arXiv subjects

Felipe Hawthorne

Publications and source records attributed to Felipe Hawthorne.

9 recordsLinked to original sources

arXiv2026

Exact mean-field phase diagram for self-avoiding active particles in a lattice

We investigate motility-induced phase separation in a lattice gas of self-propelled particles with hard-core exclusion, where an internal director biases particle hopping along the lattice coordination directions while undergoing rotational diffusion, together with a thermal-like translational diffusion. Rather than employing stochastic simulations, we adopt a master-equation formalism within a general mean-field approximation. By linearizing the mean-field master equation around the homogeneous stationary state and applying Bloch's theorem, the stability analysis is reduced to a $z$-dimensional tight-binding eigenvalue problem. A perturbation expansion in the wavenumber near $\vk = 0$ then yields the spinodal surface in closed analytical form for six Bravais lattices: linear, square, hexagonal, simple cubic, body-centered cubic, and face-centered cubic. The influence of lattice geometry is shown to enter exclusively through a single coefficient $\mathcal{A}$ which we evaluate exactly for each case. We further show that translational diffusion smooths the interface between the dense and dilute phases. Finally, we determine the rotational probability currents associated with the inhomogeneous stationary states, a distinctive signature of the broken detailed balance underlying active-system dynamics.

cond-mat.soft↗

arXiv2026

CARBON-2D Topological Descriptor (C2DTD): An Interpretable and Physics-Informed Representation for Two-Dimensional Carbon Networks

Two-dimensional (2D) carbon networks, from pristine graphene to defect-rich and amorphous monolayers, exhibit a complex structure-energy landscape governed not only by local bonding but also by medium-range order and network topology. Capturing these multi-scale effects in a compact, interpretable, and data-efficient manner remains a major challenge for machine learning (ML) in low-dimensional materials. In this work, we introduce the CARBON-2D Topological Descriptor (C2DTD), a physically informed structural representation specifically designed for 2D carbon systems. The descriptor integrates local geometric statistics, a compact radial structural signature, and explicit primitive ring topology into a fixed-length, invariant vector that is both computationally efficient and directly interpretable. Benchmarking on diverse datasets of 2D carbon allotropes and defect-engineered graphene sheets demonstrates that C2DTD achieves robust predictive performance in small-data regimes, outperforming generic high-dimensional featurization schemes while preserving physical transparency. Unsupervised manifold analysis reveals a smoother alignment between descriptor space and the DFT energy landscape, and feature-importance and ablation studies confirm that ring topology emerges as a dominant energetic driver, particularly under vacancy-induced reconstruction. Furthermore, controlled simulations with 5-15% random vacancies show that C2DTD naturally captures the progressive transition from hexagon-dominated graphene to topologically disordered networks, enabling both dataset-level and structure-specific interpretation. Owing to its compactness, interpretability, and strong physics-based inductive bias, C2DTD provides a fast and generalizable framework for data-driven modeling, defect analysis, and high-throughput screening of 2D carbon materials.

cond-mat.mtrl-sci↗

arXiv2026

Interpretable Machine Learning of Nanoparticle Stability through Topological Layer Embeddings

The stability of chemically complex nanoparticles is governed by an immense configurational space arising from heterogeneous local atomic environments across surface and interior regions. Efficiently identifying low-energy configurations within this space remains a central challenge for first-principles-based materials discovery, particularly when the available reference data are limited. Here, we introduce a data-efficient and physically interpretable machine-learning framework based on a fragmented, layer-resolved descriptor that explicitly decomposes nanoparticles into surface, intermediate, and core environments using a topology-driven definition. This representation preserves a compact and fixed feature dimensionality while retaining spatial resolution, enabling controlled emphasis on different regions of the nanoparticle through physically motivated weighting schemes. Coupled with gradient-boosted decision tree models and a ranking-based learning strategy, the proposed framework enables accurate identification of the most stable nanoparticle configurations using only a few hundred density functional theory reference calculations. Ranking performance metrics demonstrate near-saturation of correlation, high top-k recall, and rapidly vanishing regret at moderate training-set sizes, highlighting the strong data efficiency of the approach. Beyond predictive performance, layer-weighting and SHAP-based interpretability analyses reveal how surface segregation, coordination topology, and local chemical disorder contribute differently to stability across spatial regions of the nanoparticle. These insights provide a transparent physical interpretation of the learned models and establish a natural pathway toward active learning-driven exploration of complex nanoparticle configurational spaces.

cond-mat.mtrl-sci↗

arXiv2025

Melanin-Based Compounds as Low-Cost Sensors for Nitroaromatics: Theoretical Insights on Molecular Interactions and Optoelectronic Responses

Nitroaromatic compounds (NACs) are used in various industrial applications including dyes, inks, herbicides, pharmaceuticals, and explosives. Due to their toxicity and environmental persistence, reliable detection and monitoring methods are required. Hybrid organic--inorganic structures have shown potential for NAC sensing; however, their complex synthesis, high processing costs, and limited reproducibility hinder practical implementation, highlighting the need for simpler and more accessible materials. In this study, we employed density functional theory (DFT)-based calculations to evaluate the electronic, optical, and reactive properties of two melanin-based oligomeric systems, aiming to assess their potential use as NAC detectors. Our results indicate the potential of these materials to detect a series of nitroaromatic compounds such as 2,4-DNP, 2,4-DNT, 2,6-DNT, TNP, and TNT by electrical and infrared optical measurements. Born--Oppenheimer molecular dynamics (BOMD) simulations reveal the thermal stability of the adsorption process, confirming effective substrate--analyte interaction under different temperature conditions. To the best of our knowledge, this compound has not been proposed for sensing applications. Its low cost and facile synthesis make it a promising candidate for the development of environmentally friendly organic NAC sensors.

cond-mat.mtrl-sci↗

arXiv2025

Role of Translational Noise in Motility-Induced Phase Separation of Hard Active Particles

Self-propelled particles, like motile cells and artificial colloids, can spontaneously form macroscopic clusters. This phenomenon is called motility-induced phase separation (MIPS) and occurs even without attractive forces, provided that the self-propulsion direction fluctuates slowly. In addition to rotational noise, these particles may experience translational noise, not coupled to rotational noise, due to environmental fluctuations. We study the role of translational noise in the clustering of active Brownian hard disks. To tease apart the contribution of translational noise, we model excluded-volume interactions through a Monte-Carlo-like overlap rejection approach. We find that increasing translational diffusivity has a non-monotonic effect on clustering. At low values, it makes clusters more compact and rounded (less filamentous), eventually promoting genuine MIPS. For sufficiently higher translational diffusivity, clusters evaporate. We develop a theory for the cluster mass distribution, and employ a hydrodynamic approach with parameters taken from the simulation, that explains the clustering phase diagram.

cond-mat.soft↗

arXiv2025

Efficient and Accurate Machine Learning Interatomic Potential for Graphene: Capturing Stress-Strain and Vibrational Properties

Machine learning interatomic potentials (MLIPs) offer an efficient and accurate framework for large-scale molecular dynamics (MD) simulations, effectively bridging the gap between classical force fields and \textit{ab initio} methods. In this work, we present a reactive MLIP for graphene, trained on an extensive dataset generated via \textit{ab initio} molecular dynamics (AIMD) simulations. The model accurately reproduces key mechanical and vibrational properties, including stress-strain behavior, elastic constants, phonon dispersion, and vibrational density of states. Notably, it captures temperature-dependent fracture mechanisms and the emergence of linear acetylenic carbon chains upon tearing. The phonon analysis also reveals the expected quadratic ZA mode and excellent agreement with experimental and DFT benchmarks. Our MLIP scales linearly with system size, enabling simulations of large graphene sheets with \textit{ab initio}-level precision. This work delivers a robust and transferable MLIP, alongside an accessible training workflow that can be extended to other materials.

cond-mat.mtrl-sci↗

arXiv2024

Nanoconfined Water Phase Transitions in Infinite Graphene Slits: Molecular Dynamics Simulations and Mean-Field Insights

Recent experimental and computational studies have demonstrated that nanoconfinement profoundly alters the phase behavior of water, facilitating complex phase transitions at pressures and temperatures far lower than typically observed in bulk systems. When combined with adsorption, nanoconfinement substantially enhances water uptake, primarily due to condensation occurring at the onset of the isotherm curve-a phenomenon intimately related to the facilitated formation of hydrogen bond networks. In this study, we adopt a dual approach to investigate water confined within infinite graphene slits. Our Molecular Dynamics simulations reveal hysteresis across all investigated temperatures. Unlike in finite slits, where hysteresis arises due to surface tension effects at the edges, in the case of infinite slits, the hysteresis is the result of a genuine phase transition at the nanoscale. We analyze the spatial and orientational arrangements of the water molecules, demonstrating how the graphene surface promotes the formation of a hydrogen bond network in the adjacent water layers. The remarkably low pressure required for water uptake in this nano-environment is explained at the mean-field level using a simple interacting lattice model. This is attributed to the exponential dependence of the critical pressure on the adsorbate-adsorbent interaction.

cond-mat.mtrl-sci↗

arXiv2023

Thermodynamics of a minimal collective heat engine: Comparison between engine designs

Collective effects have attracted remarkable recent interest, not only for their presence in several systems in nature but also for the possibility of being used for the construction of efficient engine setups. Notwithstanding, little is known about the influence of the engine design and most studies are restricted to the simplest cases (e.g. simultaneous contact with two thermal baths), not necessarily constituting a realistic setup implementation. Aimed at partially filling this gap, we introduce the collisional/sequential description for a minimal model for collective effects, composed of two interacting nanomachines placed in contact with a distinct thermal reservoir and nonequilibrium worksource at each stage/stroke. Thermodynamic quantities are exactly obtained irrespectively the model details. Distinct kinds of engines are investigated and the influence of the interaction, temperature, period, and time asymmetry have been undertaken. Results show that a careful design of interaction provides a superior performance than the interactionless case, including optimal power outputs and efficiencies at maximum power greater than known bounds or even the system presenting efficiencies close to the ideal (Carnot) limit. We also show that the case of the system simultaneously placed in contact with two thermal reservoirs constitutes a particular case of our framework.

cond-mat.stat-mech↗

arXiv2023

Nonequilibrium thermodynamics of the majority vote model

The majority vote model is one of the simplest opinion systems yielding distinct phase transitions and has garnered significant interest in recent years. However, its original formulation is not, in general, thermodynamically consistent, precluding the achievement of quantities such as power and heat, as well as their behaviors at phase transition regimes. Here, we circumvent this limitation by introducing the idea of a distinct heat bath per local configuration, in such a way that each neighborhood value is associated with a distinct and well-defined thermal bath. Thermodynamic properties are derived for a generic majority vote model, irrespective of its neighborhood and lattice topology. The behavior of energy/heat fluxes at phase transitions, whether continuous or discontinuous, in regular and complex topologies, is investigated in detail. Unraveling the contribution of each local configuration explains the nature of the phase diagram and reveals how dissipation arises from the dynamics.

cond-mat.stat-mech↗