order discrete operator without a significant difference in computing time.

A semantic web approach to uplift decentralized household energy data

In a decentralized household energy system comprised of various devices such as home appliances, electric vehicles, and solar panels, end-users are able to dig deeper into the system’s details and further achieve energy sustainability if they are presented with data on the electric energy consumption and production at the granularity of the device. However, many databases in this field are siloed from other domains, including solely information pertaining to energy. This may result in the loss of information (e.g. weather) on each device’s energy use. Meanwhile, a large number of these datasets have been extensively used in computational modeling techniques such as machine learning models. While such computational approaches achieve great accuracy and performance by concentrating only on a local view of datasets, model reliability cannot be guaranteed since such models are very vulnerable to data input fluctuations when information omission is taken into account. This article tackles the data isolation issue in the field of smart energy systems by examining Semantic Web methods on top of a household energy system. We offer an ontology-based approach for managing decentralized data at the device-level resolution in a system. As a consequence, the scope of the data associated with each device may easily be expanded in an interoperable manner throughout the Web, and additional information, such as weather, can be obtained from the Web, provided that the data is organized according to W3C standards.

Effect of solid rubber fenders on the structural damage due to collisions between a ship-shaped offshore installation and an offshore supply vessel

ABSTRACT The effect of solid rubber fenders on the structural damage due to collisions between a ship-shaped offshore installation and an offshore supply vessel was investigated. An LS-DYNA computational modelling technique was developed to simulate the kinetic energy absorption behaviour of solid rubber fenders. Physical crushing testing was performed on rubber fender models to validate the computational model under different collision speeds. Traditional LS-DYNA computational models to simulate the structural crashworthiness of ship-shaped offshore hull structures colliding with an offshore supply vessel were combined with the developed rubber fender model. The computational models were applied to a hypothetical very-large-crude-oil-carrier (VLCC) class floating production storage and offloading unit (FPSO) hull that collides with an offshore supply vessel equipped with rubber fenders in the forecastle deck area, and the effects of rubber fenders on the collision energy absorption characteristics were examined in association with the structural damage of both striking OSV and struck FPSO hull structures. The findings and insights derived from the study were summarised.

In silico investigation of Alsin RLD conformational dynamics and phosphoinositides binding mechanism.

Alsin is a protein known for its major role in neuronal homeostasis and whose mutation is associated with early-onset neurodegenerative diseases. It has been shown that its relocalization from the cytoplasm to the cell membrane is crucial to induce early endosomes maturation. In particular, evidences suggest that the N-terminal regulator of chromosome condensation 1 like domain (RLD) is necessary for membrane association thanks to its affinity to phosphoinositides, membrane lipids involved in the regulation of several signaling processes. Interestingly, this domain showed affinity towards phosphatidylinositol 3-phosphate [PI(3)P], which is highly expressed in endosomes membrane. However, Alsin structure has not been experimentally resolved yet and molecular mechanisms associated with its biological functions are mostly unknown. In this work, Alsin RLD has been investigated through computational molecular modeling techniques to analyze its conformational dynamics and obtain a representative 3D model of this domain. Moreover, a putative phosphoinositide binding site has been proposed and PI(3)P interaction mechanism studied. Results highlight the substantial conformational stability of Alsin RLD secondary structure and suggest the role of one highly flexible region in the phosphoinositides selectivity of this domain.

A review of computational modeling techniques for wet waste valorization: Research trends and future perspectives

The conversion of wet waste (e.g., food waste, sewage sludge, and animal manure) into bioenergy is a promising strategy for sustainable energy generation and waste management. Although experimental efforts have driven waste conversion technologies (WCTs) to various degrees of maturity, computational modeling has equally contributed to this endeavor. This review focuses on the application of modeling techniques, including computational fluid dynamics (CFD), process simulation (PS), and machine learning (ML) on WCTs including anaerobic digestion, hydrothermal carbonization, gasification, pyrolysis and incineration. It addresses in a concise manner on how CFD models aid in understanding of the complex process and their molecular kinetics; while PS and ML models help in understanding the reaction kinetics, variable-response relationship, techno-economic assessment and sensitivity analysis. Relevant modeling approaches with their pros and cons are summarized and case studies are presented for each WCT. Moreover, a comparative evaluation among the three modeling techniques, along with their recent and ongoing developments are highlighted. Hybrid frameworks derived by combining mechanistic and ML models are proposed, which are expected to advance future wet waste valorization strategies for sustainable clean energy production and waste management.

Evolution of Modeling for Lightweight Structures: Creating the Munich Olympic Stadium Roof (1967–72)

The experimental structural qualities of the Munich Olympic Stadium roof compelled formative innovations in modeling techniques responsive to those conditions. The novel use of large-scale physical models allowed the team to navigate the complicated nature of generative form-finding for lightweight structures by balancing the various parameters of forms and forces. Although physical modeling was initially beneficial, it became obsolete as refinement and confirmation became requisite. The cable-net structure exceeded conventional capacity for analysis, which drove the engineers to evolve their confirmative modeling methods. Instead of abandoning the parametric logic of these physical models, the design team embraced this approach and applied their complementary interdisciplinary expertise to create two groundbreaking computational modeling techniques that are still in use today. Their modeling techniques exceeded their mandate for simple confirmation and evolved into the first generative and optimizing parametric modeling options for lightweight structures. The findings will show how the evolving use of these models and the collaborative complications that ensued situate contemporary challenges of experimental design, technology, and practice.

Hydrometeorological disasters during COVID-19: Insights from topic modeling of global aid reports

Since the beginning of the COVID-19 pandemic, the world has experienced numerous hydrometeorological disasters along with it. The pandemic has made disaster relief work more challenging for humanitarian organizations and governments. This study aims to provide an overview of the topics/issues of concern in the countries while responding to hydrometeorological extreme events (e.g., floods and cyclones) during the pandemic. Latent Dirichlet Allocation (LDA), a computational topic modeling technique, is employed to reduce the numerous (i.e., 1771) humanitarian reports/news to key terms and meaningful topics for 24 countries. Several insights are derived from the LDA results. It is identified that countries have suffered multiple crises (such as locust attacks, epidemics and conflicts) during the pandemic. Maintaining social distancing while disaster evacuation and circumventing the lockdown for relief work have been difficult. Children are an important topic for most countries; however, other vulnerable groups such as women and the disabled also need to be focused upon. Hygiene is not a highly weighted topic, which is of concern during a pandemic that mandates good sanitation to control it effectively. However, health is of great importance for almost all countries. The novelty of the paper lies in its interdisciplinary approach (usage of a computational technique in disaster management studies) and the timely examination of disaster management experiences during the ongoing pandemic. The insights presented in the study may be helpful for researchers and policy-makers to initiate further bottom-up work to address the challenges in responding to hydrometeorological disasters during a pandemic.

ANALYSIS OF MODERN METHODS FOR TECHNICAL SURVEY OF BUILDINGS AND STRUCTURES

The article analyzes modern methods for monitoring the technical operation of buildings and structures. Technical inspection of buildings and structures is a separate type of construction activity. The necessity for a technical survey is caused by the physical and moral deterioration of building structures and changes of terms of use, as well as the absence in building codes of an explicit account of the time factor. Ensuring the operability of structures at a specific moment of existence requires a set of measures that differ from the design, manufacture and installation of building elements associated with the creation of normal conditions for the life of enterprises in industrial buildings and structures and, above all, the safety of the operation process. 
 Specialists in the technical operation of buildings and structures have new tools for more accurate determination of defects and supervision of buildings and structures. The emergence and development of IT technologies, in particular, the use of mathematical, computer modeling techniques have demonstrated unprecedented opportunities for a comprehensive approach to testing the performance of buildings. Most modern methods of technical inspection of buildings do not require significant financial and labor costs; they can be directly implemented both during the planning of the facility and during its operation without reducing the bearing capacity of structures, unlike classical methods (visual, visual-instrumental, etc.). Their goal is to control the technical condition of building structures and early diagnosis of threats to the existence of the object. The integrated use of these methods at different stages of the life cycle of buildings will increase their service life and the comfort and safety of staying in them.

Modeling the Solid Electrolyte Interphase: Machine Learning as a Game Changer?

AbstractThe solid electrolyte interphase (SEI) is a complex passivation layer that forms in situ on many battery electrodes such as lithium‐intercalated graphite or lithium metal anodes. Its essential function is to prevent the electrolyte from continuous electrochemical degradation, while simultaneously allowing ions to pass through, thus constituting an electronically insulating, but ionically conducting material. Its properties crucially affect the overall performance and aging of a battery cell. Despite decades of intense research, understanding the SEI's precise formation mechanism, structure, composition, and evolution remains a conundrum. State‐of‐the‐art computational modeling techniques are powerful tools to gain additional insights, although confronted with a trade‐off between accuracy and accessible time‐ and length scales. In this review, it is discussed how recent advances in data‐driven models, especially the development of fast and accurate surrogate simulators and deep generative models, can work with physics‐based and physics‐informed approaches to enable the next generation of breakthroughs in this field. Machine learning‐enhanced multiscale models can provide new pathways to inverse the design of interphases with desired properties.

Applying the language acquisition model to the solution small language processing tasks

The problem of building a computer model of a small language was under solution. The relevance of this task is due to the following considerations: the need to eliminate the information inequality between speakers of different languages; the need for new tools for the study of poorly understood languages, as well as innovative approaches to language modeling in the low-resource context; the problem of supporting and developing small languages.There are three main objectives in solving the problem of small natural language processing at the stage of describing the problem situation: to justify the problem of modeling language in the context of resource scarcity as a special task in the field of natural languages processing, to review the literature on the relevant topic, to develop the concept of language acquisition model with a relatively small number of available resources. Computer modeling techniques using neural networks, semi-supervised learning and reinforcement learning were involved.The paper provides a review of the literature on modeling the learning of vocabulary, morphology, and grammar of a child's native language. Based on the current understanding of the language acquisition and existing computer models of this process, the architecture of the system of small language processing, which is taught through modeling of ontogenesis, is proposed. The main components of the system and the principles of their interaction are highlighted. The system is based on a module built on the basis of modern dialogical language models and taught in some rich-resources language (e.g., English). During training, an intermediate layer is used which represents statements in some abstract form, for example, in the symbols of formal semantics. The relationship between the formal recording of utterances and their translation into the target low-resource language is learned by modeling the child's acquisition of vocabulary and grammar of the language. One of components stands for the non-linguistic context in which language learning takes place.This article explores the problem of modeling small languages. A detailed substantiation of the relevance of modeling small languages is given: the social significance of the problem is noted, the benefits for linguistics, ethnography, ethnology and cultural anthropology are shown. The ineffectiveness of approaches applied to large languages in conditions of a lack of resources is noted. A model of language learning by means of ontogenesis simulation is proposed, which is based both on the results obtained in the field of computer modeling and on the data of psycholinguistics.

An application of Artificial Neural Network (ANN) to predict the friction coefficient of nuclear grade graphite

In scientific research, computer modeling techniques are widely used. Artificial neural networks are now well established and prominent in the literature when computationally based methodologies are used. New advancements in these domains have benefited and continue to benefit the materials science and engineering research community, with new applications and levels of sophistication appearing regularly. However, with this greater utilization comes a growing tendency for neural network approaches to be misapplied, reducing their potential effectiveness. Due to its good prediction quality, an artificial Neural Network (ANN) is a valuable mathematical tool for solving complex scientific and technical problems. The study presents an approach to predicting the tribological properties of nuclear grade graphite using ANN. The data required to predict the frictional behavior of nuclear grade graphite was developed experimentally and compared with ANN software (Alyuda Neuro intelligence) for analysis and verification, taking input variables such as temperature, time, and sliding distance, and friction force. The study proceeds experimentally to train the neural network, test, and validate. The study concluded that the developed ANN model and backpropagation Alyuda Neuro intelligence could predict the frictional characteristics of nuclear grade graphite with a correlation coefficient of 0.9995 and a mean absolute error of 0.0030%.

Numerical assessment of a 3D human upper respiratory tract model: effect of anatomical structure on asymmetric tidal pulmonary ventilation characteristics

The analysis of airway ventilation characteristic is important for diagnosis and pathological assistance for respiratory diseases. It is therefore imperative to study the impact of anatomical features on the internal flow field. The article is focused on an in-silico study on impact of anatomical structure of human upper respiratory tract on transient asymmetric tidal pulmonary ventilation characteristics. Therefore, a three-dimensional human airways model is reconstructed from nasal cavity up to seventh generation bronchi from computed tomography (CT) images of a 48-year-old healthy man using computational modelling technique. A validated low Reynolds number (LRN) realisable k - ε turbulence model is used to capture the internal flow mixed turbulence characteristics. The numerical simulations were performed for asymmetric low and high tidal pulmonary ventilation (ALTPV, 10 L/min and AHTPV, 40 L/min). The numerical analysis assists to predict the near realistic airway ventilation phenomena and internal flow physics in the upper respiratory tract.

Computer modeling of the area of carotid endarterectomy with patches of various forms

To describe geometric models of carotid artery bifurcation and computer modeling of carotid endarterectomy (CEA) with patches of various configurations. The method was demonstrated on a reconstructed model of intact vessel based on preoperative CT of the affected vessel in a certain patient. Blood flow is modeled by computational fluid dynamics using Doppler ultrasound data. Risk factors were assessed considering hemodynamic parameters of vascular wall associated with WSS. We studied hemodynamic results of 10 virtual CEA with patches of various shapes on the example of a reconstructed intact artery in a particular patient. Patch implantation is aimed at prevention of carotid artery narrowing since simple suture without a patch can reduce circumference of the artery by 4-5 mm. This result adversely affects blood flow. On the other hand, too wide a patch creates aneurysm-like deformation of internal carotid artery bulb. It is not optimal due to a large recirculation zone. It was found that patch width approximately equal to 3 mm ensures an optimal hemodynamic result. Deviations from this median value, both upward and downward, impair hemodynamics while the absence of a patch results the worst result. The proposed computer modeling technique is able to provide a personalized patch selection for CEA with low risk of restenosis in long-term follow-up period.

Performance of brick-filled reinforced concrete composite wall strengthened with C-FRP laminate(s) under blast loading

From the safety of the structure and security point of view, the role of free-standing compound wall is still relevant. Such unreinforced walls have limited flexural capacity under the effects of blast shockwaves and may suffer from severe damage with catastrophic out-of-plane failure consequences. The previous novel study was conducted by the authors on free-standing reinforced concrete (RC) wall, 6000 mm × 2500 mm × 230 mm (length × height × thickness), having (i) 70 mm wide cavity, the cavity filled with (ii) bricks on edges, and (iii) sand as softcore materials, subjected to explosive charge weights of 3.50 and 7.20 kg-TNT at standoff distance 3.50 m and height of burst 1.25 m using the dynamic computer code, ABAQUS/Explicit-v.6.15. The cavity wall filled with softcore material as bricks was found to give an outstanding performance while that without softcore displayed an inferior response than the wall with sand as softcore. In the present work, the authors have further extended the research by investigating the performance of the free-standing brick-filled RC composite wall under the explosive weights of 1, 5, 10, 15, and 20 kg-TNT at a very close standoff distance (0.50 m). A well-known Concrete Damage Plasticity Model (CDPM) considering strain rate effects is used to define the constitutive relation of concrete and infilled bricks. The nonlinear behavior of the reinforcing bars is also taken into account. Coupled-Eulerian-Lagrangian (CEL), an advanced computational modeling technique, is adopted to simulate the explosion effect on the wall. Radial cracks mainly at the blast height level develop under explosion loads ≤ 5 kg-TNT. For higher explosive loads, the bulging of the concrete walls occurs. To make the wall sustain the damage due to the blast, it is strengthened with a single layer of the carbon-fiber-reinforced-polymer (C-FRP) laminate of minimum thickness 0.15 mm. The load-carrying mechanisms are explained. Effect on strength parameters of the wall contributing to its improved blast performance is discussed. Multi-layers of the C-FRP are also considered against higher explosive loads for satisfactory blast performance of the wall.

Modeling of the deformation impact of the main structure framework on the stress and strain state of its individual parts

The paper presents the calculation of the metal frame of the stairwell under the action of static loads, which was performed using the Dlubal RFEM application software package. The computer modeling technique for the study of metal structures in this software environment was adapted in accordance with building regulations. The general stress calculation in software for the finite element analysis was performed and the selected sections were checked in the RF-STEEL EC3 calculation module for two groups of limit states. Various options for calculating a stairwell made of a metal frame are analyzed. The influence of deformations of the main framework of the structure on the stress-strain state of its individual part was evaluated using software.

A Conserved Allosteric Site on Drug-Metabolizing CYPs: A Systematic Computational Assessment.

Cytochrome P450 enzymes (CYPs) are the largest group of enzymes involved in human drug metabolism. Ligand tunnels connect their active site buried at the core of the membrane-anchored protein to the surrounding solvent environment. Recently, evidence of a superficial allosteric site, here denoted as hotspot 1 (H1), involved in the regulation of ligand access in a soluble prokaryotic CYP emerged. Here, we applied multi-scale computational modeling techniques to study the conservation and functionality of this allosteric site in the nine most relevant mammalian CYPs responsible for approximately 70% of drug metabolism. In total, we systematically analyzed over 44 s of trajectories from conventional MD, cosolvent MD, and metadynamics simulations. Our bioinformatic analysis and simulations with organic probe molecules revealed the site to be well conserved in the CYP2 family with the exception of CYP2E1. In the presence of a ligand bound to the H1 site, we could observe an enlargement of a ligand tunnel in several members of the CYP2 family. Further, we could detect the facilitation of ligand translocation by H1 interactions with statistical significance in CYP2C8 and CYP2D6, even though all other enzymes except for CYP2C19, CYP2E1, and CYP3A4 presented a similar trend. As the detailed comprehension of ligand access and egress phenomena remains one of the most relevant challenges in the field, this work contributes to its elucidation and ultimately helps in estimating the selectivity of metabolic transformations using computational techniques.