arXiv subjects

Lara Kabalan

Publications and source records attributed to Lara Kabalan.

4 recordsLinked to original sources

arXiv2026

Microscopic origin of polytype-dependent melting in SiC revealed by machine-learning molecular dynamics

Predicting how crystal structure influences high-temperature stability remains a key challenge in materials modelling and design. Silicon carbide (SiC), one of the most thermally and chemically stable materials known, provides an ideal system for studying this problem because its many polytypes preserve similar local tetrahedral bonding while differing in long-range stacking geometry. Here, we combine phase-coexistence machine-learning molecular dynamics with finite-temperature phonon analysis, enabled by a fine-tuned MACE interatomic potential that accurately describes crystalline, high-temperature, and disordered configurations across multiple SiC polytypes. We identify a clear relative stability ordering, 3C > 2H > 9R, reflected consistently in structural disordering, interlayer sliding, and finite-temperature phonon spectra. Across all polytypes, melting initiates through the formation of short C-C contacts and carbon-rich local regions, followed by a progressive loss of tetrahedral Si-C connectivity. The reduced stability of the long-period 9R polytype is traced to low-frequency transverse-acoustic shear modes associated with relative bilayer sliding, which are already present in the 0 K phonon spectra and soften further at high temperature. These modes generate larger lateral bilayer displacements, linking enhanced interlayer sliding to local chemical disordering and ultimately melting. More broadly, our results show that high-temperature stability in polytypic covalent materials is governed not only by local bond strength, but also by stacking-dependent transverse dynamics.

cond-mat.mtrl-sci↗

arXiv2025

Computational tuning of the elastic properties of low- and high-entropy ultra-high temperature ceramics

Ultra-high temperature ceramics (UHTCs) represent a class of crystalline materials for extreme environments. They can withstand extremely high temperatures but are mechanically difficult to work with due to their inherent brittleness. Mixture compounds, in particular high-entropy mixtures, offer a pathway to tune the physical properties of UHTCs such as their elastic constants. Here we fine-tune the MACE-MPA-0 universal machine-learning potential on rocksalt carbide UHTCs containing group IV-V metals and demonstrate that not only do the elastic constants deviate from the rule of mixtures approximation in the high-entropy limit, but also in the low-entropy limit of binary and ternary mixtures. We find that this is caused by distortion imposed by the lattice mismatch, enabling the tuning of the physical properties of UHTC mixtures in both low- and high-entropy compounds. We identify a three-component mixture compound, HfCVCZrC, as the best balance between synthesizability and toughness, and apply our developed MACE-UHTC model to identify a range of non-equimolar candidate compositions of this compound which may enable the synthesis of a mixture UHTC with a Young's modulus up to 40 GPa below that of ZrC.

cond-mat.mtrl-sci↗

arXiv2024

Hessian QM9: A quantum chemistry database of molecular Hessians in implicit solvents

A significant challenge in computational chemistry is developing approximations that accelerate \emph{ab initio} methods while preserving accuracy. Machine learning interatomic potentials (MLIPs) have emerged as a promising solution for constructing atomistic potentials that can be transferred across different molecular and crystalline systems. Most MLIPs are trained only on energies and forces in vacuum, while an improved description of the potential energy surface could be achieved by including the curvature of the potential energy surface. We present Hessian QM9, the first database of equilibrium configurations and numerical Hessian matrices, consisting of 41,645 molecules from the QM9 dataset at the $\omega$B97x/6-31G* level. Molecular Hessians were calculated in vacuum, as well as water, tetrahydrofuran, and toluene using an implicit solvation model. To demonstrate the utility of this dataset, we show that incorporating second derivatives of the potential energy surface into the loss function of a MLIP significantly improves the prediction of vibrational frequencies in all solvent environments, thus making this dataset extremely useful for studying organic molecules in realistic solvent environments for experimental characterization.

physics.chem-ph↗

arXiv2024

Roadmap on Data-Centric Materials Science

Science is and always has been based on data, but the terms "data-centric" and the "4th paradigm of" materials research indicate a radical change in how information is retrieved, handled and research is performed. It signifies a transformative shift towards managing vast data collections, digital repositories, and innovative data analytics methods. The integration of Artificial Intelligence (AI) and its subset Machine Learning (ML), has become pivotal in addressing all these challenges. This Roadmap on Data-Centric Materials Science explores fundamental concepts and methodologies, illustrating diverse applications in electronic-structure theory, soft matter theory, microstructure research, and experimental techniques like photoemission, atom probe tomography, and electron microscopy. While the roadmap delves into specific areas within the broad interdisciplinary field of materials science, the provided examples elucidate key concepts applicable to a wider range of topics. The discussed instances offer insights into addressing the multifaceted challenges encountered in contemporary materials research.

cond-mat.mtrl-sci↗