Recent advances in chemical system simulations are driven by multi-scale modeling, machine learning, and significant improvements in theoretical frameworks and computational power. AI plays a pivotal role in materials discovery and optimization by enabling systematic screening and quantitative predictions. Machine learning methods leverage high-quality, materials-specific data, which are often sparse, time-consuming, and expensive to obtain. These methods benefit significantly from physics-driven descriptors obtained through atomistic simulations. The critical importance of microstructure phenomena for corrosion-resistant alloy engineering has led to the development of advanced techniques for building accurate models and conducting extensive simulations. This presentation will discuss recent developments in multi-scale modeling and phase-field simulation techniques within the MedeA materials modeling environment and showcase applications relevant to corrosion research. The MedeA simulation environment fosters molecular-level understanding and generates key materials property data with exceptionally fine time and space resolution. MedeA integrates advanced electronic structure methods with classical molecular dynamics and Monte Carlo methods, builders, analysis tools, and workflows for automated property prediction, the informatics method Polymer Expert, and finite element methods for capturing microstructure evolution.

In the treatment of many fuel gases such as biogas, natural gas, syngas, and so on, tertiary alkanolamines play an important role in the selective removal of H2S with respect to CO2. The selectivity might be required for various reasons: to respect more stringent H2S specifications, to optimize the performance of the Claus unit, to lower the cost of CO2 capture, and so on. The H2S/CO2 selectivity is mainly kinetic and, to a lesser extent, thermodynamic. A novel experimental setup has been put in place to measure the time evolution of the simultaneous absorption of H2S and CO2. The results of an extensive experimental campaign with 18 different aqueous tertiary alkanolamine solvents (13 mol % amine, 87 mol % H2O) are presented. Although the absorption of H2S is expected to be a very fast proton transfer, a significant variation in H2S absorption rates and thus in selectivity is observed. This could not only be explained by the pKa or the viscosity of the amines. Therefore, an accurate quantitative molecular simulations-based kinetic model is developed and validated. The study allowed us to better understand the molecular origin of selectivity, as well as to identify amines with a higher selectivity than aqueous MDEA (MethylDiEthanolAmine), the standard industrial selective solvent.

The chemical absorption of CO2 and H2S in aqueous tertiary amines is a well-known acid-base reaction. Kinetic and vapor-liquid equilibrium experiments show that the addition of an amide such as HMPA, which is known to be a strong liquid structure maker, significantly inhibits the acid-base reactions. The impact is more pronounced for CO2 than for H2S absorption. Despite the presence of water in the solvent, the absorption becomes almost physical. Due to hydrogen bonding and the hydrophobic effect, each amide molecule is involved in a cluster containing several water molecules, thus rendering the water molecules less available to participate in the reaction and to solvate HS- and HCO3- ions. This effect is absent when ethylene glycol, a weak structure maker, is added, even in large quantities. This study demonstrates the importance of solvent structure in the study of chemical reactions. State-of-the-art molecular dynamics simulations of the water-HMPA system could not reproduce the strongly negative excess volume of the mixture. This illustrates the need for more accurate force fields to simulate the structuring effect and their impact on chemical reactions.

Carbon capture and storage technologies are projected to increasingly contribute to cleaner energy transitions by significantly reducing CO 2 emissions from fossil fuel-driven power and industrial plants. The industry standard technology for CO 2 capture is chemical absorption with aqueous alkanolamines, which are often being mixed with an activator, piperazine, to increase the overall CO 2 absorption rate. Inefficiency of the process due to the parasitic energy required for thermal regeneration of the solvent drives the search for new tertiary amines with better kinetics. Improving the efficiency of experimental screening using computational tools is challenging due to the complex nature of chemical absorption. We have developed a novel computational approach that combines kinetic experiments, molecular simulations and machine learning for the in silico screening of hundreds of prospective candidates and identify a class of tertiary amines that absorbs CO 2 faster than a typical commercial solvent when mixed with piperazine, which was confirmed experimentally.

Molecular simulations and chemoinformatics are used quite extensively to identify new adsorbents for CO2. The use of those digital tools is far less common in the search for new solvents (absorption) to capture CO2. This is primarily related to the complexity of the absorption process, mainly due to its reactive nature. This paper highlights and builds on several recent significant advances in computer-aided experimental developments in the field of absorption. First, we focus on physical solvents. We previously developed and experimentally validated a machine-learning tool to predict the absorption capacity of CO2 as well as its selectivity with respect to other gases (H2, N2, CO, H2, and H2S). In this paper we investigate whether multi-task learning (MTL), a technique that aims to improve machine learning efficacy by simultaneously modelling several properties, can improve the model performances. The accuracy of both models, with and without MTL, is, however, comparable. Second, we study the impact of the addition of a physical co-solvent to an aqueous amine solvent on the CO2 absorption rate. Physical co-solvents with a specific value lower than water are expected to reduce the solvent regeneration energy. A recently developed and validated molecular simulations-based tool to predict CO2 absorption rates in aqueous tertiary amines is applied to solvents of aqueous tertiary amines with physical co-solvents. A comparison with new experimental data is made. Both the model and the experimental data predict a lower CO2 absorption rate in the presence of most physical co-solvents studied.

Reliable process modeling and simulation is required to optimize the design and performance of acid gas removal units, which play a key role in the H2S and CO2 removal from natural gas, and in the CO2 capture from flue gasses. Many physico-chemical properties that are key input parameters to the process models are obtained from time-consuming and sometimes dangerous experimental campaigns (H2S) or are not (easily) accessible experimentally and are currently estimated. In this work, we investigated which of these properties could quickly and reliably be obtained using molecular simulations, to improve the accuracy of the process models and to reduce the need for long or difficult experiments. The absorption properties of H2S, CO2, and CH4 in aqueous MethylDiEthanolAmine (MDEA) were computed using a combination of forcefield-based molecular dynamics, Monte Carlo methods, and quantum mechanical approaches. The results are compared to experimental data and/or currently used estimates in the AspenPlus process model. It was found that the key properties which might reliably and relatively easily be obtained by molecular simulations are physical: density, phase behavior, Henry coefficients, diffusion coefficients, viscosity, and surface tension. Quantum mechanical calculations based on the widely used density functional theory are instrumental in exploring reaction mechanisms and in determining the structure of transition states, but obtaining accurate free energies of reactions remains a challenge, especially when energy differences of less than 1 kJ mol(-1) are necessary for quantitative predictions. Such an accuracy can only be reached if the solvent effects, which are detrimental, are correctly included in the model [1]. (c) 2022 Published by Elsevier B.V.

The recombination of laser-dissociated iodine molecules dissolved in CCl4 is explored by time-resolved x-ray diffraction. The x-raypulses employed in our experiments were generated by the ESRF synchrotron in Grenoble. The solvent contribution to the measured signals was eliminated using appropriate experimental procedures. Motions of iodine atoms were then studied from 200 ps to 10 ps. Different relaxation processes are shown to operate in this time domain. It is proved that the iodine recombination follows two reaction paths, taking place in the electronic states X and A/A' of I2, respectively. In spite of widely different experimental approaches, laser optical and x-ray studies provide a similar picture of this prototype reaction.

Aqueous tertiary amine solutions are increasingly used in industrial CO2 capture operations because they are more energy-efficient than primary or secondary amines and demonstrate higher CO2 absorption capacity. Yet, tertiary amine solutions have a significant drawback in that they tend to have lower CO2 absorption rates. To identify tertiary amines that absorb CO2 faster, it would be efficacious to have a quantitative and predictive model of the rate-controlling processes. Despite numerous attempts to date, this goal has been elusive. The present computational approach achieves this goal by focusing on the reaction of CO2 with OH- forming HCO3-. The performance of the resulting model is demonstrated for a consistent experimental data set of the absorption rates of CO2 for 24 different aqueous tertiary amine solvents. The key to the new model's success is the manner in which the free energy barrier for the reaction of CO2 with OH- is evaluated from the differences among the solvation free energies of CO2, OH-, and HCO3-, while the pKa of the amines controls the concentration of OH-. These solvation energies are obtained from molecular dynamics simulations. The experimental value of the free energy of reaction of CO2 with pure water is combined with information about measured rates of absorption of CO2 in an aqueous amine solvent in order to calibrate the absorption rate model. This model achieves a relative accuracy better than 0.1 kJ mol-1 for the free energies of activation for CO2 absorption in aqueous amine solutions and 0.07 g L-1 min-1 for the absorption rate of CO2. Such high accuracies are necessary to predict the correct experimental ranking of CO2 absorption rates, thus providing a quantitative approach of practical interest.

Predicting engineering properties of materials prior to their synthesis enables the integration of their design into the overall engineering process. In this context, the present article discusses the foundation and requirements of software platforms for predicting materials properties through modeling and simulation at the electronic, atomistic, and mesoscopic levels, addressing functionality, verification, validation, robustness, ease of use, interoperability, support, and related criteria. Based on these requirements, an assessment is made of the current state revealing two critical points in the large-scale industrial deployment of atomistic modeling, namely (i) the ability to describe multicomponent systems and to compute their structural and functional properties with sufficient accuracy and (ii) the expertise needed for translating complex engineering problems into viable modeling strategies and deriving results of direct value for the engineering process. Progress with these challenges is undeniable, as illustrated here by examples from structural and functional materials including metal alloys, polymers, battery materials, and fluids. Perspectives on the evolution of modeling software platforms show the need for fundamental research to improve the predictive power of models as well as coordination and support actions to accelerate industrial deployment.

Electronic structure calculations have become a powerful foundation for computational materials engineering. Four major factors have enabled this unprecedented evolution, namely (i) the development of density functional theory (DFT), (ii) the creation of highly efficient computer programs to solve the Kohn-Sham equations, (iii) the integration of these programs into productivityoriented computational environments, and (iv) the phenomenal increase of computing power. In this context, we describe recent applications of the Vienna Ab-initio Simulation Package (VASP) within the MedeA ® computational environment, which provides interoperability with a comprehensive range of modeling and simulation tools. The focus is on technological applications including microelectronic materials, Li-ion batteries, high-performance ceramics, silicon carbide, and Zr alloys for nuclear power generation. A discussion of current trends including high-throughput calculations concludes this article.

We use density functional theory, newly parameterized molecular dynamics simulations, and last generation 15 N dynamic nuclear polarization surface enhanced solid-state NMR spectroscopy (DNP SENS) to understand graft-host interactions and effects imposed by the metal-organic framework (MOF) host on peptide conformations in a peptide-functionalized MOF. Focusing on two grafts typified by MIL-68-proline (-Pro) and MIL-68-glycine-proline (-Gly-Pro), we identified the most likely peptide conformations adopted in the functionalized hybrid frameworks. We found that hydrogen bond interactions between the graft and the surface hydroxyl groups of the MOF are essential in determining the peptides conformation(s). DNP SENS methodology shows unprecedented signal enhancements when applied to these peptide-functionalized MOFs. The calculated chemical shifts of selected MIL-68-NH-Pro and MIL-68-NH-Gly-Pro conformations are in a good agreement with the experimentally obtained 15 N NMR signals. The study shows that the conformations of peptides when grafted in a MOF host are unlikely to be freely distributed, and conformational selection is directed by strong host-guest interactions.

The condensation reactions between Ge(OH)(4) and Si(OH)(4) units in solution are studied to understand the mechanism and stable species during the initial steps of the formation process of Ge containing zeolites under basic conditions. The free energy of formation of (OH)(3)Ge-O-Ge-(OH)(2)O-, (OH)(3)Si-O-SiOH)(2)O-, (OH)(3)Ge-O-SiOH)(2)O- and (OH)(3)Si-O-Ge-(OH)(2)O- dimers is calculated with ab initio molecular dynamics and thermodynamic integration, including an explicit description of the water solvent molecules. Calculations show that the attack of the conjugated base (Ge(OH)(3)O- and Si(OH)(3)O-) proceeds with a smaller barrier at the Ge center. In addition, the formation of the pure germanate dimer is more favorable than that of the germano-silicate structure. These results explain the experimental observation of Ge-Ge and Si-Ge dimer species in solutions, with a few Si-Si ones.

This work demonstrates the systematic prediction of thermodynamic properties for batches of thousands of molecules using automated procedures. This is accomplished with newly developed tools and functions within the Material Exploration and Design Analysis (MedeA ) software environment, which handles the automatic execution of sequences of tasks for large numbers of molecules including the creation of 3D molecular models from 1D representations, systematic exploration of possible conformers for each molecule, the creation and submission of computational tasks for property calculations on parallel computers, and the post-processing for comparison with available experimental properties. After the description of the different MedeA functionalities and methods that make it easy to perform such large number of computations, we illustrate the strength and power of the approach with selected examples from molecular mechanics and quantum chemical simulations. Specifically, comparisons of thermochemical data with quantum-based heat capacities and standard energies of formation have been obtained for more than 2 000 compounds, yielding average deviations with experiments of less than 4% with the Design Institute for Physical PRoperties (DIPPR) database. The automatic calculation of the density of molecular fluids is demonstrated for 192 systems. The relaxation to minimum-energy structures and the calculation of vibrational frequencies of 5 869 molecules are evaluated automatically using a semi-empirical quantum mechanical approach with a success rate of 99.9%. The present approach is scalable to large number of molecules, thus opening exciting possibilities with the advent of exascale computing. Résumé— Simulations atomistiques automatiques et systématiques dans l’environnement logiciel de MedeA : Application à EU-REACH — Ce travail démontre notre capacité à prédire systématiquement les propriétés thermodynamiques par lot de plusieurs milliers de molécules en utilisant des procédures automatiques. Ceci est accompli à l’aide de nouveaux outils et fonctions intégrés dans l’environnement logiciel de MedeA (Material Exploration and Design Analysis), qui manipule l’exécution automatique de séquences de tâches sur de grands nombres de molécules comprenant la création de modèles moléculaires 3D à partir de représentations 1D, l’exploration systématique des conformations possibles pour chaque molécules, la création et la soumission de tâches informatiques pour calculer des propriétés sur des ordinateurs parallèles, et le post-traitement par comparaison avec les propriétés experimentales disponibles. Après la description des différentes fonctionnalités et méthodes de MedeA qui facilitent grandement le traitement de si grand nombre Oil & Gas Science and Technology – Rev. IFP Energies nouvelles, Vol. 70 (2015), No. 3, pp. 405-417 X. Rozanska et al., published by IFP Energies nouvelles, 2014 DOI: 10.2516/ogst/2014041 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. de calculs, nous illustrons la puissance et la force de l’approche avec des exemples sélectionnés à partir de simulations de mécanique moléculaire et chimie quantique. En particulier, la comparaison des données thermochimiques moléculaires obtenues par chimie quantique, notamment les énergies de formation et les chaleurs spécifiques moléculaires, avec les valeurs expérimentales a été obtenue pour plus de 2 000 composés, conduisant à des déviations entre des valeurs expérimentales tirées des bases de données de la DIPPR (Design Institute for Physical PRoperties) entre autres et les valeurs calculées de moins de 4 %. Le calcul automatique de la densité de fluides moléculaires est démontré pour 192 systèmes. Les relaxations dans leurs structures d’énergies minimales et le calcul des fréquences vibratoires de 5 869 molécules sont évalués automatiquement en utilisant une approche de mécanique quantique semi-empirique avec un taux de succès de 99,9 %. La présente approche peut être étendue à de grand nombre de molécules, ouvrant ainsi des possibilités excitantes en vue de l’avènement du calcul à l’échelle exascale.

(27)Al NMR is the method of choice for studying grafted Al species on a large area solid support, such as co-catalysts for α-olefin oligomerization involving mesoporous silica materials. Here, we show how to interpret the (27)Al solid-state NMR spectrum and parameters for various types of Al monomeric and dimeric alkyl and halogen compounds grafted on silica, based on the trends obtained from first-principles calculations. Since most alkylaluminum species tend to form dimers in the gas phase, we chose as prototypes both the AlMe3 monomer and the Al2Me6 dimer. On top of that the influence of chlorine substituents on the NMR parameters is explored considering all possible isomers. There are two main effects on the Al NMR parameters observed in the case of monomers: (i) the larger π-donating character of the ligands (from Me to Cl for example) leads to a decrease of the quadrupolar coupling constant CQ and (ii) the larger σ-attracting character of the ligand (from Cl to F for example) yields an upfield variation of the Al chemical shift δISO while in contrast CQ is increased. The same is true also in the case of dimeric species, with an additional specific effect. By (27)Al solid state NMR we can differentiate clearly between terminal and bridge positions for the substituents. The reason for this phenomenon is explained in terms of different natural localized MO (NLMO) contributions to the CQ parameter. This aspect is important because the surface sites for this type of system are expected to be mostly dinuclear Al species, grafted on the silica surface via either two terminal or two bridging siloxy ligands.

This work demonstrates the systematic prediction of thermodynamic properties for batches of thousands of molecules using automated procedures. This is accomplished with newly developed tools and functions within the Material Exploration and Design Analysis (MedeA®) software environment, which handle the automatic execution of sequences of tasks for large numbers of molecules including the creation of 3D molecular models from 1D representations, systematic exploration of possible conformers for each molecule, the creation and submission of computational tasks for property calculations on parallel computers, and the post-processing for comparison with available experimental properties. After the description of the different MedeA® functionalities and methods that make it easy to perform such large number of computations, we illustrate the strength and power of the approach with selected examples from molecular mechanics and quantum chemical simulations. Specifically, comparisons of thermochemical data with quantum-based heat capacities and standard energies of formation have been obtained for more than 2 000 compounds, yielding average deviations with experiments of less than 4% with the Design Institute for Physical PRoperties (DIPPR) database. The automatic calculation of the density of molecular fluids is demonstrated for 192 systems. The relaxation to minimum-energy structures and the calculation of vibrational frequencies of 5 869 molecules are evaluated automatically using a semi-empirical quantum mechanical approach with a success rate of 99.9%. The present approach is scalable to large number of molecules, thus opening exciting possibilities with the advent of exascale computing.

Silica-supported chloro alkyl aluminum co-catalysts (DEAC@support) were prepared via Surface Organometallic Chemistry by contacting diethylaluminum chloride (DEAC) and high specific surface silica materials, i.e. SBA-15, MCM-41, and Aerosil SiO2. Such systems efficiently activate NiCl2(PBu3)(2) for catalytic ethene dimerization, with turnover frequency (TOF) reaching up to 498,000 moI(c2H4)/(mol(ni) h) for DEAC@MCM-41. A detailed analysis of the DEAC@SBA-15 co-catalyst structure by solid-state aluminum-27 NMR at high-field (17.6 T and 20.0 T) and ultrafast spinning rates allows to detect six sites, characterized by a distribution of quadrupolar interaction principal values C-Q and isotropic chemical shifts.delta(iso). Identification of the corresponding Al-grafted structures was possible by comparison of the experimental NMR signatures with these calculated by DFT on a wide range of models for the aluminum species (mono- versus di-nuclear, mono- versus bis-gkafted with bridging Cl or ethyl). Most of the sites were identified as dinuclear species with retention of the structure of DEAC, namely with the presence of mu(2)-Cl-ligands between two aluminum, and this probably explains the high catalytic performance of this silica-supported co-catalysts. (C) 2014 Elsevier Inc. All rights reserved.