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. 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

High throughput sampling of phase space with deep learning potentials: δ\delta-AlOOH at geophysical conditions

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. They 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 viability, success, and necessity of this approach to simulate $\delta$-AlOOH ($\delta$), 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. High-throughput sampling of phase space using molecular dynamics simulations with DP-potentials offers a panoramic view of 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

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

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

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{\deg}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

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.Comment: 51 pages, 2 figure

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

Hydrous and nominally anhydrous minerals 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 (DPs) with the strongly constrained and appropriately normed meta-generalized gradient approximation 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 (∼2900km depth and ∼135GPa) in subducting slabs. A high-throughput sampling of phase space using molecular dynamics simulations with DPs 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

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 multiconfiguration 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 and 11 GPa, H-bond configuration (HOC-11*) is generally ordered, at least in short- to midrange scale. (d) Between 11.5 and 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

Synthesis, Activity, and Application of Fluorescent Analogs of [D1G, Δ14Q]LvIC Targeting α6β4 Nicotinic Acetylcholine Receptor

α6β4* nicotinic acetylcholine receptor (nAChR) (* represents the possible presence of additional subunits) is mainly distributed in the central and peripheral nervous system and is associated with neurological diseases, such as neuropathic pain; however, the ability to explore its function and distribution is limited due to the lack of pharmacological tools. As one of the analogs of α-conotoxin (α-CTx) LvIC from Conus lividus, [D1G, Δ14Q]LvIC (Lv) selectively and potently blocks α6/α3β4 nAChR (α6/α3 represents a chimera). Here, we synthesized three fluorescent analogs of Lv by connecting fluorescent molecules 6-carboxytetramethylrhodamine succinimidyl ester (6-TAMRA-SE, R), Cy3 NHS ester (Cy3, C) and BODIPY-FL NHS ester (BDP, B) to the N-terminus of the peptide and obtained Lv-R, Lv-C, and Lv-B, respectively. The potency and selectivity of three fluorescent peptides were evaluated using two-electrode voltage-clamp recording on nAChR subtypes expressed in Xenopus laevis oocytes, and the potency and selectivity of Lv-B were almost maintained with the half-maximal inhibition (IC50) of 64 nM. Then, we explored the stability of Lv-B in artificial cerebrospinal fluid and stained rat brain slices with Lv-B. The results indicated that the stability of Lv-B was slightly improved compared to that of native Lv. Additionally, we detected the distribution of the α6β4* nAChR subtype in the cerebral cortex using green fluorescently labeled peptide and fluorescence microscopy. Our findings not only provide a visualized pharmacological tool for exploring the distribution of the α6β4* nAChR subtype in various situ tissues and organs but also extend the application of α-CTx [D1G, Δ14Q]LvIC to demonstrate the involvement of α6β4 nAChR function in pathophysiology and pharmacology