arXiv subjects

Chenxing Luo

Publications and source records attributed to Chenxing Luo.

15 recordsLinked to original sources

arXiv2026

LitCurate: A Configuration-Driven AI-Assisted Framework for Scientific Database Construction with an Application to Lower-Mantle Equation-of-State Data

The growing scientific literature contains decades of experimental and computational results that could support data-driven and physics-based modeling, yet much of this infor- mation remains locked in publications and is not readily usable for large-scale analysis or sci- entific software. Building structured databases from the literature is particularly challenging whenrelevantstudiesmustfirstbediscoveredamonglargecollectionsofpapersandreported quantities must be extracted with enough scientific context to remain usable. We present LitCurate, an open-source framework for building scientific databases from the literature using large language models within an auditable, stage-wise curation workflow. LitCurate integratesliteraturediscovery, relevancescreening, full-textprocessing, andstructuredinfor- mation extraction while retaining intermediate results and provenance, allowing researchers to inspect and revise individual stages rather than treating automated curation as a black- box process. We apply LitCurate to construct an equation-of-state database of lower-mantle and lower-mantle-relevant high-pressure mineral phases from experimental and theoretical studies, comprising 1,334 entries from 205 papers. The resulting dataset links reported equation-of-state parameters to mineral phases, compositions, equation formulations, meth- ods, and parameter constraints, and labels values as source-reported or citation-reported when provenance can be determined. The records are available through a searchable web application. By connecting scientific literature to traceable, machine-readable data, LitCu- rate provides a reusable approach for transforming accumulated literature into resources for scientific analysis and computational modeling.

cs.IR↗

arXiv2026

Lattice dynamics and the spectroscopic signatures of H-bond disorder in $\delta$-AlOOH

Raman and infrared anomalies associated with H-bond symmetrization in $\delta$-AlOOH, including mode softening and linewidth broadening at 5-10 GPa, occur at significantly lower pressures than predicted by static harmonic theory. To resolve this discrepancy, we combine harmonic phonon calculations with strongly constrained and appropriately normed (SCAN)-based deep-potential molecular dynamics and phonon quasiparticle analysis at 300 K. This framework extracts temperature- and pressure-dependent frequencies and lifetimes from long-time trajectories, capturing the branch reorganization and rapid linewidth growth characteristic of the disordering regime. Incorporating quasiparticle renormalization and directional longitudinal-optical-transverse-optical (LO-TO) splitting further yields near-quantitative agreement with the ambient-pressure OH-stretching Raman multiplet. These results identify finite-temperature dynamical effects and the progressive loss of spectral coherence as the origin of the spectroscopic signatures of H-bond symmetrization.

cond-mat.mtrl-sci↗

arXiv2026

Ab initio electronic conductivity of Fe-bearing post-perovskite

The electrical conductivity of high-pressure silicates profoundly influences the interior dynamics of rocky planets. Employing the Kubo-Greenwood formalism, we perform ab initio calculations of electronic conductivity in Fe-bearing post-perovskite under super-Earth mantle conditions, up to 4000 K and 500 GPa. Electronic structures are obtained via many-body perturbation theory, incorporating dynamical screening and correlations among localized Fe-3d orbitals. In contrast to (Fe,Mg)O, for which metallization has been reported at comparable conditions, our results indicate that post-perovskite with Earth-like Fe contents is unlikely to metallize in super-Earth mantles via band-gap closure, yielding negligible low-frequency conductivity. Any substantial conductivity would require non-electronic mechanisms, such as thermally activated small-polaron hopping, which fall beyond the scope of band conduction.

cond-mat.mtrl-sci↗

arXiv2025

Ferroelasticity, shear modulus softening, and the tetragonal-cubic transition in davemaoite

Davemaoite (Dm), the cubic phase of CaSiO3-perovskite (CaPv), is a major component of the Earth's lower mantle. Understanding its elastic behavior, including its dissolution in bridgmanite (MgSiO3-perovskite), is crucial for interpreting lower mantle seismology. Using machine-learning interatomic potentials and molecular dynamics, we investigate CaPv's elastic properties across the tetragonal-cubic transition. Our equations of state align well with experimental data at 300 K and 2,000 K, demonstrating the predictive accuracy of our trained potential. We simulate the ferroelastic hysteresis loop in tetragonal CaPv, which has yet to be investigated experimentally. We also identify a significant temperature-induced shear modulus softening near the phase transition, characteristic of ferroelastic-paraelastic transitions. Unlike previous elasticity studies, our softening region does not extend to slab geotherm conditions. We suggest that ab initio-quality computations provide a robust benchmark for shear elastic softening associated with ferroelasticity, a challenging property to measure in these materials.

cond-mat.mtrl-sci↗

arXiv2025

DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials

In recent years, machine learning potentials (MLPs) have become indispensable tools in physics, chemistry, and materials science, driving the development of software packages for molecular dynamics (MD) simulations and related applications. These packages, typically built on specific machine learning frameworks such as TensorFlow, PyTorch, or JAX, face integration challenges when advanced applications demand communication across different frameworks. The previous TensorFlow-based implementation of DeePMD-kit exemplified these limitations. In this work, we introduce DeePMD-kit version 3, a significant update featuring a multi-backend framework that supports TensorFlow, PyTorch, JAX, and PaddlePaddle backends, and demonstrate the versatility of this architecture through the integration of other MLPs packages and of Differentiable Molecular Force Field. This architecture allows seamless backend switching with minimal modifications, enabling users and developers to integrate DeePMD-kit with other packages using different machine learning frameworks. This innovation facilitates the development of more complex and interoperable workflows, paving the way for broader applications of MLPs in scientific research.

physics.chem-ph↗

arXiv2024

Machine learning potential for serpentines

Serpentines are layered hydrous magnesium silicates (MgO$\cdot$SiO$_2\cdot$H$_2$O) formed through serpentinization, a geochemical process that significantly alters the physical property of the mantle. They are hard to investigate experimentally and computationally due to the complexity of natural serpentine samples and the large number of atoms in the unit cell. We developed a machine learning (ML) potential for serpentine minerals based on density functional theory (DFT) calculation with the r$^2$SCAN meta-GGA functional for molecular dynamics simulation. We illustrate the success of this ML potential model in reproducing the high-temperature equation of states of several hydrous phases under the Earth's subduction zone conditions, including brucite, lizardite, and antigorite. In addition, we investigate the polymorphism of antigorite with periodicity $m$ = 13--24, which is believed to be all the naturally existent antigorite species. We found that antigorite with $m$ larger than 21 appears more stable than lizardite at low temperatures. This machine learning potential can be further applied to investigate more complex antigorite superstructures with multiple coexisting periodic waves.

physics.geo-ph↗

arXiv2024

Elasticity and acoustic velocities of $δ$-AlOOH at extreme conditions: a methodology assessment

Hydrous phases play a fundamental role in the deep-water cycle on Earth. Understanding their stability and thermoelastic properties is essential for constraining their abundance using seismic tomography. However, determining their elastic properties at extreme conditions is notoriously challenging. The challenges stem from the complex behavior of hydrogen bonds under high pressures and temperatures (P,Ts). In this study, we evaluate how advanced molecular dynamics simulation techniques can address these challenges by investigating the adiabatic elasticity and acoustic velocities of $δ$-AlOOH, a critical and prototypical high-pressure hydrous phase. We compared the performances of three methods to assess their viability and accuracy. The thermoelastic tensor was computed up to 140 GPa and temperatures up to 2,700 K using molecular dynamics with a DeePMD machine-learning interatomic potential based on the SCAN meta-GGA functional. The excellent agreement with ambient condition single-crystal ultrasound measurements and the correct description of velocity changes induced by H-bond disorder-symmetrization transition observed at 10 GPa in Brillouin scattering measurements underscores the accuracy and efficacy of our approach.

physics.comp-ph↗

arXiv2024

Probing the state of hydrogen in $δ$-AlOOH at mantle conditions with machine learning potential

Hydrous and nominally anhydrous minerals (NAMs) are a fundamental class of solids of enormous significance to geophysics. They are the water carriers in the deep geological water cycle and impact structural, elastic, plastic, and thermodynamic properties and phase relations in Earth's forming aggregates (rocks). They play a critical role in the geochemical and geophysical processes that shape the planet. Their complexity has prevented predictive calculations of their properties, but progress in materials simulations ushered by machine learning potentials is transforming this state of affairs. Here, we adopt a hybrid approach that combines deep learning potentials (DP) with the SCAN meta-GGA functional to simulate a prototypical hydrous system. We illustrate the success of this approach to simulate $δ$-AlOOH ($δ$), a phase capable of transporting water down to near the core-mantle boundary of the Earth (~2,900 km depth and ~135 GPa) in subducting slabs. A high-throughput sampling of phase space using molecular dynamics simulations with DP-potentials sheds light on the hydrogen-bond behavior and proton diffusion at geophysical conditions. These simulations provide a pathway for a deeper understanding of these crucial components that shape Earth's internal state.

physics.comp-ph↗

arXiv2023

Ab initio study on the stability and elasticity of brucite

Brucite (Mg(OH)$_2$) is a mineral of great interest owing to its various applications and roles in geological processes. Its structure, behavior under different conditions, and unique properties have been the subject of numerous studies and persistent debate. As a stable hydrous phase in subduction zones, its elastic anisotropy can significantly contribute to the seismological properties of these regions. We performed ab initio calculations to investigate brucite's stability, elasticity, and acoustic velocities. We tested several exchange-correlation functionals and managed to obtain stable phonons for the P$\bar{3}$ phase with r$^2$SCAN for the first time at all relevant pressures up to the mantle transition zone. We show that r$^2$SCAN performs very well in brucite, reproducing the experimental equation of state and several key structure parameters related to hydrogen positions. The room temperature elasticity results in P$\bar{3}$ reproduces the experimental results at ambient pressure. These results, together with the stable phonon dispersion of P$\bar{3}$ at all relevant pressures, indicate P$\bar{3}$ is the stable candidate phase not only at elevated pressures but also at ambient conditions. The success of r$^2$SCAN in brucite, suggests this functional should be suitable for other challenging layer-structured minerals, e.g., serpentines, of great geophysical significance.

cond-mat.mtrl-sci↗

arXiv2023

Thermoelastic properties of bridgmanite using Deep Potential Molecular Dynamics

MgSiO_3-perovskite (MgPv) plays a crucial role in the Earth's lower mantle. This study combines deep-learning potential (DP) with density functional theory (DFT) to investigate the structural and elastic properties of MgPv under lower mantle conditions. To simulate complex systems, we developed a series of potentials capable of faithfully reproducing DFT calculations using different functionals, such as LDA, PBE, PBEsol, and SCAN meta-GGA functionals. The obtained predictions exhibit remarkable reliability and consistency, closely resembling experimental measurements. Our results highlight the superior performance of the DP-SCAN and DP-LDA in accurately predicting high-temperature equations of states and elastic properties. This hybrid computational approach offers a solution to the accuracy-efficiency dilemma in obtaining precise elastic properties at high pressure and temperature conditions for minerals like MgPv, which opens a new way to study the Earth's interior state and related processes.

physics.geo-ph↗

arXiv2023

DeePMD-kit v2: A software package for Deep Potential models

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials (MLP) known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, Deep Potential - Range Correction (DPRc), Deep Potential Long Range (DPLR), GPU support for customized operators, model compression, non-von Neumann molecular dynamics (NVNMD), and improved usability, including documentation, compiled binary packages, graphical user interfaces (GUI), and application programming interfaces (API). This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, the article benchmarks the accuracy and efficiency of different models and discusses ongoing developments.

physics.chem-ph↗

arXiv2022

Ab initio calculations of third-order elastic coefficients

Third-order elasticity (TOE) theory is predictive of strain-induced changes in second-order elastic coefficients (SOECs) and can model elastic wave propagation in stressed media. Although third-order elastic tensors have been determined based on first principles in previous studies, their current definition is based on an expansion of thermodynamic energy in terms of the Lagrangian strain near the natural, or zero pressure, reference state. This definition is inconvenient for predictions of SOECs under significant initial stresses. Therefore, when TOE theory is necessary to study the strain dependence of elasticity, the seismological community has resorted to an empirical version of the theory. This study reviews the thermodynamic definition of the third-order elastic tensor and proposes using an "effective" third-order elastic tensor. An explicit expression for the effective third-order elastic tensor is given and verified. We extend the ab initio approach to calculate third-order elastic tensors under finite pressure and apply it to two cubic systems, namely, NaCl and MgO. As applications and validations, we evaluate (a) strain-induced changes in SOECs and (b) pressure derivatives of SOECs based on ab initio calculations. Good agreement between third-order elasticity-based predictions and numerically calculated values confirms the validity of our theory.

cond-mat.mtrl-sci↗

arXiv2022

Elastic anisotropy of lizardite at subduction zone conditions

Subduction zones transport water into Earth's deep interior through slab subduction. Serpentine minerals, the primary hydration product of ultramafic peridotite, are abundant in most subduction zones. Characterization of their high-temperature elasticity, particularly their anisotropy, will help us better estimate the extent of mantle serpentinization and the Earth's deep water cycle. Lizardite, the low-temperature polymorph of serpentine, is stable under the P-T conditions of cold subduction slabs (< 260°C at 2 GPa), and its high-temperature elasticity remains unknown. Here we report ab initio elasticity and acoustic wave velocities of lizardite at P-T conditions of subduction zones. Our static results agree with previous studies. Its high-temperature velocities are much higher than previous experimental-based lizardite estimates with chrysotile but closer to antigorite velocities. The elastic anisotropy of lizardite is much larger than that of antigorite and could better account for the observed large shear-wave splitting in some cold slabs such as Tonga.

physics.geo-ph↗

arXiv2022

Ab initio investigation of H-bond disordering in $δ$-AlOOH

$δ$-AlOOH ($δ$) is a high-pressure hydrous phase that participates in the deep geological water cycle. At 0 GPa, $δ$ has asymmetric hydrogen bonds (H-bonds). Under pressure, it exhibits H-bond disordering, tunneling, and finally, H-bond symmetrization at ~18 GPa. This study investigates these 300 K pressure-induced state changes in $δ$ with ab initio calculations. H-bond disordering in $δ$ was modeled using supercell multi-configuration quasiharmonic calculations. We examine: (a) energy barriers for proton jumps, (b) the pressure dependence of phonon frequencies, (c) 300 K compressibility, (d) neutron diffraction pattern anomalies, and (e) compare ab initio bond lengths with measured ones. Such thorough and systematic comparisons indicate that: (a) proton "disorder" has a restricted meaning when applied to $δ$. Nevertheless, H-bonds are disordered between 0 and 8 GPa, and a gradual change in H-bond configuration results in enhanced compressibility. (b) several structural and vibrational anomalies at ~8 GPa are consistent with the disappearance of a particular (HOC-12) H-bond configuration and its change into another one (HOC-11*). (c) between 8-11 GPa, H-bond configuration (HOC-11*) is generally ordered, at least in short- to mid-range scale. (d) between 11.5-18 GPa, H-bond lengths approach a critical value that impedes compression, resulting in decreased compressibility. In this pressure range, especially approaching H-bond symmetrization at ~18 GPa, anharmonicity and tunneling should play an essential role in the proton dynamics. Further simulations accounting for these effects are desirable to clarify the protons' state in this pressure range.

cond-mat.mtrl-sci↗

arXiv2021

cij: A Python code for quasiharmonic thermoelasticity

The Wu-Wentzcovitch semi-analytical method (SAM) is a concise and predictive formalism to calculate the high-pressure and high-temperature (high-PT) thermoelastic tensor (Cij) of crystalline materials. This method has been successfully applied to materials across different crystal systems in conjunction with ab initio calculations of static elastic coefficients and phonon frequencies. Such results have offered first-hand insights into the composition and structure of the Earth's mantle. Here we introduce the cij package, a Python implementation of the SAM-Cij formalism. It enables a thermoelasticity calculation to be initiated from a single command and fully configurable from a calculation settings file to work with solids within any crystalline system. These features allow SAM-Cij calculations to work on a personal computer and to be easily integrated as a part of high-throughput workflows. Here we show the performance of this code for three minerals from different crystal systems at their relevant PTs: diopside (monoclinic), akimotoite (trigonal), and bridgmanite (orthorhombic).

physics.comp-ph↗