arXiv subjects

Yanming Ma

Publications and source records attributed to Yanming Ma.

At least 19 recordsLinked to original sources

arXiv2026

High-Pressure Crystal Structure Database

High-pressure research is a productive route to new structures and emergent properties. However, crucial high-pressure structural information remains highly fragmented across individual publications and heterogeneous computational repositories. This fragmentation creates a major bottleneck for data-driven materials design. To bridge this gap, we introduce the High-Pressure Crystal Structure Database (HPCSD), a traceable, pressure-resolved repository that integrates experimental and theoretical high-pressure structures. HPCSD is constructed from two complementary data streams: elemental high-pressure phases and a searchable configuration space of stable and metastable phases generated via CALYPSO crystal structure prediction. To ensure rigorous comparability, all retained structures underwent re-optimization under a unified density functional theory (DFT) framework , with continuous enthalpy curves systematically generated specifically for the elemental phases across their stability fields. The initial release encompasses 77,346 consistently evaluated structural entries spanning 89 elements. An analysis reveals that pressure-induced polymorphism is ubiquitous and exhibits pronounced family-dependent trends. Structural diversity is strongly influenced by an element's electronic adaptability , with the greatest structural complexity emerging at intermediate rather than highest pressures. By providing standardized, reusable, and rigorously evaluated high-pressure structural data, HPCSD establishes a robust infrastructure to accelerate experimental phase identification, facilitate cross-study thermodynamic comparisons, and support the development of machine-learning interatomic potentials and generative models for high-pressure systems.

cond-mat.mtrl-sci↗

arXiv2026

Interface-dependent Phase Transitions and Ultrafast Hydrogen Superionic Diffusion of H2O Ice

High-pressure experiments using diamond anvils have revealed novel properties and phase behavior of H2O under extreme conditions. When contained in diamond-anvil cells, the H2O samples are usually in direct contact with the diamond anvil. However, the extent to which this interface affects measured pressure-induced properties and behavior, including coexistence lines of ice phases, remains unknown. Combining artificial neural network methods and active learning schemes with large-scale molecular dynamics simulations, we elucidate the interfacial effects on various properties of high-pressure ice phases, including superionic states, solid-solid phase transitions, and melting. The results reveal that the presence of this interface can significantly lower the hydrogen superionic transition temperature. Remarkably, the interface can also induce a spontaneous transition from bcc- to fcc-based ice following the inverse Bain mechanism. Further, we redefined a stability field of bcc and fcc ice below the melting line and predicted the existence of fcc ice at much lower pressures than previously thought. More broadly, the results emphasize the importance of interface effects in understanding a wide range of phenomena reported in experimental studies of ice under pressure, including inconsistencies between theoretical and experimental results of this fundamental system.

cond-mat.mtrl-sci↗

arXiv2026

Pressure-tuned double-dome superconductivity in KZnBi with honeycomb lattice

Materials with honeycomb lattice structures exhibit unique electronic properties arising from their distinctive atomic arrangements. Their weakly coupled nature facilitates modulation by external stimuli, which leads to a diverse range of physical phenomena, particularly superconductivity. Here, we report the discovery of a pressure-induced M-shaped double-dome superconducting phase in KZnBi with honeycomb lattice. Under applied pressure, the superconducting transition temperature Tc increases sharply and reaches a maximum value of 7 K at approximately 2.5 GPa. Following a structural phase transition from the ambient-pressure P63/mmc phase to the high-pressure Pnma phase, Tc gradually decreases. Further compression induces an electronic transition near 7 GPa, accompanied by an unexpected reentrant superconducting phase with a higher Tc of 8 K. Our theoretical calculations indicate that KZnBi undergoes a transition from a Dirac band structure to a strong topological semimetal state following the structural phase transition. These findings establish KZnBi as an ideal platform for investigating the diverse structural manifestations and intrinsic phenomena of the honeycomb lattice, demonstrating the fundamental importance of honeycomb structures in advancing superconductivity research.

cond-mat.supr-con↗

arXiv2026

Isotropic Superconductivity in Room-temperature Superconductor LaSc$_{2}$H$_{24}$

The discovery of LaSc$_{2}$H$_{24}$ represents a milestone in the quest for room-temperature superconductivity, yet the microscopic mechanism underlying its superior performance remains unclear. Through a comprehensive revisit of theoretical calculations, we uncover a pivotal transition from the anisotropic two-gap superconductivity of LaH$_{10}$ to the isotropic single-gap superconductivity in LaSc$_{2}$H$_{24}$ upon the introduction of scandium, thereby enhancing the superconducting critical temperature ($T_\mathrm{c}$). This enhancement is rooted in a critical dual role of Sc $3d$ electrons: i) the Sc-derived Jahn-Teller effect promotes hydrogen metallization via the elongation of specific interlayer H-H bonds and enhances electron-phonon coupling (EPC) through the softening of associated phonon modes; ii) Sc $3d$ electrons reconstruct the electronic structure into an MgB$_{2}$-like configuration, generating novel Sc-H-Sc $\sigma$- and $\pi$-bonding states with EPC strengths comparable to LaH$_{10}$. Crucially, the pronounced hybridization between Sc and the hydrogen cages effectively unifies these two contributions on the Fermi surface. This Sc-induced gap unification bridges the high-EPC H-H states with widespread Sc-H states, establishing an isotropic single-gap nature with a large overall EPC strength. Our findings identify this Sc-induced gap unification as the fundamental mechanism for achieving room-temperature superconductivity in LaSc$_{2}$H$_{24}$, offering a theoretical blueprint for the future design of superior superconducting hydrides.

cond-mat.supr-con↗

arXiv2025

Spin Orientation Driven Polarization in Collinear Magnets

In a collinear magnet, the predominant magnetic moments are collectively aligned along a specific spatial orientation, and this alignment may yield intriguing phenomena such as spin orientation driven polarization. It is well known that spin orientation driven polarization is a relativistic effect that widely occurs in various type-II multiferroics. However, a universal theory that describes such a phenomenon and directs the corresponding materials discovery is lacking. Here, we revisit the magnetic structures of collinear magnets and explore the spin-orientation-dependent phenomena therein. Based on symmetry principles, we analyze the spin point groups (SPGs) that are associated with collinear magnets in the non-relativistic regime, demonstrate how relativistic spin-orbit interaction reduces each SPG to various magnetic point groups that are associated with different magnetic alignments, and classify the SPGs with respect to spin orientation driven polarization. We employ our theory to elucidate the mechanisms of spin orientation driven polarization in a variety of type-II multiferroics. Combined with first-principles simulations, we further show that polarization may be driven in nonpolar collinear antiferromagnets (e.g., CuFeS$_2$) by reorienting their magnetic alignments. Our theory provides guidelines for designing and discovering materials with spin orientation driven polarization, which will benefit the development of spintronics based on type-II multiferroics and related materials.

cond-mat.mtrl-sci↗

arXiv2025

Potential-Barrier Affinity Effect in Solid Systems

Electron accumulation in interatomic regions is a fundamental quantum phenomenon dictating chemical bonding and material properties, yet its origin remains elusive across disciplines. Here, we report a quantum accumulation effect -- potential-barrier affinity (PBA) -- revealed by solving the Schr\"odinger equation for a crystalline potential. PBA effect drives significant interatomic electron accumulation when electron energy exceeds the barrier maximum. This effect essentially enhances interatomic electron density, governing microstructures and properties of condensed matter. Our theory overturns the traditional wisdom that the interstitial electron localization in electride requires potential-well constraints or hybrid orbitals, and it serves as the fundamental mechanism underlying the formation of conventional solid bonding. This work delivers a paradigm shift in understanding electron distribution and establishes a theoretical foundation for the microscopic design of material properties.

cond-mat.mtrl-sci↗

arXiv2025

Diamond quantum sensing at record high pressure up to 240 GPa

Quantum sensing utilizing nitrogen-vacancy (NV) centers in diamond has emerged as a transformative technology for probing magnetic phase transition1-4, evidencing Meissner effect of superconductors1,5-9, and visualizing stress distribution3,9 under extreme conditions. Recent development in NV configurations and hydrostatic environments have raised the operational pressures of NV centers to 140 GPa2,6,10,11, but substantial challenges remain in extending sensing capabilities into multi-megabar range, critical for research in hydrogen-rich superconductors like La-Sc-H ($T_{\text{c}}$ of 271-298 K at 195-266 GPa)12 and evolution of minerals near Earth's core13. Here we report the fabrication of shallow NV centers through ion implantation followed by high-pressure and high-temperature (HPHT) annealing, leading to increased density, improved coherence, and mitigated internal stresses, a pre-requisite for reducing their degradation under compression. This NV magnetometry enable breakthrough of pressure capabilities exceeding 240 GPa, constrained by structural integrity of the 50 um diamond anvils, suggesting that the untapped pressure limit may enable further advancements with smaller cutlets or more robust diamonds. We present compelling evidence of the Meissner effect and trapped flux at record-high pressure of 180 GPa for superconducting transition in elemental titanium (Ti) as benchmark, establishing a solid foundation for high-pressure magnetometry in exploring complex quantum phenomena at previously unreachable pressures.

quant-ph↗

arXiv2025

The nature of polar distortions in ferroelectrics

Polar distortion, the collective off-center displacements of atoms, is a fingerprint of a ferroelectric that governs its properties and functionalities. Since the 1970s, the concepts of proper, improper and triggered ferroelectrics have been established to shed light on a diversity of polar distortion mechanisms. Such concepts assign a single nature to polar distortion and are helpful to interpret how polar distortions occur in conventional ferroelectrics such as barium titanate. However, applying these concepts to complex ferroelectrics (e.g., polar orthorhombic hafnia) is notoriously challenging and can yield highly controversial arguments. Here we resolve this issue by developing a tailor-made graph theory for clarifying the nature of polar distortions in complex ferroelectrics, which emphasizes that polar distortions in such ferroelectrics usually exhibit multiple natures among proper, improper and triggered characteristics. We demonstrate the robustness of our theory by working with perovsktie superlattices and polar orthorhombic hafnia (i.e., two representative cases). We successfully identify the mixed proper-improper nature in perovsktite superlattices and reconcile the controversy on polar orthorhombic hafnia by confirming its mixed trigger-improper nature. Our work will definitely lead to a revisitation of concepts in ferroelectric physics and provide opportunities for discovering novel ferroelectrics and related phenomena.

cond-mat.mtrl-sci↗

arXiv2025

Room-Temperature Superconductivity at 298 K in Ternary La-Sc-H System at High-pressure Conditions

Room-temperature superconductor has been a century-long dream of humankind. Recent research on hydrogen-based superconductors (e.g., CaH6, LaH10, etc.) at high-pressure conditions lifts the record of superconducting critical temperature (Tc) up to ~250 kelvin. We here report the experimental synthesis of the first-ever room-temperature superconductor by compression on a mixture of La-Sc alloy and ammonia borane at pressures of 250-260 gigapascals (GPa) via a diamond anvil cell by a laser-heating technique. Superconductivity with an onset temperature of 271-298 kelvin at 195-266 GPa is observed by the measurement of zero electrical resistance and the suppression of Tc under applied magnetic fields. Synchrotron X-ray diffraction data unambiguously reveal that this superconductor crystallizes in a hexagonal structure with a stoichiometry LaSc2H24, in excellent agreement with our previous prediction1. Through thirteen reproducible experimental runs, we provide solid evidence of the realization of a room-temperature superconductor for the first time, marking a milestone in the field of superconductivity.

cond-mat.supr-con↗

arXiv2025

Bridging Theory and Experiment in Materials Discovery: Machine-Learning-Assisted Prediction of Synthesizable Structures

Even though thermodynamic energy-based crystal structure prediction (CSP) has revolutionized materials discovery, the energy-driven CSP approaches often struggle to identify experimentally realizable metastable materials synthesized through kinetically controlled pathways, creating a critical gap between theoretical predictions and experimental synthesis. Here, we propose a synthesizability-driven CSP framework that integrates symmetry-guided structure derivation with a Wyckoff encode-based machine-learning model, allowing for the efficient localization of subspaces likely to yield highly synthesizable structures. Within the identified promising subspaces, a structure-based synthesizability evaluation model, fine-tuned using recently synthesized structures to enhance predictive accuracy, is employed in conjunction with ab initio calculations to systematically identify synthesizable candidates. The framework successfully reproduces 13 experimentally known XSe (X = Sc, Ti, Mn, Fe, Ni, Cu, Zn) structures, demonstrating its effectiveness in predicting synthesizable structures. Notably, 92,310 structures are filtered from the 554,054 candidates predicted by GNoME, exhibiting great potential for promising synthesizability. Additionally, eight thermodynamically favorable Hf-X-O (X = Ti, V, and Mn) structures have been identified, among which three HfV$_2$O$_7$ candidates exhibit high synthesizability, presenting viable candidates for experimental realization and potentially associated with experimentally observed temperature-induced phase transitions. This work establishes a data-driven paradigm for machine-learning-assisted inorganic materials synthesis, highlighting its potential to bridge the gap between computational predictions and experimental realization while unlocking new opportunities for the targeted discovery of novel functional materials.

cond-mat.mtrl-sci↗

arXiv2025

Self-Optimizing Machine Learning Potential Assisted Automated Workflow for Highly Efficient Complex Systems Material Design

Machine learning interatomic potentials have revolutionized complex materials design by enabling rapid exploration of material configurational spaces via crystal structure prediction with ab initio accuracy. However, critical challenges persist in ensuring robust generalization to unknown structures and minimizing the requirement for substantial expert knowledge and time-consuming manual interventions. Here, we propose an automated crystal structure prediction framework built upon the attention-coupled neural networks potential to address these limitations. The generalizability of the potential is achieved by sampling regions across the local minima of the potential energy surface, where the self-evolving pipeline autonomously refines the potential iteratively while minimizing human intervention. The workflow is validated on Mg-Ca-H ternary and Be-P-N-O quaternary systems by exploring nearly 10 million configurations, demonstrating substantial speedup compared to first-principles calculations. These results underscore the effectiveness of our approach in accelerating the exploration and discovery of complex multi-component functional materials.

cond-mat.mtrl-sci↗

arXiv2025

Electrically switchable non-relativistic Zeeman spin splittings in collinear antiferromagnets

Magnetic or electrical manipulation of electronic spin is elementary for spin-based logic, computing, and memory, where the latter is a low-power manipulation scheme. Rashba-like spin splittings stemming from spin-orbit interaction (SOI) enable electric-field manipulation of spin, but the relativistic SOI causes spin relaxations and yields dissipative transport of spin-encoded information. Recent works suggest the occurrence of electric-field switchable non-relativistic Zeeman spin splittings (NRZSSs) in collinear antiferromagnets -- allowing for electrical manipulation of spin in the non-relativistic regime; yet, a theory elucidating the mechanisms for these NRZSSs and guiding the materials discovery remains missing. Here, we develop such a theory by analyzing the symmetries of spin point groups. We highlight the linear magnetoelectric and bilinear piezomagnetoelectric mechanisms for NRZSSs that depend linearly on electric field and are electrically switchable. First-principles calculations further confirm that FeOOH and NaMnP showcase such NRZSSs. Our theory provides guidelines for discovering light-element collinear antiferromagnets with electrically switchable NRZSSs, which are promising for the design of high-performance spin-based devices.

cond-mat.mtrl-sci↗

arXiv2025

Discovery of High-Temperature Superconducting Ternary Hydrides via Deep Learning

The discovery of novel high-temperature superconductor materials holds transformative potential for a wide array of technological applications. However, the combinatorially vast chemical and configurational search space poses a significant bottleneck for both experimental and theoretical investigations. In this study, we employ the design of high-temperature ternary superhydride superconductors as a representative case to demonstrate how this challenge can be well addressed through a deep-learning-driven theoretical framework. This framework integrates high-throughput crystal structure exploration, physics-informed screening, and accurate prediction of superconducting critical temperatures. Our approach enabled the exploration of approximately 36 million ternary hydride structures across a chemical space of 29 elements, leading to the identification of 144 potential high-Tc superconductors with predicted Tc > 200 K and superior thermodynamic stability at 200 GPa. Among these, 129 compounds spanning 27 novel structural prototypes are reported for the first time, representing a significant expansion of the known structural landscape for hydride superconductors. This work not only greatly expands the known repertoire of high-Tc hydride superconductors but also establishes a scalable and efficient methodology for navigating the complex landscape of multinary hydrides.

cond-mat.supr-con↗

arXiv2025

Observation of molecular and polymeric nitrogen stuffed NaCl ionic layers

Sodium chloride (NaCl), a ubiquitous and chemically stable compound, has been considered inert under ambient conditions. Its typical B1 structure is highly isotropic without preferential direction, favoring the growth of a three-dimensional network of strong Na-Cl ionic bonds. Here, we employ first-principles structural searching and synchrotron X-ray diffraction to unravel an unexpected chemical reaction between NaCl and N2 to produce a hybrid salt-NaCl(N2)2, where N2 molecules break the isotropic NaCl structure into two-dimensional layers upon synthesis at 50 GPa. In contrast to the insulating properties of pristine NaCl, the electronic bandgap of the N2-stuffed NaCl narrowed to 1.8 eV, becoming an indirect bandgap semiconductor. Further compression to 130 GPa induced the polymerization of N atoms into zigzag N-chains. Our findings not only demonstrate the possibility of unusual N-chemistry under extreme conditions, but also suggest a feasible approach for the design of layered NaCl frameworks to modulate the polymerization of nitrogen.

cond-mat.mtrl-sci↗

arXiv2024

CrystalFlow: A Flow-Based Generative Model for Crystalline Materials

Deep learning-based generative models have emerged as powerful tools for modeling complex data distributions and generating high-fidelity samples, offering a transformative approach to efficiently explore the configuration space of crystalline materials. In this work, we present CrystalFlow, a flow-based generative model specifically developed for the generation of crystalline materials. CrystalFlow constructs Continuous Normalizing Flows to model lattice parameters, atomic coordinates, and/or atom types, which are trained using Conditional Flow Matching techniques. Through an appropriate choice of data representation and the integration of a graph-based equivariant neural network, the model effectively captures the fundamental symmetries of crystalline materials, which ensures data-efficient learning and enables high-quality sampling. Our experiments demonstrate that CrystalFlow achieves state-of-the-art performance across standard generation benchmarks, and exhibits versatile conditional generation capabilities including producing structures optimized for specific external pressures or desired material properties. These features highlight the model's potential to address realistic crystal structure prediction challenges, offering a robust and efficient framework for advancing data-driven research in condensed matter physics and material science.

cond-mat.mtrl-sci↗

arXiv2024

Fast and stable tight-binding framework for nonlocal kinetic energy density functional reconstruction in orbital-free density functional calculations

Nonlocal kinetic energy density functionals (KEDFs) with density-dependent kernels are currently the most accurate functionals available for orbital-free density functional theory (OF-DFT) calculations. However, despite advances in numerical techniques and using only (semi)local density-dependent kernels, nonlocal KEDFs still present substantial computational costs in OF-DFT, limiting their application in large-scale material simulations. To address this challenge, we propose an efficient framework for reconstructing nonlocal KEDFs by incorporating the density functional tight-binding approach, in which the energy functionals are simplified through a first-order functional expansion based on the superposition of free-atom electron densities. This strategy allows the computationally expensive nonlocal kinetic energy and potential calculations to be performed only once during the electron density optimization process, significantly reducing computational overhead while maintaining high accuracy. Benchmark tests using advanced nonlocal KEDFs, such as revHC and LDAK-MGPA, on standard structures including Li, Mg, Al, Ga, Si, III-V semiconductors, as well as Mg$_{50}$ and Si$_{50}$ clusters, demonstrate that our method achieves orders-of-magnitude improvements in efficiency, providing a cost-effective balance between accuracy and computational speed. Additionally, the reconstructed functionals exhibit improved numerical stability for both bulk and finite systems, paving the way for developing more sophisticated KEDFs for realistic material simulations using OF-DFT.

cond-mat.mtrl-sci↗

arXiv2024

Theory of polarization-switchable electrical conductivity anisotropy in nonpolar semiconductors

The anisotropic propagation of particles is a fundamental transport phenomenon in solid state physics. As for crystalline semiconductors, the anisotropic charge transport opens novel designing routes for electronic devices, where the electrical or magnetic manipulation of anisotropic resistance provides essential guarantees. Motivated by the concept of anisotropic magnetoresistance, we develop an original theory on the electrically manipulatable anisotropic electroresistance. We show that piezoelectrics and ferroelectrics may showcase polarization-dependent anisotropic electrical conductivities between two perpendicular directions and the electrical conductivity anisotropy (ECA) is switchable by flipping the polarization. By symmetry analysis, we identify several point groups hosting the polarization-switchable ECA. These point groups simultaneously enable polarization-reversal induced conductivity change along specific directions, akin to the tunnelling electroresistance in ferroelectric tunnel junctions. First-principles-based conductivity calculations predict that piezoelectric AlP and ferroelectric KH$_2$PO$_4$ are two good semiconductors having such exotic charge transport. Our theory can motivate the design of intriguing anisotropic electronic devices (e.g., anisotropic memristor and field effect transistor).

cond-mat.mtrl-sci↗

arXiv2024

Data-driven design of high-temperature superconductivity among ternary hydrides under pressure

Recently, ternary clathrate hydrides are promising candidates for high-temperature superconductor. However, it is a formidable challenge to effectively hunt high-temperature superconductivity among multinary hydrides due to the expensive computational cost associated with large unit cells and huge stoichiometric choices. Here we present an efficiently data-driven strategy, including generated clathrate frameworks, the quick estimation of stability for each framework and superconducting critical temperature (Tc) for each hydride structure, to accelerate the discovery of high-temperature superconducting hydrides. Our strategy was initialized with more than one million input structures via zeolite databases and our generated dataset. As a result, such a strategy hitherto uncovered 14 prototypical hydrogen frameworks for clathrate hydrides, which is 1.5 times greater than the number (9) of previously reported prototypes. Remarkably, eleven ternary clathrate structures were predicted to have Tcs above 250 K at 300 GPa. Further extensive global structure-searching simulations support that Li2NaH17 and ThY2H24 are thermodynamically stable at 220 and 150 GPa, respectively, with Tcs approaching room temperature of 297 K and 303 K, which are promising for future synthesis. These results offer a platform to explore high-temperature superconductors via a great number of databases.

cond-mat.supr-con↗