Prediction of Temperature-Dependent Henry's Law Constants by Matrix Completion.

Methods for predicting Henry's law constants describing the solubility of solutes in solvents as a function of temperature are essential in chemical engineering. While isothermal properties of binary mixtures can conveniently be predicted with matrix completion methods (MCMs) from machine learning, we advance their application to the temperature-dependent prediction of in the present work by combining them with physical equations describing the temperature dependence. For training the methods, experimental data for 122 solutes and 399 solvents ranging from 173.

HANNA: hard-constraint neural network for consistent activity coefficient prediction.

We present the first hard-constraint neural network model for predicting activity coefficients (HANNA), a thermodynamic mixture property that is the basis for many applications in science and engineering. Unlike traditional neural networks, which ignore physical laws and result in inconsistent predictions, our model is designed to strictly adhere to all thermodynamic consistency criteria. By leveraging deep-set neural networks, HANNA maintains symmetry under the permutation of the components.

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.

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 describing the pair interactions in mixtures ( + ) are used.

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.

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.

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.

Polarization transfer methods for quantitative analysis of flowing mixtures with benchtop C 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 H NMR, as well as the low signal intensities and problems with the premagnetization of flowing samples in C NMR. We show here that all these problems can be overcome by using H-C polarization transfer methods. Two ternary test mixtures (one with overlapping peaks in the H 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).

Determination of self-diffusion coefficients in mixtures with benchtop C NMR spectroscopy via polarization transfer.

Nuclear magnetic resonance (NMR) is an established method to determine self-diffusion coefficients in liquids with high precision. The development of benchtop NMR spectrometers makes the method accessible to a wider community. In most cases, H NMR spectroscopy is used to determine self-diffusion coefficients due to its high sensitivity.

Extension of the Database to Transferable Force Fields.

, a web-based database for classical force fields for molecular simulations of fluids [, 10 (), 806-814], was extended to transferable force fields. Eight transferable force fields, including all-atom and united-atom type force fields, were implemented in the database: OPLS-UA, OPLS-AA, COMPASS, CHARMM, GROMOS, TraPPE, Potoff, and TAMie. These transferable force fields cover a large variety of chemical substance classes.

Molecular dynamics simulation study on the mass transfer across vapor-liquid interfaces in azeotropic mixtures.

Mass transfer through fluid interfaces is an important phenomenon in industrial applications as well as in naturally occurring processes. In this work, we investigate the mass transfer across vapor-liquid interfaces in binary mixtures using molecular dynamics simulations. We investigate the influence of interfacial properties on mass transfer by studying three binary azeotropic mixtures known to have different interfacial behaviors.

Automated nuclear magnetic resonance fingerprinting of mixtures.

Nuclear magnetic resonance (NMR) spectroscopy is a powerful tool for qualitative and quantitative analysis. However, for complex mixtures, determining the speciation from NMR spectra can be tedious and sometimes even unfeasible. On the other hand, identifying and quantifying structural groups in a mixture from NMR spectra is much easier than doing the same for components.

Molecular modeling and simulation of aqueous solutions of alkali nitrates.

A set of molecular models for the alkali nitrates (LiNO, NaNO, KNO, RbNO, and CsNO) in aqueous solutions is presented and used for predicting the thermophysical properties of these solutions with molecular dynamics simulations. The set of models is obtained from a combination of a model for the nitrate anion from the literature with a set of models for the alkali cations developed in previous works of our group. The water model is SPC/E and the Lorentz-Berthelot combining rules are used for describing the unlike interactions.

Rational method for defining and quantifying pseudo-components based on NMR spectroscopy.

Poorly specified mixtures, whose composition is unknown, are ubiquitous in chemical and biochemical engineering. In the present work, we propose a rational method for defining and quantifying pseudo-components in such mixtures that is free of assumptions. The new method requires only standard nuclear magnetic resonance (NMR) experiments and can be fully automated.

Combining Machine Learning with Physical Knowledge in Thermodynamic Modeling of Fluid Mixtures.

Thermophysical properties of fluid mixtures are important in many fields of science and engineering. However, experimental data are scarce in this field, so prediction methods are vital. Different types of physical prediction methods are available, ranging from molecular models over equations of state to models of excess properties.

Mass Transfer through Vapor-Liquid Interfaces Studied by Non-Stationary Molecular Dynamics Simulations.

Molecular dynamics (MD) simulations are highly attractive for studying the influence of interfacial effects, such as the enrichment of components, on the mass transfer through the interface. In a recent work, we have presented a steady-state MD simulation method for investigating this phenomenon and tested it using model mixtures with and without interfacial enrichment. The present study extends this work by introducing a non-stationary MD simulation method.

Comparison of Force Fields for the Prediction of Thermophysical Properties of Long Linear and Branched Alkanes.

The prediction of thermophysical properties at extreme conditions is an important application of molecular simulations. The quality of these predictions primarily depends on the quality of the employed force field. In this work, a systematic comparison of classical transferable force fields for the prediction of different thermophysical properties of alkanes at extreme conditions, as they are encountered in tribological applications, was carried out using molecular dynamics simulations.

Prediction of parameters of group contribution models of mixtures by matrix completion.

Group contribution (GC) methods are widely used for predicting the thermodynamic properties of mixtures by dividing components into structural groups. These structural groups can be combined freely so that the applicability of a GC method is only limited by the availability of its parameters for the groups of interest. For describing mixtures, pairwise interaction parameters between the groups are of prime importance.

Adsorption of conjugates of lysozyme and fluorescein isothiocyanate in hydrophobic interaction chromatography.

Bioconjugates, such as antibody-drug conjugates or fluorescent-labeled proteins, are highly interesting for various applications in medicine and biology. In their production, not only the synthesis is challenging but also the downstream processing, for which hydrophobic interaction chromatography (HIC) is often used. However, in-depth studies of the adsorption of bioconjugates in HIC are still rare.

Functionalized Controlled Porous Glasses for Producing Radical-Free Hyperpolarized Liquids by Overhauser DNP.

Overhauser dynamic nuclear polarization (ODNP) can be used as a tool for NMR signal enhancement and happens on very short time scales. Therefore, ODNP is well suited for the measurement of fast-flowing samples, even in compact magnets, which is beneficial for the real-time monitoring of chemical reactions or processes. ODNP requires the presence of unpaired electrons in the sample, which is usually accomplished by the addition of stable radicals.

Accurate measurements of self-diffusion coefficients with benchtop NMR using a QM model-based approach.

The measurement of self-diffusion coefficients using pulsed-field gradient (PFG) nuclear magnetic resonance (NMR) spectroscopy is a well-established method. Recently, benchtop NMR spectrometers with gradient coils have also been used, which greatly simplify these measurements. However, a disadvantage of benchtop NMR spectrometers is the lower resolution of the acquired NMR signals compared to high-field NMR spectrometers, which requires sophisticated analysis methods.

Making thermodynamic models of mixtures predictive by machine learning: matrix completion of pair interactions.

Predictive models of thermodynamic properties of mixtures are paramount in chemical engineering and chemistry. Classical thermodynamic models are successful in generalizing over (continuous) conditions like temperature and concentration. On the other hand, matrix completion methods (MCMs) from machine learning successfully generalize over (discrete) binary systems; these MCMs can make predictions without any data for a given binary system by implicitly learning commonalities across systems.

Investigation of Radial Shaft Seal Swelling Using a Special Tribometer and Magnetic Resonance Imaging.

Compatibility between the rubber material of radial shaft seals and the lubricants to be sealed is an important requirement that customers demand of their lubricant suppliers. Among other effects that may result from incompatibility, the penetration of lubricant components into the rubber (swelling) can impair the seal's functionality due to changes in its geometry and mechanical behavior. Typically, the penetration of a lubricant into an elastomer is evaluated after an immersion test using volumetric, gravimetric, and extraction measurements.

Influence of pH value and salts on the adsorption of lysozyme in mixed-mode chromatography.

Mixed-mode chromatography (MMC) is an interesting technique for challenging protein separation processes which typically combines adsorption mechanisms of ion exchange (IEC) and hydrophobic interaction chromatography (HIC). Adsorption equilibria in MMC depend on multiple parameters but systematic studies on their influence are scarce. In the present work, the influence of the pH value and ionic strengths up to 3000 mM of four technically relevant salts (sodium chloride, sodium sulfate, ammonium chloride, and ammonium sulfate) on the lysozyme adsorption on the mixed-mode resin Toyopearl MX-Trp-650M was studied systematically at 25℃.

Attribute-based Explanation of Non-Linear Embeddings of High-Dimensional Data.

Embeddings of high-dimensional data are widely used to explore data, to verify analysis results, and to communicate information. Their explanation, in particular with respect to the input attributes, is often difficult. With linear projects like PCA the axes can still be annotated meaningfully.