FAIR data enabling new horizons for materials research.

The prosperity and lifestyle of our society are very much governed by achievements in condensed matter physics, chemistry and materials science, because new products for sectors such as energy, the environment, health, mobility and information technology (IT) rely largely on improved or even new materials. Examples include solid-state lighting, touchscreens, batteries, implants, drug delivery and many more. The enormous amount of research data produced every day in these fields represents a gold mine of the twenty-first century. This gold mine is, however, of little value if these data are not comprehensively characterized and made available. How can we refine this feedstock; that is, turn data into knowledge and value? For this, a FAIR (findable, accessible, interoperable and reusable) data infrastructure is a must. Only then can data be readily shared and explored using data analytics and artificial intelligence (AI) methods. Making data 'findable and AI ready' (a forward-looking interpretation of the acronym) will change the way in which science is carried out today. In this Perspective, we discuss how we can prepare to make this happen for the field of materials science.

Multiscale Simulations of Self-Assembling Peptides: Surface and Core Hydrophobicity Determine Fibril Stability and Amyloid Aggregation.

Assemblies of peptides and proteins through specific intermolecular interactions set the basis for macroscopic materials found in nature. Peptides provide easily tunable hydrogen-bonding interactions, which can lead to the formation of ordered structures such as highly stable ß-sheets that can form amyloid-like supramolecular peptide nanofibrils (PNFs). PNFs are of special interest, as they could be considered as mimics of various fibrillar structures found in nature. In their ability to serve as supramolecular scaffolds, they could mimic certain features of the extracellular matrix to provide stability, interact with pathogens such as virions, and transduce signals between the outside and inside of cells. Many PNFs have been reported that reveal rich bioactivities. PNFs supporting neuronal cell growth or lentiviral gene transduction have been studied systematically, and their material properties were correlated to bioactivities. However, the impact of the structure of PNFs, their dynamics, and stabilities on their unique functions is still elusive. Herein, we provide a microscopic view of the self-assembled PNFs to unravel how the amino acid sequence of self-assembling peptides affects their secondary structure and dynamic properties of the peptides within supramolecular fibrils. Based on sequence truncation, amino acid substitution, and sequence reordering, we demonstrate that peptide-peptide aggregation propensity is critical to form bioactive ß-sheet-rich structures. In contrast to previous studies, a very high peptide aggregation propensity reduces bioactivity due to intermolecular misalignment and instabilities that emerge when fibrils are in close proximity to other fibrils in solution. Our multiscale simulation approach correlates changes in biological activity back to single amino acid modifications. Understanding these relationships could lead to future material discoveries where the molecular sequence predictably determines the macroscopic properties and biological activity. In addition, our studies may provide new insights into naturally occurring amyloid fibrils in neurodegenerative diseases.

Chilling alcohol on the computer: isothermal compressibility and the formation of hydrogen-bond clusters in liquid propan-1-ol.

Molecular dynamics simulations have been performed to compute the isothermal compressibility [Formula: see text] of liquid propan-1-ol in the temperature range [Formula: see text] K. A change in behaviour, from normal (high T) to anomalous (low T), has been identified for [Formula: see text] at [Formula: see text] K. The average number of hydrogen bonds (H-bond) per molecule turns to saturation in the same temperature interval, suggesting the formation of a relatively rigid network. Indeed, simulation results show a strong tendency to form H-bond clusters with distinct boundaries, with the average largest size and width of the size distribution growing upon decreasing temperature, in agreement with previous theoretical and experimental studies. These results also emphasise a connection between the behaviour of [Formula: see text] and the formation of nanometric structures.

Glass transition temperature of (ultra-)thin polymer films.

The glass transition temperature of confined and free-standing polymer films of varying thickness is studied by extended molecular dynamics simulations of bead-spring chains. The results are connected to the statistical properties of the polymers in the films, where the chain lengths range from short, unentangled to highly entangled. For confined films, perfect scaling of the thickness-dependent end-to-end distance and radius of gyrations normalized to their bulk values in the directions parallel and perpendicular to the surfaces is obtained. In particular, the reduced end-to-end distance in the perpendicular direction is very well described by an extended Silberberg model. For bulk polymer melts, the relation between the chain length and Tg follows the Fox-Flory equation. For films, no further confinement induced chain length effect is observed. Tg decreases and is well described by Keddie's formula, where the reduction is more pronounced for free-standing films. It is shown that Tg begins to deviate from bulk Tg at the characteristic film thickness, where the average bond orientation becomes anisotropic and the entanglement density decreases.

Determining glass transition in all-atom acrylic polymeric melt simulations using machine learning.

The functionality of many polymeric materials depends on their glass transition temperatures (Tg). In computer simulations, Tg is often calculated from the gradual change in macroscopic properties. Precise determination of this change depends on the fitting protocols. We previously proposed a robust data-driven approach to determine Tg from the molecular dynamics simulation data of a coarse-grained semiflexible polymer model. In contrast to the global macroscopic properties, our method relies on high-resolution microscopic details. Here, we demonstrate the generality of our approach by using various dimensionality reduction and clustering methods and apply it to an atomistic model of acrylic polymers. Our study reveals the explicit contribution of the side chain and backbone residues in influencing the determination of the glass transition temperature.

The Effects of Proline Isomerization on the Solvation Behavior of Elastin-Like Polypeptides in Water-Ethanol Mixtures.

Elastin-like polypeptides (ELPs) are well-known proline-rich stimulus-responsive polymers. They have broad applications ranging from drug delivery to green chemistry. Recently, the authors have shown that the cis/trans proline isomerization can be used to regulate their conformational behavior while keeping the lower critical solution temperature (LCST) unchanged in pure water. In aqueous ethanol mixtures, ELPs typically exhibit an expanded-collapsed-expanded transition known as the co-non-solvency phenomenon. Since it is unclear how proline isomerization affects the solvation behavior of ELPs in aqueous ethanol mixtures, an all-atom insight on single ELPs has been provided to address this question. It is found that if all proline residues are in the cis state, the peptides only experience a collapsed-expanded transition as ethanol concentration increases, i.e., the initial collapse vanishes because cis isomers prefer the compact structures in pure water. The data from the authors also suggest that proline isomerization does not change the shift in solvation free energy of an ELP with given sequence, but it varies the affinity of the peptide to both the solvent and cosolvent molecules.

Ligand-protein interactions in lysozyme investigated through a dual-resolution model.

A fully atomistic (AT) modeling of biological macromolecules at relevant length- and time-scales is often cumbersome or not even desirable, both in terms of computational effort required and a posteriori analysis. This difficulty can be overcome with the use of multiresolution models, in which different regions of the same system are concurrently described at different levels of detail. In enzymes, computationally expensive AT detail is crucial in the modeling of the active site in order to capture, for example, the chemically subtle process of ligand binding. In contrast, important yet more collective properties of the remainder of the protein can be reproduced with a coarser description. In the present work, we demonstrate the effectiveness of this approach through the calculation of the binding free energy of hen egg white lysozyme with the inhibitor di-N-acetylchitotriose. Particular attention is payed to the impact of the mapping, that is, the selection of AT and coarse-grained residues, on the binding free energy. It is shown that, in spite of small variations of the binding free energy with respect to the active site resolution, the separate contributions coming from different energetic terms (such as electrostatic and van der Waals interactions) manifest a stronger dependence on the mapping, thus pointing to the existence of an optimal level of intermediate resolution.

Precision Anisotropic Brush Polymers by Sequence Controlled Chemistry.

The programming of nanomaterials at molecular length-scales to control architecture and function represents a pinnacle in soft materials synthesis. Although elusive in synthetic materials, Nature has evolutionarily refined macromolecular synthesis with perfect atomic resolution across three-dimensional space that serves specific functions. We show that biomolecules, specifically proteins, provide an intrinsic macromolecular backbone for the construction of anisotropic brush polymers with monodisperse lengths via grafting-from strategy. Using human serum albumin as a model, its sequence was exploited to chemically transform a single cysteine, such that the expression of said functionality is asymmetrically placed along the backbone of the eventual brush polymer. This positional monofunctionalization strategy was connected with biotin-streptavidin interactions to demonstrate the capabilities for site-specific self-assembly to create higher ordered architectures. Supported by systematic experimental and computational studies, we envisioned that this macromolecular platform provides unique avenues and perspectives in macromolecular design for both nanoscience and biomedicine.

Free-energy landscape of polymer-crystal polymorphism.

Polymorphism rationalizes how processing can control the final structure of a material. The rugged free-energy landscape and exceedingly slow kinetics in the solid state have so far hampered computational investigations. We report for the first time the free-energy landscape of a polymorphic crystalline polymer, syndiotactic polystyrene. Coarse-grained metadynamics simulations allow us to efficiently sample the landscape at large. The free-energy difference between the two main polymorphs, α and ß, is further investigated by quantum-chemical calculations. The results of the two methods are in line with experimental observations: they predict ß as the more stable polymorph under standard conditions. Critically, the free-energy landscape suggests how the α polymorph may lead to experimentally observed kinetic traps. The combination of multiscale modeling, enhanced sampling, and quantum-chemical calculations offers an appealing strategy to uncover complex free-energy landscapes with polymorphic behavior.

Defects and defect engineering in Soft Matter.

Soft matter covers a wide range of materials based on linear or branched polymers, gels and rubbers, amphiphilic (macro)molecules, colloids, and self-assembled structures. These materials have applications in various industries, all highly important for our daily life, and they control all biological functions; therefore, controlling and tailoring their properties is crucial. One way to approach this target is defect engineering, which aims to control defects in the material's structure, and/or to purposely add defects into it to trigger specific functions. While this approach has been a striking success story in crystalline inorganic hard matter, both for mechanical and electronic properties, and has also been applied to organic hard materials, defect engineering is rarely used in soft matter design. In this review, we present a survey on investigations on defects and/or defect engineering in nine classes of soft matter composed of liquid crystals, colloids, linear polymers with moderate degree of branching, hyperbranched polymers and dendrimers, conjugated polymers, polymeric networks, self-assembled amphiphiles and proteins, block copolymers and supramolecular polymers. This overview proposes a promising role of this approach for tuning the properties of soft matter.

Efficient equilibration of confined and free-standing films of highly entangled polymer melts.

Equilibration of polymer melts containing highly entangled long polymer chains in confinement or with free surfaces is a challenge for computer simulations. We approach this problem by first studying polymer melts based on the soft-sphere coarse-grained model confined between two walls with periodic boundary conditions in two directions parallel to the walls. Then, we insert the microscopic details of the underlying bead-spring model. Tuning the strength of the wall potential, the monomer density of confined polymer melts in equilibrium is kept at the bulk density even near the walls. In a weak confining regime, we observe the same conformational properties of chains as in the bulk melt showing that our confined polymer melts have reached their equilibrated state. Our methodology provides an efficient way of equilibrating large polymer films with different thicknesses and is not confined to a specific underlying microscopic model. Switching off the wall potential in the direction perpendicular to the walls enables to study free-standing highly entangled polymer films or polymer films with one supporting substrate.

Melts of nonconcatenated rings in spherical confinement.

Motivated by the chromosomes enclosed in a cell nucleus, we study a spherically confined system of a small number of long unknotted and nonconcatenated polymer rings in a melt and systematically compare it with the bulk results. We find that universal scaling exponents of the bulk system also apply in the confined case; however, certain important differences arise. First, due to confinement effects, the static and threading properties of the rings depend on their radial position within the confining sphere. Second, the rings' dynamics is overall subdiffusive, but anisotropic along the directions parallel and perpendicular to the sphere's radius. The radial center of mass displacements of the rings are in general much smaller than the angular ones, which is caused by the confinement-induced inhomogeneous radial distribution of the whole rings within the sphere. Finally, we find enhanced contact times between rings as compared to the bulk, which indicates slow and predominantly coordinated pathways of the relaxation of the system.

Open-boundary Hamiltonian adaptive resolution. From grand canonical to non-equilibrium molecular dynamics simulations.

We propose an open-boundary molecular dynamics method in which an atomistic system is in contact with an infinite particle reservoir at constant temperature, volume, and chemical potential. In practice, following the Hamiltonian adaptive resolution strategy, the system is partitioned into a domain of interest and a reservoir of non-interacting, ideal gas particles. An external potential, applied only in the interfacial region, balances the excess chemical potential of the system. To ensure that the size of the reservoir is infinite, we introduce a particle insertion/deletion algorithm to control the density in the ideal gas region. We show that it is possible to study non-equilibrium phenomena with this open-boundary molecular dynamics method. To this aim, we consider a prototypical confined liquid under the influence of an external constant density gradient. The resulting pressure-driven flow across the atomistic system exhibits a velocity profile consistent with the corresponding solution of the Navier-Stokes equation. This method conserves, on average, linear momentum and closely resembles experimental conditions. Moreover, it can be used to study various direct and indirect out-of-equilibrium conditions in complex molecular systems.

Hierarchical modelling of polystyrene melts: from soft blobs to atomistic resolution.

We demonstrate that hierarchical backmapping strategies incorporating generic blob-based models can equilibrate melts of high-molecular-weight polymers, described with chemically specific, atomistic models. The central idea is first to represent polymers by chains of large soft blobs (spheres) and efficiently equilibrate the melt on large scales. Then, the degrees of freedom of more detailed models are reinserted step by step. The procedure terminates when the atomistic description is reached. Reinsertions are feasible computationally because the fine-grained melt must be re-equilibrated only locally. We consider polystyrene (PS) which is sufficiently complex to serve method development because of stereo-chemistry and bulky side groups. Our backmapping strategy bridges mesoscopic and atomistic scales by incorporating a blob-based, a moderately coarse-grained (CG), and a united-atom model of PS. We demonstrate that the generic blob-based model can be parameterised to reproduce the mesoscale properties of a specific polymer - here PS. The moderately CG model captures stereo-chemistry. To perform backmapping we improve and adjust several fine-graining techniques. We prove equilibration of backmapped PS melts by comparing their structural and conformational properties with reference data from smaller systems, equilibrated with less efficient methods.

A coarse-grained polymer model for studying the glass transition.

To study the cooling behavior and the glass transition of polymer melts in bulk and with free surfaces, a coarse-grained weakly semi-flexible polymer model is developed. Based on a standard bead spring model with purely repulsive interactions, an attractive potential between non-bonded monomers is added such that the pressure of polymer melts is tuned to zero. Additionally, the commonly used bond bending potential controlling the chain stiffness is replaced by a new bond bending potential. For this model, we show that the Kuhn length and the internal distances along the chains in the melt only very weakly depend on the temperature, just as for typical experimental systems. The glass transition is observed by the temperature dependency of the melt density and the characteristic non-Arrhenius slowing down of the chain mobility. The new model is set to allow for a fast switch between models, for which a wealth of data already exists.

Steering a solute between coexisting solvation states: Revisiting nonequilibrium work relations and the calculation of free energy differences.

By analogy with single-molecule pulling experiments, we present a computational framework to obtain free energy differences between complex solvation states. To illustrate our approach, we focus on the calculation of solvation free energies (SFEs). However, the method can be readily extended to cases involving more complex solutes and solvation conditions as well as to the calculation of binding free energies. The main idea is to drag the solute across the simulation box where atomistic and ideal gas representations of the solvent coexist at constant temperature and chemical potential. At finite pulling speeds, the resulting work allows one to extract SFEs via nonequilibrium relations, whereas at infinitely slow pulling speeds, this process becomes equivalent to the thermodynamic integration method. Results for small molecules well agree with literature data and pave the way to systematic studies of arbitrarily large and complex molecules.

Direct route to reproducing pair distribution functions with coarse-grained models via transformed atomistic cross correlations.

Coarse-grained (CG) models are often parameterized to reproduce one-dimensional structural correlation functions of an atomically detailed model along the degrees of freedom governing each interaction potential. While cross correlations between these degrees of freedom inform the optimal set of interaction parameters, the correlations generated from the higher-resolution simulations are often too complex to act as an accurate proxy for the CG correlations. Instead, the most popular methods determine the interaction parameters iteratively while assuming that individual interactions are uncorrelated. While these iterative methods have been validated for a wide range of systems, they also have disadvantages when parameterizing models for multicomponent systems or when refining previously established models to better reproduce particular structural features. In this work, we propose two distinct approaches for the direct (i.e., noniterative) parameterization of a CG model by adjusting the high-resolution cross correlations of an atomistic model in order to more accurately reflect correlations that will be generated by the resulting CG model. The derived models more accurately describe the low-order structural features of the underlying AA model while necessarily generating inherently distinct cross correlations compared with the atomically detailed reference model. We demonstrate the proposed methods for a one-site-per-molecule representation of liquid water, where pairwise interactions are incapable of reproducing the true tetrahedral solvation structure. We then investigate the precise role that distinct cross-correlation features play in determining the correct pair correlation functions, evaluating the importance of the placement of correlation features as well as the balance between features appearing in different solvation shells.

Efficient potential of mean force calculation from multiscale simulations: Solute insertion in a lipid membrane.

The determination of potentials of mean force for solute insertion in a lipid membrane by means of all-atom molecular dynamics simulations is often hampered by sampling issues. Recently, a multiscale method has been proposed to leverage the conformational ensemble of a lower-resolution model as starting point for higher resolution simulations. In this work, we analyze the efficiency of this method by comparing its predictions for propanol insertion into a lipid membrane against conventional atomistic umbrella sampling simulation results. The multiscale approach is confirmed to provide accurate results with a gain of one order of magnitude in computational time. We then investigate the role of the coarse-grained representation. We find that the accuracy of the results is tightly connected to the presence of a good configurational overlap between the coarse-grained and atomistic models-a general requirement when developing multiscale simulation methods.

Chain Retraction in Highly Entangled Stretched Polymer Melts.

We use computer simulations to study the relaxation of strongly deformed highly entangled polymer melts in the nonlinear viscoelastic regime, focusing on anisotropic chain conformations after isochoric elongation. The Doi-Edwards tube model and its Graham-Likhtman-McLeish-Milner (GLaMM) extension, incorporating contour length fluctuation and convective constraint release, predict a retraction of the polymer chain extension in all directions, setting in immediately after deformation. This prediction has been challenged by experiment, simulation, and other theoretical studies, questioning the general validity of the tube concept. For very long chains we observe the initial contraction of the chain extension parallel and perpendicular to the stretching direction. However, the effect is significantly weaker than predicted by the GLaMM model. We also show that the first anisotropic term of an expansion of the 2D scattering function qualitatively agrees to predictions of the GLaMM model, providing an option for direct experimental tests.

Studying polymer solutions with particle-based models linked to classical density functionals: co-non-solvency.

We demonstrate the potential of hybrid particle-based models, where interactions are introduced through functionals of local order parameters, in describing multicomponent polymer solutions. The link to a free-energy-like functional is advantageous for controlling the thermodynamics of the model. We focus on co-non-solvency - the collapse of polymer chains in dilute mixtures with two miscible good solvents, having different affinities towards the polymer. We employ a simple model where polymers and solvents are represented, respectively, by worm-like chains and single particles. Non-bonded interactions are captured by a polynomial which is third order in local densities and can, therefore, describe liquid-vapour coexistence. The parameterisation of the functional benefits from an elementary mean-field approximation to the statistical mechanics of the model. The model provides a framework for Monte Carlo simulations using a particle-to-mesh algorithm. Studies with conventional generic bead-spring and all-atom models have demonstrated that co-non-solvency is caused by preferential binding of the better solvent (termed cosolvent) with polymer. Hence, segmental loops bridged by cosolvent molecules are formed, initiating polymer collapse. The mesoscopic hybrid model differs conceptually from the conventional microscopic descriptions. Yet, it reproduces the same co-non-solvency mechanism supporting its universality. Films of adsorbed ternary solutions, showing co-non-solvency in the dilute regime, are considered at high concentrations. In this case, chains do not collapse. The properties of loops and tails of the adsorbed polymer agree with early theoretical predictions obtained for concentrated binary solutions.