arXiv subjects

Shunda Chen

Publications and source records attributed to Shunda Chen.

At least 19 recordsLinked to original sources

arXiv2026

Anisotropic Short-Range Order Modulates Ferroelectric Switching in Wurtzite ScAlN Alloys

Ferroelectric switching in wurtzite alloys is typically understood in terms of composition, strain, defects, and interfaces, while local chemical order is often neglected or treated as a secondary perturbation. Here we show that short-range order (SRO) is a previously overlooked microscopic variable that substantially influences the intrinsic switching barrier. Using first-principles canonical sampling, we find that wurtzite ScAlN develops a robust, highly anisotropic SRO that challenges the conventional random-alloy picture. This ordering suppresses in-plane Sc--N--Sc motifs while enhancing columnar mixed-cation chains along the polar $c$ axis, reflecting the symmetry-distinct polar and basal directions of the wurtzite lattice and reorganizing its polar connectivity. Relative to random-alloy structures, SRO systematically increases the intrinsic switching barrier across a broad composition range. Motif-resolved analysis further identifies the population of columnar Sc--N--Al--N--Sc motifs as the primary structural descriptor underlying switching-barrier variations among configurations with different local order. These results establish anisotropic SRO as an independent degree of freedom for tuning ferroelectric switching. More broadly, they reveal how local chemical order can couple to the symmetry-distinct directions of a polar semiconductor lattice to modify functional behavior. Our findings lay a foundation for SRO engineering as a route to tailoring switching barriers without changing alloy composition.

cond-mat.mtrl-sci↗

arXiv2026

Shining light on short-range atomic ordering in semiconductors alloys

The functional properties of semiconductors are typically controlled by tailoring their chemical composition and their state of strain, and by controlling their long-range structural order, including the presence of extended defects such as dislocations. In addition to these approaches, theoretical predictions suggest that short-range order (SRO) of atoms in group-IV semiconductor alloys can modify the bandgap, a defining property of any semiconductor. Herein, a new machine learning enabled, computation-guided methodology for extended X-ray absorption fine structure (EXAFS) analysis of SRO is used to quantify the effects of local atomic order on the bandgap of germanium-tin (GeSn) alloy single crystal nanostructures with well-controlled strain and composition. Correlative analysis of EXAFS and photoluminescence (PL) establishes the relationship between bandgap and the Warren-Cowley short-range order (WC-SRO) parameter of the GeSn alloys. It is further demonstrated that SRO can be tuned over a broad range by post-deposition annealing of the alloy crystals. This work establishes control of SRO as an important design parameter for semiconducting properties and suggests the potential for quantitative measurement and tuning of SRO in other semiconductor alloy systems.

cond-mat.mtrl-sci↗

arXiv2026

qNEP: A highly efficient neuroevolution potential with dynamic charges for large-scale atomistic simulations

Although electrostatics can be incorporated into machine-learned interatomic potentials, existing approaches are computationally very demanding, limiting large-scale, long-time simulations of electrostatics-driven phenomena such as dielectric response, infrared activity, and field-matter coupling. Here, we extend the neuroevolution potential (NEP), a highly efficient machine-learned interatomic potential, to a charge-aware framework (qNEP) by introducing explicit, environment-dependent partial charges. Each ionic partial charge is represented by a neural network as a function of the local descriptor vector, analogous to the NEP site-energy model. This formulation enables the direct prediction of the Born effective charge tensor for each ion and, consequently, the polarization. As a result, dielectric properties, infrared spectra, and coupling to external electric fields can be evaluated within a unified framework. We derive consistent expressions for the forces and virials that explicitly account for the position dependence of the partial charges. The qNEP method has been implemented in the free-and-open-source GPUMD package, with support for both Ewald summation and particle-particle particle-mesh treatments of electrostatics. We demonstrate the accuracy and efficiency of the qNEP approach through representative applications to water, Li7La3Zr2O12, BaTiO3, and a magnesium-water interface. These results show that qNEP enables accurate atomistic simulations with explicit long-range electrostatics, scalable to million-atom systems on nanosecond time scales using consumer-grade GPUs.

physics.comp-ph↗

arXiv2025

NEP89: Universal neuroevolution potential for inorganic and organic materials across 89 elements

While machine-learned interatomic potentials offer near-quantum-mechanical accuracy for atomistic simulations, many are material-specific or computationally intensive, limiting their broader use. Here, we introduce NEP89, a foundation model based on neuroevolution potential architecture, delivering near-empirical-potential speed and high accuracy across 89 elements. A compact yet comprehensive training dataset covering inorganic and organic materials was curated through descriptor-space subsampling and iterative refinement across multiple datasets. NEP89 achieves competitive accuracy compared to representative foundation models while being three to four orders of magnitude more computationally efficient, enabling previously impractical large-scale atomistic simulations of inorganic and organic systems. In addition to its out-of-the-box applicability to diverse scenarios, including million-atom-scale compression of compositionally complex alloys, ion diffusion in solid-state electrolytes and water, rocksalt dissolution, methane combustion, and protein-ligand dynamics, NEP89 also supports fine-tuning for rapid adaptation to user-specific applications, such as mechanical, thermal, structural, and spectral properties of two-dimensional materials, metallic glasses, and organic crystals.

cond-mat.mtrl-sci↗

arXiv2025

Optimizing thermoelectric performance of graphene antidot lattices via quantum transport and machine-learning molecular dynamics simulations

Thermoelectric materials, which can convert waste heat to electricity or be utilized as solid-state coolers, hold promise for sustainable energy applications. However, optimizing thermoelectric performance remains a significant challenge due to the complex interplay between electronic and thermal transport properties. In this work, we systematically optimize $ZT$ in graphene antidot lattices (GALs), nanostructured graphene sheets with periodic nanopores characterized by two geometric parameters: the hexagonal unit cell side length $L$ and the antidot radius $R$. The lattice thermal conductivity is determined through machine-learned potential-driven molecular dynamics (MD) simulations, while electronic transport properties are computed using linear-scaling quantum transport in combination with MD trajectories based on a bond-length-dependent tight-binding model. This method is able to account for electron-phonon scattering, allowing access to diffusive transport in large-scale systems, overcoming limitations of previous methods based on nonequilibrium Green function formalism. Our results show that the introduction of the antidots effectively decouples lattice and electronic transport and lead to a favorable and significant violation of the Wiedemann-Franz law. We find that optimal $ZT$ values occur in GALs with intermediate $L$ and $R$, closely correlated with peak power factor values. Notably, thermoelectric performance peaks near room temperature, with maximal $ZT$ values approaching 2, highlighting GALs as promising candidates for high-performance thermoelectric energy conversion.

cond-mat.mes-hall↗

arXiv2025

Metastability and Ostwald Step Rule in the Crystallisation of Diamond and Graphite from Molten Carbon

The crystallisation of carbon from the melt under extreme conditions is highly relevant to earth and planetary science, materials manufacturing, and nuclear fusion research. The thermodynamic conditions near the graphite-diamond-liquid (GDL) triple point are especially of interest for geological and technological applications, but high-pressure flash heating experiments aiming to resolve this region of the phase diagram of carbon exhibit large discrepancies. Experimental challenges are often related to the persistence of metastable crystalline or glassy phases, superheated crystals, or supercooled liquids. A deeper understanding of the crystallisation kinetics of diamond and graphite is crucial for effectively interpreting the outcomes of these experiments. Here, we reveal the microscopic mechanisms of diamond and graphite nucleation from liquid carbon through molecular simulations with first-principles machine learning potentials. Our simulations accurately reproduce the experimental phase diagram of carbon in the region around the GDL triple point and show that liquid carbon crystallises spontaneously upon cooling at constant pressure. Surprisingly, metastable graphite crystallises in the domain of diamond thermodynamic stability at pressures above the triple point. Furthermore, whereas diamond crystallises through a classical nucleation pathway, graphite follows a two-step process in which low-density fluctuations forego ordering. Calculations of the nucleation rates of the two competing phases confirm this result and reveal a manifestation of Ostwald's step rule where the strong metastability of graphite hinders the transformation to the stable diamond phase. Our results provide a new key to interpreting melting and recrystallisation experiments and shed light on nucleation kinetics in polymorphic materials with deep metastable states.

cond-mat.mtrl-sci↗

arXiv2025

Semiconductor-compatible topological digital alloys

Recently, GeSn alloys have attracted much interest for direct-gap infrared photonics and as potential topological materials which are compatible with the semiconductor industry. However, for photonics, the high-Sn content required leads to low detectivity, associated with poor material quality, and the (>35%) Sn required for topological properties have been out of reach experimentally. Here, we demonstrate that by patterning the Sn distribution within Ge, the electronic properties have a far greater tunability than is possible with the random alloy. For the GeSn \delta-digital alloy (DA) formed by confining Sn atoms in atomic layer(s) along the [111] direction of Ge, we show that ~10% Sn can lead to a triple-point semimetal. These findings are understood in terms of Sn ordering causing spatial separation of Sn and Ge band edges, leading to band inversion. This mechanism can also lead to a weak topological insulator, Weyl semimetal, and enables tunable direct bandgaps down to 2 meV, covering the entire infrared range. This DA induced topological properties are also identified in compound semiconductors, such as InAs1-xSbx, showing the general applicability of the DA design for realizing topological properties on conventional semiconductor platforms. Our findings not only point to a new class of currently unexplored topological systems accessible by epitaxy, but also establish the promise of low-Sn GeSn DAs for application as infrared laser diodes and photodetectors in Si photonic integrated circuits and infrared image sensors.

cond-mat.mtrl-sci↗

arXiv2025

Advances in modeling complex materials: The rise of neuroevolution potentials

Interatomic potentials are essential for driving molecular dynamics (MD) simulations, directly impacting the reliability of predictions regarding the physical and chemical properties of materials. In recent years, machine-learned potentials (MLPs), trained against first-principles calculations, have become a new paradigm in materials modeling as they provide a desirable balance between accuracy and computational cost. The neuroevolution potential (NEP) approach, implemented in the open-source GPUMD software, has emerged as a promising machine-learned potential, exhibiting impressive accuracy and exceptional computational efficiency. This review provides a comprehensive discussion on the methodological and practical aspects of the NEP approach, along with a detailed comparison with other representative state-of-the-art MLP approaches in terms of training accuracy, property prediction, and computational efficiency. We also demonstrate the application of the NEP approach to perform accurate and efficient MD simulations, addressing complex challenges that traditional force fields typically can not tackle. Key examples include structural properties of liquid and amorphous materials, chemical order in complex alloy systems, phase transitions, surface reconstruction, material growth, primary radiation damage, fracture in two-dimensional materials, nanoscale tribology, and mechanical behavior of compositionally complex alloys under various mechanical loadings. This review concludes with a summary and perspectives on future extensions to further advance this rapidly evolving field.

cond-mat.mtrl-sci↗

arXiv2024

NEP-MB-pol: A unified machine-learned framework for fast and accurate prediction of water's thermodynamic and transport properties

Water's unique hydrogen-bonding network and anomalous properties pose significant challenges for accurately modeling its structural, thermodynamic, and transport behavior across varied conditions. Although machine-learned potentials have advanced the prediction of individual properties, a unified computational framework capable of simultaneously capturing water's complex and subtle properties with high accuracy has remained elusive. Here, we address this challenge by introducing NEP-MB-pol, a highly accurate and efficient neuroevolution potential (NEP) trained on extensive many-body polarization (MB-pol) reference data approaching coupled-cluster-level accuracy, combined with path-integral molecular dynamics and quantum-correction techniques to incorporate nuclear quantum effects. This NEP-MB-pol framework reproduces experimentally measured structural, thermodynamic, and transport properties of water across a broad temperature range, achieving simultaneous, fast, and accurate prediction of self-diffusion coefficient, viscosity, and thermal conductivity. Our approach provides a unified and robust tool for exploring thermodynamic and transport properties of water under diverse conditions, with significant potential for broader applications across research fields.

physics.chem-ph↗

arXiv2024

Million-atom heat transport simulations of polycrystalline graphene approaching first-principles accuracy enabled by neuroevolution potential on desktop GPUs

First-principles molecular dynamics simulations of heat transport in systems with large-scale structural features are challenging due to their high computational cost. Here, using polycrystalline graphene as a case study, we demonstrate the feasibility of simulating heat transport with near first-principles accuracy in systems containing over 1.4 million atoms, achievable even with consumer desktop GPUs. This is enabled by the highly efficient neuroevolution potential (NEP) approach, as implemented in the open-source GPUMD package. Leveraging the NEP model's accuracy and efficiency, we quantify the reduction in thermal conductivity of polycrystalline graphene due to grain boundaries with varying grain sizes, resolving contributions from in-plane and out-of-plane (flexural) phonon modes. Additionally, we find that grain boundaries can lead to finite thermal conductivity even under significant tensile strain, in contrast to the divergent behavior observed in pristine graphene under similar conditions, indicating that grain boundaries may play a crucial role in thermal transport in low-dimensional momentum-conserving systems. These findings could offer insights for interpreting experimental observations, given the widespread presence of both large-scale grain boundaries and external strains in real materials. The demonstrated ability to simulate millions of atoms with near-first-principles accuracy on consumer desktop GPUs using the NEP approach will help make large-scale high-fidelity atomistic simulations more accessible to the broader research community.

cond-mat.mtrl-sci↗

arXiv2024

Highly efficient path-integral molecular dynamics simulations with GPUMD using neuroevolution potentials: Case studies on thermal properties of materials

Path-integral molecular dynamics (PIMD) simulations are crucial for accurately capturing nuclear quantum effects in materials. However, their computational intensity and reliance on multiple software packages often limit their applicability at large scales. Here, we present an integration of PIMD methods, including thermostatted ring-polymer molecular dynamics (TRPMD), into the open-source GPUMD package, combined with highly accurate and efficient machine-learned neuroevolution potential (NEP) models. This approach achieves almost the accuracy of first-principles calculations with the computational efficiency of empirical potentials, enabling large-scale atomistic simulations that incorporate nuclear quantum effects. We demonstrate the efficacy of the combined NEP-PIMD approach by examining various thermal properties of diverse materials, including lithium hydride (LiH), three porous metal-organic frameworks (MOFs), liquid water, and elemental aluminum. For LiH, our NEP-PIMD simulations successfully capture the isotope effect, reproducing the experimentally observed dependence of the lattice parameter on the reduced mass. For MOFs, our results reveal that achieving good agreement with experimental data requires consideration of both nuclear quantum effects and dispersive interactions. For water, our PIMD simulations capture the significant impact of nuclear quantum effects on its microscopic structure. For aluminum, the TRPMD method effectively captures thermal expansion and phonon properties, aligning well with quantum mechanical predictions. This efficient NEP-PIMD approach opens new avenues for exploring complex material properties influenced by nuclear quantum effects, with potential applications across a broad range of materials.

cond-mat.mtrl-sci↗

arXiv2024

Atomic short-range order: a new degree of freedom for band engineering of GeSn semiconductor alloys

Chemical short-range order (SRO) in alloys denotes the statistical preference or avoidance between atomic species on neighboring lattice sites. Here, we highlight SRO as a powerful new mechanism for semiconductor alloy band engineering. Atom probe tomography reveals a significantly higher probability of Sn-Sn first nearest neighbors (1NNs) in thin-film GeSn alloys grown by molecular beam epitaxy (MBE) vs. chemical vapor deposition (CVD). Remarkably, although lower Sn concentration typically widens the bandgap, we find that the stronger presence of Sn-Sn 1NN pairs in MBE samples overrides this trend, resulting in a narrower bandgap despite having 2 at.\% lower Sn content than CVD samples. First-principles modeling corroborates this effect, attributing these SRO variations to distinctive surface terminations and growth temperatures between MBE and CVD. These findings establish SRO as a new degree of freedom for semiconductor band engineering beyond composition, strain, and quantum confinement, unlocking novel device mechanisms for the post-Moore era.

cond-mat.mtrl-sci↗

arXiv2024

Solute segregation in polycrystalline aluminum from hybrid Monte Carlo and molecular dynamics simulations with a unified neuroevolution potential

One of the most effective methods to enhance the strength of aluminum alloys involves modifying grain boundaries (GBs) through solute segregation. However, the fundamental mechanisms of solute segregation and their impacts on material properties remain elusive. In this study, we implemented highly efficient hybrid Monte Carlo and molecular dynamics (MCMD) algorithms in the graphics process units molecular dynamics (GPUMD) package. Using this efficient MCMD approach combined with a general-purpose machine-learning-based neuroevolution potential (NEP) for 16 elemental metals and their alloys, we simulated the segregation of 15 solutes in polycrystalline Al. Our results elucidate the segregation behavior and trends of 15 solutes in polycrystalline Al. Additionally, we investigated the impact of solutes on the strength of polycrystalline Al. The mechanisms underlying solute strengthening and embrittlement were analyzed at the atomistic level, revealing the importance of GB cohesion, as well as the nucleation and movement of Shockley dislocations, in determining the material's strength. We anticipate that our developed methods, along with our insights into solute segregation behavior in polycrystalline Al, will be valuable for the design of Al alloys and other multi-component materials, including medium-entropy materials, high-entropy materials, and complex concentrated alloys.

cond-mat.mtrl-sci↗

arXiv2024

Molecular dynamics simulations of heat transport using machine-learned potentials: A mini review and tutorial on GPUMD with neuroevolution potentials

Molecular dynamics (MD) simulations play an important role in understanding and engineering heat transport properties of complex materials. An essential requirement for reliably predicting heat transport properties is the use of accurate and efficient interatomic potentials. Recently, machine-learned potentials (MLPs) have shown great promise in providing the required accuracy for a broad range of materials. In this mini review and tutorial, we delve into the fundamentals of heat transport, explore pertinent MD simulation methods, and survey the applications of MLPs in MD simulations of heat transport. Furthermore, we provide a step-by-step tutorial on developing MLPs for highly efficient and predictive heat transport simulations, utilizing the neuroevolution potentials (NEPs) as implemented in the GPUMD package. Our aim with this mini review and tutorial is to empower researchers with valuable insights into cutting-edge methodologies that can significantly enhance the accuracy and efficiency of MD simulations for heat transport studies.

cond-mat.mtrl-sci↗

arXiv2023

General-purpose machine-learned potential for 16 elemental metals and their alloys

Machine-learned potentials (MLPs) have exhibited remarkable accuracy, yet the lack of general-purpose MLPs for a broad spectrum of elements and their alloys limits their applicability. Here, we present a feasible approach for constructing a unified general-purpose MLP for numerous elements, demonstrated through a model (UNEP-v1) for 16 elemental metals and their alloys. To achieve a complete representation of the chemical space, we show, via principal component analysis and diverse test datasets, that employing one-component and two-component systems suffices. Our unified UNEP-v1 model exhibits superior performance across various physical properties compared to a widely used embedded-atom method potential, while maintaining remarkable efficiency. We demonstrate our approach's effectiveness through reproducing experimentally observed chemical order and stable phases, and large-scale simulations of plasticity and primary radiation damage in MoTaVW alloys. This work represents a significant leap towards a unified general-purpose MLP encompassing the periodic table, with profound implications for materials science.

cond-mat.mtrl-sci↗

arXiv2023

Combining linear-scaling quantum transport and machine-learning molecular dynamics to study thermal and electronic transports in complex materials

We propose an efficient approach for simultaneous prediction of thermal and electronic transport properties in complex materials. Firstly, a highly efficient machine-learned neuroevolution potential is trained using reference data from quantum-mechanical density-functional theory calculations. This trained potential is then applied in large-scale molecular dynamics simulations, enabling the generation of realistic structures and accurate characterization of thermal transport properties. In addition, molecular dynamics simulations of atoms and linear-scaling quantum transport calculations of electrons are coupled to account for the electron-phonon scattering and other disorders that affect the charge carriers governing the electronic transport properties. We demonstrate the usefulness of this unified approach by studying thermoelectric transport properties of a graphene antidot lattice.

cond-mat.mtrl-sci↗

arXiv2023

Local Alloy Order in a Ge1-xSnx/Ge Epitaxial Layer

The local ordering of atoms in alloys directly has a strong impact on their electronic and optical properties. This is particularly relevant in nonrandom alloys, especially if they are deposited using far from the equilibrium processes, as is the case of epitaxial Ge1-xSnx layers. In this work, we investigate the arrangement of Ge and Sn atoms in optoelectronic grade Ge1-xSnx epitaxial layers featuring a Sn content in the 5-14% range by using polarization-dependent Raman spectroscopy and density-functional-theory calculations. The thorough analysis of the polarization-dependent spectra in parallel and perpendicular configuration allowed us to properly tag all the observed vibrational modes, and to shed light on that associated to disorder-assisted Raman transitions. Indeed, with the help of large-scale atomistic simulations, we were able to highlight how the presence of Sn atoms, that modify the local environments of Ge atoms, gives rise to two spectral features at different Raman shifts, corresponding to distortions of the atomic bonds. This analysis provides a valuable framework for advancing the understanding of the vibrational properties in Ge1-xSnx alloys, particularly with regard to the impact of local ordering of the different atomic species.

cond-mat.mtrl-sci↗

arXiv2022

Coexistence of two types of short-range order in SiGeSn medium-entropy alloys

Short-range chemical order (SRO) has been recently demonstrated to play a decisive role in modulating a wide range of physical properties in medium-entropy alloy (MEA) and high-entropy alloy (HEA). The enormous configurational space of these alloys implies multiple forms of SRO are likely to develop concurrently but such structural diversity has not been reported. Here we show, through extensive {\em ab initio}-based sampling study, that SiGeSn medium-entropy alloys spontaneously develop two distinct forms of SRO. Remarkably, the two types of SROs, which carry different energies, distinct degrees of local ordering, and dissimilar electronic structures, are found to co-exist in a wide range of compositions of SiGeSn alloys. The co-existence of two SROs is rationalized through their virtual degeneracy of thermodynamic stability, due to the subtle balance in the change of enthalpy and configurational entropy upon the transformation between the two SROs. Such co-existence of SROs thus suggests an inherent structural heterogeneity, a diffuse electronic structure, and a new route for band engineering in SiGeSn MEA. More generally, our finding indicates the possible ubiquity of the co-existence of multiple forms of SRO in a broad range of MEAs and HEAs, which has profound implications on their diverse physical properties.

cond-mat.mtrl-sci↗