2015/02/04 by Claudio Cazorla, Cazorla, Claudio · 1 citation
Physics and Astronomy · Chemistry · Materials Science · #Advanced Chemical Physics Studies #Metal-Organic Frameworks: Synthesis and Applications #Machine Learning in Materials Science
paper · pdf · doi:10.48550/arxiv.1502.01100
With the advent of new synthesis and large-scale production technologies,\nnanostructured gas-adsorbent materials (GAM) like carbon nanocomposites and\nmetal-organic frameworks are becoming increasingly more influential in our\neveryday lives. First-principles methods based on density functional theory\n(DFT) have been pivotal in establishing the rational design of GAM, a factor\nwhich has tremendously boosted their development. However, DFT methods are not\nperfect and due to the stringent accuracy thresholds demanded in modelling of\nGAM (i.e., exact binding energies to within ~0.01 eV) these techniques may\nprovide erroneous conclusions in some challenging situations. Examples of\nproblematic circumstances include gas-adsorption processes in which both\nelectronic long-range exchange and nonlocal correlations are important, and\nsystems where many-body energy and Coulomb screening effects cannot be\ndisregarded. In this critical review, we analyse recent efforts done in the\nassessment of the performance of DFT methods in the prediction and\nunderstanding of GAM. Our inquiry is constrained to the areas of hydrogen\nstorage and carbon capture and sequestration, for which we expose a number of\nunresolved modelling controversies and define a set of best practice simulation\nprinciples. Also, we identify the subtle problems found in the generalization\nof DFT benchmark studies performed in model cluster systems to real materials,\nand discuss effective approaches to circumvent them. The increasing awareness\nof the strengths and imperfections of DFT methods in the simulation of\ngas-adsorption phenomena should lead in the medium term to more precise, and\nhence even more fruitful, ab initio engineering of GAM.\n