Modelling of framework materials at multiple scales: current practices and open questions

Abstract

<jats:p>The last decade has seen an explosion of the family of framework materials and their study, from both the experimental and computational points of view. We propose here a short highlight of the current state of methodologies for modelling framework materials at multiple scales, putting together a brief review of new methods and recent endeavours in this area, as well as outlining some of the open challenges in this field. We will detail advances in atomistic simulation methods, the development of material databases and the growing use of machine learning for the prediction of properties.</jats:p> <jats:p>This article is part of the theme issue ‘Mineralomimesis: natural and synthetic frameworks in science and technology’.</jats:p>

Design, Parameterization, and Implementation of Atomic Force Fields for Adsorption in Nanoporous Materials

“…One such example is screening of a large set of structures for specific applications. [347,352,353,[469][470][471] Here, the problem of false positives would not matter too much because it is used to reduce the millions of structures to a small set of promising structures. This reduced dataset, usually a few dozen, are then re-evaluated and ranked using conventional simulation approaches.…”

“…However, if one sees this approach as an additional tool amongst many others, then one could take advantage of it in situations where it really could do great things. One such example is screening of a large set of structures for specific applications . Here, the problem of false positives would not matter too much because it is used to reduce the millions of structures to a small set of promising structures.…”

Recent progress on discovery and properties prediction of energy materials: Simple machine learning meets complex quantum chemistry

“…By using QC, physicists have achieved great success in describing simple microphysics. Considering the complex chemical reactions, QC appears deficient in determining the chemical behavior of an atom [15]. Even though, calculations can provide detailed thermodynamics and kinetics information, such as Gibbs free energy, binding energy [39], or energy band [40].…”

“…Chemists sought for desired materials or material preparation methods usually rely on trial and error method, which based on numerous failed experiments and by-products [12,13]. Recently, combinatorial quantum chemistry (QC) and high-throughput screening are expected to bridge the gap [14,15]. Theory calculations can not only confirm the reliability of data but predict untried methods.…”

Introduction: minerals to metal-organic frameworks

“…Huskić & Friščić remind us some minerals can themselves be considered MOFs, using the natural processes by which these are formed and degrade as inspiration for environmentally friendly preparations of synthetic analogues [22]. Coudert and co-workers emphasize the importance of multi-scale modelling to give a realistic computational picture of these materials, and review the use of materials databases, machine learning, and other big-data methods that are increasingly prominent ways to develop these models [23].…”