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second, or FLOPS) have been reported in the past two years. There is every indication that this trend will continue, with exaflop computing (1018 FLOPS) expected around 2018, although achieving high levels of computational efficiency will be challenged by the emergence of multicore chips. We have entered an era in which Moore’s law is achieved by doubling the number of cores in each microprocessor chip, rather than increasing clock speed, adding another level of complexity to the programming model for scientific computing. Second, the community of materials simulations scholars is constantly innovating, so new algorithms often result in multiple-orders-of-magnitude speed increases without any change in hardware. Examples abound, particularly in MC methods for classical simulation (such as multispin methods for Ising systems and continuum configuration bias methods for complex molecules). There are new O(N) methods for first principles calculations in which the cost of the computation scales linearly with the number of electrons, N, rather than by the nonlinear N 3 – N 7 scaling of current methods. The combination of algorithmic and hardware advances will result in first principles and molecular simulation computations becoming far more routine. De novo predictions of the properties of a proposed new solvent require first principles methods to obtain gas phase properties, calibrate classical force fields, and model chemical reactions, and classical molecular simulations to obtain condensed-phase properties. (First principles methods include quantum chemistry methods; density functional theory [DFT]; and ab initio molecular dynamics, in which the classical equations of motion for atoms are solved using forces calculated on the fly from quantum chemistry or DFT methods.) Several methods exist for automated fitting of classical force fields to first principles calculations, but currently they are not widely practiced. To develop the capability of providing the key physical and chemical properties needed for novel carbon capture process design, a suite of molecular modeling tools based on state-of-the-art first principles and molecular simulation tools is required. A partnership between end-users of the predicted properties (process design researchers) and the molecular modeling community is needed to identify the properties required. In addition, a mechanism is needed to ensure computed properties are made available in standards-compliant properties databases used by process designers. New molecular theory for novel solvent design Continuing improvements in theory, algorithms, and computing hardware speed are resulting in computationally intensive methods (e.g., quantum chemistry and molecular simulation) that provide routes to key physical and chemical properties considered equal in accuracy and precision to experimental measurements. For example, in gas phase thermochemistry, many companies moved from experiment to computational quantum chemistry methods more than a decade ago to obtain free energies of formation and reaction.3 The reason is that quantum chemistry methods provide equivalent or higher accuracy at much lower cost. Developing the computational molecular-based approach to properties prediction that was described earlier will result in a large properties database for novel solvent systems and the capability of adding to it in ways that directly provide data for group contributions. Thus the opportunity exists to create a new, molecular-based theoretical approach to physical properties prediction to replace the current engineering group-contribution methods. The idea of using modern equations of state based on molecular theory (such as variants of the statistical associating fluid theory and other statistical-mechanics-based approaches) in a group-contribution 54PDF Image | 2020 Carbon Capture
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