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Scalable Autoregressive Deep Surrogates for Dendritic Microstructure Dynamics
Authors:
Kaihua Ji,
Luning Sun,
Shusen Liu,
Fei Zhou,
Tae Wook Heo
Abstract:
Microstructural pattern formation, such as dendrite growth, occurs widely in materials and energy systems, significantly influencing material properties and functional performance. While the phase-field method has emerged as a powerful computational tool for modeling microstructure dynamics, its high computational cost limits its integration into practical materials design workflows. Here, we intr…
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Microstructural pattern formation, such as dendrite growth, occurs widely in materials and energy systems, significantly influencing material properties and functional performance. While the phase-field method has emerged as a powerful computational tool for modeling microstructure dynamics, its high computational cost limits its integration into practical materials design workflows. Here, we introduce a machine-learning framework using autoregressive deep surrogates trained on short trajectories from quantitative phase-field simulations of alloy solidification in limited spatial domains. Once trained, these surrogates accurately predict dendritic evolution at scalable length and time scales, achieving a speed-up of more than two orders of magnitude. Demonstrations in isothermal growth and in directional solidification of a dilute Al-Cu alloy validate their ability to predict microstructure evolution. Quantitative comparisons with phase-field benchmarks further show excellent agreement in the tip-selection constant, morphological symmetry, and primary spacing evolution.
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Submitted 5 November, 2025;
originally announced November 2025.
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Grand canonically optimized grain boundary phases in hexagonal close-packed titanium
Authors:
Enze Chen,
Tae Wook Heo,
Brandon C. Wood,
Mark Asta,
Timofey Frolov
Abstract:
Grain boundaries (GBs) profoundly influence the properties and performance of materials, emphasizing the importance of understanding the GB structure and phase behavior. As recent computational studies have demonstrated the existence of multiple GB phases associated with varying the atomic density at the interface, we introduce a validated, open-source GRand canonical Interface Predictor (GRIP) to…
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Grain boundaries (GBs) profoundly influence the properties and performance of materials, emphasizing the importance of understanding the GB structure and phase behavior. As recent computational studies have demonstrated the existence of multiple GB phases associated with varying the atomic density at the interface, we introduce a validated, open-source GRand canonical Interface Predictor (GRIP) tool that automates high-throughput, grand canonical optimization of GB structures. While previous studies of GB phases have almost exclusively focused on cubic systems, we demonstrate the utility of GRIP in an application to hexagonal close-packed titanium. We perform a systematic high-throughput exploration of tilt GBs in titanium and discover previously unreported structures and phase transitions. In low-angle boundaries, we demonstrate a coupling between point defect absorption and the change in the GB dislocation network topology due to GB phase transformations, which has important implications for the accommodation of radiation-induced defects.
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Submitted 5 April, 2024;
originally announced April 2024.
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Hydrogen in Disordered Titania: Connecting Local Chemistry, Structure, and Stoichiometry through Accelerated Exploration
Authors:
James Chapman,
Kyoung E. Kweon,
Yakun Zhu,
Kyle Bushick,
Leonardus Bimo Bayu Aji,
Christopher Colla,
Nir Goldman,
Nathan Keilbart,
Roger Qui,
Tae Wook Heo,
Brandon C. Wood
Abstract:
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,…
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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 are computed using exhaustive density functional theory calculations on representative configurations. We leverage this methodology to show that hydrogen binding energetics are described by local oxygen coordination, which in turn is affected by stoichiometry. Together these results imply that hydrogen incorporation and transport in TiO$_x$ can be tailored through compositional engineering, with implications for improving performance and durability of titanium-derived alloys in hydrogen environments.
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Submitted 23 August, 2022;
originally announced August 2022.
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Quantifying Disorder One Atom at a Time Using an Interpretable Graph Neural Network Paradigm
Authors:
James Chapman,
Tim Hsu,
Xiao Chen,
Tae Wook Heo,
Brandon C. Wood
Abstract:
Quantifying the level of atomic disorder within materials is critical to understanding how evolving local structural environments dictate performance and durability. Here, we leverage graph neural networks to define a physically interpretable metric for local disorder. This metric encodes the diversity of the local atomic configurations as a continuous spectrum between the solid and liquid phases,…
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Quantifying the level of atomic disorder within materials is critical to understanding how evolving local structural environments dictate performance and durability. Here, we leverage graph neural networks to define a physically interpretable metric for local disorder. This metric encodes the diversity of the local atomic configurations as a continuous spectrum between the solid and liquid phases, quantified against a distribution of thermal perturbations. We apply this novel methodology to three prototypical examples with varying levels of disorder: (1) solid-liquid interfaces, (2) polycrystalline microstructures, and (3) grain boundaries. Using elemental aluminum as a case study, we show how our paradigm can track the spatio-temporal evolution of interfaces, incorporating a mathematically defined description of the spatial boundary between order and disorder. We further show how to extract physics-preserved gradients from our continuous disorder fields, which may be used to understand and predict materials performance and failure. Overall, our framework provides an intuitive and generalizable pathway to quantify the relationship between complex local atomic structure and coarse-grained materials phenomena.
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Submitted 16 June, 2022; v1 submitted 18 March, 2022;
originally announced March 2022.
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Modeling the thermodynamics of the FeTi hydrogenation under para-equilibrium: an ab-initio and experimental study
Authors:
Ebert Alvares,
Paul Jerabek,
Yuanyuan Shang,
Archa Santhosh,
Claudio Pistidda,
Tae Wook Heo,
Bo Sundman,
Martin Dornheim
Abstract:
FeTi-based hydrides have recently re-attracted attention as stationary hydrogen storage materials due to favorable reversibility, good sorption kinetics and relatively low costs compared to alternative intermetallic hydrides. Employing the OpenCalphad software, the thermodynamics of the (FeTi)$_{1-x}$H$_{x}$ (0 $\leq x \leq$ 1) system were assessed as a key basis for modeling hydrogenation of FeTi…
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FeTi-based hydrides have recently re-attracted attention as stationary hydrogen storage materials due to favorable reversibility, good sorption kinetics and relatively low costs compared to alternative intermetallic hydrides. Employing the OpenCalphad software, the thermodynamics of the (FeTi)$_{1-x}$H$_{x}$ (0 $\leq x \leq$ 1) system were assessed as a key basis for modeling hydrogenation of FeTi-based alloys. New thermodynamic data were acquired from our experimental pressure-composition-isotherm (PCI) curves, as well as first-principles calculations utilizing density functional theory (DFT). The thermodynamic phase models were carefully selected based on critical analysis of literature information and \emph{ab-initio} investigations. Key thermodynamic properties such as dissociation pressure, formation enthalpies and phase diagrams were calculated in good agreement to our performed experiments and literature-reported data. This work provides an initial perspective, which can be extended to account for higher-order thermodynamic assessments and subsequently enables the design of novel FeTi-based hydrides. In addition, the assessed thermodynamic data can serve as key inputs for kinetic models and hydride microstructure simulations.
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Submitted 14 April, 2022; v1 submitted 26 November, 2021;
originally announced November 2021.
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Semi-Automated Creation of Density Functional Tight Binding Models Through Leveraging Chebyshev Polynomial-based Force Fields
Authors:
Nir Goldman,
Kyoung Eun Kweon,
Babak Sadigh,
Tae Wook Heo,
Rebecca K. Lindsey,
C. Huy Pham,
Laurence E. Fried,
Bálint Aradi,
Kiel Holliday,
Jason R. Jeffries,
Brandon C. Wood
Abstract:
Density Functional Tight Binding (DFTB) is an attractive method for accelerated quantum simulations of condensed matter due to its enhanced computational efficiency over standard Density Functional Theory approaches. However, DFTB models can be challenging to determine for individual systems of interest, especially for metallic and interfacial systems where different bonding arrangements can lead…
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Density Functional Tight Binding (DFTB) is an attractive method for accelerated quantum simulations of condensed matter due to its enhanced computational efficiency over standard Density Functional Theory approaches. However, DFTB models can be challenging to determine for individual systems of interest, especially for metallic and interfacial systems where different bonding arrangements can lead to significant changes in electronic states. In this regard, we have created a rapid-screening approach for determining systematically improvable DFTB interaction potentials that can yield transferable models for a variety of conditions. Our method leverages a recent reactive molecular dynamics force field where many-body interactions are represented by linear combinations of Chebyshev polynomials. This allows for the efficient creation of multi-center representations with relative ease, requiring only a small investment in initial DFT calculations. We have focused our workflow on TiH$_2$ as a model system and show that a relatively small training set based on unit-cell sized calculations yields a model accurate for both bulk and surface properties. Our approach is easy to implement and can yield accurate DFTB models over a broad range of thermodynamic conditions, where physical and chemical properties can be difficult to interrogate directly and there is historically a significant reliance on theoretical approaches for interpretation and validation of experimental results.
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Submitted 14 April, 2021; v1 submitted 6 February, 2021;
originally announced February 2021.