A versatile and user-friendly “expert system” for de novo polymer design, named Polymer Expert, has been developed and implemented. Polymer Expert can be used to rapidly generate novel candidate polymer repeat units to meet desired performance targets. It is anticipated to accelerate innovation through materials science in industries that use polymers and polymer matrix composites. It was implemented by (1) generating an initial repeat unit database, (2) expanding this initial database into a large analog repeat unit database, (3) performing calculations for all repeat units in the large analog database by using quantitative structure–property relationships (QSPR) of broad applicability, and (4) integrating the resulting searchable library of repeat units and their predicted properties (PEARL, acronym for Polymer Expert Analog Repeat-unit Library) as a new module in a materials modeling and simulation software suite. Its use is illustrated by identifying biobased alternatives for poly(ethylene terephthalate) (PET) and bisphenol-A polycarbonate (BPAPC), alternatives for highly crystalline polypropylene homopolymer (PPHP) and 10% glass fiber containing polypropylene (PP10GF), and polymers that may provide unusually high dielectric constants. Many promising candidates were unobvious and unlikely to have been identified without using a polymer informatics approach. Future work will focus on improving the quality of candidate repeat units by refining the QSPR method, enhancing the diversity of candidate repeat units by expanding PEARL, providing additional interactive search options, and converting Polymer Expert into a versatile R&D platform that users can customize for their own needs.

A highly efficient computational approach for the screening of Li ion conducting materials is presented and its performance is demonstrated for olivine-type oxides and thiophosphates. The approach is based on a topological analysis of the electrostatic (Coulomb) potential obtained from a single density functional theory calculation augmented by a Born-Mayer-type repulsive term between Li ions and the anions of the material. This 3D-corrugation descriptor enables the automatic determination of diffusion pathways in one, two, and three dimensions and reproduces migration barriers obtained from density functional theory calculations using nudged elastic band method within approximately 0.1 eV. Importantly, it correlates with Li ion conductivity. This approach thus offers an efficient tool for evaluating, ranking, and optimizing materials with high Li-ion conductivity.

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.

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.

Asphaltenes are heavy crude oil compounds, defined as soluble in toluene and precipitating in alkanes. To understand the relation between asphaltene structure and aggregation, we perform equilibrium molecular dynamics with Large-scale Atomic and Molecular Massively Parallel Software (LAMMPS), using the atomistic force field PCFF+ in the MedeA (R) environment. The following three molecular models are considered: the continental model (1350g/mol) that has a large polyaromatic core and long alkyl chains, the island model (780g/mol) that has a smaller polyaromatic unit and shorter chains and the archipelago model (1350g/mol) that has three polyaromatic nuclei bridged with alkyl chains. The aggregation in a given solvent is monitored by visualising solvent-free configurations over 15ns trajectories at 350K. Nanoaggregates are characterised by stacked polyaromatic units separated by 0.33-0.4nm. Irreversible aggregation is found with the continental model in both solvents. Aggregation of the island model is significant in n-heptane and low in toluene. The archipelago model does not aggregate significantly. Our results confirm that the island model is a reasonable average model of asphaltenes [Headen TF, Boek ES, Skipper NT. Energy Fuels 2009;23:1220-1229]. The open structure of nanoaggregates and the limited number of stacked molecules are also in agreement with previous interpretations of experimental data [Fenistein D. et al. Langmuir 1998;14:1013-1020].

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.

The atomistic and molecular simulation environment MedeA (MedeA: Materials Exploration and Design Analysis, version 2.14.6; Material Design, Inc.: Angel Fire, NM, 19982014; http://www.materialsdesign.com in its functionalities and graphical user interface has been enhanced to prepare and submit on the order of 1000 simulations on different structures, and to collect and help in the analysis of the results. We illustrate this with the determination of the accuracy of the semiempirical (SE) package MOPAC2012 (Stewart, J. J. P. MOPAC2012; Stewart Computational Chemistry: Colorado Springs, CO, USA, 2012; http://OpenMOPAC.net) with the PM7 method (Stewart, J. J. P. Optimization of parameters for semiempirical methods VI: more modifications to the NDDO approximations and reoptimization of parameters. J. Mol. Model. 2013, 19, 132) to compute frequencies of vibration and thermodynamic properties, specifically the zero point energies, ideal gas heat capacity at constant pressure, entropy, and Gibbs free energy, between 200 and 1000 K for 795 organic molecules. The results were compared with experimental data and density functional theory (DFT) values (using B3LYP/TZVP and BP86/TZVP DFT methods). This comparison showed that the PM7 frequencies of vibration above 2500 cm(1) are systematically underestimated. An a posteriori correction using a linear relationship rescaling of the frequencies permitted resetting to zero the average relative deviations with respect to experimental reference values. This frequency correction also removed the bias from the zero point energies, ideal gas heat capacity, and entropy average deviations from the PM7 results. The root-mean-square deviation (RMSD) of PM7 and the DFT heat capacities of 160 organic molecules were equivalent with respect to experimental values, being about 5 %, 2.5 %, and 3 % at 300 K, 600 K, and 1000 K, respectively. The RMSD of PM7, when compared to the DFT values, became 4 %, 2 %, and 1 % for the same temperatures when the analysis was extended to a set of 795 molecules. In the case of the ideal gas entropies, the RMSD of the PM7 relative to DFT values were between 5 % and 4 % between 300 K and 1000 K, respectively. The RMSD of the Gibbs free energies of PM7 were 15 kJ mol(1) and 30 kJ mol(1) at 300 K and 1000 K, respectively. The efficiency of this semiempirical approach was tested on a set of approximately 5800 molecules. This set was processed in about a day, thus demonstrating the scalability of the approach to big data sets.

This paper illustrates the use of Monte Carlo (MC) simulations to study a wide range of systems of interest for biomass conversion into high-added value chemicals and biofuels. The interest is focused on the use of molecular simulation to predict the physical-chemical properties of pure compounds and mixtures at a wide range of temperature and pressure conditions, as well as to provide insight on the mechanisms involved and the molecular characteristics that are related to the macroscopic behaviour of such complex systems. A systematic scaling study has been performed in which our implementation of the MC method is scaled well up to 4 to 12 processors. We have automated tasks such as the implementation of default MC frequencies, statistical parameters, convergence analysis and determination of statistical averages. On this basis, we could determine the equilibrium properties for approximately 100 compounds (alcohols, ethers, ketones, aldehydes, esters, glycols) with the TraPPE-UA and the anisotropic united atoms (AUA) forcefields. TraPPE-UA provides a good prediction of liquid density, and AUA provides a better determination of saturation pressures and normal boiling temperature. Liquid-vapour phase diagrams of binary mixtures are also provided to illustrate the predictive capability of MC simulations. Viewing graphs or configurations allows to validate convergence analysis and to understand the hydrogen-bonded systems.

Sulfur Deactivation of NOx Storage Catalysts: A Multiscale Modeling Approach — Lean NOx Trap (LNT) catalysts, a promising solution for reducing the noxious nitrogen oxide emissions from the lean burn and Diesel engines, are technologically limited by the presence of sulfur in the exhaust gas stream. Sulfur stemming from both fuels and lubricating oils is oxidized during the combustion event and mainly exists as SOx (SO2 and SO3) in the exhaust. Sulfur oxides interact strongly with the NOx trapping material of a LNT to form thermodynamically favored sulfate species, consequently leading to the blockage of NOx sorption sites and altering the catalyst operation. Molecular and kinetic modeling represent a valuable tool for predicting system behavior and evaluating catalytic performances. The present paper demonstrates how fundamental ab initio calculations can be used as a valuable source for designing kinetic models developed in the IFP Exhaust library, intended for vehicle simulations. The concrete example we chose to illustrate our Oil & Gas Science and Technology – Rev. IFP Energies nouvelles, Vol. 68 (2013), No. 6, pp. 995-1005 Copyright 2013, IFP Energies nouvelles DOI: 10.2516/ogst/2013123 approach was SO3 adsorption on the model NOx storage material, BaO. SO3 adsorption was described for various sites (terraces, surface steps and kinks and bulk) for a closer description of a real storage material. Additional rate and sensitivity analyses provided a deeper understanding of the poisoning phenomena.

We use periodic Density Functional Theory (DFT) method to generate the electrostatic potentials of adsorption materials and use them in Grand Canonical Monte Carlo simulations of fluid adsorption isotherms. This permits us to consider complex solids showing defects and without a priori knowledge of their electrostatic parameters for the Monte Carlo simulations. We apply the method to aluminophosphate and silicate solids of ZON type and evaluate their affinity to adsorb and separate CO2-N-2(H2O) mixtures.

The development of industrial software, the decreasing cost of computing time, and the availability of well-tested forcefields make molecular simulation increasingly attractive for chemical engineers. We present here several applications of Monte-Carlo simulation techniques, applied to the adsorption of fluids in microporous solids such as zeolites and model carbons (pores < 2 nm). Adsorption was computed in the Grand Canonical ensemble with the MedeA®-GIBBS software, using energy grids to decrease computing time. MedeA®-GIBBS has been used for simulations in the NVT or NPT ensembles to obtain the density and fugacities of fluid phases. Simulation results are compared with experimental pure component isotherms in zeolites (hydrocarbon gases, water, alkanes, aromatics, ethanethiol, etc.), and mixtures (methane-ethane, n-hexane-benzene), over a large range of temperatures. Hexane/benzene selectivity inversions between silicalite and Na-faujasites are well predicted with published forcefields, providing an insight on the underlying mechanisms. Also, the adsorption isotherms in Na-faujasites for light gases or ethane-thiol are well described. Regarding organic adsorbents, models of mature kerogen or coal were built in agreement with known chemistry of these systems. Obtaining realistic kerogen densities with the simple relaxation approach considered here is encouraging for the investigation of other organic systems. Computing excess sorption curves in qualitative agreement with those recently measured on dry samples of gas shale is also favorable. Although still preliminary, such applications illustrate the strength of molecular modeling in understanding complex systems in conditions where experiments are difficult.

We use periodic Density Functional Theory (DFT) method to generate the electrostatic potentials of adsorption materials and use them in Grand Canonical Monte Carlo simulations of fluid adsorption isotherms. This permits us to consider complex solids showing defects and without a priori knowledge of their electrostatic parameters for the Monte Carlo simulations. We apply the method to aluminophosphate and silicate solids of ZON type and evaluate their affinity to adsorb and separate CO2-N2(H2O) mixtures. Nous utilisons la théorie de la fonctionnelle de la densité (DFT en anglais) pour engendrer le potentiel électrostatique en tout point de la microporosité de matériaux adsorbants et nous utilisons cette information pour modéliser l’adsorption par simulation de Monte Carlo dans l’ensemble Grand Canonique. Ceci nous permet de simuler des solides complexes montrant des défauts, sans qu’il soit nécessaire de définir a priori leurs paramètres électrostatiques. Nous appliquons cette méthode aux aluminophosphates et silicates cristallins microporeux de type ZON et nous évaluons les sélectivités d’adsorption et séparation dans des systèmes CO2-N2 ou CO2-H2O.

The simulation of vibrational properties and finite temperature effects based on ab initio calculation of phonons within the direct approach is discussed. The implementation of the approach within an automated computational framework is outlined, and applications in rather diverse fields are demonstrated: phonon dispersion of GaAs, Kohn anomaly in Niobium, rattling modes in thermoelectric skutterudites, reaction enthalpies and formation enthalpies of hydrides and hydrogen storage materials, phase transformations, surface reconstruction of Si( 111), and adsorption of CO molecules on a Ni( 001) surface.

We present in this paper a theoretical analysis that relates an irregularity measure of a fitness function to the so-called GA-deception. This approach is a con­ti­nua­tion of a work that has presented a deception a­na­ly­sis of Holder functions. The analysis developed here is a generalization of this work in two ways¸: we first use a «bitwise regularity» instead of a Holder exponent as a basis for our deception analysis, second, we perform a similar deception analysis of a GA with uniform crossover. We finally propose to use the bitwise regularity coefficients in order to analyze the influence of a chromosome encoding on the GA efficiency, and present experiments with bits permutations and Gray encoding.