A Spatial and Operations Modeling Method for Automatic Parking Systems

Automatic or Automated Parking Systems (APSs) would have spatial configurations that depend on both or any of the following factors: 1) its operations and 2) the physical structure design. Without a modeling method, designing and configuring an APS could be relatively challenging and result to special instances of APSs. On the other hand, a modeling method for this purpose would facilitate representing the spaces and operations used by APSs thus aid their design and configuration, and possibly make them adaptive to physical space constraints. This study developed such method for modeling the spaces and operations of APSs allowing their design and configuration to be highly flexible. It involved defining an approach for spatial representations and establishing a model for representing the operations of autonomous parking devices of APSs. The implementation of the spatial representations and operations model into a data structure suitable for computer programming was also described. A number of configuration examples based on those offered by current APS service providersand a few hypothetical APS designs were used to test the applicability of the method. The test was facilitated by simulation software that allowed input of varied APS configurations, input of basic car parking and retrieval operations, and showing results of such operations. Results show that basic operations are correctly executed thus indicating that the model is applicable. Keywords: modeling method, Automatic Parking Systems (APS), autonomous parking devices, spatial model, parking device operations mode

Machine Learning Nucleation Collective Variables with Graph Neural Networks

The efficient calculation of nucleation collective variables (CVs) is one of the main limitations to the application of enhanced sampling methods to the investigation of nucleation processes in realistic environments. Here we discuss the development of a graph-based model for the approximation of nucleation CVs that enables orders-of-magnitude gains in computational efficiency in the on-the-fly evaluation of nucleation CVs. By performing simulations on a nucleating colloidal system mimicking a multistep nucleation process from solution, we assess the model's efficiency in both postprocessing and on-the-fly biasing of nucleation trajectories with pulling, umbrella sampling, and metadynamics simulations. Moreover, we probe and discuss the transferability of graph-based models of nucleation CVs across systems using the model of a CV based on sixth-order Steinhardt parameters trained on a colloidal system to drive the nucleation of crystalline copper from its melt. Our approach is general and potentially transferable to more complex systems as well as to different CVs

A foundation model for atomistic materials chemistry

Machine-learned force fields have transformed the atomistic modelling of materials by enabling simulations of ab initio quality on unprecedented time and length scales. However, they are currently limited by: (i) the significant computational and human effort that must go into development and validation of potentials for each particular system of interest; and (ii) a general lack of transferability from one chemical system to the next. Here, using the state-of-the-art MACE architecture we introduce a single general-purpose ML model, trained on a public database of 150k inorganic crystals, that is capable of running stable molecular dynamics on molecules and materials. We demonstrate the power of the MACE-MP-0 model - and its qualitative and at times quantitative accuracy - on a diverse set problems in the physical sciences, including the properties of solids, liquids, gases, chemical reactions, interfaces and even the dynamics of a small protein. The model can be applied out of the box and as a starting or "foundation model" for any atomistic system of interest and is thus a step towards democratising the revolution of ML force fields by lowering the barriers to entry.Comment: 119 pages, 63 figures, 37MB PD

In situ investigations on the electrochemical polymerization and properties of polyaniline thin films by surface plasmon optical techniques

The combination of in situ surface plasmon resonance spectroscopy (SPS) and surface plasmon field-enhanced light scattering (SPLS) with an electrochemical method was used to simultaneously investigate the optical and electrochemical properties of polyaniline films on a planar gold electrode. First, the electropolymerization process of aniline and then the doping-dedoping process of the resulting polyaniline film were investigated using SPS and SPLS. The electropolymerization of aniline was achieved by applying a cycling potential known from cyclic voltammetry. Potential cycling resulted in distinct oscillations sensitively monitored with both techniques. Information was obtained on the change of the dielectric constant of the film and of the film thickness, corresponding to morphology transitions of the polyaniline film. The time- differential SPS kinetic reflectivity curve was correlated with cyclic voltammetry. SPLS was also applied in a reversed attenuated total reflection configuration to obtain more correlation with the electrochemical behavior and optical properties of the polyaniline film, Thus, this combination of experimental approaches allows for the simultaneous elucidation of optical and electrochemical properties of an ultrathin conducting polymer film