Ion Exchange Synthesizes a Metastable Layered Polymorph of MgZrN 2 and MgHfN 2 Semiconductors
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High-pressure β -Sn germanium may transform into diverse metastable allotropes with distinctive nanostructures and unique physical properties via multiple pathways under decompression. However, the mechanism and transition kinetics remain poorly understood. Here, we investigate the formation of metastable phases and nanostructures in germanium via controllable transition pathways of β -Sn Ge under rapid decompression at different rates. High-resolution transmission electron microscopy reveals three distinct metastable phases with the distinctive nanostructures: an almost perfect st12 Ge crystal, nanosized bc8/r8 structures with amorphous boundaries, and amorphous Ge with nanosized clusters (0.8–2.5 nm). Fast in situ x-ray diffraction and x-ray absorption measurements indicate that these nanostructured products form in certain pressure regions via distinct kinetic pathways and are strongly correlated with nucleation rates and electronic transitions mediated by compression rate, temperature, and stress. This work provides deep insight into the controllable synthesis of metastable materials with unique crystal symmetries and nanostructures for potential applications.
Abstract We evaluate the ability of machine learning to predict whether a hypothetical crystal structure can be synthesized and explain those predictions to scientists. Fine‐tuned large language models (LLMs) trained on a human‐readable text description of the target crystal structure perform comparably to previous bespoke convolutional graph neural network methods, but better prediction quality can be achieved by training a positive‐unlabeled learning model on a text‐embedding representation of the structure. An LLM‐based workflow can then be used to generate human‐readable explanations for the types of factors governing synthesizability, extract the underlying physical rules, and assess the veracity of those rules. These explanations can guide chemists in modifying or optimizing non‐synthesizable hypothetical structures to make them more feasible for materials design.
Abstract We evaluate the ability of machine learning to predict whether a hypothetical crystal structure can be synthesized and explain those predictions to scientists. Fine‐tuned large language models (LLMs) trained on a human‐readable text description of the target crystal structure perform comparably to previous bespoke convolutional graph neural network methods, but better prediction quality can be achieved by training a positive‐unlabeled learning model on a text‐embedding representation of the structure. An LLM‐based workflow can then be used to generate human‐readable explanations for the types of factors governing synthesizability, extract the underlying physical rules, and assess the veracity of those rules. These explanations can guide chemists in modifying or optimizing non‐synthesizable hypothetical structures to make them more feasible for materials design.
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The demand for multifunctional materials has motivated the move from near-equilibrium materials to metastable i.e. out-of-equilibrium phases that can meet several desired target properties. The search for such metastable phases with exotic properties is non-trivial and often serendipitous. Inverse design approaches based on evolutionary search have been powerful tools, but such traditional searches have focused on identifying primarily stable and metastable materials with the lowest enthalpy. The inverse design of materials, with a focus on a desired property such as, for example, hardness is a challenging task because of the expensive computational cost involved in sampling multiple structures. The recent advances in machine learning have brought new powerful AI techniques to the forefront which can potentially revolutionize the inverse design and discovery of materials, especially metastable phases capable of meeting multifunctionality. Here, in this work, we develop and apply an automated reinforcement learning workflow for inverse design that integrates first principles physics and atomistic simulations with machine learning (ML), and high-performance computing to allow rapid exploration of the superhard and ultrahard metastable phases of Carbon. We demonstrate an automatic machine learning based inverse design workflow to map new undiscovered metastable states ranging from near equilibrium to those far-from-equilibrium that satisfy multiple property objectives, specifically bulk moduli, shear moduli and hardness. We create a comprehensive library of carbon stable and metastable phases with varying hardness and subsequently shortlist 10 top performing candidate carbon structures, including two newly reported phases, based on their hardness and characterize their temperature dependent mechanical properties. A neural network model is built using featurization of allotropes of carbon to predict the quasi-harmonic Gibbs free energies. The Gibbs free energies of the top performing phases are analyzed to get an estimate of the experimental synthesizability of these superhard and ultrahard carbon phases. In general, we show using machine learning based inverse design approaches how hitherto inaccessible metastable states can be identified and potentially synthesized to meet the demand for multifunctional materials.
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Controlling the polytypes of close-packed structures of spherical colloids is still a challenging problem despite their wide occurrence. Here, in this work, we show that systematic engineering of the polytype structures of close-packed colloids is possible by using the conformational entropy of polymer chains confined in the interstitial space of colloid crystals. Our interstitial space analysis shows that the hexagonal close-packed (HCP) structures offer larger local interstitial space domains, and the structure director chains in favor of HCP counteract the entropic advantages of the face-centered cubic (FCC) lattices. Using model block copolymer colloids and the known lattice entropy of FCC, a proportionality parameter, β CP = 1.91 × 10 –3 ± 3.67 × 10 –4 , for quantifying the conformational entropy contribution toward HCP structures is extracted. This work demonstrates that the interstitial space of colloid crystals serves as a new structure engineering tool for the self-assembly of colloids.
Magnesium chloride hydrates show a rich diversity of structural forms, with different hydration numbers ranging from 1 to 12. Here, we show that this structural versatility is also present within the same hydration numbers, with the formation of a new phase of magnesium chloride decahydrate (MgCl 2 ·10H 2 O-II) stable at the conditions of 0.03–0.40(14) GPa at 234(11) K and up to 2.11(3) GPa at 220(2) K, as determined by in situ single-crystal X-ray diffraction. The hydrogen bonding interactions of MgCl 2 ·10H 2 O-II are identified and the bulk modulus determined at B 0 = 18.9(12) GPa at 220(2) K. We discuss the implication of this high-pressure MgCl 2 hydrate for icy moon compositional investigations and showcase the structural and density changes between the various hydrates in MgCl 2 ·nH 2 O.
The short- and long-range order of III–V materials under high pressure has long been the subject of debate, with advancements in structural characterization leading to significant revisions to the accepted structural models. Despite these revisions, previous high-pressure structural assignments in the In–Bi system include the site-disordered β-Sn structure type, a structure type demonstrated to be nonexistent in analogous III–V systems. While X-ray diffraction is consistent with site disordering in InBi at high pressure, cluster expansion calculations indicate that disordering requires temperatures above 3000 K. Here, we propose InBi as a model material for studying unique high-pressure planar defects due to its highly anisotropic stress-dependent properties and structure. Specifically, we identify two sets of planar defects that mimic the diffraction pattern of a site disordered β-Sn structure type and are compatible with the calculated disorder barrier. We derive these defects by symmetry relations over crystallographic transitions. Density functional theory calculations of the proposed defects suggest that these defects are stabilized by diminishing interlayer separations with pressure. Further, we find that one of the proposed defects closely resembles a bulk high-pressure phase of InBi, InBi-ϵ, and we assert that the proposed defects order upon heating, acting as a template for InBi-ϵ growth. The proposed defects and their electronic structure provide a basis for the trend of superconducting critical temperature with increasing pressure. These methods for identifying defects are generalizable to other materials with reports of site disorder at high pressure, prompting a broader search for related high-pressure defects.
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Solid-solid phase change materials (SS-PCMs) hold promise for energy storage/dissipation in batteries and energetic materials. Yet, phase change kinetics for SS-PCMs undergoing metastable to semi-stable/stable phase transformations remain relatively ill-studied because trapping metastable phases remain challenging. Recently, we demonstrated the kinetic entrapment and stabilization of a highly disordered and amorphous Al-oxide phase m-AlO x @C (x~2.5-3.0) via laser ablation synthesis in solution (LASiS). We report here, to our knowledge, the first chemical kinetics analysis for S-S phase transition of the m-AlO 3 @C nanocomposites (< 5–8 nm sizes) into semi-stable equilibrium alumina phases (θ/γ-Al 2 O 3 ) via disproportionation reaction, while releasing excess trapped gases. Our results indicate the atomic density of the AlO 3 structures to be ~5–10 times less than that of the final Al 2 O 3 phases, which led to the hypothesis of a volume shrinkage process during their phase transition. Temperature-dependent X-ray diffraction studies reveal the high-temperature phase transition for m-AlO 3 → θ/γ-Al 2 O 3 to follow contracting volume kinetics model, thereby validating our earlier hypothesis. Using the geometric volume contraction model, reaction kinetics analyses from Arrhenius plots reveal the activation energy barrier for the phase transition to be ~270±11 kJ/mol. This makes the activation energy barrier nearly identical to the oxidation of micron-sized Al particles.
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During the synthetic exploration targeting the polycrystalline compound LK-99, an unexpected phase, Pb 5 (PO 4 ) 3 OH δ , was identified as a byproduct. We elucidated the composition of this compound through single-crystal X-ray diffraction analysis. Subsequent synthesis of the target compounds was achieved via high-temperature solid-state pellet reactions. The newly identified Pb 5 (PO 4 ) 3 OH δ has an orthorhombic crystal structure with space group Pnma , representing a unique structure differing from the hexagonal apatite phases of Pb 10 (PO 4 ) 6 O and Pb 5 (PO 4 ) 3 OH. Comprehensive temperature- and magnetic-field-dependent magnetization studies unveiled a temperature-independent magnetic characteristic of Pb 5 (PO 4 ) 3 OH δ . Solid-state nuclear magnetic resonance spectroscopy was employed to decipher the origins of the phase stability and confirm the presence of hydrogen atoms in Pb 5 (PO 4 ) 3 OH δ . These investigations revealed the presence of protonated oxygen sites, in addition to the interstitial water molecules within the structure, which may play critical roles in stabilizing the orthorhombic phase.