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We introduce CrystalFormer, a transformer-based autoregressive model specifically designed for space group-controlled generation of crystalline materials. The incorporation of space group symmetry significantly simplifies the crystal space, which is crucial for data and compute efficient generative modeling of crystalline materials. Leveraging the prominent discrete and sequential nature of the Wyckoff positions, CrystalFormer learns to generate crystals by directly predicting the species and locations of symmetry-inequivalent atoms in the unit cell. We demonstrate the advantages of CrystalFormer in standard tasks such as symmetric structure initialization and element substitution compared to conventional methods implemented in popular crystal structure prediction software. Moreover, we showcase the application of CrystalFormer of property-guided materials design in a plug-and-play manner. Our analysis shows that CrystalFormer ingests sensible solid-state chemistry knowledge and heuristics by compressing the material dataset, thus enabling systematic exploration of crystalline materials. The simplicity, generality, and flexibility of CrystalFormer position it as a promising architecture to be the foundational model of the entire crystalline materials space, heralding a new era in materials modeling and discovery., Comment: 26 pages, 11 figures

Recent advances in deep learning generative models (GMs) have created high capabilities in accessing and assessing complex high-dimensional data, allowing superior efficiency in navigating vast material configuration space in search of viable structures. Coupling such capabilities with physically significant data to construct trained models for materials discovery is crucial to moving this emerging field forward. Here, we present a universal GM for crystal structure prediction (CSP) via a conditional crystal diffusion variational autoencoder (Cond-CDVAE) approach, which is tailored to allow user-defined material and physical parameters such as composition and pressure. This model is trained on an expansive dataset containing over 670,000 local minimum structures, including a rich spectrum of high-pressure structures, along with ambient-pressure structures in Materials Project database. We demonstrate that the Cond-CDVAE model can generate physically plausible structures with high fidelity under diverse pressure conditions without necessitating local optimization, accurately predicting 59.3% of the 3,547 unseen ambient-pressure experimental structures within 800 structure samplings, with the accuracy rate climbing to 83.2% for structures comprising fewer than 20 atoms per unit cell. These results meet or exceed those achieved via conventional CSP methods based on global optimization. The present findings showcase substantial potential of GMs in the realm of CSP.

The rapid advancements in artificial intelligence (AI) are catalyzing transformative changes in atomic modeling, simulation, and design. AI-driven potential energy models have demonstrated the capability to conduct large-scale, long-duration simulations with the accuracy of ab initio electronic structure methods. However, the model generation process remains a bottleneck for large-scale applications. We propose a shift towards a model-centric ecosystem, wherein a large atomic model (LAM), pre-trained across multiple disciplines, can be efficiently fine-tuned and distilled for various downstream tasks, thereby establishing a new framework for molecular modeling. In this study, we introduce the DPA-2 architecture as a prototype for LAMs. Pre-trained on a diverse array of chemical and materials systems using a multi-task approach, DPA-2 demonstrates superior generalization capabilities across multiple downstream tasks compared to the traditional single-task pre-training and fine-tuning methodologies. Our approach sets the stage for the development and broad application of LAMs in molecular and materials simulation research.

Temperature and quantum effects induce the structural complexity of condensed hydrogen, and therefore they are expected to impact nontrivially the structures of clathrate hydrides. Exemplified by clathrate calcium hydride, we show through ab initio (path-integral) molecular dynamics simulations that these effects are indeed pivotal at 100-200 GPa. The quantum equation of states derived at 300 K suggests that the synthesized samples in previous experiments were berthollide-like CaH$_{6-\delta}$, with the stoichiometric defect $\delta$ increasing smoothly during decompression. The change of composition provides an explanation for the experimental observation of positive pressure dependence of superconducting T$_c$ below 165 GPa. Furthermore, we find significant proton diffusion in CaH$_{6-\delta}$ at 150-300 K, with the diffusion coefficient reaching 10$^{-7}$ cm$^{2}$/s. This suggests a coexistence of superconductivity and proton diffusion in clathrate hydrides. Our findings underline the importance of temperature, quantum and defect effects to the understandings of the structure and pertinent physics in high-T$_c$ superconducting clathrate hydrides., Comment: 9 pages, 3 figures

Lithium (Li) is a prototypical simple metal at ambient conditions, but exhibits remarkable changes in structural and electronic properties under compression. There has been intense debate about the structure of dense Li, and recent experiments offered fresh evidence for new yet undetermined crystalline phases near the enigmatic melting minimum region in the pressure-temperature phase diagram of Li. Here, we report on an extensive exploration of the energy landscape of Li using an advanced crystal structure search method combined with a machine learning approach, which greatly expands the scale of structure search, leading to the prediction of four complex Li crystal phases containing up to 192 atoms in the unit cell that are energetically competitive with known Li structures. These findings provide a viable solution to the newly observed yet unidentified crystalline phases of Li, and showcase the predictive power of the global structure search method for discovering complex crystal structures in conjunction with accurate machine-learning potentials., Comment: 6 pages, 4 figures

The recent discovery of the extraordinary-log (E-Log) criticality is a celebrated achievement in modern critical theory and calls for generalization. Using large-scale Monte Carlo simulations, we study the critical phenomena of plane defects in three- and four-dimensional O($n$) critical systems. In three dimensions, we provide the first numerical proof for the E-Log criticality of plane defects. In particular, for $n=2$, the critical exponent $\hat{q}$ of two-point correlation and the renormalization-group parameter $\alpha$ of helicity modulus conform to the scaling relation $\hat{q}=(n-1)/(2 \pi \alpha)$, whereas the results for $n \geq 3$ violate this scaling relation. In four dimensions, it is strikingly found that the E-Log criticality also emerges in the plane defect. These findings have numerous potential realizations and would boost the ongoing advancement of conformal field theory., Comment: 24 pages, 11 figures, including supplemental material. v2: expanded version. To appear in Physical Review Letters

There has been much recent progress on exotic surface critical behavior, yet the classical-quantum correspondence of special and extraordinary-log criticality remains largely unclear. Employing worm Monte Carlo simulations, we explore the surface criticality at an emergent superfluid-Mott insulator critical point in the Villain representation, which is believed to connect classical and quantum O(2) critical systems. We observe a special transition with the thermal and magnetic renormalization exponents $y_t=0.58(1)$ and $y_h=1.690(1)$ respectively, which are close to recent estimates from models with discrete spin variables. The existence of extraordinary-log universality is evidenced by the critical exponent $\hat{q}=0.58(2)$ from two-point correlation and the renormalization-group parameter $\alpha=0.28(1)$ from superfluid stiffness, which obey the scaling relation of extraordinary-log critical theory and recover the logarithmic finite-size scaling of critical superfluid stiffness in open-edge quantum Bose-Hubbard model. Our results bridge recent observations of surface critical behavior in the classical statistical mechanical models [Parisen Toldin, Phys. Rev. Lett. 126, 135701 (2021); Hu $et$ $al.$, $ibid.$ 127, 120603 (2021); Parisen Toldin $et$ $al.$, $ibid.$ 128, 215701 (2022)] and the open-edge quantum Bose-Hubbard model [Sun $et$ $al.$, Phys. Rev. B 106, 224502 (2022)]., Comment: 11 pages, 6 figures

The coexistence of superconductivity and topology holds the potential to realize exotic quantum states of matter. Here we report that superconductivity induced by high pressure in three thallium-based materials, covering the phase transition from a normal insulator (TlBiS2) to a topological insulator (TlBiSe2) through a Dirac semimetal (TlBiSeS). By increasing the pressure up to 60 GPa, we observe superconductivity phase diagrams with maximal Tc values at 6.0-8.1 K. Our density-functional theory calculations reveal topological surface states in superconductivity phases for all three compounds. Our study paves the path to explore topological superconductivity and topological phase transitions., Comment: 39 pages, 5+22 figures

The interaction between an ultrastrong laser and a cone-like target is an efficient approach to generate high power radiations like attosecond pulses and terahertz waves. The object is to study the $\gamma$-ray generation under this configuration with the help of 2D particle-in-cell simulations. It is deciphered that electrons experience three stages including injection, acceleration and scattering to emit high energy photons via nonlinear compton scattering (NCS). These spatial-separated attosecond $\gamma$-ray pulses own high peak brilliance ($>10^{22}$ photons/($\rm s\cdot\rm mm^2\cdot\rm mrad^2\cdot0.1\%BW$)) and high energy (6MeV) under the case of normalized laser intensity $a_0=30$ ($\mathrm{I=2\times10^{21}W/cm^2}$). Besides, the cone target turns out to be an order of magnitude more efficient in energy transfer compared with a planar one., Comment: 14 pages, 6 figures

Recognition of structure prototypes from tremendous known inorganic crystal structures has been an important subject beneficial for material science research and new materials design. The existing databases of inorganic crystal structure prototypes were mostly constructed by classifying materials in terms of the crystallographic space group information. Herein, we employed a distinct strategy to construct the inorganic crystal structure prototype database, relying on the classification of materials in terms of local atomic environments (LAE) accompanied by unsupervised machine learning method. Specifically, we adopted a hierarchical clustering approach onto all experimentally known inorganic crystal structures data to identify structure prototypes. The criterion for hierarchical clustering is the LAE represented by the state-of-the-art structure fingerprints of the improved bond-orientational order parameters and the smooth overlap of atomic positions. This allows us to build up a LAE-based Inorganic Crystal Structure Prototype Database (LAE-ICSPD) containing 15,613 structure prototypes with defined stoichiometries. In addition, we have developed a Structure Prototype Generator Infrastructure (SPGI) package, which is a useful toolkit for structure prototype generation. Our developed SPGI toolkit and LAE-ICSPD are beneficial for investigating inorganic materials in a global way as well as accelerating materials discovery process in the data-driven mode., Comment: 27 pages, 4 figures, with Supporting Information

The recent discovery of extraordinary-log universality has generated intense interest in classical and quantum boundary critical phenomena. Despite tremendous efforts, the existence of quantum extraordinary-log universality remains extremely controversial. Here, by utilizing quantum Monte Carlo simulations, we study the quantum edge criticality of a two-dimensional Bose-Hubbard model featuring emergent bulk criticality. On top of an insulating bulk, the open edges experience a Kosterlitz-Thouless-like transition into the superfluid phase when the hopping strength is sufficiently enhanced on edges. At the bulk critical point, the open edges exhibit the special, ordinary, and extraordinary critical phases. In the extraordinary phase, logarithms are involved in the finite-size scaling of two-point correlation and superfluid stiffness, which admit a classical-quantum correspondence for the extraordinary-log universality. Thanks to modern quantum emulators for interacting bosons in lattices, the edge critical phases might be realized in experiments., Comment: 11 pages, 6 figures

Crystal structure prediction has been a subject of topical interest, but remains a substantial challenge, especially for complex structures as it deals with the global minimization of the extremely rugged high-dimensional potential energy surface. In this manuscript, a symmetry-orientated divide-and-conquer scheme was proposed to construct a symmetry tree graph, where the entire search space is decomposed into a finite number of symmetry-dependent subspaces. An artificial intelligence-based symmetry selection strategy was subsequently devised to select the low-lying subspaces with high symmetries for global exploration and in-depth exploitation. Our approach can significantly simplify the problem of crystal structure prediction by avoiding exploration of the most complex P1 subspace on the entire search space and have the advantage of preserving the crystal symmetry during structure evolution, making it well suitable for predicting the complex crystal structures. The effectiveness of the method has been validated by successful prediction of the candidate structures of binary Lennard-Jones mixtures and high-pressure phase of ice, containing more than one hundred atoms in the simulation cell. The work, therefore, opens up an opportunity towards achieving the long-sought goal for crystal structure prediction of complex systems.

The geological conundrum of sulfur excess refers to the finding that predicted amounts of sulfur, in the form of SO2, discharged in volcanic eruptions much exceeds the sulfur available for degassing from the erupted magma. Exploring the source of the excess sulfur has been the subject of considerable interest. Here, from a systematic computational investigation of sulfur-oxygen compounds under pressure, a hitherto unknown S3O4 compound containing a mixture of sulfur oxidation states +II and +IV emerges and is predicted to be stabilized above a pressure of 79 GPa. We predict that S3O4 can be produced via multiple redox reactions involving subducted S-bearing minerals (e.g., sulfates and sulfides) at high pressure conditions relevant to the deep lower mantle, and conversely be decomposed into SO2 and S at shallow depths of Earth. Therefore, S3O4 can be considered as a key intermediate compound to promote the decomposition of sulfates to release SO2, which offers an alternative source of the excess sulfur released during explosive eruptions. These findings provide a possible resolution to the geological paradox of excess sulfur degassing and a viable mechanism for the understanding of S exchange between surface and the lower mantle for the deep sulfur cycle., Comment: 18 pages, 12 figures

Universality is a pillar of modern critical phenomena. The standard scenario is that the two-point correlation algebraically decreases with the distance $r$ as $g(r) \sim r^{2-d-\eta}$, with $d$ the spatial dimension and $\eta$ the anomalous dimension. Very recently, a logarithmic universality was proposed to describe the extraordinary surface transition of O($N$) system. In this logarithmic universality, $g(r)$ decays in a power of logarithmic distance as $g(r) \sim ({\rm ln}r)^{-\hat{\eta}}$, dramatically different from the standard scenario. We explore the three-dimensional $XY$ model by Monte Carlo simulations, and provide strong evidence for the emergence of logarithmic universality. Moreover, we propose that the finite-size scaling of $g(r,L)$ has a two-distance behavior: simultaneously containing a large-distance plateau whose height decays logarithmically with $L$ as $g(L) \sim ({\rm ln}L)^{-\hat{\eta}'}$ as well as the $r$-dependent term $g(r) \sim ({\rm ln}r)^{-\hat{\eta}}$, with ${\hat{\eta}'} \approx {\hat{\eta}}-1$. The critical exponent $\hat{\eta}'$, characterizing the height of the plateau, obeys the scaling relation $\hat{\eta}'=(N-1)/(2\pi \alpha)$ with the RG parameter $\alpha$ of helicity modulus. Our picture can also explain the recent numerical results of a Heisenberg system. The advances on logarithmic universality significantly expand our understanding of critical universality., Comment: 6+11 pages, 4+4 figures