Atomistic modeling of the structure and diffusion processes at Al(110)/Si(001) interphase boundaries obtained by vapor deposition
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Li, Yang (ORCID:0000000311710852)↗
SEARCH · Engineering Papers
Results for “atomistic models”
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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Atomistic Modeling of the Interphase Structure between Alumina and Al12Fe2Cr Quasicrystal Approximant
Molecular dynamics simulations are performed to study the stability of an fcc metallic interlayer phase between an iron aluminide quasicrystal approximant (QCA) phase (84.2 Al, 5.7 Fe, 5.4 Ni, and 4.7 at.% Cr) and aluminum oxides (α-Al 2 O 3 and γ-Al 2 O 3 ). The presence of the interlayer phase was experimentally observed in some regions of the inner diameter surface of a TPBAR cladding tube. The simulations employed existing many-body (MEAM and EAM) interatomic potentials to describe atomic interactions in the interlayer and QCA phases, and Buckingham+Coulomb pair interactions for the alumina and between the alumina and the QCA (or fcc). Interface systems of QCA/alumina, fec/alumina, and QCA/fcc are modeled and the fracture energy of the interfaces is calculated. Based on the fracture energies, for the γ-Al 2 O 3 , both MEAM and EAM suggest that the fcc interlayer phase would not form. For the α-Al 2 O 3 , the MEAM predicts that the fcc phase would form, however, the EAM predicts that it forms only if the α-Al 2 O 3 is Al-terminated at the interface. For the O-terminated α-Al 2 O 3 , the EAM predicts the fcc phase would not form, consistent with the results for γ-Al 2 O 3 .
36 MATERIALS SCIENCE↗
Solvated Ionic Liquid-based Electrolytes: Experimental Characterization, Atomistic Modeling and Applications in Energy Storage
This study aimed on understanding the molecular structure and transport mechanisms of high-concentration lithium electrolytes, with a particular focus on solvate ionic liquids (SILs) based on glyme solvents, for use in next-generation, high-voltage energy storage systems. The hypothesis is that the composition of the solvents in these electrolytes can be designed and adjusted to control cation solvation, enabling charge transport via an ion-hopping mechanism. This mechanism resembles Grotthuss conduction of protons in water and is independent of electrolyte viscosity. The experimental studies employed linear and nonlinear infrared spectroscopy (FTIR and 2D IR), electrochemical measurements, and vibrational probes.
25 ENERGY STORAGE↗
Atomistic Modeling of materials for fusion energy using machine learned interatomic potentials
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Cusentino, Mary Alice [Sandia National Laboratorie↗
Atomistic Modeling of Fusion Energy Materials using machine learned interatomic potentials
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Cusentino, Mary Alice [Sandia National Laboratorie↗
A semi-atomistic model for flaw-induced fracture in non-ductile materials
Semiatomistic model for nonductile fracture based on analysis of static and dynamic stress concentrations in uniaxial fiber-matrix composite materials
MATHEMATICAL MODEL↗
Atomistic Modeling of Surface and Bulk Properties of Cu, Pd and the Cu-Pd System
The BFS (Bozzolo-Ferrante-Smith) method for alloys is applied to the study of the Cu-Pd system. A variety of issues are analyzed and discussed, including the properties of pure Cu or Pd crystals (surface energies, surface relaxations), Pd/Cu and Cu/Pd surface alloys, segregation of Pd (or Cu) in Cu (or Pd), concentration dependence of the lattice parameter of the high temperature fcc CuPd solid solution, the formation and properties of low temperature ordered phases, and order-disorder transition temperatures. Emphasis is made on the ability of the method to describe these properties on the basis of a minimum set of BFS universal parameters that uniquely characterize the Cu-Pd system.
Bozzolo, Guillermo↗
Growth of Pt/Cu(100): An Atomistic Modeling Comparison with the Pd/Cu(100) Surface Alloy
The Bozzolo, Ferrante, and Smith (BFS) method for alloys is applied to the study of Pt deposition on Cu(100). The formation of a Cu-Pt surface alloy is discussed within the framework of previous results for Pd/Cu(100). In spite of the fact that both Pd and Pt share the same basic behavior when deposited on Cu, it is seen that subtle differences become responsible for the differences in growth observed at higher cover-ages. In agreement with experiment, all the main features of Pt/Cu(100) and Pd/Cu(100) are obtained by means of a simple modeling scheme, and explained in terms of a few basic ingredients that emerge from the BFS analysis.
Demarco, Gustavo↗
Atomistic Modeling of Nanostructures via the BFS Quantum Approximate Method
Ideally, computational modeling techniques for nanoscopic physics would be able to perform free of limitations on the type and number of elements, while providing comparable accuracy when dealing with bulk or surface problems. Computational efficiency is also desirable, if not mandatory, for properly dealing with the complexity of typical nano-strucured systems. A quantum approximate technique, the BFS method for alloys, which attempts to meet these demands, is introduced for the calculation of the energetics of nanostructures. The versatility of the technique is demonstrated through analysis of diverse systems, including multi-phase precipitation in a five element Ni-Al-Ti-Cr-Cu alloy and the formation of mixed composition Co-Cu islands on a metallic Cu(III) substrate.
Bozzolo, Guillermo↗
Atomistic Modeling of Pd Site Preference in NiTi
An analysis of the site subsitution behavior of Pd in NiTi was performed using the BFS method for alloys. Through a combination of Monte Carlo simulations and detailed atom-by-atom energetic analyses of various computational cells, representing compositions of NiTi with up to 10 at% Pd, a detailed understanding of site occupancy of Pd in NiTi was revealed. Pd subsituted at the expense of Ni in a NiTi alloy will prefer the Ni-sites. Pd subsituted at the expense of Ti shows a very weak preference for Ti-sites that diminishes as the amount of Pd in the alloy increases and as the temperature increases.
Bozzolo, Guillermo↗
Quantum Approximate Methods for the Atomistic Modeling of Multicomponent Alloys
This chapter describes the role of quantum approximate methods in the understanding of complex multicomponent alloys at the atomic level. The need to accelerate materials design programs based on economical and efficient modeling techniques provides the framework for the introduction of approximations and simplifications in otherwise rigorous theoretical schemes. As a promising example of the role that such approximate methods might have in the development of complex systems, the BFS method for alloys is presented and applied to Ru-rich Ni-base superalloys and also to the NiAI(Ti,Cu) system, highlighting the benefits that can be obtained from introducing simple modeling techniques to the investigation of such complex systems.
Bozzolo, Guillermo↗
Electrostatic Properties of PE and PTFE Subjected to Atmospheric Pressure Plasma Treatment; Correlation of Experimental Results with Atomistic Modeling
The use of an atmospheric pressure glow discharge (APGD) plasma was used at KSC to increase the hydrophilicity of spaceport materials to enhance their surface charge dissipation and prevent possible ESD in spaceport operations. Significant decreases in charge decay times were observed after tribocharging the materials using the standard KSC tribocharging test. The polarity and amount of charge transferred was dependent upon the effective work function differences between the respective materials. In this study, polyethylene (PE) and polytetrafluoroethylene (PTFE) were exposed to a He+O2 APGD. The pre and post treatment surface chemistry was analyzed by X-ray photoelectron spectroscopy and contact angle measurements. Semi-empirical and ab initio calculations were performed to correlate the experimental results with some plausible molecular and electronic structure features of the oxidation process. For the PE, significant surface oxidation was observed, as indicated by XPS showing C-O, C=O, and O-C=O bonding, and a decrease in the surface contact angle from 98.9 deg to 61.2 deg. For the PTFE, no C-O bonding appeared and the surface contact angle increased indicating the APGD only succeeded in cleaning the PTFE surface without affecting the surface structure. The calculations using the PM3 and DFT methods were performed on single and multiple oligomers to simulate a wide variety of oxidation scenarios. Calculated work function results suggest that regardless of oxidation mechanism, e.g. -OH, =0 or a combination thereof, the experimentally observed levels of surface oxidation are unlikely to lead to a significant change in the electronic structure of PE and that its increased hydrophilic properties are the primary reason for the observed changes in its electrostatic behavior. The calculations for PTFE argue strongly against significant oxidation of that material, as confirmed by the XPS results.
Trigwell, Steve↗
Electrostatic Properties of Polymers Subjected to Atmospheric Pressure Plasma Treatment; Correlation of Experimental Results with Atomistic Modeling
this study, PE, PTFE, PS and PMMA were exposed to a He+O2, APGD and pre and post treatment surface chemistries were analyzed by X-ray photoelectron spectroscopy and contact angle measurements. Semi-empirical and ab-initio calculations were performed to correlate the experimental results with sonic plausible molecular and electronic structure features of the oxidation process. For the PE and PS, significant surface oxidation showing C-O, C=O, and O-C=O bonding, and a decrease in the surface contact angles was observed. For the PTFE and PM MA, little change in the surface composition was observed. The molecular modeling calculations were performed on single and multiple oligomers and showed regardless of oxidation mechanism, e.g. -OH, =O or a combination thereof, experimentally observed levels of surface oxidation were unlikely to lead to a significant change in the electronic structure of PE and PS, and that the increased hydrophilic properties are the primary reason for the observed changes in its electrostatic behavior. Calculations for PTFE and PMMA argue strongly against significant oxidation of those materials, as confirmed by the XPS results.
Trigwell, S.↗
Final Report (October 2024): University of Tennessee, Knoxville (UTK) contribution to: FusMatML: Machine Learning Atomistic Modeling for Fusion Materials Collaborative Project led by Dr. Aidan Thompson, Sandia National Laboratory
The rapid growth of the field of Machine Learning Inter-Atomic Potentials (MLIAP) has lead to a profusion of methods, all of which have some similarity to each other, but each also restricted to particular design choices, often arrived at in a rather ad hoc fashion. Beyond anecdotal evidence, and some benchmarking studies on specific problems, little progress has been made in developing design principles for MLIAPs. The goal of this project is to use machine learning, data science, and uncertainty quantification methods to optimize the design choices for MLIAP.
Density functional theory, Helium and Hydrogen↗
A foundation model for atomistic materials chemistry
Atomistic simulations of matter, especially those that leverage first-principles (ab initio) electronic structure theory, provide a microscopic view of the world, underpinning much of our understanding of chemistry and materials science. Over the last decade or so, machine-learned force fields have transformed atomistic modeling by enabling simulations of ab initio quality over unprecedented time and length scales. However, early machine-learning (ML) force fields have largely been limited by (i) the substantial computational and human effort required to develop and validate potentials for each particular system of interest and (ii) a general lack of transferability from one chemical system to the next. Here, we show that it is possible to create a general-purpose atomistic ML model, trained on a public dataset of moderate size, that is capable of running stable molecular dynamics for a wide range of molecules and materials. We demonstrate the power of the MACE-MP-0 model-and its qualitative and at times quantitative accuracy-on a diverse set of problems in the physical sciences, including properties of solids, liquids, gases, chemical reactions, interfaces, and even the dynamics of a small protein. The model can be applied out of the box as a starting or "foundation" model for any atomistic system of interest and, when desired, can be fine-tuned on just a handful of application-specific data points to reach ab initio accuracy. Establishing that a stable force-field model can cover almost all materials changes atomistic modeling in a fundamental way: experienced users obtain reliable results much faster, and beginners face a lower barrier to entry. Foundation models thus represent a step toward democratizing the revolution in atomic-scale modeling that has been brought about by ML force fields.
Batatia, Ilyes↗
HydraGNN_Predictive_GFM_2024 - Ensemble of predictive graph foundation models for ground state atomistic materials modeling
We provide the ensemble of fifteen pre-trained graph foundation models (GFMs) for atomistic materials modeling applications. Each one of the fifteen GFMs has been trained on five open-source datasets that (once aggregated) amount to over 154 million atomistic structures, which cover over two-thirds of the natural elements of the periodic table and that comprises a broad set of organic and inorganic compounds. This vast set of atomistic structures comprises ground state configurations that are dynamically stable (i.e., equilibrated structures with atomic forces approximately close to zero values) as well as dynamically unstable structures (i.e., non-equilibrium structures with non-negligible non-zero values of atomic forces). The ensemble of datasets aggregated does NOT include excited states. The datasets have been curated to remove atomistic structures with spectral norm of the force tensor above 100 eV/angstrom. Moreover, a linear term of the energy was computed for each dataset using a linear regression model that uses the chemical concentration of each natural element as regressor. The linear term predicted by the linear regression model has been subtracted from each original energy value to perform a re-alignment of the energy values across different electronic structures approximation theories performed to generate the diverse multi-source, multi-fidelity datasets. The folder "ADIOS_files" contains the set of pre-processed datasets in Adaptable I/O System (ADIOS) format (https://www.exascaleproject.org/research-project/adios/) that have been used for the development and training of GFMs in this work. The "ADIOS_files" directory contains 6 sub-directories named as follows: - ANI1x-v3.bp - MPTrj-v3.bp - OC2020-20M-v3.bp - OC2020-v3.bp - OC2022-v3.bp - qm7x-v3.bp Each sub-directory contains the pre-processed datasets converted in Adaptable I/O System (ADIOS) format (https://www.exascaleproject.org/research-project/adios/) that have been used to the development, training, and performance testing of the ensemble go predictive graph foundation models. Each GFM was developed using HydraGNN (https://github.com/ORNL/HydraGNN) as underlying graph neural network (GNN) architecture. The multi-task learning (MTL) capability of HydraGNN was used to simultaneously train the GFMs on labeled values for direct predictions of energy (a total system property of an atomistic structure that measures the chemical stability) and atomic forces (an atomic level property of an atomistic structure that measures the dynamical stability). The hyper parameters of the GFM have been tuned using scalable hyperparameter optimization (HPO) algorithms implemented in the software DeepHyper (https://github.com/deephyper/deephyper). The pre-training of each HPO trial was performed using distributed data parallelism (DDP) to scale the training across 128 compute nodes of the exascale OLCF supercomputer Frontier. Each HPO trial was trained only for 10 epochs and an early stopping was performed to avoid wasting significant computational resources on GNN architectures that were clearly underperforming. For each HPO trial, the 'omnistat' tool developed by (AMD Research - Advanced Micro Device) was used to measure the total energy consumption in kWh. The ensemble of GFMs was obtained by selecting the fifteen best performing HPO trials. Four models have been selected for their clear advantage in accuracy, and these are the GFMs with IDs 229, 156, 147, 260. Additional eleven models have been selected based on judicious balance between accuracy and energy consumption needed for training, and these are the GFMs with IDs 165, 78, 137, 1, 175, 171, 181, 67, 179, 167, 351. Each selected GFM of the ensemble was continued to cumulate a total of at most 30 epochs. In some cases, the total number of epochs actually performed was les than 30 due to two combined factors: (1) the size of the GFM (i.e., the number of model parameters to train) and (2) the total wall-clock time for which the computational resources could be allocated on OLCF-Frontier. The "Ensemble_of_models" directory contains 15 sub-directories named as follows: - gfm_0.229 - gfm_0.156 - gfm_0.147 - gfm_0.260 - gfm_0.165 - gfm_0.78 - gfm_0.137 - gfm_0.1 - gfm_0.175 - gfm_0.171 - gfm_0.181 - gfm_0.67 - gfm_0.179 - gfm_0.167 - gfm_0.351 Each one of these sub-directories refers to one of the fifteen HPO trials that have been selected to continue the pre-training with at most 30 epochs. With each sub-directory associated with a specific HPO trial, the following files can be found: - config.json: file for argument parsing to develop and train an HydraGNN architecture - gfm_0.ID_epoch_N.pk: file with model parameters for HPO ID trial after N epochs of training The ensemble of fifteen GFM architectures was used for (1) ensemble averaging to stabilize the predictions of energy and atomic forces after pre-training for post-processing analysis and (2) ensemble uncertainty quantification (UQ). The code used to develop, pre-train, and load the pre-trained models for post-processing analysis is available on the ORNL-GitHub at the following link: https://github.com/ORNL/HydraGNN/tree/Predictive_GFM_2024
36 MATERIALS SCIENCE↗
Molecular Modeling for Calculation of Mechanical Properties of Epoxies with Moisture Ingress
Atomistic models of epoxy structures were built in order to assess the effect of crosslink degree, moisture content and temperature on the calculated properties of a typical representative generic epoxy. Each atomistic model had approximately 7000 atoms and was contained within a periodic boundary condition cell with edge lengths of about 4 nm. Four atomistic models were built with a range of crosslink degree and moisture content. Each of these structures was simulated at three temperatures: 300 K, 350 K, and 400 K. Elastic constants were calculated for these structures by monitoring the stress tensor as a function of applied strain deformations to the periodic boundary conditions. The mechanical properties showed reasonably consistent behavior with respect to these parameters. The moduli decreased with decreasing crosslink degree with increasing temperature. The moduli generally decreased with increasing moisture content, although this effect was not as consistent as that seen for temperature and crosslink degree.
Clancy, Thomas C.↗
Foundation models for atomistic simulation of chemistry and materials
Conventional computational methods for modeling chemical and materials systems are limited by system size and timescale, forcing a trade-off between quantum-mechanical accuracy and the sampling needed for realistic observables. Large language and vision foundation models — pre-trained on massive datasets using transformer architectures — have revolutionized many fields. It is thus interesting to ask whether a foundation model — subject to suitable data, parameter scaling and training — could enable learned simulations of chemistry and materials. Here, in this study, we review the field of machine-learned interatomic potentials (MLIPs) and posit that scaling up large and diverse chemical and materials datasets and highly expressive architectures using advanced training strategies should result in models that are: more efficient, transferable, robust to out-of-distribution scenarios, and easier to fine-tune to a variety of downstream physical observables than models trained from scratch on small datasets corresponding to specific, targeted atomistic simulation tasks. We provide specific criteria for creating such large-scale MLIP foundation models, coordinated strategies for their development, evaluation and deployment, and highlight potential emergent capabilities that could transform predictive simulations in chemistry and materials science and accelerate discovery across multiple technological domains.
Yuan, Eric C.-Y. [University of California, Berkel↗