Engineering topics

Chapman, James

Publications and source records attributed to Chapman, James.

8 records

DOE OSTI2024

Spectroscopy-guided discovery of three-dimensional structures of disordered materials with diffusion models

Spectroscopy techniques such as x-ray absorption near edge structure (XANES) provide valuable insights into the atomic structures of materials, yet the inverse prediction of precise structures from spectroscopic data remains a formidable challenge. In this study, we introduce a framework that combines generative artificial intelligence models with XANES spectroscopy to predict three-dimensional atomic structures of disordered systems, using amorphous carbon (a-C) as a model system. In this work, we introduce a new framework based on the diffusion model, a recent generative machine learning method, to predict 3D structures of disordered materials from a target property. For demonstration, we apply the model to identify the atomic structures of a-C as a representative material system from the target XANES spectra. We show that conditional generation guided by XANES spectra reproduces key features of the target structures. Furthermore, we show that our model can steer the generative process to tailor atomic arrangements for a specific XANES spectrum. Finally, our generative model exhibits a remarkable scale-agnostic property, thereby enabling generation of realistic, large-scale structures through learning from a small-scale dataset (i.e. with small unit cells). Our work represents a significant stride in bridging the gap between materials characterization and atomic structure determination; in addition, it can be leveraged for materials discovery in exploring various material properties as targeted.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Score-based denoising for atomic structure identification

We propose an effective method for removing thermal vibrations that complicate the task of analyzing complex dynamics in atomistic simulation of condensed matter. Our method iteratively subtracts thermal noises or perturbations in atomic positions using a denoising score function trained on synthetically noised but otherwise perfect crystal lattices. The resulting denoised structures clearly reveal underlying crystal order while retaining disorder associated with crystal defects. Purely geometric, agnostic to interatomic potentials, and trained without inputs from explicit simulations, our denoiser can be applied to simulation data generated from vastly different interatomic interactions. The denoiser is shown to improve existing classification methods, such as common neighbor analysis and polyhedral template matching, reaching perfect classification accuracy on a recent benchmark dataset of thermally perturbed structures up to the melting point. Demonstrated here in a wide variety of atomistic simulation contexts, the denoiser is general, robust, and readily extendable to delineate order from disorder in structurally and chemically complex materials.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Effects of Nonequilibrium Atomic Structure on Ionic Diffusivity in LLZO: A Classical and Machine Learning Molecular Dynamics Study

To improve the performance of electrochemical devices, it is essential to understand the effects of nonequilibrium motifs in solids, such as grain boundaries, amorphous phases, and highly strained regions, on atomic-scale transport and stability. Molecular dynamics simulations are used to explore the combined effect of far-from-equilibrium atomic structures and the choice of interatomic potential on ionic diffusivity predictions for Li 7 La 3 Zr 2 O 12 (LLZO), a promising solid electrolyte for all-solid-state batteries. Amorphization and high strain are considered using both classical Buckingham interatomic potentials and machine learning force fields. Here we find that both crystalline expansion and amorphization tend to slow diffusion, although the different physical encodings in the two potentials impact the properties in different ways. We trace these variations to a combination of structural and transport factors, the contributions of which are deconvoluted computationally. Graph-based analysis reveals that the variations for amorphous LLZO arise from the connectivity of diffusion pathways within the predicted structures, which generally correlates with diffusivity and is notably higher for structures generated by the machine learning force fields. Our study provides additional insight into the relationship between atomic structure and diffusivity in LLZO, while also highlighting the need for care in choosing and validating potentials to simulate far from equilibrium structures.

25 ENERGY STORAGE↗

DOE OSTI2023

Universal and interpretable classification of atomistic structural transitions via unsupervised graph learning

Materials processing often occurs under extreme dynamic conditions leading to a multitude of unique structural environments. These structural environments generally occur at high temperatures and/or high pressures, often under non-equilibrium conditions, which results in drastic changes in the material's structure over time. Computational techniques, such as molecular dynamics simulations, can probe the atomic regime under these extreme conditions. However, characterizing the resulting diverse atomistic structures as a material undergoes extreme changes in its structure has proved challenging due to the inherently non-linear relationship between structures as large-scale changes occur. Here, we introduce SODAS++, a universal graph neural network framework, that can accurately and intuitively quantify the atomistic structural evolution corresponding to the transition between any two arbitrary phases. We showcase SODAS++ for both solid–solid and solid–liquid transitions for systems of increasing geometric and chemical complexity, such as colloidal systems, elemental Al, rutile and amorphous TiO 2 , and the non-stoichiometric ternary alloy Ag 26 Au 5 Cu 19 . Finally, we show that SODAS++ can accurately quantify all transitions in a physically interpretable manner, showcasing the power of unsupervised graph neural network encodings for capturing the complex and non-linear pathway, a material's structure takes as it evolves.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Hydrogen in disordered titania: connecting local chemistry, structure, and stoichiometry through accelerated exploration

Hydrogen incorporation in native surface oxides of metal alloys often controls the onset of metal hydriding, with implications for materials corrosion and hydrogen storage. A key representative example is titania, which forms as a passivating layer on a variety of titanium alloys for structural and functional applications. These oxides tend to be structurally diverse, featuring polymorphic phases, grain boundaries, and amorphous regions that generate a disparate set of unique local environments for hydrogen. Here, we introduce a workflow that can efficiently and accurately navigate this complexity. First, a machine learning force field, trained on ab initio molecular dynamics simulations, was used to generate amorphous configurations. Density functional theory calculations were then performed on these structures to identify local oxygen environments, which were compared against experimental observations. Second, to classify subtle differences across the disordered configuration space, we employ a graph-based sampling procedure. Finally, local hydrogen binding energies and hopping kinetics are computed using exhaustive density functional theory calculations on representative configurations. Here, we leverage this methodology to show that hydrogen binding energetics are described by local oxygen coordination, which in turn is affected by stoichiometry, and form the basis of hopping kinetics and diffusion. Together these results imply that hydrogen incorporation and transport in TiO x can be tailored through compositional engineering, with implications for improving the performance and durability of titanium-derived alloys in hydrogen environments.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Quantifying the atomistic free-volume morphology of materials with graph theory

Here, we introduce a new computational methodology for the identification and characterization of free volume within/around atomistic configurations. This scheme employs a three-stage workflow, by which spheres are iteratively grown inside of voxels, and ultimately converted to planar graphs, which are then characterized via a graph-based order parameter. Our approach is computationally efficient, physically intuitive, and universally transferable to any material system. Validation of our methodology is performed on several sets of materials problems: (1) classification of unique free volumes in various crystal phases, (2) autonomous detection and classification of complex surface defects during epitaxial growth simulations, (3) characterization of free volume defects in metals/alloys, and (4) quantification of the spatio-temporal behavior of nano-scale free volume morphologies as a function of both temperature and free-volume size. Our method accurately identifies and characterizes unique free volumes over a multitude of systems and length scales, indicating its potential for future use in understanding the relationship between free volume morphology and material properties under both static and dynamic conditions.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Amorphization of Pseudocapacitive T−Nb 2 O 5 Accelerates Lithium Diffusivity as Revealed Using Tunable Isomorphic Architectures

Abstract Intercalation pseudocapacitance can combine capacitor‐like power densities with battery‐like energy densities. Such surface‐limited behavior requires rapid diffusion where amorphization can increase solid‐state diffusivity. Here intercalation pseudocapacitive materials with tailored extents of amorphization in T‐Nb 2 O 5 are first reported. Amorphization was characterized with WAXS, XPS, XAFS, and EPR which suggested a peroxide‐rich (O 2 2− ) surface that was consistent with DFT predictions. A series of tunable isomorphic architectures enabled comparisons while independently varying transport parameters. Through process of elimination, solid‐state lithium diffusion was identified as the dominant diffusive‐constraint dictating the maximum voltage sweep rate for surface‐limited kinetics ( v SLT ), termed the Surface‐Limited Threshold (SLT). The v SLT increased with amorphization however stable cycling required crystalline T‐Nb 2 O 5 . A current‐response model using series‐impedances well‐matched these observations. This perspective revealed that amorphization of T‐Nb 2 O 5 enhanced solid‐state diffusion by 12.2 % and increased surface‐limitations by 17.0 % (stable samples). This approach enabled retaining 95 % lithiation capacity at ∼800 mV s −1 (1,600 C‐rate equivalent).

25 ENERGY STORAGE↗

DOE OSTI2014

TOWARD COMPLETE STATISTICS OF MASSIVE BINARY STARS: PENULTIMATE RESULTS FROM THE CYGNUS OB2 RADIAL VELOCITY SURVEY

We analyze orbital solutions for 48 massive multiple-star systems in the Cygnus OB2 association, 23 of which are newly presented here, to find that the observed distribution of orbital periods is approximately uniform in log P for P < 45 days, but it is not scale-free. Inflections in the cumulative distribution near 6 days, 14 days, and 45 days suggest key physical scales of ≅0.2, ≅0.4, and ≅1 A.U. where yet-to-be-identified phenomena create distinct features. No single power law provides a statistically compelling prescription, but if features are ignored, a power law with exponent β ≅ –0.22 provides a crude approximation over P = 1.4-2000 days, as does a piece-wise linear function with a break near 45 days. The cumulative period distribution flattens at P > 45 days, even after correction for completeness, indicating either a lower binary fraction or a shift toward low-mass companions. A high degree of similarity (91% likelihood) between the Cyg OB2 period distribution and that of other surveys suggests that the binary properties at P ≲ 25 days are determined by local physics of disk/clump fragmentation and are relatively insensitive to environmental and evolutionary factors. Fully 30% of the unbiased parent sample is a binary with period P < 45 days. Completeness corrections imply a binary fraction near 55% for P < 5000 days. The observed distribution of mass ratios 0.2 < q < 1 is consistent with uniform, while the observed distribution of eccentricities 0.1 < e < 0.6 is consistent with uniform plus an excess of e ≅ 0 systems. We identify six stars, all supergiants, that exhibit aperiodic velocity variations of ∼30 km s{sup –1} attributed to atmospheric fluctuations.

79 ASTRONOMY AND ASTROPHYSICS↗