arXiv subjects

Davide Donadio

Publications and source records attributed to Davide Donadio.

At least 19 recordsLinked to original sources

arXiv2026

Structural Origin of Water Heat Capacity Anomaly from Classical and Quantum Simulations

Water isobaric heat capacity is anomalously large under ambient conditions and exhibits a sharp maximum upon supercooling. Using classical and path-integral molecular dynamics with accurate machine-learning interatomic potentials, we show that nuclear quantum effects primarily act by suppressing high-frequency vibrations, while the anomalous temperature dependence of the isobaric heat capacity originates from structural fluctuations, quantified by the second-solvent-shell intruder order parameter. A simple two-state mapping reveals an effective enthalpy scale of about 4 kJ/mol associated with the interconversion of low- and high-density-like local structures, providing a microscopic link between their population changes and the excess heat capacity from supercooled to ambient conditions.

cond-mat.stat-mech↗

arXiv2026

Stability and superstructural ordering of alkali-triel-pnictide clathrates A$_8$T$_{27}$Pn$_{19}$

Clathrates are a class of inclusion compounds that offer various useful and surprising phenomena, including superconductivity, thermoelectricity, and the potential for high-density ion storage. Stability conditions within the Alkali-Triel-Pnictide A$_8$T$_{27}$Pn$_{19}$ family of unconventional clathrates are investigated with high-throughput density functional theory calculations, establishing trends in formation energy, structural and electronic properties. Electronic structure calculations and first-principles molecular dynamics simulations show that the ionization potential of guest alkaline atoms strongly influences the stability of electron-exact clathrates and affects their rattler behavior. Targeted reactive synthesis from elemental precursors is attempted, resulting in two novel ternary compounds. However, the targeted clathrate phases are not obtained. Further analysis reveals that the stability of ATPn clathrate compounds containing heavy elements, such as bismuth, depends strongly on spin-orbit effects, which are often neglected in high-throughput studies that compute formation energies. Finally, chemically induced superstructural ordering is described in relation to Wyckoff sites in the prototypical type-I clathrate unit cell.

cond-mat.mtrl-sci↗

arXiv2026

kALDo 2.0: Scalable Thermal Transport from First Principles and Machine Learning Potentials

We introduce kALDo2.0, an open-source Python package for computing vibrational, elastic, and thermal transport properties of solids from first principles and machine-learned interatomic potentials. Building on the anharmonic lattice dynamics (ALD) framework, kALDo2.0 provides efficient CPU and GPU-accelerated implementations of the Boltzmann transport equation (BTE) for crystals and the quasi-harmonic Green-Kubo (QHGK) method. QHGK extends thermal transport predictions beyond crystals to disordered materials, including glasses, alloys, and complex nanostructures. kALDo2.0 introduces native integration with modern machine-learned potentials (MLPs), enabling thermal transport workflows that combine the accuracy of first-principles methods with the scalability of classical force fields. It also features comprehensive support for temperature-dependent effective potentials workflows, flexible storage backends for large-scale calculations, and advanced quantification of anharmonicity. The software seamlessly interfaces with electronic structure codes (Quantum ESPRESSO, VASP), molecular dynamics packages (LAMMPS), and MLPs (ACE, NEP, MACE, MatterSim, Orb), enabling thermal transport studies from 0 K to finite temperatures. kALDo2.0 implements multiple BTE solution strategies and essential physical corrections, including isotopic scattering and non-analytical terms for polar materials. A modular Python architecture with lazy evaluation and multiple storage formats (ASCII, NumPy, HDF5) enables simulations of systems containing up to tens of thousands of atoms. This paper describes the theoretical framework, implementation details, software architecture, and validation examples demonstrating kALDo2.0's capabilities for studying complex materials, including halide perovskites with strong anharmonicity and polar oxides requiring long-range electrostatic corrections.

cond-mat.mtrl-sci↗

arXiv2025

Pseudo-grand canonical molecular dynamics via volumetrically controlled osmotic pressure

Molecular dynamics simulations are typically constrained to have a fixed number of particles, which limits our capability to simulate chemical and physical processes where the composition of the system changes during the simulation time. Typical examples are the calculation of nucleation and crystal growth rates in heterogeneous solutions where the driving force depends on the composition of the fluid. Constant chemical potential molecular dynamics simulations would instead be required to compute time-independent growth and nucleation rates. While this can, in principle, be achieved through the addition and deletion of particles using the grand canonical partition function, this is very inefficient in the condensed phase due to the low acceptance probability of these events. Adaptive resolution schemes, which use a reservoir of non-interacting particles that can be transformed into solute particles, circumvent this problem, but at the cost of relatively complicated code implementations. In this work, a simpler approach is proposed that uses harmonic volumetric restraints to control the solute osmotic pressure, which can be considered a proxy for the system's chemical potential. The osmotic pressure regulator is demonstrated to reproduce the expected properties of ideal gases and ideal solutions. Using the mW water model, the osmotic pressure regulator is shown to provide a constant growth rate for ice in the presence of an electrolyte solution, unlike what standard molecular dynamics simulations would produce.

physics.chem-ph↗

arXiv2025

Metastability and Ostwald Step Rule in the Crystallisation of Diamond and Graphite from Molten Carbon

The crystallisation of carbon from the melt under extreme conditions is highly relevant to earth and planetary science, materials manufacturing, and nuclear fusion research. The thermodynamic conditions near the graphite-diamond-liquid (GDL) triple point are especially of interest for geological and technological applications, but high-pressure flash heating experiments aiming to resolve this region of the phase diagram of carbon exhibit large discrepancies. Experimental challenges are often related to the persistence of metastable crystalline or glassy phases, superheated crystals, or supercooled liquids. A deeper understanding of the crystallisation kinetics of diamond and graphite is crucial for effectively interpreting the outcomes of these experiments. Here, we reveal the microscopic mechanisms of diamond and graphite nucleation from liquid carbon through molecular simulations with first-principles machine learning potentials. Our simulations accurately reproduce the experimental phase diagram of carbon in the region around the GDL triple point and show that liquid carbon crystallises spontaneously upon cooling at constant pressure. Surprisingly, metastable graphite crystallises in the domain of diamond thermodynamic stability at pressures above the triple point. Furthermore, whereas diamond crystallises through a classical nucleation pathway, graphite follows a two-step process in which low-density fluctuations forego ordering. Calculations of the nucleation rates of the two competing phases confirm this result and reveal a manifestation of Ostwald's step rule where the strong metastability of graphite hinders the transformation to the stable diamond phase. Our results provide a new key to interpreting melting and recrystallisation experiments and shed light on nucleation kinetics in polymorphic materials with deep metastable states.

cond-mat.mtrl-sci↗

arXiv2025

Probing the Limit of Heat Transfer in Inorganic Crystals with Deep Learning

Heat transfer is a fundamental property of matter. Research spanning decades has attempted to discover materials with exceptional thermal conductivity, yet the upper limit remains unknown. Using deep learning accelerated crystal structure prediction and first-principles calculation, we systematically explore the thermal conductivity landscape of inorganic crystals. We brute-force over half a million ordered crystalline structures, encompassing an extensive coverage of local energy minima in binary compounds with up to four atoms per primitive cell. We confirm diamond sets the upper bound of thermal conductivity within our search space, very likely also among all stable crystalline solids at ambient conditions. We also identify over 20 novel crystals surpassing silicon in thermal conductivity, validated by density functional theory. These include a semiconductor TaN with ultrahigh thermal conductivity (~900 $\mathrm{W\cdot m^{-1}\cdot K^{-1}}$), and metallic compounds such as MnV that exhibit high lattice and electronic thermal conductivity simultaneously, a distinctive feature not observed before. These results as well as the deep learning-driven screening method, redefine the landscape of thermal transport and establish a large open-access database for future materials discovery.

cond-mat.mtrl-sci↗

arXiv2024

PLUMED Tutorials: a collaborative, community-driven learning ecosystem

In computational physics, chemistry, and biology, the implementation of new techniques in a shared and open source software lowers barriers to entry and promotes rapid scientific progress. However, effectively training new software users presents several challenges. Common methods like direct knowledge transfer and in-person workshops are limited in reach and comprehensiveness. Furthermore, while the COVID-19 pandemic highlighted the benefits of online training, traditional online tutorials can quickly become outdated and may not cover all the software's functionalities. To address these issues, here we introduce ``PLUMED Tutorials'', a collaborative model for developing, sharing, and updating online tutorials. This initiative utilizes repository management and continuous integration to ensure compatibility with software updates. Moreover, the tutorials are interconnected to form a structured learning path and are enriched with automatic annotations to provide broader context. This paper illustrates the development, features, and advantages of PLUMED Tutorials, aiming to foster an open community for creating and sharing educational resources.

physics.ed-ph↗

arXiv2024

Molecular Fingerprints of Ice Surfaces in Sum Frequency Generation Spectra: a First Principles Machine Learning Study

Understanding the molecular-level structure and dynamics of ice surfaces is crucial for deciphering several chemical, physical, and atmospheric processes. Vibrational sum-frequency generation (SFG) spectroscopy is the most prominent tool for probing the molecular-level structure of the air--ice interface as it is a surface-specific technique, but the molecular interpretation of SFG spectra is challenging. This study utilizes a machine-learning potential, along with dipole and polarizability models trained on ab initio data, to calculate the SFG spectrum of the air--ice interface. At temperatures below ice surface premelting, our simulations support the presence of a proton-ordered arrangement at the Ice Ih surface, similar to that seen in Ice XI. Additionally, our simulations provide insight into the assignment of SFG peaks to specific molecular configurations where possible and assess the contribution of subsurface layers to the overall SFG spectrum. These insights enhance our understanding and interpretation of vibrational studies of environmental chemistry at the ice surface.

physics.chem-ph↗

arXiv2024

High Pressure Structural Behavior of Silicon Telluride (Si2Te3) Nanoplates

The high-pressure behavior of silicon telluride (Si2Te3), a two-dimensional (2D) layered material, was investigated using synchrotron X-ray powder diffraction in a diamond anvil cell to 11.5 GPa coupled with first-principles theory. Si2Te3 undergoes a phase transition at < 1 GPa from a trigonal to a hexagonal crystal structure. At higher pressures (> 8.5 GPa), X-ray diffraction showed the appearance of new peaks possibly coincident with a new phase transition, though we suspect Si2Te3 retains a hexagonal structure. Density functional theory calculations of the band structure reveal metallization above 9.1 GPa consistent with previous measurements of the Raman spectra and disappearance of color and transparency at pressure. The theoretical Raman spectra reproduce the prominent features of the experiment, though a deeper analysis suggests that the orientation of Si dimers dramatically influences the vibrational response. Given the complex structure of Si2Te3, simulation of the resulting high-pressure phase is complicated by disordered vacancies and the initial orientations of Si-Si dimers in the crushed layered phase.

cond-mat.mtrl-sci↗

arXiv2024

Elastic moduli and thermal conductivity of quantum materials at finite temperature

We describe a theoretical and computational approach to calculate the vibrational, elastic, and thermal properties of materials from the low-temperature quantum regime to the high-temperature anharmonic regime. This approach is based on anharmonic lattice dynamics and the Boltzmann transport equation. It relies on second and third-order force constant tensors estimated by fitting temperature-dependent empirical potentials (TDEP) from path-integral quantum simulations with a first-principles machine learning Hamiltonian. The temperature-renormalized harmonic force constants are used to calculate the elastic moduli and the phonon modes of materials. Harmonic and anharmonic force constants are combined to solve the phonon Boltzmann transport equation to compute the lattice thermal conductivity. We demonstrate the effectiveness of this approach on bulk crystalline silicon in the temperature range from 50 to 1200~K, showing substantial improvement in the prediction of the temperature dependence of the target properties compared to experiments.

cond-mat.mtrl-sci↗

arXiv2024

Effects of colored disorder on the heat conductivity of SiGe alloys from first principles

Semiconducting alloys, in particular SiGe, have been employed for several decades as high-temperature thermoelectric materials. Devising strategies to reduce their thermal conductivity may provide a substantial improvement in their thermoelectric performance also at lower temperatures. We have carried out an ab initio investigation of the thermal conductivity of SiGe alloys with random and spatially correlated mass disorder employing the Quasi-Harmonic Green-Kubo (QHGK) theory with force constants computed by density functional theory. Leveraging QHGK and the hydrodynamic extrapolation to achieve size convergence, we obtained a detailed understanding of lattice heat conduction in SiGe and demonstrated that colored disorder suppresses thermal transport across the acoustic vibrational spectrum, leading to up to a 4-fold enhancement in the intrinsic thermoelectric figure of merit.

cond-mat.mtrl-sci↗

arXiv2024

Nuclear Quantum Effects on the Electronic Structure of Water and Ice

The electronic properties and optical response of ice and water are intricately shaped by their molecular structure, including the quantum mechanical nature of hydrogen atoms. In spite of numerous studies appeared over decades, a comprehensive understanding of the effect of the nuclear quantum motion on the electronic structure of water and ice at finite temperatures remains elusive. Here, we utilize molecular simulations that harness the efficiency of machine-learning potentials and many-body perturbation theory to assess the impact of nuclear quantum effects on the electronic structure of water and hexagonal ice. By comparing the results of path-integral and classical simulations, we find that including nuclear quantum effects leads to a larger renormalization of the fundamental gap of ice, compared to that of water, eventually leading to a comparable gap in the two systems, consistent with experimental estimates. Our calculations suggest that the quantum fluctuations responsible for an increased delocalization of protons in ice, relative to water, are a key factor leading to the enhancement of nuclear quantum effects on the electronic structure of ice.

physics.chem-ph↗

arXiv2023

Non-Fourier heat transport in nanosystems

Energy transfer in small nanosized systems can be very different from that in their macroscopic counterparts due to reduced dimensionality, interaction with surfaces, disorder, and large fluctuations. Those ingredients may induce non-diffusive heat transfer that requires to be taken into account on small scales. We provide an overview of the recent advances in this field from the points of view of nonequilibrium statistical mechanics and atomistic simulations. We summarize the underlying basic properties leading to violations of the standard diffusive picture of heat transport and its universal features, with some historical perspective. We complete this scenario by illustrating also the effects of long-range interaction and integrability on non-diffusive transport. Then we discuss how all of these features can be exploited for thermal management, rectification and to improve the efficiency of energy conversion. We conclude with a review on recent achievements in atomistic simulations of anomalous heat transport in single polymers, nanotubes and two-dimensional materials. A short account of the existing experimental literature is also given.}

cond-mat.stat-mech↗

arXiv2022

Effect of Sodium Chloride Adsorption on the Surface Premelting of Ice

We characterise the structural properties of the quasi-liquid layer (QLL) at two low-index ice surfaces in the presence of sodium chloride (Na$^+/$Cl$^-$) ions by molecular dynamics simulations. We find that the presence of a high surface density of Na$^+/$Cl$^-$ pairs changes the surface melting behaviour from step-wise to gradual melting. The ions lead to an overall increase of the thickness and the disorder of the QLL, and to a low-temperature roughening transition of the air-ice interface. The local molecular structure of the QLL is similar to that of liquid water, and the differences between the basal and primary prismatic surface are attenuated by the presence of Na$^+/$Cl$^-$ pairs. These changes modify the crystal growth rates of different facets and the solvation environment at the surface of sea-water ice with a potential impact on light scattering and environmental chemical reactions.

physics.chem-ph↗

arXiv2022

UV-Visible Absorption Spectra of Solvated Molecules by Quantum Chemical Machine Learning

Predicting UV-visible absorption spectra is essential to understanding photochemical processes and designing energy materials. Quantum chemical methods can deliver accurate calculations of UV-visible absorption spectra, but they are computationally expensive, especially for large systems or when one computes line shapes from thermal averages. Here, we present an approach to predicting UV-visible absorption spectra of solvated aromatic molecules by quantum chemistry (QC) and machine learning (ML). We show that a ML model, trained on the high-level QC calculation of the excitation energy of a set of aromatic molecules, can accurately predict the line shape of the lowest-energy UV-visible absorption band of several related molecules with less than 0.1 eV deviation with respect to reference experimental spectra. Applying linear decomposition analysis on the excitation energies, we unveil that our ML models probe vertical excitations of these aromatic molecules primarily by learning the atomic environment of their phenyl rings, which align with the physical origin of the $π\rightarrowπ^\star$ electronic transition. Our study provides an effective workflow that combines ML with quantum chemical methods to accelerate the calculations of UV-visible absorption spectra for various molecular systems.

physics.chem-ph↗

arXiv2021

Phonon dynamics and thermal conductivity of PtSe2 thin films: Impact of crystallinity and film thickness on heat dissipation

We present a comparative investigation of the influence of crystallinity and film thickness on the acoustic and thermal properties of 2D layered PtSe2 thin films of varying thickness (0.6-24 nm) by combining a set of experimental techniques, namely, frequency domain thermo-reflectance, low-frequency Raman and pump-probe coherent phonon spectroscopy. We find a 35% reduction in the cross-plane thermal conductivity of polycrystalline films with thickness larger than 12 nm compared to the crystalline films of the same thickness due to phonon grain boundary scattering. Density functional theory calculations are in good agreement with the experiments and further reveal the ballistic nature of cross-plane heat transport in PtSe2 up to a certain thickness (~20 nm). In addition, our experiments revealed strong interlayer interactions in PtSe2, short acoustic phonon lifetimes in the range of picoseconds, out-of-plane elastic constant C33=31.8 GPa and layer-dependent group velocity ranging from 1340 m/s in bilayer PtSe2 to 1873 m/s in 8 layers of PtSe2. The potential of tuning the lattice cross-plane thermal conductivity of layered 2D materials with the level of crystallinity and the real-time observation of coherent phonon dynamics, which have direct implications on the cooling and transport of electrons, open a new playground for research in 2D thermoelectric devices and provide guidelines for thermal management in 2D electronics.

cond-mat.mtrl-sci↗

arXiv2021

Engineering thermal transport across layered graphene-MoS2 superlattices

Layering two-dimensional van der Waals materials provides unprecedented control over atomic placement, which could enable tailoring of vibrational spectra and heat flow at the sub-nanometer scale. Here, using spatially-resolved ultrafast thermoreflectance and spectroscopy, we uncover the design rules governing cross-plane heat transport in superlattices assembled from monolayers of graphene (G) and MoS2 (M). Using a combinatorial experimental approach, we probe nine different stacking sequences: G, GG, MG, GGG, GMG, GGMG, GMGG, GMMG, GMGMG and identify the effects of vibrational mismatch, interlayer adhesion, and junction asymmetry on thermal transport. Pure G sequences display signatures of quasi-ballistic transport, whereas adding even a single M layer strongly disrupts heat conduction. The experimental data are described well by molecular dynamics simulations which include thermal expansion, accounting for the effect of finite temperature on the interlayer spacing. The simulations show that a change of only 1.5% in the layer separation can lead to a nearly 100% increase of the thermal resistance. Using these design rules, we experimentally demonstrate a 5-layer GMGMG superlattice with an ultralow effective cross-plane thermal conductivity comparable to air, paving the way for a new class of thermal metamaterials with extreme properties.

cond-mat.mes-hall↗

arXiv2021

Ultrahigh convergent thermal conductivity of carbon nanotubes from comprehensive atomistic modeling

Anomalous heat transport in one-dimensional nanostructures, such as nanotubes and nanowires, is a widely debated problem in condensed matter and statistical physics, with contradicting pieces of evidence from experiments and simulations. Using a comprehensive modeling approach, comprised of lattice dynamics and molecular dynamics simulations, we proved that the infinite length limit of the thermal conductivity of a (10,0) single-wall carbon nanotube is finite but this limit is reached only for macroscopic lengths due to thermal phonon mean free path of several millimeters. Our calculations showed that the extremely high thermal conductivity of this system at room temperature is dictated by quantum effects. Modal analysis showed that the divergent nature of thermal conductivity, observed in one-dimensional model systems, is suppressed in carbon nanotubes by anharmonic scattering channels provided by the flexural and optical modes with polarization in the plane orthogonal to the transport direction.

cond-mat.mes-hall↗