arXiv subjects

Mikko Karttunen

Publications and source records attributed to Mikko Karttunen.

At least 19 recordsLinked to original sources

arXiv2026

Nuclearity of Copper Clusters on hBN/SiC Heterostructure Modulates Molecular Adsorption

Defect engineering can transform inert two-dimensional (2D) materials into chemically active and electronically tunable platforms by creating anchoring sites for metal atoms and clusters. Nevertheless, precise control over the formation, thermodynamic and kinetic stability, electronic structure, and chemical reactivity of metal species confined at these defect sites remains a challenge. Here, we use density functional theory (DFT) calculations assisted by machine-learning molecular dynamics (MLMD) simulations to elucidate the stability, electronic structure, and reactivity of Cu clusters anchored at boron vacancies (VB) in hBN/SiC heterostructures. Systematic variation of the Cu-to-vacancy ratio reveals a transition from isolated Cu atoms to multiatom Cu clusters at VB sites, with cluster growth reshaping the stability, electronic structure, and surface reactivity. Our results show that a single VB defect can be passivated by three Cu atoms, which compensate the local charge deficiency and stabilize the defect through Cu-N coordination. Capturing further Cu introduces localized midgap states that could influence the reactivity of the Cu-decorated defect sites. We probe the response of the Cu-decorated surface to chemically relevant gases CO, H2, O2, N2, H2S, and CO2, revealing implications for surface reactivity and stability. The calculations show pronounced cluster-size-dependent reactivity of Cu clusters at VB sites, with CO forming strong Cu-C bonds and O2 undergoing enhanced adsorption and molecular activation. Overall, this work identifies defect-engineered hBN/SiC as a versatile 2D platform for stabilizing Cu clusters and tuning gas-surface reactivity. By correlating Cu nuclearity at VB sites with electronic structure, molecular activation, and environmental robustness, our findings provide design guidelines for nuclearity-dependent metal functionalization of 2D heterostructures.

cond-mat.mtrl-sci↗

arXiv2026

Dislocation-loop formation is a first-order phase transition

Dislocation loops are the elementary product of radiation damage in crystals, limiting reactor-component lifetimes, power-electronics reliability and the coherence of solid-state qubits. Their nucleation has been simulated for six decades but never reduced to a thermodynamic law. We show that dislocation-loop formation is a \emph{first-order phase transition}, and construct its Ginzburg--Landau free energy, with the loop area as order parameter, entirely from atomistic simulation. In diamond, carbon self-interstitials condense into planar precursors that collapse abruptly into a prismatic $\tfrac{1}{2}\langle110\rangle$ loop across a 3.7-electronvolt barrier, with pressure--volume work supplying only 2\% of the energy released. The reduced free energy proves material-independent: the vacancy platelet-to-loop collapse in body-centred-cubic iron falls on the same one-parameter family, placing loop nucleation on a transferable thermodynamic footing.

cond-mat.mtrl-sci↗

arXiv2026

Machine-learned prediction of carbon interstitial clusters in diamond

Diamond hosts optically active point defects central to quantum technologies, yet the carbon self-interstitials introduced during growth and irradiation compete with them and form new defects whose configurational landscape is poorly charted, as subtle energy differences govern the competing minima and pathways. Here we build an interstitial-focused dataset by active learning and benchmark three machine-learning interatomic potentials -- GAP, NEP and the equivariant MACE -- against density functional theory for energies, forces and migration barriers. MACE reproduces the reference energetics and relative stabilities, whereas the others can misorder the ground states. Annealing molecular dynamics with the validated potentials uncovers a series of previously unreported carbon interstitial clusters, from di- to octa-interstitials -- several introducing in-gap states of interest as colour centres -- and shows that their metastability is governed by kinetically accessible pathways rather than energetic ordering. These results chart the interstitial defect landscape and accelerate defect discovery for quantum technologies.

physics.comp-ph↗

arXiv2026

Robust phonon engineering and symmetry-selective lattice dynamics in CrSBr$_{1-x}$Cl$_{x}$

Atomic substitution provides a controlled route to engineer lattice dynamics in low-symmetry two-dimensional materials. Here, by combining polarization-resolved Raman spectroscopy and first-principles calculations, we investigate the evolution of phonon characteristics in CrSBr$_{1-x}$Cl$_{x}$ ($0 \leq x \leq \sim 0.5$) upon partial substitution of Br with Cl atoms. Progressive Cl substitution of Br induces systematic shifts of parent CrSBr out-of-plane $A_\textrm{g}$ phonon modes and activates additional Raman features. These features persist across different polarization configurations and excitation energies, reflecting substitution-induced symmetry lowering and local lattice perturbations. Explicit supercell phonon calculations combined with Raman $Γ$-density-of-states simulations identify these features as symmetry-lowered descendants of parent modes arising from alloy disorder. Complementary strain-dependent calculations reveal that anisotropic lattice compression plays a key role in renormalizing Cr-S dominated phonons. Under near-resonant excitation, stimulated Raman scattering-like amplification remains observable with increasing Cl content, highlighting the resilience of anisotropic electron-phonon coupling in this system.

cond-mat.mtrl-sci↗

arXiv2025

Parallel and GPU accelerated code for phase-field and reaction-diffusion simulations

We present SymPhas 2.0, a major update of the compile-time symbolic algebra simulation framework SymPhas for phase-field and reaction-diffusion models. This release introduces significant expansions and enhancements that enable the definition of a phase-field model directly from the free-energy functional via compile-time evaluated functional differentiation. It also introduces directional derivatives, symbolic summation, tensor-valued expressions, and compile-time derived finite difference stencils of arbitrary order and accuracy. Furthermore, the code has been parallelized for CPUs with MPI, and GPU computing has been added using CUDA (Compute Unified Device Architecture). For the latter, symbolic expressions are compiled into optimized CUDA kernels, allowing large-scale simulations to execute entirely on the GPU. For large systems ($32,768^2$ in 2D and $1,024^3$ in 3D with double precision), speedups up to $\sim \!\!1,000 \times$ were obtained compared to the first version of SymPhas using multi-threaded CPU execution on a single system. These developments establish SymPhas 2.0 as a flexible and scalable framework for efficient implementation of phase-field and reaction-diffusion models on GPU-based high-performance computing platforms.

physics.comp-ph↗

arXiv2025

From Radiation Dose to Cellular Dynamics: A Discrete Model for Simulating Cancer Therapy

Radiation therapy is one of the most common cancer treatments, and dose optimization and targeting of radiation are crucial since both cancerous and healthy cells are affected. Different mathematical and computational approaches have been developed for this task. The most common mathematical approach, dating back to the late 1970's, is the linear-quadratic (LQ) model for the survival probability given the radiation dose. Most simulation models consider tissue as a continuum rather than consisting of discrete cells. While reasonable for large-scale models (e.g., human organs), continuum approaches necessarily neglect cellular-scale effects, which may play a role in growth, morphology, and metastasis of tumors. Here, we propose a method for modeling the effect of radiation on cells based on the mechanobiological \textsc{CellSim3D} simulation model for growth, division, and proliferation of cells. To model the effect of a radiation beam, we incorporate a Monte Carlo procedure into \textsc{CellSim3D} with the LQ model by introducing a survival probability at each beam delivery. Effective removal of dead cells by phagocytosis was also implemented. Systems with two types of cells were simulated: stiff slowly proliferating healthy cells and soft rapidly proliferating cancer cells. For model verification, the results were compared to prostate cancer (PC-3 cell line) data for different doses and we found good agreement. In addition, we simulated proliferating systems and analyzed the probability density of the contact forces. We determined the state of the system with respect to the jamming transition and found very good agreement with experiments.

physics.bio-ph↗

arXiv2025

Living Capillary Bridges

Biological tissues exhibit complex behaviors with their dynamics often resembling inert soft matter such as liquids, polymers, colloids, and liquid crystals. These analogies enable physics-based approaches for investigations of emergent behaviors in biological processes. A well-studied case is the spreading of cellular aggregates on solid surfaces, where they display dynamics similar to viscous droplets. \textit{In vivo}, however, cells and tissues are in a confined environment with varying geometries and mechanical properties to which they need to adapt. In this work, we compressed cellular aggregates between two solid surfaces and studied their dynamics using microscopy, and computer simulations. The confined cellular aggregates transitioned from compressed spheres into dynamic living capillary bridges exhibiting bridge thinning and a convex-to-concave meniscus curvature transition. We found that the stability of the bridge is determined by the interplay between cell growth and cell spreading on the confining surfaces. This interaction leads to bridge rupture at a critical length scale determined by the distance between the plates. The force distributions, formation and stability regimes of the living capillary bridges were characterized with full 3D computer simulations that included cell division, migration and growth dynamics, directly showing how mechanical principles govern the behavior of the living bridges; cellular aggregates display jamming and stiffening analogously to granular matter, and cell division along the long axis enhances thinning. Based on our results, we propose a new class of active soft matter behavior, where cellular aggregates exhibit liquid-like adaptation to confinement, but with self-organized rupturing driven by biological activity.

physics.bio-ph↗

arXiv2024

Learning glass transition temperatures via dimensionality reduction with data from computer simulations: Polymers as the pilot case

Machine learning (ML) methods provide advanced means for understanding inherent patterns within large and complex datasets. Here, we employ the principal component analysis (PCA) and the diffusion map (DM) techniques to evaluate the glass transition temperature ($T_\mathrm{g}$) from low-dimensional representations of all-atom molecular dynamic (MD) simulations of polylactide (PLA) and poly(3-hydroxybutyrate) (PHB). Four molecular descriptors were considered: radial distribution functions (RDFs), mean square displacements (MSDs), relative square displacements (RSDs), and dihedral angles (DAs). By applying a Gaussian Mixture Model (GMM) to analyze the PCA and DM projections, and by quantifying their log-likelihoods as a density-based metric, a distinct separation into two populations corresponding to melt and glass states was revealed. This separation enabled the $T_\mathrm{g}$ evaluation from a cooling-induced sharp increase in the overlap between log-likelihood distributions at different temperatures. $T_\mathrm{g}$ values derived from the RDF and MSD descriptors using DM closely matched the standard computer simulation-based dilatometric and dynamic $T_\mathrm{g}$ values for both PLA and PHB models. This was not the case for PCA. The DM-transformed DA and RSD data resulted in $T_\mathrm{g}$ values in agreement with experimental ones. Overall, the fusion of atomistic simulations and diffusion maps complemented with the Gaussian Mixture Models presents a promising framework for computing $T_\mathrm{g}$ and studying the glass transition in a unified way across various molecular descriptors for glass-forming materials.

cond-mat.soft↗

arXiv2024

Jamming Crossovers in a Confined Driven Polymer in Solution

We use lattice-Boltzmann molecular dynamics (LBMD) simulations to study the compression of a confined polymer immersed in a fluid and pushed by a large spherical colloid with a diameter comparable to the channel width. We examined the chain's deformation with both purely repulsive and weakly attractive Lennard-Jones (LJ) potentials applied between the monomers. The sphere's velocity was varied over 3 orders of magnitude. The chain is in a non-dense state at low pushing velocities for both repulsive and attractive monomer interactions. When the velocity of the spherical colloid exceeds a threshold $v^*$, the back end of the chain transitions to a high density state with low mean square monomer displacement (MSD) values. The front end, however, remains in a non-dense state with high MSD indicating a pseudo two-state coexistence. This crossover is also revealed through volume per monomer and MSD as a function of the sphere's velocity. We also studied polymer dynamics by investigating folding events at different times.

cond-mat.soft↗

arXiv2023

Non-Stokesian dynamics of magnetic helical nanoswimmers under confinement

Electromagnetically propelled helical nanoswimmers offer great potential for nanorobotic applications. Here, the effect of confinement on their propulsion is characterized using lattice-Boltzmann simulations. Two principal mechanisms give rise to their forward motion under confinement: 1) pure swimming, and 2) the thrust created by the differential pressure due to confinement. Under strong confinement, they face greater rotational drag, but display a faster propulsion for fixed driving frequency in agreement with experimental findings. This is due to the increased differential pressure created by the boundary walls when they are sufficiently close to each other and the particle. Two new analytical relations are presented: 1) for predicting the swimming speed of an unconfined particle as a function of its angular speed and geometrical properties, and 2) an empirical expression to accurately predict the propulsion speed of a confined swimmer as a function of the degree of confinement and its unconfined swimming speed. At low driving frequencies and degrees of confinement, the systems retain the expected linear behavior consistent with the predictions of the Stokes equation. However, as the driving frequency and/or the degree of confinement increase, their impact on propulsion leads to increasing deviations from the Stokesian regime and emergence of nonlinear behavior.

physics.flu-dyn↗

arXiv2023

Characterization of partial wetting by CMAS droplets using multiphase many-body dissipative particle dynamics and data-driven discovery based on PINNs

The molten sand, a mixture of calcia, magnesia, alumina, and silicate, known as CMAS, is characterized by its high viscosity, density, and surface tension. The unique properties of CMAS make it a challenging material to deal with in high-temperature applications, requiring innovative solutions and materials to prevent its buildup and damage to critical equipment. Here, we use multiphase many-body dissipative particle dynamics (mDPD) simulations to study the wetting dynamics of highly viscous molten CMAS droplets. The simulations are performed in three dimensions, with varying initial droplet sizes and equilibrium contact angles. We propose a coarse parametric ordinary differential equation (ODE) that captures the spreading radius behavior of the CMAS droplets. The ODE parameters are then identified based on the Physics-Informed Neural Network (PINN) framework. Subsequently, the closed form dependency of parameter values found by PINN on the initial radii and contact angles are given using symbolic regression. Finally, we employ Bayesian PINNs (B-PINNs) to assess and quantify the uncertainty associated with the discovered parameters. In brief, this study provides insight into spreading dynamics of CMAS droplets by fusing simple parametric ODE modeling and state-of-the-art machine learning techniques.

physics.flu-dyn↗

arXiv2023

Designing architectured ceramics for transient thermal applications using finite element and deep learning

Topologically interlocking architectures can generate tough ceramics with attractive thermo-mechanical properties. This concept can make the material design pathway a challenging task, since modeling the whole design space is neither effective nor feasible. We propose an approach to design high-performance architectured ceramics using machine learning (ML) with data from finite element analysis (FEA). Convolutional neural networks (CNNs) and Multilayer Perceptrons (MLPs) are used as the deep learning approaches. A limited set of FEA simulation data containing a variety of architectural design parameters is used to train our neural networks, including learning how independent and dependent design parameters are related. A trained network is then used to predict the optimum structure from the configurations. A FEA simulation is run on the best predictions of both MLP and CNN algorithms to evaluate the performance of our networks. Although a limited amount of simulation data are available, our networks are effective in predicting the transient thermo-mechanical responses of possible panel designs. For example, the optimal design after using CNN prediction resulted in $\approx \! 30\%$ improvement in terms of edge temperature.

cs.CE↗

arXiv2023

A Framework Based on Symbolic Regression Coupled with eXtended Physics-Informed Neural Networks for Gray-Box Learning of Equations of Motion from Data

We propose a framework and an algorithm to uncover the unknown parts of nonlinear equations directly from data. The framework is based on eXtended Physics-Informed Neural Networks (X-PINNs), domain decomposition in space-time, but we augment the original X-PINN method by imposing flux continuity across the domain interfaces. The well-known Allen-Cahn equation is used to demonstrate the approach. The Frobenius matrix norm is used to evaluate the accuracy of the X-PINN predictions and the results show excellent performance. In addition, symbolic regression is employed to determine the closed form of the unknown part of the equation from the data, and the results confirm the accuracy of the X-PINNs based approach. To test the framework in a situation resembling real-world data, random noise is added to the datasets to mimic scenarios such as the presence of thermal noise or instrument errors. The results show that the framework is stable against significant amount of noise. As the final part, we determine the minimal amount of data required for training the neural network. The framework is able to predict the correct form and coefficients of the underlying dynamical equation when at least 50\% data is used for training.

cond-mat.dis-nn↗

arXiv2023

Effect of substrate heterogeneity and topology on epithelial tissue growth dynamics

Tissue growth kinetics and interface dynamics depend on the properties of the tissue environment and cell-cell interactions. In cellular environments, substrate heterogeneity and geometry arise from a variety factors, such as the structure of the extracellular matrix and nutrient concentration. We used the CellSim3D model, a kinetic division simulator, to investigate the growth kinetics and interface roughness dynamics of epithelial tissue growth on heterogeneous substrates with varying topologies. The results show that the presence of quenched disorder has a clear effect on the colony morphology and the roughness scaling of the interface in the moving interface regime. In a medium with quenched disorder, the tissue interface has a smaller interface roughness exponent, $α$, and a larger growth exponent, $β$. The scaling exponents also depend on the topology of the substrate and cannot be categorized by well-known universality classes.

cond-mat.soft↗

arXiv2022

Machine Learning-Driven Process of Alumina Ceramics Laser Machining

Laser machining is a highly flexible non-contact manufacturing technique that has been employed widely across academia and industry. Due to nonlinear interactions between light and matter, simulation methods are extremely crucial, as they help enhance the machining quality by offering comprehension of the inter-relationships between the laser processing parameters. On the other hand, experimental processing parameter optimization recommends a systematic, and consequently time-consuming, investigation over the available processing parameter space. An intelligent strategy is to employ machine learning (ML) techniques to capture the relationship between picosecond laser machining parameters for finding proper parameter combinations to create the desired cuts on industrial-grade alumina ceramic with deep, smooth and defect-free patterns. Laser parameters such as beam amplitude and frequency, scanner passing speed and the number of passes over the surface, as well as the vertical distance of the scanner from the sample surface, are used for predicting the depth, top width, and bottom width of the engraved channels using ML models. Owing to the complex correlation between laser parameters, it is shown that Neural Networks (NN) are the most efficient in predicting the outputs. Equipped with an ML model that captures the interconnection between laser parameters and the engraved channel dimensions, one can predict the required input parameters to achieve a target channel geometry. This strategy significantly reduces the cost and effort of experimental laser machining during the development phase, without compromising accuracy or performance. The developed techniques can be applied to a wide range of ceramic laser machining processes.

cs.CE↗

arXiv2022

Machine learning based data-driven discovery of nonlinear phase-field dynamics

One of the main questions regarding complex systems at large scales concerns the effective interactions and driving forces that emerge from the detailed microscopic properties. Coarse-grained models aim to describe complex systems in terms of coarse-scale equations with a reduced number of degrees of freedom. Recent developments in machine learning (ML) algorithms have significantly empowered the discovery process of the governing equations directly from data. However, it remains difficult to discover partial differential equations (PDEs) with high-order derivatives. In this paper, we present new data-driven architectures based on multi-layer perceptron (MLP), convolutional neural network (CNN), and a combination of CNN and long short-term memory (CNN-LSTM) structures for discovering the non-linear equations of motion for phase-field models with non-conserved and conserved order parameters. The well-known Allen--Cahn, Cahn--Hilliard, and the phase-field crystal (PFC) models were used as the test cases. Two conceptually different types of implementations were used: (a) guided by physical intuition (such as local dependence of the derivatives) and (b) in the absence of any physical assumptions (black-box model). We show that not only can we effectively learn the time derivatives of the field in both scenarios, but we can also use the data-driven PDEs to propagate the field in time and achieve results in good agreement with the original PDEs.

physics.comp-ph↗

arXiv2022

Epithelial Tissue Growth Dynamics: Universal or Not?

Universality of interfacial roughness in growing epithelial tissue has remained a controversial issue. Kardar-Parisi-Zhang (KPZ) and Molecular Beam Epitaxy (MBE) universality classes have been reported among other behaviors including total lack of universality. Here, we utilize a kinetic division model for deformable cells to investigate cell-colony scaling. With seemingly minor model changes, it can reproduce both KPZ- and MBE-like scaling in configurations that mimic the respective experiments. This result neutralizes the apparent scaling controversy. It can be speculated that this diversity in growth behavior is beneficial for efficient evolution and versatile growth dynamics.

cond-mat.soft↗

arXiv2022

Temperature-resilient anapole modes associated with TE polarization in semiconductor nanowire

Polarization-dependent scattering anisotropy of cylindrical nanowires has numerous potential applications in, for example, nanoantennas, photothermal therapy, thermophotovoltaics, catalysis, sensing, optical filters and switches. In all these applications, temperature-dependent material properties play an important role and often adversely impact performance depending on the dominance of either radiative or dissipative damping. Here, we employ numerical modeling based on Mie scattering theory to investigate and compare the temperature and polarization-dependent optical anisotropy of metallic (gold, Au) nanowires with indirect (silicon, Si) and direct (gallium arsenide, GaAs) bandgap semiconducting nanowires. Results indicate that plasmonic scattering resonances in semiconductors, within the absorption band, deteriorate with an increase in temperature whereas those occurring away from the absorption band strengthen as a result of the increase in phononic contribution. Indirect-bandgap thin ($20 \,\mathrm{nm}$) Si nanowires present low absorption efficiencies for both the transverse electric (TE, $E_{\perp}$) and magnetic (TM, $E_{\parallel}$) modes, and high scattering efficiencies for the TM mode at shorter wavelengths making them suitable as highly efficient scatterers. Temperature-resilient higher-order anapole modes with their characteristic high absorption and low scattering efficiencies are also observed in the semiconductor nanowires ($r \! = \! 125 \! - \! 130$ nm) for the TE polarization. Herein, the GaAs nanowires present $3 \! - \! 7$ times greater absorption efficiencies compared to the Si nanowires making them especially suitable for temperature-resilient applications such as scanning near-field optical microscopy (SNOM), localized heating, non-invasive sensing or detection that require strong localization of energy in the near field.

cond-mat.mes-hall↗