Effects of Degrees of Freedom on Calculating Diffusion Properties in Nanoporous Materials.

If one carries out a molecular simulation of N particles using periodic boundary conditions, linear momentum is conserved, and hence, the number of degrees of freedom is set to 3N - 3. In most programs, this number of degrees of freedom is the default setting. However, if one carries out a molecular simulation in an external field, one needs to ensure that degrees of freedom are changed from this default setting to 3N, as in an external field the velocity of the center of mass can change. Using the correct degrees of freedom is important in calculating the temperature and in some algorithms to simulate at constant temperature. For sufficiently large systems, the difference between 3N and 3N - 3 is negligible. However, there are systems in which the comparison with experimental data requires molecular dynamics simulations of a small number of particles. In this work, we illustrate the effect of an incorrect setting of degrees of freedom in molecular dynamic simulations studying the diffusion properties of guest molecules in nanoporous materials. We show that previously published results have reported a surprising diffusion dependence on the loading, which could be traced back to an incorrect setting of the degrees of freedom. As the correct settings are convoluted and counterintuitive in some of the most commonly used molecular dynamics programs, we carried out a systematic study on the consequences of the various commonly used (incorrect) settings. Our conclusion is that for systems smaller than 50 particles the results are most likely unreliable as these are either performed at an incorrect temperature or the temperature is incorrectly used in some of the results. Furthermore, a novel and efficient method to calculate diffusion coefficients of guest molecules into nanoporous materials at zero-loading conditions is introduced.

Geometric landscapes for material discovery within energy-structure-function maps.

Porous molecular crystals are an emerging class of porous materials formed by crystallisation of molecules with weak intermolecular interactions, which distinguishes them from extended nanoporous materials like metal-organic frameworks (MOFs). To aid discovery of porous molecular crystals for desired applications, energy-structure-function (ESF) maps were developed that combine a priori prediction of both the crystal structure and its functional properties. However, it is a challenge to represent the high-dimensional structural and functional landscapes of an ESF map and to identify energetically favourable and functionally interesting polymorphs among the 1000s to 10 000s of structures typically on a single ESF map. Here, we introduce geometric landscapes, a representation for ESF maps based on geometric similarity, quantified by persistent homology. We show that this representation allows the exploration of complex ESF maps, automatically pinpointing interesting crystalline phases available to the molecule. Furthermore, we show that geometric landscapes can serve as an accountable descriptor for porous materials to predict their performance for gas adsorption applications. A machine learning model trained using this geometric similarity could reach a remarkable accuracy in predicting the materials' performance for methane storage applications.

Material patterning on substrates by manipulation of fluidic behavior.

Patterned materials on substrates are of great importance for a wide variety of applications. In solution-based approaches to material patterning, fluidic flow is inevitable. Here we demonstrate not only the importance of fluidic behavior but also the methodology of engineering the flow pattern to guide the material crystallization and assembly. We show by both experiment and simulation that substrate heating, which is generally used to accelerate evaporation, produces irregular complex vortexes. Instead, a top-heating-bottom-cooling (THBC) set-up offers an inverse temperature gradient and results in a single Marangoni vortex, which is desired for ordered nanomaterial patterning near the contact line. We then realize the fabrication of large-scale patterns of iodide perovskite crystals on different substrates under THBC conditions. We further demonstrate that harnessing the flow behavior is a general strategy with great feasibility to pattern various functional materials ranging from inorganic, organic, hybrid to biological categories on different substrates, presenting great potential for practical applications.