arXiv subjects

Peter I. C. Cooke

Publications and source records attributed to Peter I. C. Cooke.

8 recordsLinked to original sources

arXiv2026

Benchmarking empirical and machine-learned interatomic potentials using phase diagram predictions for Lead

We compare the predicted phase behaviour of lead (Pb) using three different interatomic potential models, including an embedded atom method (EAM), a modified embedded atom method (MEAM), and a neural network-based machine-learned model in the form of an ephemeral data-derived potential (EDDP). Using nested sampling and replica-exchange nested sampling simulations, we computed thermodynamic and structural properties at pressures up to 60 GPa, mapping both melting behaviour and solid-phase stability. Both the EAM and MEAM models predict the face-centred cubic (FCC) phase to remain stable up to approximately 60 GPa. In contrast, the EDDP model captures the experimentally-observed FCC-to-hexagonal close-packed (HCP) transition at around 15 GPa. These results highlight the importance of training data and model flexibility in accurately describing high-pressure phase behaviour, and demonstrate the effectiveness of nested sampling as a robust framework for exploring phase stability in materials. Particularly, the combination of nested sampling with modern machine-learned interatomic potentials - delivering near ab initio accuracy at tractable cost - opens the door to truly predictive and exhaustive exploration. EDDPs trained on diverse, out-of-equilibrium configurations appear particularly well suited to this task, offering a robust and transferable framework for unbiased phase discovery.

cond-mat.mtrl-sci↗

arXiv2026

Competing Hydrogenation Pathways to Metastable CaH$_6$ Revealed by Machine-Learning-Potential Molecular Dynamics

The synthesis of the high-$T_c$ superhydride CaH$_6$ has stimulated significant interest in understanding synthesis pathways for metastable hydrides. However, the microscopic mechanisms governing such hydrogenation reactions remain poorly understood. Here, we show that machine-learning potential molecular dynamics (MLP-MD) simulations can reproduce and distinguish competing reaction pathways leading to metastable and stable hydrides. By simulating hydrogenation reactions at CaH$_2$/H$_2$ and CaH$_4$/H$_2$ interfaces, we identify two distinct pathways that produce clathrate-type CaH$_6$ and A15-type CaH$_{5.75}$, respectively. CaH$_{5.75}$ lies on the convex hull but requires extensive Ca sublattice rearrangement and therefore forms only at elevated temperatures. In contrast, CaH$_6$ becomes kinetically accessible when CaH$_2$ is used as the precursor. The crystallographic compatibility between the Ca sublattice of CaH$_2$ and the bcc framework of CaH$_6$ enables a martensitic-like topotactic transformation that bypasses the reconstructive pathway leading to CaH$_{5.75}$. These results reveal how precursor structure and thermodynamic stability compete to determine superhydride formation pathways and demonstrate that machine-learning molecular dynamics can directly capture the kinetic selection of metastable phases in reactive materials systems.

cond-mat.mtrl-sci↗

arXiv2025

Metastability and high-Tc superconductivity in A15-type ternary hydride YSbH6 at moderate pressure

The discovery of high-temperature superconductors remains a central challenge in materials science. Hydrogen-rich compounds are among the most promising candidates, as they can exhibit phonon-mediated superconductivity at elevated critical temperatures, though their stabilization typically requires extreme pressures. % Here, we report the identification of YSbH$_{6}$ as a promising superconductor by a multi-stage high-throughput screening on ternary A15-type hydrides, followed by a high-throughput computational search of the Y--Sb--H system, accelerated by ephemeral data derived potentials. % The cubic $Pm\Bar{3}$ YSbH$_{6}$ phase exhibits a predicted critical temperature of 118\,K at 50\,GPa, among the highest $T_{\rm c}$ reported to date for an A15-hydride at this pressure. Thermodynamic analysis shows that YSbH$_{6}$ lies $\sim$100\,meV/atom above the convex hull at 50\,GPa, but only 26\,meV/atom above the hull at 120\,GPa, suggesting possible metastability and synthesis at similar high pressure conditions. The phase is dynamically stable over a wide pressure range (20--120\,GPa), displays kinetic stability at 50\,GPa and elastic stability at 20 and 50\,GPa, key ingredients for long-lived metastable behaviour at moderate pressures. % These results highlight YSbH$_{6}$ as a benchmark case illustrating the balance between high-$T_{\rm c}$ performance and limited thermodynamic stability in ternary hydrides, and underscore the importance of combined dynamic, thermodynamic, kinetic and elastic stability analyses for guiding experimental synthesis of metastable superconductors.

cond-mat.supr-con↗

arXiv2025

Vacancy-free cubic superconducting NbN enabled by quantum anharmonicity

Niobium nitride (NbN) is renowned for its exceptional mechanical, electronic, magnetic, and superconducting properties. The ideal 1:1 stoichiometric $δ$-NbN cubic phase, however, is known to be dynamically unstable, and repeated experimental observations have indicated that vacancies are necessary for its stabilization. In this work, we demonstrate that when the structure is fully relaxed and allowed to distort under quantum anharmonic effects, a previously unreported stable cubic phase with space group $P\bar{4}3m$ emerges - 65 meV/atom lower in free energy than the ideal $δ$ phase. This discovery is enabled by state-of-the-art first-principles calculations accelerated by machine-learned interatomic potentials. To evaluate the vibrational and superconducting properties with quantum anharmonic effects accounted for, we use the stochastic self-consistent harmonic approximation (SSCHA) and molecular dynamics spectral energy density (SED) methods. Electron-phonon coupling calculations based on the SSCHA phonon dispersion yield a superconducting transition temperature of $T_\text{c}$ = 20 K, which aligns closely with experimentally reported values for near-stoichiometric NbN. These findings challenge the long-held assumption that vacancies are essential for stabilizing cubic NbN and point to the potential of synthesizing the ideal 1:1 stoichiometric phase as a route to achieving enhanced superconducting performance in this technologically significant material.

cond-mat.supr-con↗

arXiv2024

Understanding solid nitrogen through machine learning simulation

We construct a fast, transferable, general purpose, machine-learning interatomic potential suitable for large-scale simulations of $N_2$. The potential is trained only on high quality quantum chemical molecule-molecule interactions, no condensed phase information is used. The potential reproduces the experimental phase diagram including the melt curve and the molecular solid phases of nitrogen up to 10 GPa. This demonstrates that many-molecule interactions are unnecessary to explain the condensed phases of $N_2$. With increased pressure, transitions are observed from cubic ($α-N_2$), which optimises quadrupole-quadrupole interactions, through tetragonal ($γ-N_2$) which allows more efficient packing, through to monoclinic ($λ-N_2$) which packs still more efficiently. On heating, we obtain the hcp 3D rotor phase ($β-N_2$) and, at pressure, the cubic $δ-N_2$ phase which contains both 3D and 2D rotors, tetragonal $δ^\star-N_2$ phase with 2D rotors and the rhombohedral $ε-N_2$. Molecular dynamics demonstrates where these phases are indeed rotors, rather than frustrated order. The model does not support the existence of the wide range of bondlengths reported for the complex $ι-N_2$ phase. The thermodynamic transitions involve both shifts of molecular centres and rotations of molecules. We simulate these phase transitions between finding that the onset of rotation is rapid whereas motion of molecular centres is inhibited and the cause of the observed sluggishness of transitions. Routine density functional theory calculations give a similar picture to the potential.

physics.comp-ph↗

arXiv2023

Developments and Further Applications of Ephemeral Data Derived Potentials

Machine-learned interatomic potentials are fast becoming an indispensable tool in computational materials science. One approach is the ephemeral data-derived potential (EDDP), which was designed to accelerate atomistic structure prediction. The EDDP is simple and cost-efficient. It relies on training data generated in small unit cells and is fit using a lightweight neural network, leading to smooth interactions which exhibit the robust transferability essential for structure prediction. Here, we present a variety of applications of EDDPs, enabled by recent developments of the open-source EDDP software. New features include interfaces to phonon and molecular dynamics codes, as well as deployment of the ensemble deviation for estimating the confidence in EDDP predictions. Through case studies ranging from elemental carbon and lead to the binary scandium hydride and the ternary zinc cyanide, we demonstrate that EDDPs can be trained to cover wide ranges of pressures and stoichiometries, and used to evaluate phonons, phase diagrams, superionicity, and thermal expansion. These developments complement continued success in accelerated structure prediction.

cond-mat.mtrl-sci↗

arXiv2022

Calculating the Raman Signal Beyond Perturbation Theory for a Diatomic Molecular Crystal

We calculate the eigenstates of a diatomic molecule in a range of model mean-field potentials, and evaluate the evolution of their associated Raman spectra with field strength. We demonstrate that dramatic changes in the appearance of the Raman spectrum for a diatomic molecule occur without any associated change in the symmetry of the surrounding potential. The limiting cases of the quantum eigenstates correspond, in the classical sense, to free rotation, and libration of well-oriented molecules. However, there are also many mixed modes which are neither rotons nor librons. The consequence for Raman spectroscopy is a series of complications - the non-harmonic potential splits the Raman active modes, and breaks the selection rules on forbidden modes. The mass-dependence of the various states is different - rotors, oscillators and reorientations have $1/m$, $1/\sqrt{m}$ and weaker mass dependence respectively. This may allow one to identify the character of the mode with isotope spectroscopy. However it is complicated by mixed modes and transitions between two different eigenstates with different character. We conclude that significant changes in the Raman spectrum of molecular systems are insufficient to demonstrate a phase transition since such changes can also occur in a fixed symmetry potential upon increasing field strength.

cond-mat.mtrl-sci↗

arXiv2020

The raman signal of a hindered rotor

We present a method for calculation of Raman modes of the quantum solid phase I solid hydrogen and deuterium. We use the mean-field assumption that the quantised excitations are localized on one molecule. This is done by explicit solution of the time-dependent Schroedinger equation in an angle-dependent potential, and direct calculation of the polarisation. We show that in the free-rotor limit, the H$_2$ and D$_2$ frequencies differ by a factor of 2, which evolves toward $\sqrt{2}$ as the modes acquire librational character due to stronger interactions. The ratio overshoots $\sqrt{2}$ if anharmonic terms weaken the harmonic potential. We also use density functional theory and molecular dynamics to calculate the E$_{2_g}$ optical phonon frequency and the Raman linewidths. The molecular dynamics shows that the molecules are not free rotors except at very low pressure and high temperature, and become like oscillators as phase II is approached. We fit the interaction strengths to experimental frequencies, but good agreement for intensities requires us to also include strong preferred-orientation and stimulated Raman effects between S$_0$(1) and S$_0$(0) contributions. The experimental Raman spectrum for phase II cannot be reproduced, showing that the mean-field assumption is invalid in that case.

cond-mat.mtrl-sci↗