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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Resolving Lonsdaleite's decade-long controversy: Atomistic insights into a metastable diamond polymorph

Lonsdaleite, a theoretically proposed hexagonal diamond polymorph, has remained at the center of a five-decade scientific controversy since its 1967 identification. While some studies claim it exhibits superior hardness through compression-induced structural changes, others contend it is merely a stacking-faulted cubic diamond. Meteoritic samples and synthetic preparations have yielded conflicting evidence, with even advanced characterisation techniques like XRD and TEM failing to provide definitive proof. In this work, we employ first-principles density functional theory (DFT) and molecular dynamics (MD) simulations to generate unambiguous theoretical fingerprints through XRD, Raman, and SAED patterns that distinguish true Lonsdaleite from cubic diamond and its defective variants. Our atomistic approach quantifies the thermodynamic metastability of Lonsdaleite under realistic pressure-temperature conditions, reveals distinct spectral signatures through simulated Raman and resolves the structural ambiguity through generalised stacking fault energy analysis. By establishing clear criteria for definitive identification, this study provides long-awaited clarity to the Lonsdaleite debate while offering a robust computational framework for characterising metastable carbon phases in meteoritic, synthetic and industrial materials.

DFT↗
DOE OSTI2026

Leveraging Polymorphism in YbCuBi to Map Transport and Elastic Properties

AMX Zintl compounds with the hexagonal ZrBeSi structure have gained significant attention for their remarkable vacancy tolerance and low thermal conductivity. Their 2D honeycomb sublattice, composed of M–X covalent bonds, is believed to contribute to high anharmonicity and unusual thermal transport properties. In this study, we explore the temperature-dependent polymorphism of YbCuBi as a model system to investigate the relationship between the structure and elastic and thermal transport properties in AMX Zintls. YbCuBi undergoes a structural transition from the “flat” Cu–Bi layers in the ZrBeSi structure to corrugated layers in the LiGaGe structure below 410 K, resulting in a distortion of its centrosymmetric structure. To probe the effects of this crystallographic transition, we employ inelastic neutron scattering and temperature-dependent resonant ultrasound spectroscopy. These experimental findings, coupled with first-principles calculations and thermal conductivity measurements, allow us to elucidate a direct relationship between corrugation of the honeycomb lattice and the observed changes in elastic and thermal transport properties. These insights can be extended to other Zintl phases with similar structure types, providing a platform for the rational design of functional materials with tailored thermal properties.

Chemical structure↗
DOE OSTI2025

Fe 0.6 Pd 0.4 Te 2 : A New Polymorph of FeTe 2 and PdTe 2 Stable at Ambient Pressure

Whereas pyrite space group is a high pressure polymorph of FeTe 2 , here we report pyrite-type single crystal Fe 0.6 Pd 0.4 Te 2 prepared using Pd substitution on Fe atomic site with ambient pressure crystal growth methods. Here, Fe 0.6(1) Pd 0.4(1) Te 2 single crystals show metal behavior above 15 K abided by Bloch-Grüneisen relation, and display resistivity upturn below 15 K due to disorder-related scattering of correlated electronic states.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2025

LeaPP: Learning Pathways to Polymorphs through Machine Learning Analysis of Atomic Trajectories

Understanding the mechanisms underlying crystal nucleation and growth is crucial for many technological applications. Due to the short length and time scales involved, crystal nucleation is often studied using molecular simulations. Most existing approaches to extract the nucleation mechanism from simulations focus on the analysis of static snapshots of the configurations, potentially overlooking subtle local fluctuations and the history of the particles involved in the formation of solid nuclei. Here, in this work, we propose a novel methodology called LeaPP that categorizes nucleation trajectories based on the temporal information of their constituent particles. We leverage the time evolution of the local environment of the crystallizing particles to encapsulate the relationship between the structure and dynamics and distinguish between different evolving particle paths. Identification of the distinct particle paths further enables characterizing the nucleation trajectories into different pathways. Collectively, LeaPP provides a more nuanced understanding of nucleation through an unsupervised approach with lesser dependence on traditional order parameters. Furthermore, the pathways identified by LeaPP are predictive of the resulting polymorph. We demonstrate LeaPP on three different systems─Lennard-Jones-like particles, Ni 3 Al, and water on surfaces. The general methodology underlying LeaPP─considering the time evolution of the building blocks─applies to a wide range of self-assembly problems.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Achieving Phase Control of Polymorphic Tungsten Carbide Catalysts

The polymorphism of tungsten carbide (W x C) and the challenge of selectively synthesizing pure phases have impeded a precise understanding of catalytic structure−property relationships. This study establishes a framework for phase-selective synthesis of W x C through controlling carburization kinetics. By maintaining particle sizes below 10 nm, β-W 2 C is selectively synthesized using gaseous carbon precursors (CH 4 /H 2 ) via temperature-programmed carburization (TPC). Our findings reveal that W 2 C stabilization is predominantly dictated by particle size and carburization kinetics rather than support interactions, providing a tunable approach to synthesize tungsten carbide catalysts. We elucidate the mechanistic pathway of WO x carburization, demonstrating that CH 4 activation occurs at mild temperatures via lattice oxygen. Our reactor studies establish ex situ synthesized β- W 2 C as an active and stable catalyst for the reverse water-gas shift (RWGS) reaction. However, the need for passivation and reduction pretreatment leads to a complex surface structure with diminished intrinsic activity. In contrast, our in situ synthesis protocol for β-W 2 C eliminates the need for passivation and exhibits increased CO STY during RWGS, illustrating the intrinsically higher activity compared to metallic W, WC 1−x (0.5 < x < 1), and stoichiometric WC.

CO2 conversion↗
DOE OSTI2024

Identification of large polarons and exciton polarons in rutile and anatase polymorphs of titanium dioxide

Titanium dioxide (TiO 2 ) is a wide-gap semiconductor with numerous applications in photocatalysis, photovoltaics, and neuromorphic computing. The unique functional properties of this material critically depend on its ability to transport charge in the form of polarons, namely narrow electron wavepackets accompanied by local distortions of the crystal lattice. It is currently well established that the most important polymorphs of TiO 2 , the rutile and anatase phases, harbor small electron polarons and small hole polarons, respectively. However, whether additional polaronic species exist in TiO 2 , and under which conditions, remain open questions. Here, we provide definitive answers to these questions by exploring the rich landscape of polaron quasiparticles in TiO 2 via recently developed ab initio techniques. In addition to the already known small polarons, we identify three species, namely a large hole polaron in rutile, a large quasi-two-dimensional electron polaron in anatase, and a large exciton polaron in anatase. These findings complete the puzzle on the polaron physics of TiO 2 and pave the way for systematically probing and manipulating polarons in a broad class of complex oxides and quantum materials.

Dai, Zhenbang↗
DOE OSTI2024

High-pressure synthesis and neutron scattering study of tantalum hydride TaH 1.23(5) and a tantalum polymorph with A15-type structure

A unique type of tantalum hydride was synthesized by exposing tantalum dihydride to the high hydrogen pressure of 9 GPa and a temperature of using toroid-type high-pressure chambers. The samples of this hydride were cooled down to 100 K, recovered to ambient pressure, and studied in a metastable state by hot extraction, powder x-ray and neutron diffraction, and inelastic neutron scattering. X-ray diffraction demonstrated that this hydride had an A15-type crystal structure of metal lattice (space group , Ta atoms at the 2a and 6c Wyckoff positions) and a lattice parameter of a = 5.150(5) Å at T = 85 K. The hydrogen content determined by hot extraction was H/Ta - 1.23(5). Hydrogen desorption during heating the sample in vacuum proceeded in two steps—first, Δ⁢H⁡/Ta=0.2 was desorbed at around -70 °⁢C, and then the rest of the hydrogen was desorbed between 100 ⁢°C and 390 ⁢°⁢C. The A⁢15-type metal lattice was preserved upon hydrogen removal, leaving a unique polymorph of tantalum. Neutron diffraction of A⁢15-TaH 1.23⁢(5) demonstrated that hydrogen atoms occupy the 24⁢k and 16⁢i Wyckoff sites in the crystal structure, and annealing at 250 K resulted in a decrease of the 24⁢k and an increase of the 16⁢i site occupancies. Inelastic neutron scattering revealed four vibrational modes in the fundamental band of A⁢15-TaH 1.23⁢(5) at 72, 135, 145, and 166 meV, the first three and the last one of which were tentatively assigned to the vibrations of H atoms at the 24⁢k and 16⁢i sites, respectively. No superconductivity was found in A⁢15-TaH 1.1 and hydrogen-free A15-Ta at temperatures down to 1.5 K.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Efficient machine learning interatomic potentials robust for liquid and multiple solid polymorphs of NaF and KF

Achieving atomic-level understanding of crystallization of molten salts is of importance to a wide range of technological applications. Recent work [Fan et al., Proc. Natl. Acad. Sci. USA 122, e2425702122 (2025)] revealed that crystal nucleation in molten LiF salt is a multistage process according to the molecular-dynamics (MD) simulations based on an atomic cluster expansion (ACE) machine-learning interatomic potential (MLIP). In order to understand the influence of increasing cation size on nucleation pathways and nucleation rates of molten fluoride salts, here we develop two new ACE MLIPs for NaF and KF. The two ACE MLIPs feature DFT-SCAN-level accuracy for liquid and multiple solid polymorphs over a wide temperature (0–2000 K) and pressure (0–100 GPa) range, and also reproduce well a number of experimental data for solid and liquid equilibrium properties. The efficiency of the two ACE MLIPs enable million-atom-scale or microsecond-scale MD simulations. The two general-purpose ACE MLIPs are expected to be useful for atomistic simulations for different purposes, in addition to studying crystallization of molten NaF and KF salts.

Crystal melting↗
DOE OSTI2024

Contact-dependent growth inhibition (CDI) systems deploy a large family of polymorphic ionophoric toxins for inter-bacterial competition

Contact-dependent growth inhibition (CDI) is a widespread form of inter-bacterial competition mediated by CdiA effector proteins. CdiA is presented on the inhibitor cell surface and delivers its toxic C-terminal region (CdiA-CT) into neighboring bacteria upon contact. Inhibitor cells also produce CdiI immunity proteins, which neutralize CdiA-CT toxins to prevent auto-inhibition. Here, we describe a diverse group of CDI ionophore toxins that dissipate the transmembrane potential in target bacteria. These CdiA-CT toxins are composed of two distinct domains based on AlphaFold2 modeling. The C-terminal ionophore domains are all predicted to form five-helix bundles capable of spanning the cell membrane. The N-terminal "entry" domains are variable in structure and appear to hijack different integral membrane proteins to promote toxin assembly into the lipid bilayer. The CDI ionophores deployed by E. coli isolates partition into six major groups based on their entry domain structures. Comparative sequence analyses led to the identification of receptor proteins for ionophore toxins from groups 1 & 3 (AcrB), group 2 (SecY) and groups 4 (YciB). Using forward genetic approaches, we identify novel receptors for the group 5 and 6 ionophores. Group 5 exploits homologous putrescine import proteins encoded by puuP and plaP, and group 6 toxins recognize di/tripeptide transporters encoded by paralogous dtpA and dtpB genes. Finally, we find that the ionophore domains exhibit significant intra-group sequence variation, particularly at positions that are predicted to interact with CdiI. Accordingly, the corresponding immunity proteins are also highly polymorphic, typically sharing only ~30% sequence identity with members of the same group. Competition experiments confirm that the immunity proteins are specific for their cognate ionophores and provide no protection against other toxins from the same group. The specificity of this protein interaction network provides a mechanism for self/nonself discrimination between E. coli isolates.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Sequence, structure prediction, and epitope analysis of the polymorphic membrane protein family in Chlamydia trachomatis

The polymorphic membrane proteins (Pmps) are a family of autotransporters that play an important role in infection, adhesion and immunity in Chlamydia trachomatis. Here we show that the characteristic GGA(I,L,V) and FxxN tetrapeptide repeats fit into a larger repeat sequence, which correspond to the coils of a large beta-helical domain in high quality structure predictions. Analysis of the protein using structure prediction algorithms provided novel insight to the chlamydial Pmp family of proteins. While the tetrapeptide motifs themselves are predicted to play a structural role in folding and close stacking of the beta-helical backbone of the passenger domain, we found many of the interesting features of Pmps are localized to the side loops jutting out from the beta helix including protease cleavage, host cell adhesion, and B-cell epitopes; while T-cell epitopes are predominantly found in the beta-helix itself. This analysis more accurately defines the Pmp family of Chlamydia and may better inform rational vaccine design and functional studies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

LiMnO2 polymorphs

Energies of LiMnO2 polymorphs computed within various DFT approximations

36 MATERIALS SCIENCE↗
DOE OSTI2025

Formation of distinctive nanostructured metastable polymorphs mediated by kinetic transition pathways in germanium

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.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Explainable Synthesizability Prediction of Inorganic Crystal Polymorphs Using Large Language Models

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.

Kim, Seongmin [Department of Chemical and Biologic↗
DOE OSTI2025

Explainable Synthesizability Prediction of Inorganic Crystal Polymorphs Using Large Language Models

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.

Kim, Seongmin [Department of Chemical and Biologic↗
DOE OSTI2025

Machine learning enabled discovery of superhard and ultrahard carbon polymorphs

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.

Balasubramanian, Karthik [Univ. of Illinois, Chica↗