Elucidating the behavior of the SARS-CoV-2 virus surface at vapor-liquid interfaces using molecular dynamics simulation.

Aerosols play a major role in the transmission of the SARS-CoV-2 virus. The behavior of the virus within aerosols is therefore of fundamental importance. On the surface of a SARS-CoV-2 virus, there are about 40 spike proteins, which each have a length of about 20 nm. They are glycosylated trimers, which are highly flexible, due to their structure. These spike proteins play a central role in the intrusion of the virus into human host cells and are, therefore, a focus of vaccine development. In this work, we have studied the behavior of spike proteins of the SARS-CoV-2 virus in the presence of a vapor-liquid interface by molecular dynamics (MD) simulations. Systematically, the behavior of the spike protein at different distances to a vapor-liquid interface were studied. The results reveal that the spike protein of the SARS-CoV-2 virus is repelled from the vapor-liquid interface and has a strong affinity to stay inside the bulk liquid phase. Therefore, the spike protein bends when a vapor-liquid interface approaches the top of the protein. This has important consequences for understanding the behavior of the virus during the dry-out of aerosol droplets.

Hierarchical Multicriteria Optimization of Molecular Models of Water.

Many widely used molecular models of water are built from a single Lennard-Jones site on which three point charges are positioned, one negative and two positive ones. Models from that class, denoted LJ3PC here, are computationally efficient, but it is well known that they cannot represent all relevant properties of water simultaneously with good accuracy. Despite the importance of the LJ3PC water model class, its inherent limitations in simultaneously describing different properties of water have never been studied systematically. This task can only be solved by multicriteria optimization (MCO). However, due to its computational cost, applying MCO to molecular models is a formidable task. We have recently introduced the reduced units method (RUM) to cope with this problem. In the present work, we apply the RUM in a hierarchical scheme to optimize LJ3PC water models taking into account five objectives: the representation of vapor pressure, saturated liquid density, self-diffusion coefficient, shear viscosity, and relative permittivity. Of the six parameters of the LJ3PC models, five were varied; only the H-O-H bond angle, which is usually chosen based on physical arguments, was kept constant. Our hierarchical RUM-based approach yields a Pareto set that contains attractive new water models. Furthermore, the results give an idea of what can be achieved by molecular modeling of water with models from the LJ3PC class.

KnowTD─An Actionable Knowledge Representation System for Thermodynamics.

We demonstrate that thermodynamic knowledge acquired by humans can be transferred to computers so that the machine can use it to solve thermodynamic problems and produce explainable solutions with a guarantee of correctness. The actionable knowledge representation system that we have created for this purpose is called KnowTD. It is based on an ontology of thermodynamics that represents knowledge of thermodynamic theory, material properties, and thermodynamic problems. The ontology is coupled with a reasoner that sets up the problem to be solved based on user input, extracts the correct, pertinent equations from the ontology, solves the resulting mathematical problem, and returns the solution to the user, together with an explanation of how it was obtained. KnowTD is presently limited to simple thermodynamic problems, similar to those discussed in an introductory course in Engineering Thermodynamics. This covers the basic theory and working principles of thermodynamics. KnowTD is designed in a modular way and is easily extendable.

Prediction of pair interactions in mixtures by matrix completion.

Molecular simulations enable the prediction of physicochemical properties of mixtures based on pair-interaction models of the pure components and combining rules to describe the unlike interactions. However, if no adjustment to experimental data is made, the existing combining rules often do not yield sufficiently accurate predictions of mixture data. To address this problem, adjustable binary parameters ξij describing the pair interactions in mixtures (i + j) are used. In this work, we present the first method for predicting ξij for unstudied mixtures based on a matrix completion method (MCM) from machine learning (ML). Considering molecular simulations of Henry's law constants as an example, we demonstrate that ξij for unstudied mixtures can be predicted with high accuracy. Using the predicted ξij significantly increases the accuracy of the Henry's law constant predictions compared to using the default ξij = 1. Our approach is generic and can be transferred to molecular simulations of other mixture properties and even to combining rules in equations of state, granting predictive access to the description of unlike intermolecular interactions.

Polarization transfer methods for quantitative analysis of flowing mixtures with benchtop 13C NMR spectroscopy.

Benchtop NMR spectroscopy is attractive for process monitoring; however, there are still drawbacks that often hamper its use, namely, the comparatively low spectral resolution in 1H NMR, as well as the low signal intensities and problems with the premagnetization of flowing samples in 13C NMR. We show here that all these problems can be overcome by using 1H-13C polarization transfer methods. Two ternary test mixtures (one with overlapping peaks in the 1H NMR spectrum and one with well-separated peaks, which was used as a reference) were studied with a 1 T benchtop NMR spectrometer using the polarization transfer sequence PENDANT (polarization enhancement that is nurtured during attached nucleus testing). The mixtures were analyzed quantitatively in stationary as well as in flow experiments by PENDANT enhanced 13C NMR experiments, and the results were compared with those from the gravimetric sample preparation and from standard 1H and 13C NMR spectroscopy. Furthermore, as a proxy for a process monitoring application, continuous dilution experiments were carried out, and the composition of the mixture was monitored in a flow setup by 13C NMR benchtop spectroscopy with PENDANT. The results demonstrate the high potential of polarization transfer methods for applications in quantitative process analysis with benchtop NMR instruments, in particular with flowing samples.

A child of prediction. On the History, Ontology, and Computation of the Lennard-Jonesium.

The Lennard-Jones (LJ) fluid, named after mathematician-physicist-chemist Sir John Lennard-Jones (1894-1954), occupies a special place among fluids. It is an ideal entity, defined as the fluid whose particles interact according to the Lennard-Jones potential. This paper expounds the history of the LJ fluid to throw light on the tensions between theory and computational practice. The paper argues for the following claims. Firstly, the computational approach-even prior to the computer-pragmatically aims at prediction, not truth. Secondly, computer simulation methods, especially "molecular dynamics" (MD), triggered a change in epistemology. Now, simulated model fluids became targets of investigation in their own right. The urge for prediction turned the LJ fluid into the most investigated fluid in engineering thermodynamics. Thirdly, MD took a huge upswing in the 1990s, due to exploratory options in simulation. We discuss how, under these conditions, predictive success might be fraught with problems of reproducibility.