Artificial Hydrocarbon Networks Fuzzy Inference System

This paper presents a novel fuzzy inference model based on artificial hydrocarbon networks, a computational algorithm for modeling problems based on chemical hydrocarbon compounds. In particular, the proposed fuzzy-molecular inference model (FIM-model) uses molecular units of information to partition the output space in the defuzzification step. Moreover, these molecules are linguistic units that can be partially understandable due to the organized structure of the topology and metadata parameters involved in artificial hydrocarbon networks. In addition, a position controller for a direct current (DC) motor was implemented using the proposed FIM-model in type-1 and type-2 fuzzy inference systems. Experimental results demonstrate that the fuzzy-molecular inference model can be applied as an alternative of type-2 Mamdani’s fuzzy control systems because the set of molecular units can deal with dynamic uncertainties mostly present in real-world control applications

Next generation 3D pharmacophore modeling

3D pharmacophore models are three‐dimensional ensembles of chemically defined interactions of a ligand in its bioactive conformation. They represent an elegant way to decipher chemically encoded ligand information and have therefore become a valuable tool in drug design. In this review, we provide an overview on the basic concept of this method and summarize key studies for applying 3D pharmacophore models in virtual screening and mechanistic studies for protein functionality. Moreover, we discuss recent developments in the field. The combination of 3D pharmacophore models with molecular dynamics simulations could be a quantum leap forward since these approaches consider macromolecule–ligand interactions as dynamic and therefore show a physiologically relevant interaction pattern. Other trends include the efficient usage of 3D pharmacophore information in machine learning and artificial intelligence applications or freely accessible web servers for 3D pharmacophore modeling. The recent developments show that 3D pharmacophore modeling is a vibrant field with various applications in drug discovery and beyond

Theoretical-experimental study on protein-ligand interactions based on thermodynamics methods, molecular docking and perturbation models

The current doctoral thesis focuses on understanding the thermodynamic events of protein-ligand interactions which have been of paramount importance from traditional Medicinal Chemistry to Nanobiotechnology. Particular attention has been made on the application of state-of-the-art methodologies to address thermodynamic studies of the protein-ligand interactions by integrating structure-based molecular docking techniques, classical fractal approaches to solve protein-ligand complementarity problems, perturbation models to study allosteric signal propagation, predictive nano-quantitative structure-toxicity relationship models coupled with powerful experimental validation techniques. The contributions provided by this work could open an unlimited horizon to the fields of Drug-Discovery, Materials Sciences, Molecular Diagnosis, and Environmental Health Sciences

Classical and reactive molecular dynamics: Principles and applications in combustion and energy systems

Molecular dynamics (MD) has evolved into a ubiquitous, versatile and powerful computational method for fundamental research in science branches such as biology, chemistry, biomedicine and physics over the past 60 years. Powered by rapidly advanced supercomputing technologies in recent decades, MD has entered the engineering domain as a first-principle predictive method for material properties, physicochemical processes, and even as a design tool. Such developments have far-reaching consequences, and are covered for the first time in the present paper, with a focus on MD for combustion and energy systems encompassing topics like gas/liquid/solid fuel oxidation, pyrolysis, catalytic combustion, heterogeneous combustion, electrochemistry, nanoparticle synthesis, heat transfer, phase change, and fluid mechanics. First, the theoretical framework of the MD methodology is described systemically, covering both classical and reactive MD. The emphasis is on the development of the reactive force field (ReaxFF) MD, which enables chemical reactions to be simulated within the MD framework, utilizing quantum chemistry calculations and/or experimental data for the force field training. Second, details of the numerical methods, boundary conditions, post-processing and computational costs of MD simulations are provided. This is followed by a critical review of selected applications of classical and reactive MD methods in combustion and energy systems. It is demonstrated that the ReaxFF MD has been successfully deployed to gain fundamental insights into pyrolysis and/or oxidation of gas/liquid/solid fuels, revealing detailed energy changes and chemical pathways. Moreover, the complex physico-chemical dynamic processes in catalytic reactions, soot formation, and flame synthesis of nanoparticles are made plainly visible from an atomistic perspective. Flow, heat transfer and phase change phenomena are also scrutinized by MD simulations. Unprecedented details of nanoscale processes such as droplet collision, fuel droplet evaporation, and CO2 capture and storage under subcritical and supercritical conditions are examined at the atomic level. Finally, the outlook for atomistic simulations of combustion and energy systems is discussed in the context of emerging computing platforms, machine learning and multiscale modelling

Development and application of force fields for molecular simulations

Simulationen weicher Materie umfassen ein breites Spektrum von Anwendungen, wie z. B. die Modellierung von Biomolekülen, Polymeren und Materialien für die organische Elektronik. Um die Längen- und Zeitskalen relevanter Phänomene zu erreichen, werden die Wechselwirkungen in diesen Systemen üblicherweise durch recheneffiziente analytische Kraftfelder berechnet. Ein Teil dieser Arbeit beschreibt eine Beispielanwendung für die kraftfeldbasierte Modellierung von amorphen organischen Halbleitern. Der konventionelle Kraftfeldansatz führt jedoch Parameter ein, die aus für das betrachtete Molekül geeigneten Parametersätzen zugewiesen werden müssen. Vor allem aufgrund der einfachen Funktionsausdrücke für die nicht-kovalenten Wechselwirkungen erfordert das Verfahren zur Bestimmung dieser Parametersätze empirische Zielwerte, die nicht immer verfügbar sind. Bottom-up-Ansätze, wie z. B. Bottom-up-Kraftfelder mit festen Funktionsausdrücken oder Potentiale basierend auf neuronalen Netzen, zielen darauf ab, die experimentellen Daten durch Ergebnisse aus ab initio Rechnungen zu ersetzen. Für die Anwendung in umfangreichen Molekulardynamiksimulationen weisen diese Methoden noch offene Herausforderungen auf. Feste Funktionsausdrücke leiden unter einer begrenzten Flexibilität, die ab initio Potentialenergieoberfläche zu reproduzieren und erfordern manuelle Typdefinitionen, um die Anzahl der Parameter zu reduzieren. Potentiale, die auf neuronalen Netzen basieren, verbessern beide Aspekte, aber ihre hohen Rechenanforderungen begrenzen die zugänglichen Längen- und Zeitskalen. In dieser Arbeit wird ein neuartiger Bottom-up-Ansatz zur Modellierung nicht-kovalenter Wechselwirkungen vorgestellt, der für großskalige Simulationen konzipiert ist. Das Konzept effizienter additiver Wechselwirkungen wird mit der Flexibilität künstlicher neuronaler Netze für die Interpolation verschiedener chemischer Zusammensetzungen und geometrischer Anordnungen kombiniert. Die Anwendung des Modells wird in Molekulardynamiksimulationen demonstriert, und der Vergleich der berechneten thermodynamischen Eigenschaften mehrerer kleiner organischer Moleküle mit experimentellen Daten und konventionellen Kraftfeldern zeigt eine vielversprechende Vorhersageleistung. Zusätzlich bewahrt das Modell die Energiezerlegung in physikalisch motivierte Komponenten, die von der symmetrieangepassten Störungstheorie, die für die ab initio Referenzrechnungen verwendet wird, bereitgestellt wird. Diese Trennbarkeit und die Unabhängigkeit von empirischen Daten machen dieses Modell potenziell nützlich für zukünftige Materialdesign-Anwendungen

Estimation of in-situ fluid properties from the combined interpretation of nuclear, dielectric, optical, and magnetic resonance measurements

During the last few decades, the quantification of hydrocarbon pore volume from borehole measurements has been widely studied for reservoir descriptions. Relatively less effort has been devoted to estimating in-situ fluid properties because (1) acquiring fluid samples is expensive, (2) reservoir fluids are a complex mixture of various miscible and non-miscible phases, and (3) they depend on environmental factors such as temperature and pressure. This dissertation investigates the properties of fluid mixtures based on various manifestations of their electromagnetic properties from the MHz to the THz frequency ranges. A variety of fluids, including water, alcohol, alkane, aromatics, cyclics, ether, and their mixtures, are analyzed with both laboratory experiments and numerical simulations. A new method is introduced to quantify in-situ hydrocarbon properties from borehole nuclear measurements. The inversion-based estimation method allows depth-continuous assessment of compositional gradients at in-situ conditions and provides thermodynamically consistent interpretations of reservoir fluids that depend greatly on phase behavior. Applications of this interpretation method to measurements acquired in two field examples, including one in a gas-oil transition zone, yielded reliable and verifiable hydrocarbon compositions. Dielectric properties of polar liquid mixtures were analyzed in the frequency range from 20 MHz to 20 GHz at ambient conditions. The Havriliak-Negami (HN) model was adapted for the estimation of dielectric permittivity and relaxation time. These experimental dielectric properties were compared to Molecular Dynamics (MD) simulations. Additionally, thermodynamic properties, including excess enthalpy, density, number of hydrogen bonds, and effective self-diffusion coefficient, were computed to cross-validate experimental results. Properties predicted from MD simulations are in excellent agreement with experimental measurements. The three most common optical spectroscopy techniques, i.e. Near Infrared (NIR), Infrared, and Raman, were applied for the estimation of compositions and physical properties of liquid mixtures. Several analytical techniques, including Principal Component Analysis (PCA), Radial Basis Functions (RBF), Partial Least-Squares Regression (PLSR), and Artificial Neural Networks (ANN), were separately implemented for each spectrum to build correlations between spectral data and properties of liquid mixtures. Results show that the proposed methods yield prediction errors from 1.5% to 22.2% smaller than those obtained with standard multivariate methods. Furthermore, the errors can be decreased by combining NIR, Infrared, and Raman spectroscopy measurements. Lastly, the ¹H NMR longitudinal relaxation properties of various liquid mixtures were examined with the objective of detecting individual components. Relaxation times and diffusion coefficients obtained via MD simulations for these mixtures are in agreement with experimental data. Also, the ¹H-¹H dipole-dipole relaxations for fluid mixtures were decomposed into the relaxations emanate from the intramolecular and intermolecular interactions. The quantification of intermolecular interactions between the same molecules and different molecules reveals how much each component contributes to the total NMR longitudinal relaxation of the mixture as well as the level of interactions between different fluids. Both experimental and numerical simulation results documented in this dissertation indicate that selecting measurement techniques that can capture the physical property of interest and maximize the physical contrasts between different components is important for reliable and accurate in-situ fluid identificationPetroleum and Geosystems Engineerin

Machine learning for property prediction and optimization of polymeric nanocomposites: a state-of-the-art

Recently, the field of polymer nanocomposites has been an area of high scientific and industrial attention due to noteworthy improvements attained in these materials, arising from the synergetic combination of properties of a polymeric matrix and an organic or inorganic nanomaterial. The enhanced performance of those materials typically involves superior mechanical strength, toughness and stiffness, electrical and thermal conductivity, better flame retardancy and a higher barrier to moisture and gases. Nanocomposites can also display unique design possibilities, which provide exceptional advantages in developing multifunctional materials with desired properties for specific applications. On the other hand, machine learning (ML) has been recognized as a powerful predictive tool for data-driven multi-physical modelling, leading to unprecedented insights and an exploration of the system's properties beyond the capability of traditional computational and experimental analyses. This article aims to provide a brief overview of the most important findings related to the application of ML for the rational design of polymeric nanocomposites. Prediction, optimization, feature identification and uncertainty quantification are presented along with different ML algorithms used in the field of polymeric nanocomposites for property prediction, and selected examples are discussed. Finally, conclusions and future perspectives are highlighted

A New Atomistic Simulation Framework for Mechanochemical Reaction Analysis of Mechanophore Embedded Nanocomposites

abstract: A hybrid molecular dynamics (MD) simulation framework is developed to emulate mechanochemical reaction of mechanophores in epoxy-based nanocomposites. Two different force fields, a classical force field and a bond order based force field are hybridized to mimic the experimental processes from specimen preparation to mechanical loading test. Ultra-violet photodimerization for mechanophore synthesis and epoxy curing for thermoset polymer generation are successfully simulated by developing a numerical covalent bond generation method using the classical force field within the framework. Mechanical loading tests to activate mechanophores are also virtually conducted by deforming the volume of a simulation unit cell. The unit cell deformation leads to covalent bond elongation and subsequent bond breakage, which is captured using the bond order based force field. The outcome of the virtual loading test is used for local work analysis, which enables a quantitative study of mechanophore activation. Through the local work analysis, the onset and evolution of mechanophore activation indicating damage initiation and propagation are estimated; ultimately, the mechanophore sensitivity to external stress is evaluated. The virtual loading tests also provide accurate estimations of mechanical properties such as elastic, shear, bulk modulus, yield strain/strength, and Poisson’s ratio of the system. Experimental studies are performed in conjunction with the simulation work to validate the hybrid MD simulation framework. Less than 2% error in estimations of glass transition temperature (Tg) is observed with experimentally measured Tgs by use of differential scanning calorimetry. Virtual loading tests successfully reproduce the stress-strain curve capturing the effect of mechanophore inclusion on mechanical properties of epoxy polymer; comparable changes in Young’s modulus and yield strength are observed in experiments and simulations. Early damage signal detection, which is identified in experiments by observing increased intensity before the yield strain, is captured in simulations by showing that the critical strain representing the onset of the mechanophore activation occurs before the estimated yield strain. It is anticipated that the experimentally validated hybrid MD framework presented in this dissertation will provide a low-cost alternative to additional experiments that are required for optimizing material design parameters to improve damage sensing capability and mechanical properties. In addition to the study of mechanochemical reaction analysis, an atomistic model of interphase in carbon fiber reinforced composites is developed. Physical entanglement between semi-crystalline carbon fiber surface and polymer matrix is captured by introducing voids in multiple graphene layers, which allow polymer matrix to intertwine with graphene layers. The hybrid MD framework is used with some modifications to estimate interphase properties that include the effect of the physical entanglement. The results are compared with existing carbon fiber surface models that assume that carbon fiber has a crystalline structure and hence are unable to capture the physical entanglement. Results indicate that the current model shows larger stress gradients across the material interphase. These large stress gradients increase the viscoplasticity and damage effects at the interphase. The results are important for improved prediction of the nonlinear response and damage evolution in composite materials.Dissertation/ThesisDoctoral Dissertation Mechanical Engineering 201

More is Different: Modern Computational Modeling for Heterogeneous Catalysis

La combinació d'observacions experimentals i estudis de la Density Functional Theory (DFT) és un dels pilars de la investigació química moderna. Atès que permeten recopilar informació física addicional d'un sistema químic, difícilment accessible a través de l'entorn experimental, aquests estudis es fan servir àmpliament per modelar i predir el comportament d'una gran varietat de compostos químics en entorns únics. A la catàlisi heterogènia, els models DFT s'utilitzen habitualment per avaluar la interacció entre els compostos moleculars i els catalitzadors, vinculant aquestes interpretacions amb els resultats experimentals. Tanmateix, l'alta complexitat trobada tant als escenaris catalítics com a la reactivitat, implica la necessitat de metodologies sofisticades que requereixen automatització, emmagatzematge i anàlisi per estudiar correctament aquests sistemes. Aquest treball presenta el desenvolupament i la combinació de múltiples metodologies per avaluar correctament la complexitat d'aquests sistemes químics. A més, aquest treball mostra com s'han utilitzat les tècniques proporcionades per estudiar noves configuracions catalítiques d'interès acadèmic i industrial.La combinación de observaciones experimentales y estudios de la Density Functional Theory (DFT) es uno de los pilares de la investigación química moderna. Dado que permiten recopilar información física adicional de un sistema químico, difícilmente accesible a través del entorno experimental, estos estudios se emplean ampliamente para modelar y predecir el comportamiento de una gran variedad de compuestos químicos en entornos únicos. En la catálisis heterogénea, los modelos DFT se emplean habitualmente para evaluar la interacción entre los compuestos moleculares y los catalizadores, vinculando estas interpretaciones con los resultados experimentales. Sin embargo, la alta complejidad encontrada tanto en los escenarios catalíticos como en la reactividad, implica la necesidad de metodologías sofisticadas que requieren de automatización, almacenamiento y análisis para estudiar correctamente estos sistemas. Este trabajo presenta el desarrollo y la combinación de múltiples metodologías con el objetivo de evaluar correctamente la complejidad de estos sistemas químicos. Además, este trabajo muestra cómo las técnicas proporcionadas se han utilizado para estudiar nuevas configuraciones catalíticas de interés académico e industrial.The combination of Experimental observations and Density Functional Theory studies is one of the pillars of modern chemical research. As they enable the collection of additional physical information of a chemical system, hardly accessible via the experimental setting, Density Functional Theory studies are widely employed to model and predict the behavior of a diverse variety of chemical compounds under unique environments. Particularly, in heterogeneous catalysis, Density Functional Theory models are commonly employed to evaluate the interaction between molecular compounds and catalysts, lately linking these interpretations with experimental results. However, high complexity found in both, catalytic settings and reactivity, implies the need of sophisticated methodologies involving automation, storage and analysis to correctly study these systems. Here, I present the development and combination of multiple methodologies, aiming at correctly asses complexity. Also, this work shows how the provided techniques have been actively used to study novel catalytic settings of academic and industrial interest