Computational models provide crucial insights into complex biological processes such as cancer evolution, but their mechanistic nature often makes them nonlinear and parameter-rich, complicating calibration. We systematically evaluate parameter probability distributions in cell migration models using Bayesian calibration across four complementary strategies: parametric and surrogate models, each with and without explicit model discrepancy. This approach enables joint analysis of parameter uncertainty, predictive performance, and interpretability. Applied to a real data experiment of glioblastoma progression in microfluidic devices, surrogate models achieve higher computational efficiency and predictive accuracy, whereas parametric models yield more reliable parameter estimates due to their mechanistic grounding. Incorporating model discrepancy exposes structural limitations, clarifying where model refinement is necessary. Together, these comparisons offer practical guidance for calibrating and improving computational models of complex biological systems.

The origin of microbial cells required the emergence of metabolism, an autocatalytic network of roughly 400 enzymatically catalyzed chemical reactions that synthesize the building blocks of life: amino acids, nucleotides and cofactors. Proposals for metabolic origin are theoretical in nature [1-9], empirical studies addressing the origin and early evolution of the 400-reaction chemical network itself are lacking. Here we identify intermediate states in the primordial assembly of metabolism from its inorganic origins, using structure-refined clusters for metabolic enzymes of prokaryotic genomes. We show that metabolism in the last universal common ancestor (LUCA) was enzymatically incomplete, undergoing final assembly independently in the lineages leading to bacteria and archaea, with metal catalysts that predated both enzymes and cofactors providing essential functions. Over half of modern core metabolism corresponds to laboratory reactions catalyzed by native transition metals--Fe(0), Co(0), Ni(0) and their alloys--under conditions of serpentinizing hydrothermal vents. As the hitherto elusive source of primordial aqueous phosphorylation, we show that phosphite, a constituent of serpentinizing systems [10], phosphorylates AMP [11] to ADP using native metals in water. Seventeen cofactors that transfer electrons, nitrogen, and carbon units to substrates in modern metabolism [12] can be functionally replaced by environmental transition metals [13-19]. The data reveal that cofactors are synthesized late in enzymatic metabolism and are required in reactions preceding their synthesis, specifying the existence at origins of simpler precursors, which we identify here as native metals. Cofactors liberated metabolism from a requirement for solid state catalysis at a phosphorylating hydrothermal vent, engendering its autocatalytic state.

Slip transfer and blocking across grain boundaries was studied in a Ti foil with a strong rolling texture deformed in tension. Prior to deformation, the shape of the grains and the orientation of the grain boundaries were quantified through laboratory scale diffraction contrast tomography (LabDCT). Mechanical deformation led to the activation of prismatic slip, and slip transfer/blocking was assessed in > 300 grain boundaries by means of slip trace analysis and electron backscatter diffraction. A categorical model was employed to accurately assess slip transfer, and the "F1 score" of various slip transfer criteria proposed in the literature was evaluated for the first time from 3D grain boundary information. Remarkably, for the prismatic-dominated slip transfer in the current Ti sample, the results show that the best predictions of slip transfer/blocking are provided by the angle \k{appa}, which is directly related to the residual Burgers vector, and by the Luster-Morris parameter m'. In contrast, metrics based on the twist angle {\theta} and on the LRB criterion were not able to predict accurately slip transfer/blocking. Thus, the extensive analysis of the 3D grain boundary data and the novel application of LabDCT was able to help clarify the role of grain boundary orientation on the mechanisms of plastic deformation in polycrystals with strong prismatic-dominated slip.

Phase-field models are widely employed to simulate microstructure evolution during processes such as solidification or heat treatment. The resulting partial differential equations, often strongly coupled together, may be solved by a broad range of numerical methods, but this often results in a high computational cost, which calls for advanced numerical methods to accelerate their resolution. Here, we quantitatively test the efficiency and accuracy of semi-implicit Fourier spectral-based methods, implemented in Python programming language and parallelized on a graphics processing unit (GPU), for solving a phase-field model coupling Cahn-Hilliard and Allen-Cahn equations. We compare computational performance and accuracy with a standard explicit finite difference (FD) implementation with similar GPU parallelization on the same hardware. For a similar spatial discretization, the semi-implicit Fourier spectral (FS) solvers outperform the FD resolution as soon as the time step can be taken 5 to 6 times higher than afforded for the stability of the FD scheme. The accuracy of the FS methods also remains excellent even for coarse grids, while that of FD deteriorates significantly. Therefore, for an equivalent level of accuracy, semi-implicit FS methods severely outperform explicit FD, by up to 4 orders of magnitude, as they allow much coarser spatial and temporal discretization.

The introduction of nanostructured interlayers is one of the most promising strategies for interlaminar reinforcement in structural composites. In this work, we study the failure mechanism and interlayer microstructure of aerospace-grade structural composites reinforced with thin veils of carbon nanotube produced using an industrialised spinning process. Samples of unidirectional carbon fibre/epoxy matrix composites interleaved with different composition CNT veils were prepared using hot press method and tested for interlaminar fracture toughness (IFT), measured in Mode-I (opening) and Mode-II (in-plane shear), and for interlaminar shear strength (ILSS), evaluated by the short beam shear (SBS) test. The crack propagation mode could be directly determined through fractography analysis by electron microscopy and resin/CNT spatial discrimination by Raman spectroscopy, showing a clear correlation between interlaminar reinforcement and the balance between cohesive/adhesive failure mode at the interlayer region. Composites with full resin infiltration of the CNT veils give a large increase of Mode II IFT (88%) to 1500 J/m2 and a slight enhancement of apparent interlaminar shear strength (6.5%), but a decrease of Mode I IFT (-21%). The results help establish the role of interlayer infiltration, interlaminar crossings and formation of a carbon fibre bridgings, for interlaminar reinforcement with interleaves.

We present a quantitative benchmark of multiscale models for dendritic growth simulations. We focus on approaches based on phase-field, dendritic needle network, and grain envelope dynamics. As a first step, we focus on isothermal growth of an equiaxed grain in a supersaturated liquid in three dimensions. A quantitative phase-field formulation for solidification of a dilute binary alloy is used as the reference benchmark. We study the effect of numerical and modeling parameters in both needle-based and envelope-based approaches, in terms of their capacity to quantitatively reproduce phase-field reference results. In light of this benchmark, we discuss the capabilities and limitations of each approach in quantitatively and efficiently predicting transient and steady states of dendritic growth. We identify parameters that yield a good compromise between accuracy and computational efficiency in both needle-based and envelope-based models. We expect that these results will guide further developments and utilization of these models, and ultimately pave the way to a quantitative bridging of the dendrite tip scale with that of entire experiments and solidification processes.

Copolymers are highly versatile materials with a vast range of possible chemical compositions. By using computational methods for property prediction, the design of copolymers can be accelerated, allowing for the prioritization of candidates with favorable properties. In this study, we utilized two distinct representations of molecular ensembles to predict the seven different physical polymer properties copolymers using machine learning: we used a random forest (RF) model to predict polymer properties from molecular descriptors, and a graph neural network (GNN) to predict the same properties from 2D polymer graphs under both a single- and multi-task setting. To train and evaluate the models, we constructed a data set from molecular dynamic simulations for 140 binary copolymers with varying monomer compositions and configurations. Our results demonstrate that descriptors-based RFs excel at predicting density and specific heat capacities at constant pressure (Cp) and volume (Cv) because these properties are strongly tied to specific molecular features captured by molecular descriptors. In contrast, graph representations better predict expansion coefficients ({\gamma}, {\alpha}) and bulk modulus (K), which depend more on complex structural interactions better captured by graph-based models. This study underscores the importance of choosing appropriate representations for predicting molecular properties. Our findings demonstrate how machine learning models can expedite copolymer discovery with learnable structure- property relationships, streamlining polymer design and advancing the development of high-performance materials for diverse applications.

The deformation mechanisms of an extruded Mg-5Y-0.08Ca (wt. %) alloy were analyzed by means of micropillar compression tests on single crystals along different orientations -- selected to activate specific deformation modes -- as well as slip trace analysis, transmission electron microscopy and transmission Kikuchi diffraction. The polycrystalline alloy presented a remarkable ductility in tension (~32%) and negligible differences in the yield strength between tension and compression. It was found that the presence of Y and Ca in solid solution led to a huge increase in the CRSS for basal slip (29 $\pm$ 5 MPa), pyramidal slip (203 $\pm$ 7 MPa) and tensile twin nucleation (above 148 MPa), while the CRSS for prismatic slip only increases up to 105 $\pm$ 4 MPa. The changes in the CRSS for slip and tensile twinning in Mg-Y-Ca alloys expectedly modify the dominant deformation mechanisms in polycrystals. In particular, tensile twinning is replaced by prismatic slip during compressive deformation along the a-axis. The reduction of twinning (which generally induces strong anisotropy in the plastic deformation in textured alloys), and the activation of prismatic slip (which provides an additional plastic deformation mechanism with limited hardening) were responsible for the large tensile ductility of the alloy.

A natural embodiment for multifunctional materials combining energy-storing capabilities and structural mechanical properties are layered structures, similar to both laminate structural composites and electrochemical energy storage devices. A structural composite with integrated electric double layer capacitive storage is produced by resin infusion of a lay up including woven glass fabric used as mechanical reinforcement, carbon nanotube non-woven fabrics as electrodes/current collectors and a polymer electrolyte. The energy-storing layer is patterned with holes, which after integration form resin plugs for mechanical interconnection between layers, similar to rivets. Finite element modelling is used to optimise rivet shape and areal density on interlaminar shear properties. Galvanostatic charge discharge tests during three point bending show no degradation of properties after large deflections or repeated load/unload cycling at 3.5 V.This mechanical tolerance is a consequence of the elimination of metallic current collectors and the effective integration of multifunctional materials, as observed by electron microscopy and X-ray computed tomography. In contrast, control samples with metallic current collectors, analogous to embedded devices, rapidly degrade upon repeated bending.

The mechanisms of dislocation/precipitate interaction as well as the critical resolved shear stress were determined as a function of temperature in a Mg-4 wt. % Zn alloy by means of micropillar compression tests. It was found that the mechanical properties were independent of the micropillar size when the cross-section was $>$ 3 x 3 $\mu$m$^2$. Transmission electron microscopy showed that deformation involved a mixture of dislocation bowing around the precipitates and precipitate shearing. The initial yield strength was compatible with the predictions of the Orowan model for dislocation bowing around the precipitates. Nevertheless, precipitate shearing was dominant afterwards, leading to the formation of slip bands in which the rod precipitates were transformed into globular particles, limiting the strain hardening. The importance of precipitate shearing increased with temperature and was responsible for the reduction in the mechanical properties of the alloy from 23C to 100C.

Efficient exploration of multicomponent material composition spaces is often limited by time and financial constraints, particularly when mixture and synthesis constraints exist. Traditional methods like Latin hypercube sampling (LHS) struggle with constrained problems especially in high dimensions, while emerging approaches like Bayesian optimization (BO) face challenges in early-stage exploration. This article introduces ConstrAined Sequential laTin hypeRcube sampling methOd (CASTRO), an open-source tool designed to address these challenges. CASTRO is optimized for uniform sampling in constrained small- to moderate-dimensional spaces, with scalability to higher dimensions through future adaptations. CASTRO uses a divide-and-conquer strategy to decompose problems into parallel subproblems, improving efficiency and scalability. It effectively handles equality-mixture constraints, ensuring comprehensive design space coverage and leveraging LHS and LHS with multidimensional uniformity (LHSMDU). It also integrates prior experimental knowledge, making it well-suited for efficient exploration within limited budgets. Validation through two material design case studies, a four-dimensional problem with near-uniform distributions and a nine-dimensional problem with additional synthesis constraints, demonstrates CASTRO's effectiveness in exploring constrained design spaces for materials science, pharmaceuticals and chemicals. The software and case studies are available on GitHub.

Predicting the structural response of advanced multiphase alloys and understanding the underlying microscopic mechanisms that are responsible for it are two critically important roles modeling plays in alloy development. An alloys demonstration of superior properties, such as high strength, creep resistance, high ductility, and fracture toughness, is not sufficient to secure its use in widespread application. Still, a good model is needed, to take measurable alloy properties, such as microstructure and chemical composition, and forecast how the alloy will perform in specified mechanical deformation conditions, including temperature, time, and rate. In this bulletin, we highlight recent achievements by multiscale modeling in elucidating the coupled effects of alloying, microstructure, and the dynamics of mechanisms on the mechanical properties of polycrystalline alloys. Much of the understanding gained by these efforts relied on integration of computational tools that varied over many length and time scales, from first principles density functional theory, atomistic simulation methods, dislocation and defect theory, micromechanics, phase field modeling, single crystal plasticity, and polycrystalline plasticity.

The practical application of sodium-ion hybrid capacitors is limited by their low energy densities resulted from the kinetics mismatch between cathodes and anodes, and the fire safety related to the flammable electrolyte-separator system. Hence, we report a rational design of metal-organic frameworks (MOFs, UiO-66) modified PVDF-HFP separator. High tensile strength and dimensional thermal stability of the separator reduce the risk of electrode short circuit caused by the separator deformation. MCC test demonstrates a reduction of 75% in peak heat release rate (pHRR), indicating an enhanced fire-resistant property of the separator. This is due to the transformation of UiO-66 into ZrO2 accompanied by the consumption of oxygen and the formation of the barrier char that suppresses further heat release. Quasi-solid-state electrolyte prepared based on this separator presents an enhanced ionic conductivity of 2.44 mS*cm-1 and Na-ion transference number of 0.55, which are related to the high porosity ( >70%) and electrolyte uptake (~ 320%) of the separator. Moreover, the open metal sites of UiO-66 can capture PF6- and consequently liberate the Na+ for faster migration, thus reducing the kinetics mismatch between cathodes and anodes. Such multifunctional separator enables the quasi-solid-state Na-ion hybrid capacitor to achieve high energy density (182 Wh*kg-1 @31 W*kg-1) and power density (5280 W*kg-1 @22 Wh*kg-1), as well as excellent cyclic stability (10000 cycles @1000 mA*g-1). Keywords: Quasi-solid-state; PVDF-HFP; Metal-organic frameworks; Dimensional thermal stability; Fire safety; Selective charge transfer

A high-throughput correlative study of the local mechanical properties, chemical composition and crystallographic orientation has been carried out in selected areas of cast Inconel 718 specimens subjected to three different tempers. The specimens showed a strong Nb segregation at the scale of the dendrite arms, with local Nb contents that varied between 2 wt.% in the core of the dendrite arms to 8 wt.% in the interdendritic regions and 25 wt.% within the second phase particles (MC carbides, Laves phases and {\delta} phase needles). The nanohardness was found to correlate strongly with the local Nb content and the temper condition. On the contrary, the indentation elastic moduli were not influenced by the local chemical composition or temper condition, but directly correlated with the crystallographic grain orientation, due to the high elastic anisotropy of nickel alloys.

This paper reviews the current state-of-the-art in the simulation of the mechanical behavior of polycrystalline materials by means of computational homogenization. The key ingredients of this modelling strategy are presented in detail starting with the parameters needed to describe polycrystalline microstructures and the digital representation of such microstructures in a suitable format to perform computational homogenization. The different crystal plasticity frameworks that can describe the physical mechanisms of deformation in single crystals (dislocation slip and twinning) at the microscopic level are presented next. This is followed by the description of computational homogenization methods based on mean-field approximations by means of the viscoplastic self-consistent approach, or on the full-field simulation of the mechanical response of a representative polycrystalline volume element by means of the finite element method or the fast Fourier transform-based method. Multiscale frameworks based on the combination of mean-field homogenization and the finite element method are presented next to model the plastic deformation of polycrystalline specimens of arbitrary geometry under complex mechanical loading. Examples of application to predict the strength, fatigue life, damage, and texture evolution under different conditions are presented to illustrate the capabilities of the different models. Finally, current challenges and future research directions in this field are summarized.

A roadblock in utilizing InGaAs for scaled-down electronic devices is its anomalous dopant diffusion behavior; specifically, existing models are not able to explain available experimental data on beryllium diffusion consistently. In this paper, we propose a comprehensive model, taking self-interstitial migration and Be interaction with Ga and In into account. Density functional theory (DFT) calculations are first used to calculate the energy parameters and charge states of possible diffusion mechanisms. Based on the DFT results, continuum modeling and kinetic Monte Carlo simulations are then performed. The model is able to reproduce experimental Be concentration profiles. Our results suggest that the Frank-Turnbull mechanism is not likely, instead, kick-out reactions are the dominant mechanism. Due to a large reaction energy difference, the Ga interstitial and the In interstitial play different roles in the kick-out reactions, contrary to what is usually assumed. The DFT calculations also suggest that the influence of As on Be diffusion may not be negligible.

The effect of slip transfer on the deformation mechanisms of Al bicrystals was explored using a rate-dependent dislocation-based crystal plasticity model. Three different types of grain boundaries (GBs) were included in the model by modifying the rate of dislocation accumulation near the GB in the Kocks-Mecking law, leading to fully-opaque (dislocation blocking), fully-transparent and partially-transparent GBs. In the latter, slip transmission is only allowed in pairs of SS in neighbour grains that are suitably oriented for slip transfer according to the Luster-Morris parameter. Modifications of the GB character led to important changes in the deformation mechanisms at the GB. In general, bicrystals with fully-opaque boundaries showed an increase in the dislocation density near the GB, which was associated with an increase in the Von Mises stress. In contrast, the dislocation pile-ups and the stress concentration were less pronounced in the case of partially-transparent boundaries as the slip in one grain can progress into the next grain with some degree of continuity. No stress concentrations were found at these boundaries for fully-transparent boundaries, and there was continuity of strain across the boundary, which is not typical of most experimentally observed GBs. Simulations of ideal bicrystals oriented for favorable slip transfer on the most highly favored slip system in grains with high Schmid factors for slip transfer depends on the number of active SS in operation in the neighborhood and that most boundaries will lead to nearly opaque conditions while some boundaries will be transparent. Finally, the model was applied to a particular experimentally observed GB in which slip transfer was clearly operating indicating that the model predicted a nearly transparent GB.

Additive manufacturing of Ti-6Al-4V alloy via laser powder-bed fusion leads to non-equilibrium $\alpha'$ martensitic microstructures, with high strength but poor ductility and toughness. These properties may be modified by heat treatments, whereby the $\alpha'$ phase decomposes into equilibrium $\alpha+\beta$ structures, while possibly conserving microstructural features and length scales of the $\alpha'$ lath structure. Here, we combine experimental and computational methods to explore the kinetics of martensite decomposition. Experiments rely on in-situ characterisation (electron microscopy and diffraction) during multi-step heat treatment from 400$^{\circ}$C up to the alloy $\beta$-transus temperature (995$^{\circ}$C). Computational simulations rely on an experimentally-informed computationally-efficient phase-field model. Experiments confirmed that as-built microstructures were fully composed of martensitic $\alpha'$ laths. During martensite decomposition, nucleation of the $\beta$ phase occurs primarily along $\alpha'$ lath boundaries, with traces of $\beta$ nucleation along crystalline defects. Phase-field results, using electron backscatter diffraction maps of as-built microstructures as initial conditions, are compared directly with in-situ characterisation data. Experiments and simulations confirmed that, while full decomposition into stable $\alpha+\beta$ phases may be complete at 650$^{\circ}$C provided sufficient annealing time, visible morphological evolution of the microstructure was only observed for $T\geq\,$700$^{\circ}$C, without modification of the prior-$\beta$ grain structure.

In this work, we report on the fabrication of continuous transparent and flexible supercapacitors by depositing a CNT network onto a polymer electrolyte membrane directly from an aerogel of ultra-long CNTs produced floating in the gas phase. The supercapacitors combine record power density of $1370 kW kg^{-1}$at high transmittance ($ca. 70%$), high electrochemical stability during extended cycling ($>94$ capacitance retention over $20 000 cycles$) as well as against repeated $180{\deg}$ flexural deformation. They represent a significant enhancement of 1-3 orders of magnitude compared to the prior-art transparent supercapacitors based on graphene, CNTs, and rGO. These features mainly arise from the exceptionally long length of the CNTs, which makes the material behave as a bulk conductor instead of an aspect ratio-limited percolating network, even for electrodes with $>90$ transparency. The electrical and capacitive figures-of-merit for the transparent conductor are $FoMe = 2.7$, and $FoMc = 0.46 F S^{-1} cm^{-2}$ respectively

We present a mathematical formulation of a multiscale model for solidification with convective flow in the liquid phase. The model is an extension of the dendritic needle network approach for crystal growth in a binary alloy. We propose a simple numerical implementation based on finite differences and step-wise approximations of parabolic dendritic branches of arbitrary orientation. Results of the two-dimensional model are verified against reference benchmark solutions for steady, unsteady, and buoyant flow, as well as steady-state dendritic growth in the diffusive regime. Simulations of equiaxed growth under forced flow yield dendrite tip velocities within 10% of quantitative phase-field results from the literature. Finally, we perform illustrative simulations of polycrystalline solidification using physical parameters for an aluminum-10wt%copper alloy. Resulting microstructures show notable differences when taking into account natural buoyancy in comparison to a purely diffusive transport regime. The resulting model opens new avenues for computationally and quantitatively investigating the influence of fluid flow and gravity-induced buoyancy upon the selection of dendritic microstructures. Further ongoing developments include an equivalent formulation for directional solidification conditions and the implementation of the model in three dimensions, which is critical for quantitative comparison to experimental measurements.