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The need for improved functionalities in extreme environments is fuelling interest in high-entropy ceramics 1 – 3 . Except for the computational discovery of high-entropy carbides, performed with the entropy-forming-ability descriptor 4 , most innovation has been slowly driven by experimental means 1 – 3 . Hence, advancement in the field needs more theoretical contributions. Here we introduce disordered enthalpy–entropy descriptor (DEED), a descriptor that captures the balance between entropy gains and enthalpy costs, allowing the correct classification of functional synthesizability of multicomponent ceramics, regardless of chemistry and structure. To make our calculations possible, we have developed a convolutional algorithm that drastically reduces computational resources. Moreover, DEED guides the experimental discovery of new single-phase high-entropy carbonitrides and borides. This work, integrated into the AFLOW computational ecosystem, provides an array of potential new candidates, ripe for experimental discoveries. DEED captures the balance between entropy gains and costs, allowing the correct classification of functional synthesizability of multicomponent ceramics, regardless of chemistry and structure, and provides an array of potential new candidates, ripe for experimental discoveries.

The design rules for materials are clear for applications with a single objective. For most applications, however, there are often multiple, sometimes competing objectives where there is no single best material and the design rules change to finding the set of Pareto optimal materials. In this work, we leverage an active learning algorithm that directly uses the Pareto dominance relation to compute the set of Pareto optimal materials with desirable accuracy. We apply our algorithm to de novo polymer design with a prohibitively large search space. Using molecular simulations, we compute key descriptors for dispersant applications and drastically reduce the number of materials that need to be evaluated to reconstruct the Pareto front with a desired confidence. This work showcases how simulation and machine learning techniques can be coupled to discover materials within a design space that would be intractable using conventional screening approaches. Identifying optimal materials in multiobjective optimization problems represents a challenge for new materials design approaches. Here the authors develop an active-learning algorithm to optimize the Pareto-optimal solutions successfully applied to the in silico polymer design for a dispersant-based application.

An accurate and computationally efficient molecular level description of mesoscopic behavior of ice-water systems remains a major challenge. Here, we introduce a set of machine-learned coarse-grained (CG) models (ML-BOP, ML-BOP dih , and ML-mW) that accurately describe the structure and thermodynamic anomalies of both water and ice at mesoscopic scales, all at two orders of magnitude cheaper computational cost than existing atomistic models. In a significant departure from conventional force-field fitting, we use a multilevel evolutionary strategy that trains CG models against not just energetics from first-principles and experiments but also temperature-dependent properties inferred from on-the-fly molecular dynamics (~ 10’s of milliseconds of overall trajectories). Our ML BOP models predict both the correct experimental melting point of ice and the temperature of maximum density of liquid water that remained elusive to-date. Our ML workflow navigates efficiently through the high-dimensional parameter space to even improve upon existing high-quality CG models (e.g. mW model). A computationally efficient description of ice-water systems at the mesoscopic scale is challenging due to system size and timescale limitations. Here the authors develop a machine-learned coarse-grained water model to elucidate the ice nucleation process much more efficiently than previous models.

A topological meron features a non-coplanar structure, whose order parameters in the core region are perpendicular to those near the perimeter. A meron is half of a skyrmion, and both have potential applications for information carrying and storage. Although merons and skyrmions in ferromagnetic materials can be readily obtained via inter-spin interactions, their behaviour and even existence in ferroelectric materials are still elusive. Here we observe using electron microscopy not only the atomic morphology of merons with a topological charge of 1/2, but also a periodic meron lattice in ultrathin PbTiO 3 films under tensile epitaxial strain on a SmScO 3 substrate. Phase-field simulations rationalize the formation of merons for which an epitaxial strain, as a single alterable parameter, plays a critical role in the coupling of lattice and charge. This study suggests that by engineering strain at the nanoscale it should be possible to fabricate topological polar textures, which in turn could facilitate the development of nanoscale ferroelectric devices. Merons are topological structures, but these have yet to be directly observed in ferroelectrics. Here, by epitaxially straining PbTiO 3 on a SmScO 3 substrate, electron microscopy and phase-field modelling allow the morphology and distribution of merons to be observed.

Conventional models for grain growth are based on the assumption that grain boundary (GB) velocity is proportional to GB mean curvature. We demonstrate via a series of molecular dynamics (MD) simulations that such a model is inadequate and that many physical phenomena occur during grain boundary migration for which this simple model is silent. We present a series of MD simulations designed to unravel GB migration phenomena and set it in a GB migration context that accounts for competing migration mechanisms, elasticity, temperature, and grain boundary crystallography. The resultant formulation is quantitative and validated through a series of atomistic simulations. The implications of this model for microstructural evolution is described. We show that consideration of GB migration mechanisms invites considerable complexity even under ideal conditions. However, that complexity also grants these systems enormous flexibility, and that flexibility is key to the decades-long success of conventional grain growth theories. Conventional grain growth models assume the velocity of a grain boundary is proportional to its curvature but cannot account for the many deviations observed experimentally. Here, the authors present a model that connects grain growth directly to the disconnection mechanism of grain boundary migration and can account for these deviations.

Soft colloids enable the exploration of states with densities exceeding that of random close packing, but it remains unclear whether softness controls the dynamics under these dense conditions. Experimental studies have reported conflicting results, and numerical studies have so far focused primarily on simple models that allow particles to overlap, but neglect particle deformations. This makes the concept of softness in simulations and experiments difficult to compare. Here, we propose a model system consisting of polymer rings with internal elasticity. At high packing fractions, the system displays compressed exponential decay of the intermediate scattering functions and super-diffusive behaviour of the mean-squared displacements. These features are explained in terms of the complex interplay between particle deformations and dynamic heterogeneities, which gives rise to persistent motion of ballistic particles. We also observe a striking variation of the relaxation times with increasing particle softness, clearly demonstrating the crucial role of deformation in the dynamics of realistic soft colloids.Simulations of a system comprising polymer rings with internal elasticity reveal a key role for deformation in controlling the microscopic dynamics of soft colloids.

Flexoelectricity refers to electric polarization generated by heterogeneous mechanical strains, namely strain gradients, in materials of arbitrary crystal symmetries. Despite more than 50 years of work on this effect, an accurate identification of its coupling strength remains an experimental challenge for most materials, which impedes its wide recognition. Here, we show the presence of flexoelectricity in the recently discovered polar vortices in PbTiO 3 /SrTiO 3 superlattices based on a combination of machine-learning analysis of the atomic-scale electron microscopy imaging data and phenomenological phase-field modeling. By scrutinizing the influence of flexocoupling on the global vortex structure, we match theory and experiment using computer vision methodologies to determine the flexoelectric coefficients for PbTiO 3 and SrTiO 3 . Our findings highlight the inherent, nontrivial role of flexoelectricity in the generation of emergent complex polarization morphologies and demonstrate a viable approach to delineating this effect, conducive to the deeper exploration of both topics. Flexoelectric coupling between strain gradients and polarization influences the physics of ferroelectric devices but it is difficult to directly probe its effects. Here, Li et al. use principal component analysis to compare STEM images with phase-field modeling and extract the flexoelectric contributions.

Guiding experiments to find materials with targeted properties is a crucial aspect of materials discovery and design, and typically multiple properties, which often compete, are involved. In the case of two properties, new compounds are sought that will provide improvement to existing data points lying on the Pareto front (PF) in as few experiments or calculations as possible. Here we address this problem by using the concept and methods of optimal learning to determine their suitability and performance on three materials data sets; an experimental data set of over 100 shape memory alloys, a data set of 223 M 2 AX phases obtained from density functional theory calculations, and a computational data set of 704 piezoelectric compounds. We show that the Maximin and Centroid design strategies, based on value of information criteria, are more efficient in determining points on the PF from the data than random selection, pure exploitation of the surrogate model prediction or pure exploration by maximum uncertainty from the learning model. Although the datasets varied in size and source, the Maximin algorithm showed superior performance across all the data sets, particularly when the accuracy of the machine learning model fits were not high, emphasizing that the design appears to be quite forgiving of relatively poor surrogate models.

The glass transition in soft matter systems is generally triggered by an increase in packing fraction or a decrease in temperature. It has been conjectured that the internal topology of the constituent particles, such as polymers, can cause glassiness too. However, the conjecture relies on immobilizing a fraction of the particles and is therefore difficult to fulfill experimentally. Here we show that in dense solutions of circular polymers containing (active) segments of increased mobility, the interplay of the activity and the topology of the polymers generates an unprecedented glassy state of matter. The active isotropic driving enhances mutual ring threading to the extent that the rings can relax only in a cooperative way, which dramatically increases relaxation times. Moreover, the observed phenomena feature similarities with the conformation and dynamics of the DNA fibre in living nuclei of higher eukaryotes. Glass transition in soft materials can be affected by the topology of constituent particles, but the detail remains elusive. Here, Smrek et al. show that the interplay between circular topology of ring polymers and their active segments generates a new state of matter, namely active topological glass.

The mechanical properties of the extracellular matrix dictate tissue behaviour. In epithelial tissues, laminin is a very abundant extracellular matrix component and a key supporting element. Here we show that laminin hinders the mechanoresponses of breast epithelial cells by shielding the nucleus from mechanical deformation. Coating substrates with laminin-111—unlike fibronectin or collagen I—impairs cell response to substrate rigidity and YAP nuclear localization. Blocking the laminin-specific integrin β4 increases nuclear YAP ratios in a rigidity-dependent manner without affecting the cell forces or focal adhesions. By combining mechanical perturbations and mathematical modelling, we show that β4 integrins establish a mechanical linkage between the substrate and keratin cytoskeleton, which stiffens the network and shields the nucleus from actomyosin-mediated mechanical deformation. In turn, this affects the nuclear YAP mechanoresponses, chromatin methylation and cell invasion in three dimensions. Our results demonstrate a mechanism by which tissues can regulate their sensitivity to mechanical signals.Laminin, an important component of the extracellular matrix supporting the epithelium, hinders the typical mechanoresponse of epithelial cells to an increase in substrate stiffness, by protecting the cell nucleus from mechanical deformation.