arXiv subjects

Tapio Ala-Nissila

Publications and source records attributed to Tapio Ala-Nissila.

At least 19 recordsLinked to original sources

arXiv2026

Universal entanglement embezzlement and divergent nonlocal magic from generic local chaotic quantum evolution

We show that, starting from a product state, local unitary quantum evolutions generate intermediate states which exhibit a multiscale entanglement-spectrum structure required for universal entanglement embezzlement. This facilitates entanglement extraction from a catalyst many-body state while leaving it asymptotically unchanged. Remarkably, these atypical structures emerge generically at intermediate stages, well before reaching maximum entropy where thermalization has flattened out the spectral hierarchy. The resulting state is accompanied by nonlocal nonstabilizerness that diverges with the system size, consistent with a recently established equivalence between universal embezzlement and divergent nonlocal magic. Thus, without any fine tuning, a chaotic quantum evolution generates intermediate states which form a universal family of catalytic reservoirs.

quant-ph↗

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

The ebbs and flows of quantum learning and sensing

What is the relation between subsystem quantum complexity and the emergence of computationally useful structure? We address this by studying a family of minimally tunable postvariational quantum circuits, and show how spectral nonflatness and metrological response directly control the ensemble-typical information processing power. This unveils an intermediate "learning phase" that precedes the onset of quantum chaos, characterized by pronounced nonflatness and sensitivity of readout states. The optimal information processing capacity improves with system size, while deep scrambling suppresses observable response. The results reveal how such features of random quantum dynamics can be viewed as computational resources for scalable nonlinear computation.

quant-ph↗

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

NEP89: Universal neuroevolution potential for inorganic and organic materials across 89 elements

While machine-learned interatomic potentials offer near-quantum-mechanical accuracy for atomistic simulations, many are material-specific or computationally intensive, limiting their broader use. Here, we introduce NEP89, a foundation model based on neuroevolution potential architecture, delivering near-empirical-potential speed and high accuracy across 89 elements. A compact yet comprehensive training dataset covering inorganic and organic materials was curated through descriptor-space subsampling and iterative refinement across multiple datasets. NEP89 achieves competitive accuracy compared to representative foundation models while being three to four orders of magnitude more computationally efficient, enabling previously impractical large-scale atomistic simulations of inorganic and organic systems. In addition to its out-of-the-box applicability to diverse scenarios, including million-atom-scale compression of compositionally complex alloys, ion diffusion in solid-state electrolytes and water, rocksalt dissolution, methane combustion, and protein-ligand dynamics, NEP89 also supports fine-tuning for rapid adaptation to user-specific applications, such as mechanical, thermal, structural, and spectral properties of two-dimensional materials, metallic glasses, and organic crystals.

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

TorchNEP: Ultra-Efficient and Accurate Training of Neuroevolution Potentials

Neuroevolution Potential (NEP) is one of the most efficient machine-learned interatomic potential frameworks for large-scale atomistic simulations. However, its original training strategy remains computationally demanding, limiting systematic exploration of model architectures and training protocols. Here, we present TorchNEP, a PyTorch-based implementation of NEP that combines analytically derived gradients, adaptive optimization, and a two-stage training strategy. TorchNEP accelerates training by more than two orders of magnitude while maintaining full compatibility with existing NEP models. We further show that the improvement in predictive accuracy primarily originates from the two-stage training protocol rather than the optimization algorithm itself. Across diverse benchmark datasets, TorchNEP consistently improves force and stress predictions while maintaining comparable or improved energy accuracy. Benchmark evaluations on elemental and alloy systems demonstrate enhanced predictive performance for both atomic configurations and key materials properties. Furthermore, we show that increasing model complexity does not necessarily improve predictive performance despite reducing training errors. Overall, TorchNEP provides an efficient and flexible training framework for developing more accurate and robust machine-learned interatomic potentials.

physics.comp-ph↗

arXiv2026

Optimal quantum reservoir learning in proximity to universality

The study of the boundary between classically simulable and computationally complex quantum dynamics is fundamental to understanding which physical resources may enable enhanced information-processing capabilities. We investigate this within the framework of quantum reservoir computing by introducing a tunable $N$-qubit random circuit model, where a fraction $p$ of Clifford gates are probabilistically substituted with nonstabilizing conditional-$\hat{T}$ gates. We establish a direct correspondence between the reservoir's performance on temporal processing tasks and its entanglement spectrum statistics and long-range nonstabilizer resource content. To assess scalability, we study the scaling of the anti-flatness of states in the large-$N$ limit at a fixed circuit depth ratio $d/N \sim \mathcal{O}(1)$. This is taken as a witness to concentration of measures, a known impediment to learning in thermalizing systems. We demonstrate that the learnability and scalability of the reservoir can be continuously controlled by the parameter $p$, allowing us to navigate from classically tractable to maximally expressive quantum dynamics. These architecture-agnostic results provide a general strategy for designing tunable and expressive quantum reservoirs, highlighting how certain nonclassical properties control average-case intrinsic learnability and functionality.

quant-ph↗

arXiv2026

Thermodynamics of Coherence-Selective Quantum Reset Protocols

We develop an exact theory of coherence-selective stroboscopic resetting for quadratic open quantum systems within the single-particle density-matrix formalism. We focus on the survival of coherences and the associated thermodynamic cost at the stroboscopic fixed point. To this end, we introduce a one-parameter family of reset channels that continuously interpolates between complete coherence erasure and complete coherence preservation. This unifies the reset-map description, the repeated-interaction and evolving-correlation endpoint channels, and the thermodynamic cost of environmental reinitialization. For a single fermionic level coupled to a structured semi-infinite tight-binding bath, we derive the exact affine stroboscopic map, solve for its unique fixed point, and compute the retained coherence spectrum, the post-reset occupation, and the reset heat current. We find that retained coherence increases monotonically with the retention parameter, whereas the reset heat current is generically nonmonotonic and is maximized at an intermediate operating point. Thus the protocol that stores the most coherence is not the one that dissipates the most heat. Exact operating diagrams further show that coherence-optimal and coherence-per-cost-optimal protocols are both driven toward the coherence-preserving endpoint, while the heat-optimal protocol depends strongly on the reset interval. We also show that this coherence-cost geometry survives at nonzero chemical potential as a filling-biased deformation of the same fixed-point tradeoff, rather than as an independent particle-current optimization problem. These results establish coherence-selective resetting as a distinct control principle for structured-bath open quantum systems and provide an exactly solvable benchmark for memory engineering and thermodynamic optimization under repeated environmental reinitialization.

quant-ph↗

arXiv2026

Coherence-Controlled Quantum Zeno Dynamics from Exact Reset Maps

We develop an exact framework for quantum Zeno and anti-Zeno dynamics in a broad class of open systems, whose microscopic Hamiltonians are quadratic in bosonic or fermionic operators. We treat the environment through an exact stroboscopic resetting scheme acting at the level of the single-particle density matrix (SPDM). Within this framework, we consider two cases: a repeated-interaction (RI) protocol, in which the environment block is rethermalized and all system-environment coherences are erased after each step, and an evolving-correlation (EC) protocol, in which only the environment block is reset while system-environment coherences are preserved. For RI, we derive a general short-time Zeno law for the survival probability of a single-particle excitation and show that the corresponding decay rate scales linearly with the reset interval, implying Zeno freezing in the limit of infinitely frequent resets. Beyond the short-time regime, we formulate the RI dynamics directly in terms of the exact one-cycle propagator, which allows us to analyze finite-$τ$ anti-Zeno windows without additional approximations. For EC, we obtain a continuous-reset description in which the kept single-particle correlators obey a finite-dimensional linear differential equation. In this case the drift in the system block remains finite in the frequent-reset limit, so strict freezing is absent. We illustrate these results for a single fermionic level coupled to a semi-infinite tight-binding chain acting as a structured bath. Our results identify coherence erasure versus coherence retention as the key factor controlling the reset-induced Zeno physics.

quant-ph↗

arXiv2026

Interaction-Enabled Hartree Fixed Points in Fermionic Resetting Dynamics

In resetting dynamics, a system is repeatedly coupled to and decoupled from ancillary degrees of freedom that are reinitialized between interactions. This provides a versatile route to engineer nonequilibrium steady states and constitutes a powerful and analytically transparent framework for studying nonequilibrium dynamics in quadratic fermionic models. The baseline noninteracting resetting scheme yields an affine evolution for the subsystem single-particle density matrix (SPDM), with a clear operational interpretation: a finite environment block E mediates the interaction between the subsystem S and an ideal external thermal reservoir. In this work, we develop a controlled extension of such a framework to weakly interacting systems. We introduce a Hartree mean-field treatment of density-density interactions that preserves closure of the SPDM dynamics while producing genuinely nonlinear behavior. We further construct a completely positive (CP-safe) Gaussian Lindblad embedding that reproduces the resetting dynamics in the noninteracting limit and yields a continuous-time representation of environmental thermalization when interactions are present. Our analytical results are complemented by numerical studies of a ring segmentation geometry and a minimal two-site model, revealing interaction-enabled steady states that cannot be obtained in any purely quadratic setting. Together, these results establish a general and physically consistent route for incorporating weak interactions ino resetting-based approaches to open quantum system.

quant-ph↗

arXiv2026

Nine-element machine-learned interatomic potentials for multiphase refractory alloys

New refractory alloys are being continuously designed and characterised for applications requiring good high-temperature mechanical properties and stability. Computational design from atomistic simulations is limited by interatomic potentials missing key elements, being too inaccurate, or computationally too slow for large-scale simulations. Here we present development of a refractory alloy database and two computationally efficient and general-purpose machine-learned potentials (tabGAP and NEP). We also design a cross-sampling strategy for effective sampling of training data using predictions from two potentials with completely different underlying architecture. The potentials support arbitrary alloy compositions of elements in groups four to six in the periodic table (Ti, Zr, Hf, V, Nb, Ta, Cr, Mo, W). The database is diverse yet multitargeted to enable simulations of refractory metals and alloys across different pure-metal, solid-solution, intermetallic, and glassy phases. We demonstrate the usefulness of the potentials by reproducing known pressure-, temperature-, and solute-induced phase transitions, grain boundary segregation, and simulations of radiation damage in the WTaCrVHf metallic glass.

cond-mat.mtrl-sci↗

arXiv2026

Density-dependent sodium-storage mechanisms in hard carbon materials

Understanding the sodium-storage mechanism in hard carbon (HC) anodes is crucial for advancing sodium-ion battery (SIB) technology. However, the intrinsic complexity of HC microstructures and their interactions with sodium remain not fully elucidated. We present a multiscale methodology that integrates grand-canonical Monte Carlo (GCMC) simulations with a machine-learning interatomic potential based on the Gaussian approximation potential (GAP) framework to investigate sodium insertion mechanisms in hard carbons with different levels of porosity, achieved by simulating structural models with densities ranging from 0.7 to 1.9 g cm$^{-3}$. Structural and thermodynamic analyses reveal the interplay between pore size and accessibility and the relative contributions of adsorption, intercalation, and pore filling to the overall storage capacity. Low-density carbons favor pore-filling, achieving extremely high capacities at near-zero voltages, whereas high-density carbons primarily store sodium through adsorption and intercalation, leading to lower but more stable capacities. Intermediate-density carbons ($1.3-1.6$ g cm$^{-3}$) provide the most balanced performance, combining moderate capacity (480 and 310 mAh g$^{-1}$), safe operating voltages, and minimal volume expansion ($<10$\%). These findings establish a direct correlation between carbon density and electrochemical behavior, providing atomic-scale insight into how hard carbon morphology governs sodium-storage. The proposed framework offers a rational design principle for optimizing HC-based SIB anodes toward high energy density and long-term cycling stability.

cond-mat.mtrl-sci↗

arXiv2025

Stabilisation of hBN/SiC Heterostructures with Vacancies and Transition-Metal Atoms

When two-dimensional atomic layers of different materials are brought into close proximity to form van der Waals (vdW) heterostructures, interactions between adjacent layers significantly influence their physicochemical properties. These effects seem particularly pronounced when the interface exhibits local order and near-perfect structural alignment, leading to the emergence of Moiré patterns. Using quantum mechanical density functional theory calculations, we propose a prototypical bilayer heterostructure composed of hexagonal boron nitride (hBN) and silicon carbide (SiC), characterized by a lattice mismatch of 18.77\% between their primitive unit cells. We find that the removal of boron atoms from specific lattice sites can convert the interlayer interaction from weak vdW coupling to robust localized silicon-nitrogen covalent bonding. Motivated by this, we study the binding of transition-metal adatoms and formulate design guidelines to enhance surface reactivity, thereby enabling the controlled isolation of single-metal atoms. Our machine-learning-assisted molecular dynamics simulations confirm both dynamical stability and metal anchoring feasibility at finite temperatures. Our results suggest the hBN/SiC heterostructure as a versatile platform for atomically precise transition-metal functionalization, having potential for next-generation catalytic energy-conversion technologies.

cond-mat.mtrl-sci↗

arXiv2025

Dynamical learning and quantum memory with non-Hermitian many-body systems

Non-Hermitian (NH) systems provide a fertile platform for quantum technologies, owing in part to their distinct dynamical phases. These systems can be characterized by the preservation or spontaneous breaking of parity-time reversal symmetry, significantly impacting the dynamical behavior of quantum resources such as entanglement and purity; resources which in turn govern the system's information processing and memory capacity. Here we investigate this relationship using the example of an interacting NH spin system defined on random graphs. We show that the onset of the first exceptional point - marking the real-to-complex spectral transition - also corresponds to an abrupt change in the system's learning capacity. We further demonstrate that this transition is controllable via local disorder and spin interactions strength, thereby defining a tunable learnability threshold. Within the learning phase, the system exhibits the key features required for memory-dependent reservoir computing. This makes explicit a direct link between spectral structure and computational capacity, further establishing non-Hermiticity, and more broadly engineered dissipation, as a dynamic resource for temporal quantum machine learning.

quant-ph↗

arXiv2025

Driven Polymer Translocation through a Nanopore from a Confining Channel

We consider the dynamics of pore-driven polymer translocation through a nanopore to semi-infinite space when the chain is initially confined and equilibrated in a narrow channel. To this end, we use Langevin dynamics (LD) simulations and iso-flux tension propagation (IFTP) theory to characterize local and global dynamics of the translocating chain. The dynamics of the process can be described by the IFTP theory in very good agreement with the LD simulations for all values of confinement in the channel. The theory reveals that for channels with size comparable to or less than the end-to-end distance of the unconfined chain, in which the blob theory works, the scaling form of the translocation time depends on both the chain contour length as well as the channel width. %originating from the confinement of the spatial fluctuations of the chain inside the channel. Conversely, for a very narrow channel the translocation time only depends on the chain contour length and is similar to that of a rod due to the absence of spatial chain fluctuations.

cond-mat.soft↗

arXiv2025

Quantum reservoir computing on random regular graphs

Quantum reservoir computing (QRC) is a low-complexity learning paradigm that combines the inherent dynamics of input-driven many-body quantum systems with classical learning techniques for nonlinear temporal data processing. Optimizing the QRC process and computing device is a complex task due to the dependence of many-body quantum systems to various factors. To explore this, we introduce a strongly interacting spin model on random regular graphs as the quantum component and investigate the interplay between static disorder, interactions, and graph connectivity, revealing their critical impact on quantum memory capacity and learnability accuracy. We tackle linear quantum and nonlinear classical tasks, and identify optimal learning and memory regimes through studying information localization, dynamical quantum correlations, and the many-body structure of the disordered Hamiltonian. In particular, we uncover the role of previously overlooked network connectivity and demonstrate how the presence of quantum correlations can significantly enhance the learning performance. Our findings thus provide guidelines for the optimal design of disordered analog quantum learning platforms.

quant-ph↗

arXiv2024

Density dependence of thermal conductivity in nanoporous and amorphous carbon with machine-learned molecular dynamics

Disordered forms of carbon are an important class of materials for applications such as thermal management. However, a comprehensive theoretical understanding of the structural dependence of thermal transport and the underlying microscopic mechanisms is lacking. Here we study the structure-dependent thermal conductivity of disordered carbon by employing molecular dynamics (MD) simulations driven by a machine-learned interatomic potential based on the efficient neuroevolution potential approach. Using large-scale MD simulations, we generate realistic nanoporous carbon (NP-C) samples with density varying from $0.3$ to $1.5$ g cm$^{-3}$ dominated by sp$^2$ motifs, and amorphous carbon (a-C) samples with density varying from $1.5$ to $3.5$ g cm$^{-3}$ exhibiting mixed sp$^2$ and sp$^3$ motifs. Structural properties including short- and medium-range order are characterized by atomic coordination, pair correlation function, angular distribution function and structure factor. Using the homogeneous nonequilibrium MD method and the associated quantum-statistical correction scheme, we predict a linear and a superlinear density dependence of thermal conductivity for NP-C and a-C, respectively, in good agreement with relevant experiments. The distinct density dependences are attributed to the different impacts of the sp$^2$ and sp$^3$ motifs on the spectral heat capacity, vibrational mean free paths and group velocity. We additionally highlight the significant role of structural order in regulating the thermal conductivity of disordered carbon.

cond-mat.mtrl-sci↗