arXiv subjects

Chris J. Pickard

Publications and source records attributed to Chris J. Pickard.

At least 37 records · Page 2Linked to original sources

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↗

arXiv2024

Predicted Complex Lithium Phases at Terapascal Pressures

We investigate the pressure-temperature ($p$-$T$) phase diagram of elemental lithium (Li) up to multiterapascal (TPa) pressures using ab-initio random structure search (AIRSS) and density functional theory (DFT). At zero temperature, beyond the high-pressure $Fd\bar{3}m$ diamond structure predicted in previous studies, we find eleven solid-state phase transitions to structures of greatly varying complexity, in addition to two structures that we calculate will become stable with sufficient temperature. The full $p$-$T$ dependence of the phase boundaries are computed within the vibrational harmonic approximation, and the solid-liquid melting line is calculated using ab-initio molecular dynamics simulations. Notably, between 39.1 TPa and 55.7 TPa, Li adopts an elaborate monoclinic structure with 46 atoms in the primitive unit cell, and between 71.9 TPa and $103$ TPa, an incommensurate host-guest phase of the Ba-IV type. We find that Li, hitherto predicted to be an electride at TPa pressures, abruptly loses its electride character above 16 TPa, reverting back to normal metallic behaviour with a corresponding rise in the Fermi-level electronic density of states (eDOS) and broadening of the electronic bands.

cond-mat.mtrl-sci↗

arXiv2024

On the dynamical stability of copper-doped lead apatite

The recent claim of room temperature superconductivity in a copper-doped lead apatite compound, called LK-99, has sparked remarkable interest and controversy. Subsequent experiments have largely failed to reproduce the claimed superconductivity, while theoretical works have identified multiple key features including strong electronic correlation, structural instabilities, and dopability constraints. A puzzling claim of several recent theoretical studies is that both parent and copper-doped lead apatite structures are dynamically unstable at the harmonic level, questioning decades of experimental reports of the parent compound structures and the recently proposed copper-doped structures. In this work, we demonstrate that both parent and copper-doped lead apatite structures are dynamically stable at room temperature. Anharmonic phonon-phonon interactions play a key role in stabilizing some copper-doped phases, while most phases are largely stable even at the harmonic level. We also show that dynamical stability depends on both volume and correlation strength, suggesting controllable ways of exploring the copper-doped lead apatite structural phase diagram. Our results fully reconcile the theoretical description of the structures of both parent and copper-doped lead apatite with experiment.

cond-mat.supr-con↗

arXiv2023

Microscopic theory of colour in lutetium hydride

Nitrogen-doped lutetium hydride has recently been proposed as a near-ambient-conditions superconductor. Interestingly, the sample transforms from blue to pink to red as a function of pressure, but only the pink phase is claimed to be superconducting. Subsequent experimental studies have failed to reproduce the superconductivity, but have observed pressure-driven colour changes including blue, pink, red, violet, and orange. However, discrepancies exist among these experiments regarding the sequence and pressure at which these colour changes occur. Given the claimed relationship between colour and superconductivity, understanding colour changes in nitrogen-doped lutetium hydride may hold the key to clarifying the possible superconductivity in this compound. Here, we present a full microscopic theory of colour in lutetium hydride, revealing that hydrogen-deficient LuH$_2$ is the only phase which exhibits colour changes under pressure consistent with experimental reports, with a sequence blue-violet-pink-red-orange. The concentration of hydrogen vacancies controls the precise sequence and pressure of colour changes, rationalising seemingly contradictory experiments. Nitrogen doping also modifies the colour of LuH$_2$ but it plays a secondary role compared to hydrogen vacancies. Therefore, we propose hydrogen-deficient LuH$_2$ as the key phase for exploring the superconductivity claim in the lutetium-hydrogen system. Finally, we find no phonon-mediated superconductivity near room temperature in the pink phase.

cond-mat.supr-con↗

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↗

arXiv2023

Introducing fluctuation-driven order into density functional theory using the quantum order-by-disorder framework

Density functional theory in the local or semi-local density approximation is a powerful tool for materials simulation, yet it struggles in many cases to describe collective electronic order that is driven by electronic interactions. In this work it is shown how arbitrary, fluctuation-driven electronic order may be introduced into density functional theory using the quantum order-by-disorder framework. This is a method of calculating the free energy correction due to collective spin and charge fluctuations about a state that hosts static order, in a self-consistent manner. In practical terms, the quantum order-by-disorder method is applied to the Kohn-Sham auxiliary system of density functional theory to give an order-dependent correction to the exchange-correlation functional. Calculation of fluctuation propagators within density functional theory renders the result fully first-principles. Two types of order are considered as examples -- fluctuation-driven superconductivity and spin nematic order -- and implementation schemes are presented in each case.

cond-mat.str-el↗

arXiv2023

Search for ambient superconductivity in the Lu-N-H system

Motivated by the recent report of room-temperature superconductivity at near-ambient pressure in N-doped lutetium hydride by Dasenbrock et al. [Nature 615, 244 (2023)], we performed a comprehensive, detailed study of the phase diagram of the Lu-N-H system, looking for superconducting phases. We combined ab initio crystal structure prediction with ephemeral data-derived interatomic potentials to sample over 200,000 different structures. Out of the more than 150 structures predicted to be metastable within $\sim$ 50 meV from the convex hull we identify 52 viable candidates for conventional superconductivity, for which we computed their superconducting properties from Density Functional Perturbation Theory. Although for some of these structures we do predict a finite superconducting $T_{\text{c}}$, none is even remotely compatible with room-temperature superconductivity as reported by Dasenbrock et al. Our work joins the broader community effort that has followed the report of near-ambient superconductivity, confirming beyond reasonable doubt that no conventional mechanism can explain the reported $T_{\text{c}}$ in Lu-N-H.

cond-mat.supr-con↗

arXiv2023

Ultra-fast yttrium hydride chemistry at high pressures via non-equilibrium states induced by x-ray free electron laser

Controlling the formation and stoichiometric content of desired phases of materials has become a central interest for the study of a variety of fields, notably high temperature superconductivity under extreme pressures. The further possibility of accessing metastable states by initiating reactions by x-ray triggered mechanisms over ultra-short timescales is enabled with the development of x-ray free electron lasers (XFEL). Utilizing the exceptionally high brilliance x-ray pulses from the EuXFEL, we report the synthesis of a previously unobserved yttrium hydride under high pressure, along with non-stoichiometric changes in hydrogen content as probed at a repetition rate of 4.5\,MHz using time-resolved x-ray diffraction. Exploiting non-equilibrium pathways we synthesize and characterize a hydride with yttrium cations in an \textit{A}15 structure type at 125\,GPa, predicted using crystal structure searches, with a hydrogen content between 4.0--5.75 hydrogens per cation, that is enthalpically metastable on the convex hull. We demonstrate a tailored approach to changing hydrogen content using changes in x-ray fluence that is not accessible using conventional synthesis methods, and reveals a new paradigm in metastable chemical physics.

cond-mat.mtrl-sci↗

arXiv2023

How to verify the precision of density-functional-theory implementations via reproducible and universal workflows

In the past decades many density-functional theory methods and codes adopting periodic boundary conditions have been developed and are now extensively used in condensed matter physics and materials science research. Only in 2016, however, their precision (i.e., to which extent properties computed with different codes agree among each other) was systematically assessed on elemental crystals: a first crucial step to evaluate the reliability of such computations. We discuss here general recommendations for verification studies aiming at further testing precision and transferability of density-functional-theory computational approaches and codes. We illustrate such recommendations using a greatly expanded protocol covering the whole periodic table from Z=1 to 96 and characterizing 10 prototypical cubic compounds for each element: 4 unaries and 6 oxides, spanning a wide range of coordination numbers and oxidation states. The primary outcome is a reference dataset of 960 equations of state cross-checked between two all-electron codes, then used to verify and improve nine pseudopotential-based approaches. Such effort is facilitated by deploying AiiDA common workflows that perform automatic input parameter selection, provide identical input/output interfaces across codes, and ensure full reproducibility. Finally, we discuss the extent to which the current results for total energies can be reused for different goals (e.g., obtaining formation energies).

cond-mat.mtrl-sci↗

arXiv2023

Automatic Differentiation for Orbital-Free Density Functional Theory

Differentiable programming has facilitated numerous methodological advances in scientific computing. Physics engines supporting automatic differentiation have simpler code, accelerating the development process and reducing the maintenance burden. Furthermore, fully-differentiable simulation tools enable direct evaluation of challenging derivatives - including those directly related to properties measurable by experiment - that are conventionally computed with finite difference methods. Here, we investigate automatic differentiation in the context of orbital-free density functional theory (OFDFT) simulations of materials, introducing PROFESS-AD. Its automatic evaluation of properties derived from first derivatives, including functional potentials, forces, and stresses, facilitates the development and testing of new density functionals, while its direct evaluation of properties requiring higher-order derivatives, such as bulk moduli, elastic constants, and force constants, offers more concise implementations compared to conventional finite difference methods. For these reasons, PROFESS-AD serves as an excellent prototyping tool and provides new opportunities for OFDFT.

physics.comp-ph↗

arXiv2022

The first-principles phase diagram of monolayer nanoconfined water

Water in nanoscale cavities is ubiquitous and of central importance to everyday phenomena in geology and biology. However, the properties of nanoscale water can be remarkably different from bulk, as shown e.g., by the anomalously low dielectric constant of water in nanochannels [1], near frictionless water flow [2], or the possible existence of a square ice phase [3]. Such properties suggest that nanoconfined water could be engineered for technological applications in nanouidics [4], electrolyte materials [5], and water desalination [6]. Unfortunately, challenges in experimentally characterising water on the nanoscale and the high cost of first-principles simulations have prevented the molecular level understanding required to control the behavior of water. Here we combine a range of computational approaches to enable a first-principles level investigation of a single layer of water within a graphene-like channel. We find that monolayer water exhibits surprisingly rich and diverse phase behavior that is highly sensitive to temperature and the van der Waals pressure acting within the nanochannel. In addition to multiple molecular phases with melting temperatures varying non-monotonically by over 400 degrees with pressure, we predict a hexatic phase, which is an intermediate between a solid and a liquid, and a superionic phase with a high electrical conductivity exceeding that of battery materials. Notably, this suggests that nanoconfinement could be a promising route towards superionic behavior at easily accessible conditions.

cond-mat.mtrl-sci↗

arXiv2022

Ephemeral data derived potentials for random structure search

Structure prediction has become a key task of the modern atomistic sciences, and depends on the rapid and reliable computation of the energy landscape. First principles density functional based calculations are highly reliable, faithfully describing the entire energy landscape. They are, however, computationally intensive and slow compared to interatomic potentials. Great progress has been made in the development of machine learning, or data derived, potentials, which promise to describe the entire energy landscape at first principles quality. However, compared to first principles approaches, their preparation can be time consuming and delay searching. Ab initio random structure searching (AIRSS) is a straightforward and powerful approach to structure prediction, based on the stochastic generation of sensible initial structures, and their repeated local optimisation. Here, a scheme, compatible with AIRSS, for the rapid construction of disposable, or ephemeral, data derived potentials (EDDPs) is described. These potentials are constructed using a homogeneous, separable manybody environment vector, and iterative neural network fits, sparsely combined through non-negative least squares. The approach is first tested on methane, boron nitride, elemental boron and urea. In the case of boron, an EDDP generated using data from small unit cells is used to rediscover the complex $γ$-boron structure without recourse to symmetry or fragments. Finally, an EDDP generated for silane (SiH$_4$) at 500 GPa enables the discovery of an extremely complex, dense, structure which significantly modifies silane's high pressure phase diagram. This has implications for the theoretical exploration for high temperature superconductivity in the dense hydrides, which have so far largely depended on searches in smaller unit cells.

cond-mat.mtrl-sci↗

arXiv2022

Unified chemical theory of structure and bonding in elemental metals

Most elemental metals under ambient conditions adopt simple structures such as BCC, FCC and HCP in specific groupings across the Periodic Table, and on compression, many of these elements undergo transitions to surprisingly complex structures, including open and low-symmetry phases not expected from conventional free-electron based theories of metals. First-principles calculations have been able to reproduce many observed structures and transitions, but a unified, predictive theory of bonding that underlies this behavior is not yet in hand. We propose a remarkably simple theory based on large-scale high-throughput calculations of the elements over a broad range of thermodynamic conditions. The results broaden the conventional concept of metallic bonding with a new perspective that both explains the stability of different simple structures as well as lower symmetry phases arising from electron localization at interstitial sites in these structures. The success of this simple framework reveals the important role of chemical interactions in governing the structures and electronic properties of simple metals.

cond-mat.mtrl-sci↗

arXiv2021

Accelerating Cathode Material Discovery through Ab Initio Random Structure Searching

The choice of cathode material in Li-ion batteries (LIBs) underpins their overall performance. Discovering new cathode materials is a slow process, and all major commercial cathode materials are still based on those identified in the 1990s. Materials discovery using high-throughput calculations has attracted great research interest, however, reliance on databases of existing materials begs the question of whether these approaches are applicable for finding truly novel materials. In this work, we demonstrate that ab-initio random structure searching (AIRSS), a first-principles structure prediction methods that does not rely on any pre-existing data, can locate low energy structures of complex cathode materials efficiently based only on chemical composition. We use AIRSS to explore three Fe-containing polyanion compounds as low-cost cathodes. Using known quaternary LiFePO4 and quinary LiFeSO4F cathodes as examples, we easily reproduce the known polymorphs, in addition to predicting other, hitherto unknown, low energy polymorphs, and even finding a new polymorph of LiFeSO4F which is more stable than the known ones. We then explore the phase space for Fe-containing fluoroxalates, predicting a range of redox-active phases that have yet to be experimentally synthesized, demonstrating the suitability of AIRSS as a tool for accelerating the discovery of novel cathode materials.

cond-mat.mtrl-sci↗

arXiv2021

Navigating the Ti-C-O and Al-C-O ternary systems through theory-driven discovery

Computational searches for new materials are naturally turning from binary systems, to ternary and other multicomponent systems, and beyond. Here, we select the industrially-relevant metals titanium and aluminium and report the results of an extensive structure prediction study on the ternary titanium-carbon-oxygen (Ti-C-O) and aluminium-carbon-oxygen (Al-C-O) systems. We map out for the first time the full phase stability of Ti-C-O and Al-C-O compounds using first-principles calculations, through simple, efficient and highly parallel random structure searching in conjunction with techniques based on complex network theory. These phase stabilities emerge naturally from our `data agnostic' approach, in which we map stable compounds without recourse to structural databases or other prior knowledge. A surprising find is the predicted ambient pressure stability of octet-rule-fulfiling titanium and aluminium carbonate: Ti(CO3)$_2$ and Al$_2$(CO3)$_3$, neither of which has to our knowledge been synthesised before. These materials could potentially act as carbon sequestering compounds. Our searches discover several additional stable and metastable ternary compounds supported by the Ti-C-O and Al-C-O systems.

cond-mat.mtrl-sci↗

arXiv2021

High-pressure phase behaviors of titanium dioxide revealed by a $Δ$-learning potential

Titanium dioxide has been extensively studied in the rutile or anatase phases, while its high-pressure phases are less well understood, despite that many are thought to have interesting optical, mechanical and electrochemical properties. First-principles methods such as density functional theory (DFT) are often used to compute the enthalpies of TiO$_2$ phases at 0~K, but they are expensive and thus impractical for long time-scale and large system-size simulations at finite temperatures. On the other hand, cheap empirical potentials fail to capture the relative stablities of the various polymorphs. To model the thermodynamic behaviors of ambient and high-pressure phases of TiO$_2$, we design an empirical model as a baseline, and then train a machine learning potential based on the difference between the DFT data and the empirical model. This so-called $Δ$-learning potential contains long-range electrostatic interactions, and predicts the 0~K enthalpies of stable TiO$_2$ phases that are in good agreement with DFT. We construct a pressure-temperature phase diagram of TiO$_2$ in the range $0<P<70$~GPa and $100<T<1500$~K. We then simulate dynamic phase transition processes, by compressing anatase at different temperatures. At 300~K, we observe predominantly anatase-to-baddeleyite transformation at about 20~GPa, via a martensitic two-step mechanism with highly ordered and collective atomic motion. At 2000~K, anatase can transform into cotunnite around 45-55~GPa in a thermally-activated and probabilistic manner, accompanied by diffusive movement of oxygen atoms. The pressures computed for these transitions show good agreement with experiments. Our results shed light on how to synthesize and stabilize high-pressure TiO$_2$ phases, and our method is generally applicable to other functional materials with multiple polymorphs.

cond-mat.mtrl-sci↗

arXiv2021

Visualising energy landscapes through manifold learning

Energy landscapes provide a conceptual framework for structure prediction, and a detailed understanding of their topological features is necessary to develop efficient methods for their exploration. The ability to visualise these surfaces is essential, but the high dimensionality of the corresponding configuration spaces makes this difficult. Here we present Stochastic Hyperspace Embedding and Projection (SHEAP), a method for energy landscape visualisation inspired by state-of-the-art algorithms for dimensionality reduction through manifold learning, such as t-SNE and UMAP. The performance of SHEAP is demonstrated through its application to the energy landscapes of Lennard-Jones clusters, solid-state carbon, and the quaternary system C+H+N+O. It produces meaningful and interpretable low-dimensional representations of these landscapes, reproducing well known topological features such as funnels, and providing fresh insight into their layouts. In particular, an intrinsic low dimensionality in the distribution of local minima across configuration space is revealed.

physics.comp-ph↗

arXiv2021

Structure and vibrational properties of methane up to 71 GPa

Single-crystal synchrotron X-ray diffraction, Raman spectroscopy, and first principles calculations have been used to identify the structure of the high-pressure (HP) phase of molecular methane above 20 GPa up to 71 GPa. The structure of HP phase is trigonal R3, which can be represented as a distortion of the cubic phase B, previously documented at 7-15 GPa and confirmed here. The positions of hydrogen atoms in HP phase have been 19 obtained from first principles calculations. The molecules occupy four different crystallographic sites in phases B and eleven sites in the HP phase, which result in splitting of molecular stretching modes detected in Raman spectroscopy and assigned here based on 22 a good agreement with the Raman spectra calculated from the first principles.

cond-mat.mtrl-sci↗