Data-mining the stability of 29,902 material phases reveals the thermodynamic landscape of inorganic crystalline metastability. The space of metastable materials offers promising new design opportunities for next-generation technological materials, such as complex oxides, semiconductors, pharmaceuticals, steels, and beyond. Although metastable phases are ubiquitous in both nature and technology, only a heuristic understanding of their underlying thermodynamics exists. We report a large-scale data-mining study of the Materials Project, a high-throughput database of density functional theory–calculated energetics of Inorganic Crystal Structure Database structures, to explicitly quantify the thermodynamic scale of metastability for 29,902 observed inorganic crystalline phases. We reveal the influence of chemistry and composition on the accessible thermodynamic range of crystalline metastability for polymorphic and phase-separating compounds, yielding new physical insights that can guide the design of novel metastable materials. We further assert that not all low-energy metastable compounds can necessarily be synthesized, and propose a principle of ‘remnant metastability’—that observable metastable crystalline phases are generally remnants of thermodynamic conditions where they were once the lowest free-energy phase.
Abstract. Severe regional haze pollution events occurred in eastern and central China in January 2013, which had adverse effects on the environment and public health. Extremely high levels of particulate matter with aerodynamic diameter of 2.5 μm or less (PM2.5) with dominant components of sulfate and nitrate are responsible for the haze pollution. Although heterogeneous chemistry is thought to play an important role in the production of sulfate and nitrate during haze episodes, few studies have comprehensively evaluated the effect of heterogeneous chemistry on haze formation in China by using the 3-D models due to of a lack of treatments for heterogeneous reactions in most climate and chemical transport models. In this work, the WRF-CMAQ model with newly added heterogeneous reactions is applied to East Asia to evaluate the impacts of heterogeneous chemistry and the meteorological anomaly during January 2013 on regional haze formation. As the parameterization of heterogeneous reactions on different types of particles is not well established yet, we arbitrarily selected the uptake coefficients from reactions on dust particles and then conducted several sensitivity runs to find the value that can best match observations. The revised CMAQ with heterogeneous chemistry not only captures the magnitude and temporal variation of sulfate and nitrate, but also reproduces the enhancement of relative contribution of sulfate and nitrate to PM2.5 mass from clean days to polluted haze days. These results indicate the significant role of heterogeneous chemistry in regional haze formation and improve the understanding of the haze formation mechanisms during the January 2013 episode.
Guangbao Yang, Soo Zeng Fiona Phua, Anivind Kaur Bindra
et al.
Inorganic nanoparticles with tunable and diverse properties hold tremendous potential in the field of nanomedicine, while having non‐negligible toxicity concerns in healthy tissues/organs that have resulted in their restricted clinical translation to date. In the past decade, the emergence of biodegradable or clearable inorganic nanoparticles has made it possible to completely solve this long‐standing conundrum. A comprehensive understanding of the design of these inorganic nanoparticles with their metabolic performance in the body is of crucial importance to advance clinical trials and expand their biological applications in disease diagnosis. Here, a diverse variety of biodegradable or clearable inorganic nanoparticles regarding considerations of the size, morphology, surface chemistry, and doping strategy are highlighted. Their pharmacokinetics, pathways of metabolism in the body, and time required for excretion are discussed. Some inorganic materials intrinsically responsive to various conditions in the tumor microenvironment are also introduced. Finally, an overview of the encountered challenges is provided along with an outlook for applying these inorganic nanoparticles toward future clinical translations.
Helen J. Kitchen, S. Vallance, J. L. Kennedy
et al.
Chemistry: From Fundamentals to Manufacturing Helen J. Kitchen,† Simon R. Vallance,†,‡ Jennifer L. Kennedy,†,§ Nuria Tapia-Ruiz,† Lucia Carassiti,† Andrew Harrison, A. Gavin Whittaker, Timothy D. Drysdale, Samuel W. Kingman,‡ and Duncan H. Gregory*,† †WestCHEM, School of Chemistry, University of Glasgow, Joseph Black Building, Glasgow G12 8QQ, United Kingdom ‡Department of Chemical and Environmental Engineering, University of Nottingham, University Park, Nottingham NG7 2RD, United Kingdom School of Engineering, University of Glasgow, James Watt South Building, Glasgow G12 8QQ, United Kingdom Institut Laue-Langevin, 6 rue Jules Horowitz, BP 156, F 38042, Grenoble, Cedex 9, France Tan Delta Microwaves Limited, 7 Nettlingflat, Heriot EH38 5YF, United Kingdom
A solution of the two-dimensional Schrödinger equation with Pauli-Fierz Hamiltonian and trigonometric double-well potential is obtained within the framework of the first-order of adiabatic approximation. The case of vibrational strong coupling is considered which is pertinent for polariton chemistry and (presumably) for enzymatic hydrogen transfer. We exemplify the application of the solution by calculating the proton transfer rate constant in the hydrogen bond of the Zundel ion ${\rm{H_5O_2^{+}}}$ (oxonium hydrate) within the framework of the Weiner's theory. An analytic formula is derived which provides the calculation of the proton transfer rate with the help of elements implemented in {\sl {Mathematica}}. The parameters of the model for the Zundel ion are extracted from the literature data on IR spectroscopy and quantum chemical calculations. The approach yields a vivid manifestation of the phenomenon of vibrationally enhanced tunneling, i.e., a sharp bell-shaped peak of the rate enhancement by the external vibration at its symmetric coupling to the proton coordinate. The results obtained testify that the effect of resonant activation in our model is robust and stable to variations in the types of the quadratically coupled mode (vibrational strong coupling or symmetric one).
Stratospheric aerosol injection (SAI) has been proposed as a geoengineering strategy to mitigate global warming by increasing Earth's albedo. Silica-based materials, such as diamond-doped silica aerogels, have shown promising optical properties, but their impact on stratospheric chemistry, ozone one in particular, remains largely unknown. Here, we present first-principles molecular dynamics (MD) simulations of the heterogeneous reaction between hydrogen chloride ($\mathrm{HCl}$) and chlorine nitrate ($\mathrm{ClONO_2}$), two main reservoirs of stratospheric chlorine and nitrogen species, on a dry, hydroxylated $α$-quartz silica interface. Our results reveal a barrierless reaction pathway toward the formation of chlorine gas ($\mathrm{Cl}_2$), a major contributor to stratospheric ozone loss. We design a heterogeneous kinetic model informed by our MD simulation and available experimental data: despite the barrierless formation of $\mathrm{Cl_2}$, the higher surface affinities and partial pressures of $\mathrm{HNO_3}$ and $\mathrm{HCl}$ compared to those of $\mathrm{ClONO_2}$ result in a negligible reaction probability, $γ_\mathrm{ClONO_2}$, upon chlorine nitrate collision with the silica surface. Since $γ_\mathrm{ClONO_2}$ enters as a proportionality constant in the definition of the heterogeneous reaction rate, our kinetic model indicates that the injection of silica-based aerosols may have only a limited impact on stratospheric ozone depletion driven by $\mathrm{HCl}$ and $\mathrm{ClONO_2}$ chemistry. At the same time, our findings also underscore the scarcity of experimental data, the need of better theoretical frameworks for the inclusion of MD results into kinetic models, and the urgency for further experimental validations of silica-based SAI technologies before their deployment in climate intervention strategies.
Designing inorganic crystalline materials with tailored properties is critical to technological innovation, yet current generative computational methods often struggle to efficiently explore desired targets with sufficient interpretability. Here, we present MatAgent, a generative approach for inorganic materials discovery that harnesses the powerful reasoning capabilities of large language models (LLMs). By combining a diffusion-based generative model for crystal structure estimation with a predictive model for property evaluation, MatAgent uses iterative, feedback-driven guidance to steer material exploration precisely toward user-defined targets. Integrated with external cognitive tools-including short-term memory, long-term memory, the periodic table, and a comprehensive materials knowledge base-MatAgent emulates human expert reasoning to vastly expand the accessible compositional space. Our results demonstrate that MatAgent robustly directs exploration toward desired properties while consistently achieving high compositional validity, uniqueness, and material novelty. This framework thus provides a highly interpretable, practical, and versatile AI-driven solution to accelerate the discovery and design of next-generation inorganic materials.
Cerboni Noemi, Stephens Kyle J., Shepelin Nick A.
et al.
The production of superheavy elements requires targets capable of withstanding prolonged, high-intensity heavy ion-beam bombardment. Current methods, such as molecular plating, produce actinoid films with insufficient stability under these conditions. To address this, a thermally superior solid solution between actinoids and Pd has been synthesized using the coupled reduction process and successfully tested. To further improve said technique, we aimed at confining Tb (i.e., a surrogate for late actinoid elements) within a thin Pd layer with a thickness of a typical target layer suitable for superheavy element synthesis. The thin Pd film was initially deposited onto a support composed of a Ni backing foil and a TiN layer intended to block the diffusion of Tb and Pd into the underlying Ni during coupled reduction. The thermal stability of the obtained multilayered samples and the diffusion behavior of Tb were studied by cross-sectional analysis via scanning electron microscopy coupled with focused ion beam milling and energy dispersive X-ray spectroscopy.
Ulrik Friis-Jensen, Frederik L. Johansen, Andy S. Anker
et al.
Advances in graph machine learning (ML) have been driven by applications in chemistry as graphs have remained the most expressive representations of molecules. While early graph ML methods focused primarily on small organic molecules, recently, the scope of graph ML has expanded to include inorganic materials. Modelling the periodicity and symmetry of inorganic crystalline materials poses unique challenges, which existing graph ML methods are unable to address. Moving to inorganic nanomaterials increases complexity as the scale of number of nodes within each graph can be broad ($10$ to $10^5$). The bulk of existing graph ML focuses on characterising molecules and materials by predicting target properties with graphs as input. However, the most exciting applications of graph ML will be in their generative capabilities, which is currently not at par with other domains such as images or text. We invite the graph ML community to address these open challenges by presenting two new chemically-informed large-scale inorganic (CHILI) nanomaterials datasets: A medium-scale dataset (with overall >6M nodes, >49M edges) of mono-metallic oxide nanomaterials generated from 12 selected crystal types (CHILI-3K) and a large-scale dataset (with overall >183M nodes, >1.2B edges) of nanomaterials generated from experimentally determined crystal structures (CHILI-100K). We define 11 property prediction tasks and 6 structure prediction tasks, which are of special interest for nanomaterial research. We benchmark the performance of a wide array of baseline methods and use these benchmarking results to highlight areas which need future work. To the best of our knowledge, CHILI-3K and CHILI-100K are the first open-source nanomaterial datasets of this scale -- both on the individual graph level and of the dataset as a whole -- and the only nanomaterials datasets with high structural and elemental diversity.