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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 OSTI2026

Multinuclear Solid-State NMR and NMR Crystallography of Solid Forms of Creatine and Creatinine

Creatine is a performance-enhancing supplement with two widely available commercial solid forms, namely, creatine monohydrate (creatine·H 2 O) and creatine HCl, the latter of which does not have a reported crystal structure. Moreover, commercial formulations of creatine may contain creatinine, an undesired impurity phase resulting from the self-cyclization of creatine during manufacturing. Therefore, reliable methods for characterizing the different solid forms of creatine and detecting the presence of creatinine are essential. Herein, we address these challenges using 13 C, 15 N, and 35 Cl solid-state NMR (SSNMR) spectroscopy to obtain distinct spectral fingerprints for creatine·H 2 O and creatine HCl, along with creatinine and creatinine HCl. The acquisition of these SSNMR spectra offers a robust approach for both the rapid characterization of each solid form and the detection of the impurity phases. Additionally, quadrupolar NMR crystallography-guided crystal structure prediction (QNMRX-CSP) was applied for the de novo crystal structure determination of creatine HCl, which was validated by the subsequently determined single-crystal X-ray diffraction (SCXRD) structure. Finally, to investigate the relationship between NMR parameters and structural features, 13 C and 15 N chemical shifts and 35 Cl electric field gradient (EFG) tensors were computed from geometry-optimized structures of the four solid forms by using dispersion-corrected DFT-D2* methods. Finally, this integrative approach offers a powerful framework for advancing the structural understanding and quality control of creatine-based supplements and next-generation formulations, as well as a wide range of other solid pharmaceuticals and nutraceuticals.

NMR↗
DOE OSTI2021

Computational discovery of stable and metastable ternary oxynitrides

Materials design from first principles enables exploration of uncharted chemical spaces. Extensive computational searches have been performed for mixed-cation ternary compounds, but mixed-anion systems are gaining increased interest as well. Central to computational discovery is the crystal structure prediction, where the trade-off between reliance on prototype structures and size limitations of unconstrained sampling has to be navigated. We approach this challenge by letting two complementary structure sampling approaches compete. We use the kinetically limited minimization approach for high-throughput unconstrained crystal structure prediction in smaller cells up to 21 atoms. On the other hand, ternary—and, more generally, multinary—systems often assume structures formed by atomic ordering on a lattice derived from a binary parent structure. Thus, we additionally sample atomic configurations on prototype lattices with cells up to 56 atoms. Using this approach, we searched 65 different charge-balanced oxide–nitride stoichiometries, including six known systems as the control sample. The convex hull analysis is performed both for the thermodynamic limit and for the case of synthesis with activated nitrogen sources. We identified 34 phases that are either on the convex hull or within a viable energy window for potentially metastable phases. We further performed structure sampling for “missing” binary nitrides whose energies are needed for the convex hull analysis. Among these, we discovered metastable Ce3N4 as a nitride analog of the tetravalent cerium oxide, which becomes stable under slightly activated nitrogen condition µN > +0.07 eV. Given the outsize role of CeO2 in research and application, Ce3N4 is a potentially important discovery.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

A systematic approach to generating accurate neural network potentials: the case of carbon

Abstract Availability of affordable and widely applicable interatomic potentials is the key needed to unlock the riches of modern materials modeling. Artificial neural network-based approaches for generating potentials are promising; however, neural network training requires large amounts of data, sampled adequately from an often unknown potential energy surface. Here we propose a self-consistent approach that is based on crystal structure prediction formalism and is guided by unsupervised data analysis, to construct an accurate, inexpensive, and transferable artificial neural network potential. Using this approach, we construct an interatomic potential for carbon and demonstrate its ability to reproduce first principles results on elastic and vibrational properties for diamond, graphite, and graphene, as well as energy ordering and structural properties of a wide range of crystalline and amorphous phases.

Chemistry↗
DOE OSTI2024

Random Forest Prediction of Crystal Structure from Electron Diffraction Patterns

Transmission electron microscopy (TEM) diffraction patterns are regularly used to determine the structure of crystalline materials. Electron diffraction is the most common method to solve for unknown or partially known crystal structures, as it provides direct and interpretable feedback on the orientation of crystal grains under the beam [1]. However, it remains a challenge to determine the crystal structure of a new material or even a new phase of an existing material. Analysis of such materials commonly requires manual exploration and comparison with simulated diffraction patterns. This is often a time consuming process with no obvious start point when many similar structures are possible, and this method cannot be used to determine crystal structure or orientation from structures not included in the diffraction libraries. Therefore, we have developed a machine learning model to determine the crystal structure of a material from its electron diffraction pattern.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Generative Models for Crystalline Materials

Understanding structure-property relationships in materials is fundamental in condensed matter physics and materials science. Over the past few years, machine learning (ML) has emerged as a powerful tool for advancing this understanding and accelerating materials discovery. Early ML approaches primarily focused on constructing and screening large material spaces to identify promising candidates for various applications. More recently, research efforts have increasingly shifted toward generating crystal structures using end-to-end generative models. This review analyzes the current state of generative modeling for crystal structure prediction and de novo generation. It examines crystal representations, outlines the generative models used to design crystal structures, and evaluates their respective strengths and limitations. Furthermore, the review highlights experimental considerations for evaluating generated structures and provides recommendations for suitable existing software tools. Emerging topics, such as modeling disorder and defects, integration in advanced characterization, incorporating synthetic feasibility constraints, and model explainability are explored. Ultimately, this work aims to inform both experimental scientists looking to adapt suitable ML models to their specific circumstances and ML specialists seeking to understand the unique challenges related to inverse materials design and discovery.

Metni, Houssam [Karlsruhe Inst. of Technology (KIT↗
DOE OSTI2022

Atomically thin interlayer phase from first principles enables defect-free incommensurate SnO2/CdTe interface

Advancing optoelectronic and emerging technologies increasingly requires control and design of interfaces between dissimilar materials. However, incommensurate interfaces are notoriously defective and rarely benefit from first-principles predictions, because no explicit atomic-structure models exist. Here, we adopt a bulk crystal structure prediction method to the interface geometry and apply it to SnO2/CdTe heterojunctions without and with the addition of CdCl2, a ubiquitous and beneficial, but abstruse processing step in CdTe photovoltaics. Whereas the direct SnO2/CdTe interface is highly defective, we discover a unique two-dimensional CdCl2 interphase, unrelated to the respective bulk structure. It facilitates a seamless transition from the rutile to zincblende lattices and removes defect-states from the interface bandgap. Implementing the predicted interface electronic structure in device simulations, we demonstrate the theoretical feasibility of bufferless oxide-CdTe heterojunction solar cells approaching the Shockley–Queisser limit. Our results highlight the broader potential of designing atomically thin interlayers to enable defect-free incommensurate interfaces.

14 SOLAR ENERGY↗
DOE OSTI2020

Augmenting machine learning of energy landscapes with local structural information

We present a machine learning approach for accurately predicting formation energies of binary compounds in the context of crystal structure predictions. The success of any machine learning model depends significantly on the choice of representation used to encode the relevant physical information into machine-learnable data. We test different representation schemes based on partial radial and angular distribution functions (RDF+ADF) on Al–Ni and Cd–Te structures generated using our genetic algorithm for structure prediction. We observe a remarkable improvement in predictive accuracy upon transitioning from global to atom-centered representations, resulting in a threefold decrease in prediction errors. We show that a support vector regression model using a combination of atomic radial and angular distribution functions performs best at the formation energy prediction task, providing small root mean squared errors of 3.9 meV/atom and 10.9 meV/atom for Al–Ni and Cd–Te, respectively. We test the performance of our models against common traditional descriptors and find that RDF- and ADF-based representations significantly outperform many of those in the prediction of formation energies. The high accuracy of predictions makes our machine learning models great candidates for the exploration of energy landscapes.

Honrao, Shreyas J. (ORCID:0000000166155257)↗
DOE OSTI2022

Materials under high pressure: a chemical perspective

At high pressure, the typical behavior of elements dictated by the periodic table, including oxidation numbers, stoichiometries in compounds, and reactivity, to name but a few, is altered dramatically. As pressure is applied, the energetic ordering of atomic orbitals shifts, allowing core orbitals to become chemically active, atypical electron congurations to occur, and in some cases,non-atom-centered orbitals to form in the interstices of solid structures. Strange stoichiometries, structures, and bonding motifs result. Crystal structure prediction tools, not burdened by preconceived notions about structural chemistry learned at atmospheric pressure, have been applied to great success to explore phase diagrams at high pressure, identifying novel structures in diverse chemical systems. Several of these phases have been subsequently synthesized. Experimentally, access to high-pressure regimes has been bolstered by advances in diamond anvil cell and dynamic com- pression techniques. Here, the joint efforts of experiment and theory have led to startling success stories in the realm of high-temperature superconductivity, identifying many novel phases (some of which have been synthesized) whose superconducting transition approaches room temperature.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Preference for a pressure-induced 3D structure after 1T-HfSe 2

Extensive crystal structure prediction searches provide evidence of two 3D structures with orthorhombic (Immm) and monoclinic (C2/m) space groups as reasonable candidates for the first pressure-induced phase of 1T-HfSe 2 . Our candidates are compared with two recent proposals that keep the 2D nature of the ambient conditions phase and display hexagonal (P6 3 /mmc) and monoclinic (C2/m) symmetry, although the latter has a different structure than our proposed C2/m phase. Both of these 2D-like structures are discarded based on simple thermodynamic and kinetic arguments that can be extended to explain the pressure-induced polymorphic sequence of other transition metal dichalcogenides. The computed observables of our orthorhombic phase are fully consistent with the experimental structural and Raman data observed at low and high-pressure.

36 MATERIALS SCIENCE↗
DOE OSTI2022

High-Pressure Investigation of 2,4,6-Trinitro-3-bromoanisole (TNBA): Structural Determination and Piezochromism

Understanding phase transitions in energetic materials is crucial for developing predictive models of detonation. 2,4,6-Trinitro-3-bromoanisole (TNBA), an energetic material, was studied in its single-crystal form up to pressures of 45 GPa in a diamond anvil cell. The material was characterized by using X-ray, Raman, and optical transmission measurements. From single-crystal X-ray diffraction, the ambient structure of TNBA was determined which crystallizes in the P2 1 /c space group having four molecular units per unit cell. The X-ray data up to 9.2 GPa were fitted to a third-order Birch–Murnaghan equation of state by using the parameters K o = 13.2(2.4) GPa and K p = 5.1(1.4). Between 6.8 and 7.3 GPa, a phase transition was inferred in TNBA from concurrent fading of X-ray diffraction, disappearance of Raman peaks, increase in sample fluorescence, and discontinuous color change. The new phase was consistent with an amorphous state of at least partially intact molecules judging from the presence of higher-order Raman modes and irreversibility of the Raman spectra upon release. Piezochromism was observed with the translucent yellow TNBA gradually darkening and becoming opaque black at ~25 GPa. This correlated to the absorption edge gradually shifting to the red in the visible spectrum. Signs of two possible additional structural transitions were detected in the 32.4–41.0 GPa range as suggested by a jump in the absorption edge, the irreversible changes in the absorption spectrum upon release to ambient pressure, and by the lack of Raman modes in recovered samples. Here, the crystal and electronic structures of TNBA were also investigated up to 10 GPa by using DFT calculations and crystal structure prediction (CSP) simulations. In agreement with the experimentally observed transition at 7 GPa, the simulations at 10 GPa found a bevy of polymorphs lower in enthalpy and higher in density than P2 1 /c. The lowest calculated enthalpy structure was determined to be P2 1 2 1 2 1 , being in a different space group than the ambient experimental result.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

CHARGED POINT DEFECTS AND DEFECT COMPLEXES IN PHOSPHORENE AND STRUCTURAL PREDICTION OF TAU-11, A DENSITY FUNCTIONAL THEORY APPROACH

With the advent of powerful computers, first principle calculations have become a robust and reliable tool to complement experiments in the study of materials. First principle calculations such as density functional theory can be used to guide experiments to save time and effort when it comes to new and advanced materials discovery. Study of charge defects in materials and crystal structure prediction are such areas in which density functional theory calculations facilitate the prediction of defect formation energy, charge transition levels and elemental site occupations. Experimentally, studying charge defects requires inferring results from various techniques. Likewise, determining site occupancies for similar elements using experimental techniques is challenging and sometimes impossible. In this work, we study the charged intrinsic and extrinsic defects and defect complexes in phosphorene and determine the site occupancies of Al, Fe and Si in the τ11 phase using density functional theory. In the defect work, we calculate the defect formation energy, charge transition level, binding energies and Stokes shift for vacancy, dopant substitution and dopant-vacancy defect complexes in phosphorene. We found that vacancy defect in phosphorene becomes negatively charged in n-type doping and may passivate the dopants and reduce carrier concentration and mobility. For non-metal dopants in phosphorene, we predict that O, S and Si prefer to form dopant-vacancy complexes removing the vacancy defect states from the band gap. Mn dopant-vacancy defect complex exhibits possibility of switching between two magnetic spin states. Lastly, using density functional theory, we complement the results from neutron powder diffraction to determine the site occupancies of Al and Si in τ11 phase which was used to determine the alloying element to develop a high temperature and low density alloy.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Refining T c Prediction in Hydrides via Symbolic‐Regression‐Enhanced Electron‐Localization‐Function‐Based Descriptors

Hydrogen‐based materials are able to possess extremely high superconducting critical temperatures, T c s , due to hydrogen's low atomic mass and strong electron–phonon interaction. Recently, a descriptor based on the Electron Localization Function (ELF) has enabled the rapid estimation of the T c of hydrogen‐containing compounds from electronic networking properties, but its applicability has been limited by the small size and homogeneity of the training dataset used. Herein, the model is re‐examined, compiling a publicly available combined dataset of 244 binary and ternary hydride superconductors. The analysis shows that though ELF‐based networking remains a valuable descriptor, its predictive power declines with increasing compositional complexity. However, by introducing the molecularity index, defined as the highest value of the ELF at which two hydrogen atoms connect, and applying symbolic regression, the accuracy of the predictions can be substantially enhanced. These results establish a more robust framework for assessing superconductivity in hydride materials, facilitating accelerated screening of novel candidates through integration with crystal structure prediction methods or high‐throughput searches.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

AI-assisted rapid crystal structure generation towards a target local environment

In material design, traditional crystal structure prediction approaches are expensive as they require extensive structural sampling through expensive energy minimization methods. Emerging artificial intelligence (AI) generative models have shown great promise in rapidly generating realistic crystals, but they typically handle only a few tens of atoms per unit cell. To overcome this limitation, we introduce a symmetry-informed approach, the Local Environment Geometry-Oriented Crystal Generator (LEGO-xtal). Our method generates initial structures using AI models trained on an augmented dataset, and then optimizes them using structure descriptors rather than energy-based optimization. We demonstrate its effectiveness by expanding from 25 known low-energy sp2 carbon allotropes to over 1700, all within 0.5 eV/atom of the ground-state energy of graphite. This framework offers a generalizable strategy for the targeted design of materials with modular building blocks, such as metal-organic frameworks and battery materials.

Ridwan, Osman Goni [University of North Carolina a↗
DOE OSTI2024

Suppression of Disorder in Benzamide and Thiobenzamide Crystals by Fluorine Substitution

Disorder is a common feature of molecular crystals that complicates determination of structures and can potentially affect electric and mechanical properties. Suppression of disorder is observed in otherwise severely disordered benzamide and thiobenzamide crystals by substituting hydrogen with fluorine in the ortho-position of the phenyl ring. Fluorine occupancies of 20–30% are sufficient to suppress disorder without changing the packing motif. Crystal structure prediction calculations reveal a much denser lattice energy landscape for benzamide compared to 2-fluorobenzamide, suggesting that fluorine substitution makes disorder less likely.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Structurally Constrained Evolutionary Algorithm for the Discovery and Design of Metastable Phases

Metastable materials are abundant in nature and technology, showcasing remarkable properties that inspire innovative materials design. However, traditional crystal structure prediction methods, which rely solely on energetic factors to determine a structure’s fitness, are not suitable for predicting the vast number of potentially synthesizable phases that represent a local minimum corresponding to a state in thermodynamic equilibrium. Here, we present a new approach for the prediction of metastable phases with specific structural features, and interface this method with the XTALOPT evolutionary algorithm. Our method relies on structural features that include the local crystalline order (e.g., the coordination number or chemical environment), and symmetry (e.g., Bravais lattice and space group) to filter the breeding pool of an evolutionary crystal structure search. The effectiveness of this approach is benchmarked on three known metastable systems: XeN 8 , with a two-dimensional polymeric nitrogen sublattice, brookite TiO 2 , and a high pressure BaH 4 phase that was recently characterized. Additionally, a newly predicted metastable melaminate salt, P1¯WC 3 N 6 , was found to possess an energy that is lower than two phases proposed in a recent computational study. Here, the method presented here could help in identifying the structures of compounds that have already been synthesized, and developing new synthesis targets with desired properties.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

First Principles Predictions of SnO2/CdTe and SnO2/CdCl2/CdTe Interface Structures

Interfaces become increasingly the limiting factor of solar cell efficiency, raising the interest in computational models. However, explicit atomic structure models from microscopy are rare, and hypothetical assumptions about the atomic structure become ambiguous for incoherent or highly lattice mismatched interfaces. Adopting a bulk crystal structure prediction algorithm to interfacial systems enables explicit structure models for the bare and CdCl 2 treated SnO 2 /CdTe interface.

CdTe↗