arXiv subjects

Chris J. Pickard

Publications and source records attributed to Chris J. Pickard.

At least 19 recordsLinked to original sources

arXiv2026

A low-symmetry ground state of dense two-dimensional hydrogen

Hydrogen under pressure is an extremely complex system featuring molecular dissociation, metallization, and anomalous melting. While bulk hydrogen has been studied for nearly a century, its two-dimensional counterpart remains unexplored. Using first-principles structural searches and relaxations in supercells containing up to 512 atoms, we identify ground-state structures of dense two-dimensional hydrogen showing no evidence of long-range crystalline order over the simulated length scales. Within density functional theory, these structures have lower enthalpy than all crystalline candidates considered over an intermediate pressure interval between molecular and atomic crystals. This preference already emerges with classical nuclei and persists as the supercell size increases, while the dominant structure-factor peaks grow substantially more slowly than in the crystalline reference phases. Although crystals with very large primitive cells cannot be rigorously excluded, these findings support the possibility of a disordered ground state and identify dense two-dimensional hydrogen as a promising setting for investigating competition between crystallization and structural disorder.

cond-mat.mtrl-sci↗

arXiv2026

How reproducible are first-principles simulations of liquid water?

Liquid water is fundamentally important, and its accurate computer simulation has been the driving force for myriad methodological developments. Ab initio molecular dynamics with forces obtained from density functional theory (DFT) is now a standard tool widely used by researchers. However, we reveal that previous studies of liquid water using the same widely-used density functional (revPBE-D3) exhibit significant discrepancies with one another, varying by over 20% in the diffusion coefficient and 10% in the density, raising fundamental questions about reproducibility. By combining modern long-range machine-learning interatomic potentials that enable robust statistical sampling with carefully converged DFT training data, we resolve these discrepancies, achieving consensus across six diverse community codes. Our predictions differ markedly from previous literature: we show that most previous results overestimate the density and underestimate the diffusion coefficient of revPBE-D3 water due to basis set incompleteness and pseudopotential inconsistencies, coupled with limitations in statistical sampling (in some cases). These benchmark values provide a reliable reference for validating current and future implementations of DFT-based ab initio molecular dynamics. Reaching agreement establishes confidence and credibility and serves as a prerequisite for the systematic assessment of new density functionals and numerical approximations.

physics.chem-ph↗

arXiv2026

Building a physics-aware AI ecosystem for solid-state hydrogen storage materials

Hydrogen storage remains a central bottleneck for scalable hydrogen energy systems due to the multiscale and coupled nature of the thermodynamics, kinetics, and microstructural evolution of hydrogen storage materials (HSMs). Although artificial intelligence (AI) has accelerated materials discovery, current approaches remain constrained by fragmented data, limited physical consistency, and weak integration with experimental validation. Here, we propose a unified framework that integrates coherent data infrastructure, physics-grounded modeling, and AI-driven inverse design within a closed-loop discovery paradigm. By embedding physical constraints and experimental feedback, this approach enables adaptive, physically consistent optimization, thereby establishing a pathway toward autonomous, digital-twin-enabled discovery of HSMs.

cond-mat.mtrl-sci↗

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

First-principles evidence for conventional superconductivity in a quasicrystal approximant

Quasicrystals (QCs) host long-range order without translational symmetry, a regime in which the very foundations of BCS theory are not straightforwardly applicable, yet experiments on QCs and their approximant crystals (ACs) point to conventional, $s$-wave, electron-phonon coupled superconductivity. Here we test the predictive power of the electron-phonon framework in a representative decagonal AC from first principles. Using state-of-the-art \textit{ab initio} methods, we compute the superconducting properties of the recently discovered AC Al$_{13}$Os$_4$ and quantitatively reproduce its bulk $T_\text{c}$. This constitutes, to our knowledge, the first \textit{ab initio} determination of $T_\text{c}$ for an AC and establishes that the electron-phonon framework is predictive in these systems as well. Using the generalized quasichemical approximation for alloy modeling in the decagonal Al--Os family, we predict tunable superconductivity in Al$_{13}$Os$_{4-x}$Re$_x$ and Al$_{13}$Os$_{4-x}$Ir$_x$; in particular, Al$_{13}$Re$_4$ is dynamically stable and estimated to have a $T_\text{c}$ about 30% above Al$_{13}$Os$_4$. Finally, we discuss the role of ACs as high-fidelity proxies for their parent QCs. Although long-range quasiperiodicity may introduce subtle electronic features, our findings indicate that the key ingredients for superconductivity are already encoded in the local structural motifs preserved by the AC. This places the Al--Os and Al--Re families among the most promising candidates for the highest-$T_\text{c}$ quasicrystalline superconductivity.

cond-mat.supr-con↗

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↗

arXiv2026

High-pressure stabilization of Mg2IrH7: Structural proximity to high-Tc superconductivity

Mg$_2$IrH$_6$ is a metastable complex metal hydride with a predicted superconducting transition temperature as high as 170 K at ambient pressure. Following the synthesis of isomorphic, insulating Mg$_2$IrH$_5$ at low pressure, higher-pressure studies were conducted to investigate the phase behavior and compound formation in this system. X-ray diffraction and Raman spectroscopic measurements indicate that cubic Mg$_2$IrH$_7$ is stabilized above ca. 40 GPa and coexists with a related hexagonal hydride with likely composition near Mg$_2$IrH$_5$. Electrical transport measurements show that the cubic Mg$_2$IrH$_7$ is insulating, in agreement with ab initio predictions, and persists during room-temperature decompression until $\sim$20 GPa before reverting back to the cubic Mg$_2$IrH$_5$. The experimental results confirm ground-state structure predictions in the Mg-Ir-H system, and the formation of two nearly identical phases with surrounding compositions opens new opportunities to access superconducting Mg$_2$IrH$_6$ through non-equilibrium processing pathways.

cond-mat.supr-con↗

arXiv2026

Inverse Isotope Effect in the Ternary Perovskite Hydride SrPdH/D$_{2.9}$: A Signature of Quantum Zero-Point Fluctuations

Guided by first-principles calculations, we demonstrate superconductivity in the ternary perovskite hydride SrPdH$_{3-x}$, synthesized at low pressure. Structural characterization via neutron diffraction reveals the near-stoichiometric composition SrPdD$_{2.9(2)}$ with 96\% deuterium site occupancy. Subsequent transport and magnetic susceptibility measurements establish onset superconducting transitions at $T_\text{c} = \SI{2.1}{K} $ (H) and $T_\text{c} = \SI{2.2}{K} $ (D), exhibiting an inverse isotope effect that our first-principles calculations attribute predominantly to quantum zero-point motion. The excellent agreement between theory and experiment with respect to thermodynamic stability and superconducting properties provides important validation for theory-guided superconductor discovery. This work establishes superconductivity in the perovskite hydride structural prototype -- expanding the limited family of experimentally realized ternary hydride superconductors -- and demonstrates the importance of quantum nuclear motion on the accurate theoretical treatment of low-pressure hydride superconductors.

cond-mat.supr-con↗

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

Accelerating Crystal Structure Prediction Using Data-Derived Potentials: High-Pressure Binary Hydrides

Crystal structures can be predicted from first-principles using ab initio random structure searching AIRSS and density functional theory (DFT). AIRSS provides a method to sample the potential energy landscape and DFT provides a robust and accurate description of that landscape. Classical interatomic potentials can describe energy landscapes at a significantly lower computational cost, typically at the expense of robustness and accuracy. Modern machine-learning interatomic potentials offer a compromise, with greater robustness and accuracy than classical potentials at a fraction of the computational cost of DFT. In this work, we use Ephemeral Data-Derived Potentials EDDPs to perform accelerated AIRSS calculations for the binary hydrides at 100 GPa. Since the training data is generated iteratively using AIRSS, the searches can be performed with no prior knowledge of hydrides. These potentials allow for more diverse searches, sampling a wider range of compositions, larger unit cells, and orders-of-magnitude more structures. In addition to recovering many of the known structures, the searches reveal structures such as the hydrogen-rich phases of H$_{22}$(BrH), H$_{23}$Pb, and H$_{32}$Mg, supermolecular phases of H$_{25}$Cs and H$_{26}$Rn, and many substoichiometric variants of known hydrides. Our results indicate that using the current generation of pretrained universal MLIPs to search for novel high-pressure hydrides is less effective due to model instabilities or markedly slower inference speeds and highlight the necessity of generating new, targeted data to drive further discoveries.

cond-mat.mtrl-sci↗

arXiv2025

High-throughput superconducting $T_{\mathrm{c}}$ predictions through density of states rescaling

First principles computational methods can predict the superconducting critical temperature $T_{\mathrm{c}}$ of conventional superconductors through the electron-phonon spectral function. Full convergence of this quantity requires Brillouin zone integration on very dense grids, presenting a bottleneck to high-throughput screening for high $T_{\mathrm{c}}$ systems. In this work, we show that an electron-phonon spectral function calculated at low cost on a coarse grid yields accurate $T_{\mathrm{c}}$ predictions, provided the function is rescaled to correct for the inaccurate value of the density of states at the Fermi energy on coarser grids. Compared to standard approaches, the method converges rapidly and improves the accuracy of predictions for systems with sharp features in the density of states. This approach can be directly integrated into existing materials screening workflows, enabling the rapid identification of promising candidates that might otherwise be overlooked.

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↗

arXiv2025

Distillation of atomistic foundation models across architectures and chemical domains

Machine-learned interatomic potentials have transformed computational research in the physical sciences. Recent atomistic `foundation' models have changed the field yet again: trained on many different chemical elements and domains, these potentials are widely applicable, but comparably slow and resource-intensive to run. Here we show how distillation via synthetic data can be used to cheaply transfer knowledge from atomistic foundation models to a range of different architectures, unlocking much smaller, more efficient potentials. We demonstrate speed-ups of $> 10\times$ by distilling from one graph-network architecture into another, and $> 100\times$ by leveraging the atomic cluster expansion framework. We showcase applicability across chemical and materials domains: from liquid water to hydrogen under extreme conditions; from porous silica and a hybrid halide perovskite solar-cell material to modelling organic reactions. Our work shows how distillation can support the routine and computationally efficient use of current and future atomistic foundation models in real-world scientific research.

physics.comp-ph↗

arXiv2025

Prediction and Synthesis of Mg$_4$Pt$_3$H$_6$: A Metallic Complex Transition Metal Hydride Stabilized at Ambient Pressure

The low-pressure stabilization of superconducting hydrides with high critical temperatures ($T_c$s) remains a significant challenge, and experimentally verified superconducting hydrides are generally constrained to a limited number of structural prototypes. Ternary transition-metal complex hydrides (hydrido complexes)-typically regarded as hydrogen storage materials-exhibit a large range of compounds stabilized at low pressure with recent predictions for high-$T_c$ superconductivity. Motivated by this class of materials, we investigated complex hydride formation in the Mg-Pt-H system, which has no known ternary hydride compounds. Guided by ab initio structural predictions, we successfully synthesized a novel complex transition-metal hydride, Mg$_4$Pt$_3$H$_6$, using laser-heated diamond anvil cells. The compound forms in a body-centered cubic structural prototype at moderate pressures between 8-25 GPa. Unlike the majority of known hydrido complexes, Mg$_4$Pt$_3$H$_6$ is metallic, with formal charge described as 4[Mg]$^{2+}$.3[PtH$_2$]$^{2-}$. X-ray diffraction (XRD) measurements obtained during decompression reveal that Mg$_4$Pt$_3$H$_6$ remains stable upon quenching to ambient conditions. Magnetic-field and temperature-dependent electrical transport measurements indicate ambient-pressure superconductivity with $T_c$ (50%) = 2.9 K, in reasonable agreement with theoretical calculations. These findings clarify the phase behavior in the Mg-Pt-H system and provide valuable insights for transition-metal complex hydrides as a new class of hydrogen-rich superconductors.

cond-mat.supr-con↗

arXiv2024

The Critical Metallization of Hydrogen in Pressurized LaBeH8 Hydride

Behaviours of hydrogen, such as fluidity and metallicity, are crucial for our understanding of planetary interiors and the emerging field of high-temperature superconducting hydrides. These behaviours were discovered in complex phase diagrams of hydrogen and hydrides, however, the transition mechanism of behaviours driven by temperature, pressure and chemical compression remain unclear, particularly in the processes of metallization. Until now, a comprehensive theoretical framework to quantify atomization and metallization of hydrogen in phase diagram of hydrides has been lacking. In this study, we address this gap by combining molecular dynamics and electronic structure analysis to propose a theoretical framework, which clarify the content and properties of atomic hydrogen under various temperature and pressure conditions and chemical compression exerted by non-hydrogen elements in hydrides. Applying this framework to the superconducting hydride LaBeH8, we identify three general hydrogen orderings within its phase diagram: molecular, sublattice and warm hydrogens. During the phase transition from molecule to sublattice, hydrogen exhibits different properties from three general hydrogen orderings, such as fast superionicity, metallicity and unusual atomic content response to temperature. These abnormal behaviours were defined as the critical metallization of hydrogen, which not only suggests a potential synthesis route for the metastable phase but also provides valuable insights into the complex synthetic products of superconducting hydrides.

cond-mat.mtrl-sci↗

arXiv2024

Beyond theory driven discovery: hot random search and datum derived structures

Data driven methods have transformed the prospects of the computational chemical sciences, with machine learned interatomic potentials (MLIPs) speeding up calculations by several orders of magnitude. I reflect on theory driven, as opposed to data driven, discovery based on ab initio random structure searching (AIRSS), and then introduce two methods which exploit machine learning acceleration. I show how long high throughput anneals, between direct structural relaxation, enabled by ephemeral data derived potentials (EDDPs), can be incorporated into AIRSS to bias the sampling of challenging systems towards low energy configurations. Hot AIRSS (hot-AIRSS) preserves the parallel advantage of random search, while allowing much more complex systems to be tackled. This is demonstrated through searches for complex boron structures in large unit cells. I then show how low energy carbon structures can be directly generated from a single, experimentally determined, diamond structure. An extension to the generation of random sensible structures, candidates are stochastically generated and then optimised to minimise the difference between the EDDP environment vector and that of the reference diamond structure. The distance-based cost function is captured in an actively learned EDDP. Graphite, small nanotubes and caged, fullerene-like, structures emerge from searches using this potential, along with a rich variety of tetrahedral framework structures. Using the same approach, the pyrope, Mg$_3$Al$_2$(SiO$_4$)$_3$, garnet structure is recovered from a low energy AIRSS structure generated in a smaller unit cell with a different chemical composition. The relationship of this approach to modern diffusion model based generative methods is discussed.

physics.comp-ph↗

arXiv2024

Synthesis of Mg$_2$IrH$_5$: A potential pathway to high-$T_c$ hydride superconductivity at ambient pressure

Following long-standing predictions associated with hydrogen, high-temperature superconductivity has recently been observed in several hydride-based materials. Nevertheless, these high-$T_c$ phases only exist at extremely high pressures, and achieving high transition temperatures at ambient pressure remains a major challenge. Recent predictions of the complex hydride Mg$_{2}$IrH$_{6}$ may help overcome this challenge with calculations of high-$T_c$ superconductivity (65 K$~<~T_c~<~$ 170 K) in a material that is stable at atmospheric pressure. In this work, the synthesis of Mg$_{2}$IrH$_{6}$ was targeted over a broad range of $P$-$T$ conditions, and the resulting products were characterized using X-ray diffraction (XRD) and vibrational spectroscopy, in concert with first-principles calculations. The results indicate that the charge-balanced complex hydride Mg$_{2}$IrH$_{5}$ is more stable over all conditions tested up to ca 28 GPa. The resulting hydride is isostructural with the predicted superconducting Mg$_{2}$IrH$_{6}$ phase except for a single hydrogen vacancy, which shows a favorable replacement barrier upon insertion of hydrogen into the lattice. Bulk Mg$_{2}$IrH$_{5}$ is readily accessible at mild $P$-$T$ conditions and may thus represent a convenient platform to access superconducting Mg$_{2}$IrH$_{6}$ via non-equilibrium processing methods.

cond-mat.supr-con↗

arXiv2024

Feasible route to high-temperature ambient-pressure hydride superconductivity

A key challenge in materials discovery is to find high-temperature superconductors. Hydrogen and hydride materials have long been considered promising materials displaying conventional phonon-mediated superconductivity. However, the high pressures required to stabilize these materials have restricted their application. Here, we present results from high-throughput computation, considering a wide range of high-symmetry ternary hydrides from across the periodic table at ambient pressure. This large composition space is then reduced by considering thermodynamic, dynamic, and magnetic stability, before direct estimations of the superconducting critical temperature. This approach has revealed a metastable ambient-pressure hydride superconductor, Mg$_2$IrH$_6$, with a predicted critical temperature of 160 K, comparable to the highest temperature superconducting cuprates. We propose a synthesis route via a structurally related insulator, Mg$_2$IrH$_7$, which is thermodynamically stable above 15 GPa and discuss the potential challenges in doing so.

cond-mat.supr-con↗