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Results for “CLUSTER EXPANSION”

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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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Bayesian prior construction for uncertainty quantification in first-principles statistical mechanics

First-principles statistical mechanics enables the prediction of thermodynamic and kinetic properties of materials, but is computationally expensive. Many approaches require surrogate models to calculate energies within Monte Carlo or molecular dynamics simulations. Inexpensive surrogates such as cluster expansions enable otherwise intractable calculations by interpolating data from higher accuracy methods, such as Density Functional Theory (DFT). Surrogate models introduce uncertainty into downstream calculations, in addition to any uncertainty inherent to DFT calculations. Bayesian frameworks address this by quantifying uncertainty and incorporating expert knowledge through priors. However, constructing effective priors remains challenging. This work introduces and describes practical strategies for building Bayesian cluster expansions, focusing on basis truncation, hyperparameter selection, and ground state replication. We analyze multiple basis truncation schemes, compare cross-validation to the evidence-approximation for hyperparameter optimization, and provide methods to find and enforce ground-state-preserving models through priors. Additionally, we compare the uncertainties between different approximations to DFT (LDA, PBE, SCAN) against the uncertainty introduced with the use of cluster expansion surrogate models. These approaches are demonstrated on the BCC Li x Mg 1-x and Li x Al 1-x alloys, which are both of interest for solid-state Li batteries. Our results provide guidelines for constructing and utilizing Bayesian cluster expansions, thereby improving the transparency of materials modeling. Furthermore, the approaches and insights developed in this work can be transferred to a wide range of cluster expansion surrogate models, including the atomic cluster expansion and related machine-learned interatomic potential architectures.

Alloy theory↗
DOE OSTI2012

Modeling Coverage Dependence in Surface Reaction Networks

In heterogeneous catalysis molecules adsorb on surfaces and react to form products which desorb. It has been known for a long time that the reactivity of a surface depends on coverage of intermediates, but there had been little work in quantitatively modeling this dependence or in understanding the nature of the coverage dependence. Our work aimed to develop methods for quantitatively modeling the coverage dependence of adsorption energies, and to determine the origin of the coverage dependence. We utilized density functional theory to compute the coverage dependent adsorption energies of many atomic adsorbates (H, C, N, O, and S) on the late transition metal (111) surfaces. We were able to show that the coverage dependence is due to an adsorbate-induced surface electronic structure modification mechanism of the metal atom d-orbitals. Furthermore, we showed the coverage dependence is similar for all of these adsorbates and metal surfaces, enabling the prediction of the coverage dependence of other adsorbates and metals. There are numerous ways that adsorbates can adsorb onto a surface; too many to attempt to compute every possible adsorbate configuration using density functional theory. To address this limitation we used the density functional theory results to construct a simpler model (a cluster expansion) that could be used to enumerate the configurations that could be described by a simple lattice of adsorption sites. The cluster expansion enabled us to show that the similarity in the coverage dependence exists across all coverages. Although the cluster expansion methodology can be extended in principle to multiple adsorbates, adsorption sites and alloys, in practice this is tedious and computationally expensive. As alternative this the cluster expansion approach, we developed a simple model that describes the coverage dependent mechanism that is extendable to alloy surfaces. This model allows large-scale screening studies to be performed, and was used in part of this work to identify sulfur-tolerant alloy catalysts. This grant provided full support one PhD student, Spencer Miller, who now works for Advanced Process Combinatorics, and partial support for another PhD student, Nilay Inoglu, who now works for Exxon Mobil. The work supported by this grant has been published in eight peer-reviewed publications and two book chapters which are listed in section 3. One of the publications (PRB 2010, 82, 045414) was selected as an important scientific contribution within the DOE BES Catalysis Science program for 2011. One final manuscript is nearly completed. This work was presented at four invited academic talks, including an international summer school, seven invited conference talks and 21 contributed talks and posters over the course of the grant.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2014

Excitation picture of an interacting Bose gas

Atomic Bose–Einstein condensates (BECs) can be viewed as macroscopic objects where atoms form correlated atom clusters to all orders. Therefore, the presence of a BEC makes the direct use of the cluster-expansion approach–lucrative e.g. in semiconductor quantum optics–inefficient when solving the many-body kinetics of a strongly interacting Bose. An excitation picture is introduced with a nonunitary transformation that describes the system in terms of atom clusters within the normal component alone. The nontrivial properties of this transformation are systematically studied, which yields a cluster-expansion friendly formalism for a strongly interacting Bose gas. Its connections and corrections to the standard Hartree–Fock–Bogoliubov approach are discussed and the role of the order parameter and the Bogoliubov excitations are identified. The resulting interaction effects are shown to visibly modify number fluctuations of the BEC. Even when the BEC has a nearly perfect second-order coherence, the BEC number fluctuations can still resolve interaction-generated non-Poissonian fluctuations. - Highlights: • Excitation picture expresses interacting Bose gas with few atom clusters. • Semiconductor and BEC many-body investigations are connected with cluster expansion. • Quantum statistics of BEC is identified in terms of atom clusters. • BEC number fluctuations show extreme sensitivity to many-body correlations. • Cluster-expansion friendly framework is established for an interacting Bose gas.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

PyCCE: A python package for custer correlation expansion simulations of spin qubit dynamics.

PyCCE, an open-source Python library for simulating the dynamics of spin qubits in a spin bath, is presented using the cluster-correlation expansion (CCE) method. PyCCE includes modules to generate realistic spin baths, employing coupling parameters computed from first principles with electronic structure codes, and enables the user to run simulations with either the conventional or generalized CCE method. Three use cases of the Python library are illustrated: the calculation of the Hahn-echo coherence time of the nitrogen-vacancy in diamond; the calculation of the coherence time of the basal divacancy in silicon carbide at avoided crossings; and the calculation for magnetic field orientation-dependent dynamics of a shallow donor in silicon. The complete documentation, downloadable tutorials, and installation instructions are available at https://pycce.readthedocs.io/en/latest/.

coherence↗
DOE OSTI2022

CASM — A software package for first-principles based study of multicomponent crystalline solids

CASM is a software package that enables first-principles based studies of crystalline materials. It has been designed to treat coupled chemical, mechanical, vibrational, and magnetic degrees of freedom to determine ground state and finite temperature properties of crystals. The symmetry of the underlying parent crystal structure is used to enumerate perturbations of the parent crystal structure and generate derivative structures which can be input to first-principles calculations in order to explore the ground state energy landscape. CASM algorithmically constructs cluster expansions that fully couple discrete and continuous degrees of freedom, and generates highly efficient code to evaluate the cluster expansion basis functions. Widely used machine learning methods are integrated for fitting expansion coefficients to first-principle calculations. The fully parameterized cluster expansions can be combined with (kinetic) Monte Carlo methods to calculate finite temperature thermodynamic and kinetic properties. CASM Alloy Manager identifies distinct parent crystal structures in an alloy system, creating individual projects for each, and integrating the results. The integrated infrastructure facilitates the linkage between first-principles statistical mechanics predictions with higher length scale computational methods, such as phase field simulations. In conclusion, CASM projects are designed to be easy to share in repositories, to re-use, and to extend to include additional chemical species or types of degrees of freedom.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Comparison of DeePMD, MTP, GAP, ACE and MACE Machine‐Learned Potentials for Radiation‐Damage Simulations: A User Perspective

Accurate and efficient interatomic potentials are essential for molecular dynamics (MD) simulations of radiation damage, gas diffusion, and phase stability in complex ceramics such as LiAlO 2 , especially under extreme conditions relevant to tritium production. Here, we evaluate the performance of six machine-learned interatomic potentials (MLIPs), moment tensor potential (MTP), Gaussian approximation potential, deep potential (DeePMD), atomic cluster expansion (ACE), message-passing ACE (multilayer atomic cluster expansion (MACE) pretrained) and MACE (trained from-scratch), all trained on the same density functional theory dataset with inclusion of tritium. The MLIPs are benchmarked against traditional Buckingham and ReaxFF potentials in terms of energy accuracy, density predictions, thermal equilibration behavior, threshold displacement energy (E d ), tritium diffusivity, and computational cost. Among the models, MTP shows the best overall balance between efficiency and accuracy, with low force and energy errors and realistic E d values for Li and Al. The ACE and MACE (pretrained and trained from scratch) models exhibit high E d (>200 eV) and unphysical pair interactions. DeePMD underestimates Ed due to overly repulsive behavior even at equilibrium distances. All models over-estimate tritium diffusion but the pretrained MACE model behaves well during tritium-diffusion simulations up to 500 K, maintaining diffusivities in the physically consistent 10 −11 m 2 /s range. Finally, we quantify the computational cost of each potential in large-scale atomic/molecular massively parallel simulator, finding that only MTP is more efficient than traditional empirical potentials, while others are significantly more expensive. These findings explain the trade-offs between accuracy and computational cost in MLIP development and provide essential guidance for use in high-throughput radiation damage and gas diffusion simulations in nuclear ceramics.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2021

Sparse expansions of multicomponent oxide configuration energy using coherency and redundancy

We report that compressed sensing has become a widely accepted paradigm to construct high dimensional cluster expansion models used for statistical mechanical studies of atomic configuration in complex multicomponent crystalline materials. However, strict sampling requirements necessary to obtain minimal coherence measurements for compressed sensing to guarantee accurate estimation of model parameters are difficult and in some cases impossible to satisfy due to the inability of physical systems to access certain configurations. Nevertheless, the dependence of energy on atomic configuration can still be adequately learned without these strict requirements by using compressed sensing by way of coherent measurements using redundant function sets known as frames. We develop a particular frame constructed from the union of all occupancy-based cluster expansion basis sets. We illustrate how using this highly redundant frame yields sparse expansions of the configuration energy of complex oxide materials that are competitive and often surpass the prediction accuracy and sparsity of models obtained from standard cluster expansions.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗