Structural response of α-quartz under plate-impact shock compression

Nanosecond structural evolution in shocked coesite

The phase transitions in minerals under shock are crucial for understanding meteorite impact history. Recent time-resolved x-ray diffraction (XRD) studies on silica shocked to 65 GPa proposed the formation of different high-pressure phases between fused silica and quartz. Furthermore, the dynamics of silica behavior under higher pressure need to be investigated, particularly during nonequilibrium superheating before melting. This study examines the time-dependent response of coesite, using laser-driven shock coupled with fast XRD and molecular dynamics simulations with our recently developed machine learning interatomic potential. Our results reveal a transient dense supercooled liquid crystallizes into a semi-disordered d-NiAs-type silica, followed by transforming into either seifertite or stishovite, depending on the pressure. Instead of thermodynamically stable quartz, a back-transformation to coesite phase is identified after release. The complicated phase evolution pathways in shocked coesite provide deeper insights into the high-pressure silica phases observed in the meteorite bombardments on the early Moon, Mars, and Earth.

Theoretical study of the structural and thermodynamic properties of U-He compounds under high pressure

Uranium is considered as a very important nuclear energy material because of the huge amount of energy it releases. As the main product of the spontaneous decay of uranium, it is difficult for helium to react with uranium because of its chemical inertness. Therefore, bubbles will be formed inside uranium, which could greatly reduce the performance of uranium or cause safety problems. Additionally, nuclear materials are usually operated in an environment of high-temperature and high-pressure, so it is necessary to figure out the exact state of helium inside uranium under extreme conditions. Here, we explored the structural stability of the U-He system under high pressure and high temperature by using density functional theory calculations. Two metastable phases are found between 50 and 400 GPa: U4He with space group Fmmm and U6He with space group P1̄. Both are metallic and adopt layered structures. Electron localization function calculation combined with charge density difference analysis indicates that there are covalent bonds between U and U atoms in both Fmmm-U4He and P1̄-U6He. Regarding the elastic modulus of α-U, the addition of helium has certain influence on the mechanical properties of uranium. Besides, first-principles molecular dynamics simulations were carried out to study the dynamical behavior of Fmmm-U4He and P1̄-U6He at high-temperature. It was found that Fmmm-U4He and P1̄-U6He undergo one-dimensional superionic phase transitions at 150 GPa. Our study revealed the exotic structure of U-He compounds beyond the formation of bubbles under high-pressure and high-temperature, which might be relevant to the performance and safety issues of nuclear materials under extreme conditions.

Modelling atomic and nanoscale structure in the silicon-oxygen system through active machine learning

Silicon-oxygen compounds are among the most important ones in the natural sciences, occurring as building blocks in minerals and being used in semiconductors and catalysis. Beyond the well-known silicon dioxide, there are phases with different stoichiometric composition and nanostructured composites. One of the key challenges in understanding the Si-O system is therefore to accurately account for its nanoscale heterogeneity beyond the length scale of individual atoms. Here we show that a unified computational description of the full Si-O system is indeed possible, based on atomistic machine learning coupled to an active-learning workflow. We showcase applications to very-high-pressure silica, to surfaces and aerogels, and to the structure of amorphous silicon monoxide. In a wider context, our work illustrates how structural complexity in functional materials beyond the atomic and few-nanometre length scales can be captured with active machine learning.

The Crystallization of Disordered Materials under Shock Is Governed by Their Network Topology

When the shock load is applied, materials experience incredibly high temperature and pressure conditions on picosecond timescales, usually accompanied by remarkable physical or chemical phenomena. Understanding the underlying physics that governs the kinetics of shocked materials is of great importance for both physics and materials science. Here, combining experiment and large-scale molecular dynamics simulation, the ultrafast nanoscale crystal nucleation process in shocked soda-lime silicate glass is investigated. By adopting topological constraints theory, this study finds that the propensity of nucleation is governed by the connectivity of the atomic network. The densification of local networks, which appears once the crystal starts to grow, results in the underconstrained shell around the crystal and prevents further crystallization. These results shed light on the nanoscale crystallization mechanism of shocked materials from the viewpoint of topological constraint theory.

Evidence for a rosiaite-structured high-pressure silica phase and its relation to lamellar amorphization in quartz

When affected by impact, quartz (SiO₂) undergoes an abrupt transformation to glass lamellae, the planar deformation features (PDFs). This shock effect is the most reliable indicator of impacts and is decisive in identifying catastrophic collisions in the Earth´s record such as the Chicxulub impact. Despite the significance of PDFs, there is still no consensus how they form. Here, we present time-resolved in-situ synchroton X-ray diffraction data of single-crystal quartz rapidly compressed in a dynamic diamond anvil cell. These experiments provide evidence for the transformation of quartz at pressures above 15 GPa to lamellae of a metastable rosiaite (PbSb₂O₆)-type high-pressure phase with octahedrally coordinated silicon. This phase collapses during decompression to amorphous lamellae, which closely resemble PDFs in naturally shocked quartz. The identification of rosiaite-structured silica provides thus an explanation for lamellar amorphization of quartz. Furthermore, it suggests that the mixed phase region of the Hugoniot curve may be related to the progressive formation of rosiaite-structured silica.

Topological Ordering of Memory Glass on Extended Length Scales

Identifying ordering in non-crystalline solids has been a focus of natural science since the publication of Zachariasen's random network theory in 1932, but it still remains as a great challenge of the century. Literature shows that the hierarchical structures, from the short-range order of first-shell polyhedra to the long-range order of translational periodicity, may survive after amorphization. Here, in a piece of AlPO₄, or berlinite, we combine X-ray diffraction and stochastic free-energy surface simulations to study its phase transition and structural ordering under pressure. From reversible single crystals to amorphous transitions, we now present an unambiguous view of the topological ordering in the amorphous phase, consisting of a swarm of Carpenter low-symmetry phases with the same topological linkage, trapped in a metastable intermediate stage. We propose that the remaining topological ordering is the origin of the switchable "memory glass" effect. Such topological ordering may hide in many amorphous materials through disordered short atomic displacements.

Considerations and challenges for sex-aware drug repurposing

Sex differences are essential factors in disease etiology and manifestation in many diseases such as cardiovascular disease, cancer, and neurodegeneration [33]. The biological influence of sex differences (including genomic, epigenetic, hormonal, immunological, and metabolic differences between males and females) and the lack of biomedical studies considering sex differences in their study design has led to several policies. For example, the National Institute of Health's (NIH) sex as a biological variable (SABV) and Sex and Gender Equity in Research (SAGER) policies to motivate researchers to consider sex differences [204]. However, drug repurposing, a promising alternative to traditional drug discovery by identifying novel uses for FDA-approved drugs, lacks sex-aware methods that can improve the identification of drugs that have sex-specific responses [7, 11, 14, 33]. Sex-aware drug repurposing methods either select drug candidates that are more efficacious in one sex or deprioritize drug candidates based on if they are predicted to cause a sex-bias adverse event (SBAE), unintended therapeutic effects that are more likely to occur in one sex. Computational drug repurposing methods are encouraging approaches to develop for sex-aware drug repurposing because they can prioritize sex-specific drug candidates or SBAEs at lower cost and time than traditional drug discovery. Sex-aware methods currently exist for clinical, genomic, and transcriptomic information [1, 7, 155]. They have not expanded to other data types, such as DNA variation, which has been beneficial in other drug repurposing methods that do not consider sex [114]. Additionally, some sex-aware methods suffer from poorer performance because a disproportionate number of male and female samples are available to train computational methods [7]. However, there is development potential for several different categories (i.e., data mining, ligand binding predictions, molecular associations, and networks). Low-dimensional representations of molecular association and network approaches are also especially promising candidates for future sex-aware drug repurposing methodologies because they reduce the multiple hypothesis testing burden and capture sex-specific variation better than the other methods [151, 159]. Here we review how sex influences drug response, the current state of drug repurposing including with respect to sex-bias drug response, and how model organism study design choices influence drug repurposing validation.

Plasma shielding removes prior magnetization record from impacted rocks near Santa Fe, New Mexico

The shock exposure of the Santa Fe’s impact structure in New Mexico is evidenced by large human-size shatter cones. We discovered a new magnetic mechanism that allows a magnetic detection of plasma’s presence during the impact processes. Rock fragments from the impactites were once magnetized by a geomagnetic field. Our novel approach, based on Neel’s theory, revealed more than an order of magnitude lower magnetizations in the rocks that were exposed to the shockwave. Here we present a support for a newly proposed mechanism where the shock wave appearance can generate magnetic shielding that allow keeping the magnetic grains in a superparamagnetic-like state shortly after the shock’s exposure, and leaves the individual magnetized grains in random orientations, significantly lowering the overall magnetic intensity. Our data not only clarify how an impact process allows for a reduction of magnetic paleointensity but also inspire a new direction of effort to study impact sites, using paleointensity reduction as a new impact proxy.

Metastability of Liquid Water Freezing into Ice VII under Dynamic Compression

The ubiquitous nature and unusual properties of water have motivated many studies on its metastability under temperature- or pressure-induced phase transformations. Here, nanosecond compression by a high-power laser is used to create the nonequilibrium conditions where liquid water persists well into the stable region of ice VII. Through our experiments, as well as a complementary theoretical-computational analysis based on classical nucleation theory, we report that the metastability limit of liquid water under nearly isentropic compression from ambient conditions is at least 8 GPa, higher than the 7 GPa previously reported for lower loading rates.