Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Complexity theory”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Data-driven, multi-moment fluid modeling of Landau damping

Deriving governing equations of complex physical systems based on first principles can be quite challenging when there are certain unknown terms and hidden physical mechanisms in the systems. In this work, we apply a deep learning architecture to learn fluid partial differential equations (PDEs) of a plasma system based on the data acquired from a fully kinetic model. Here, the learned multi-moment fluid PDEs are demonstrated to incorporate kinetic effect such as Landau damping. Based on the learned fluid closure, the data-driven, multi-moment fluid modeling can well reproduce all the physical quantities derived from the fully kinetic model. The calculated damping rate of Landau damping is consistent with both the fully kinetic simulation and the linear theory. The data-driven fluid modeling of PDEs for complex physical systems may be applied to improve the fluid closure and reduce the computational cost of multi-scale modeling of global systems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Probing the Framework Metal Dependent Properties of Actinide-Centered Polyoxoalkoxide Sandwich-Type Complexes

Development of a simple and scalable synthesis of (TBA) 3 [W 5 O 18 MoNO] provides for the formation of the mixed-metal lacunary polyoxoalkoxide, (TBA) 2 [W 4 O 13 (OMe) 4 MoNO][Na(MeOH)]. This complex was used to synthesize a series of polyoxoalkoxide sandwich-type complexes with the general formula (TBA) 2 [M{W 4 O 13 (OMe) 4 MoNO} 2 ], where M = Zr(IV), Hf(IV), Th(IV), U(IV), and Np(IV). Compared to the analogous all-molybdenum complexes, the series have drastically different optical and redox properties. The results indicate that framework metal substitution acts as a tool for “orbital engineering”, with Density Functional Theory (DFT) calculations revealing that the major consequence of incorporation of tungsten into the complexes is localization of LUMO and LUMO+1 on the molybdenum centers remaining in the molecule. The change in the distribution of the frontier orbitals translates to discrepancies in the electronic properties of the series. Given the rarity of polyoxometalate complexes featuring a U(V) ion, one electron oxidation of (TBA) 2 [U(IV){W 4 O 13 (OMe) 4 MoNO} 2 ] was pursued. Isolation of the corresponding U(V) centered sandwich-type complex is reported, only the second example of U(V)-polyoxometalate complex described to date.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Ultrafast Jahn‐Teller Photoswitching in Cobalt Single‐Ion Magnets

Abstract Single‐ion magnets (SIMs) constitute the ultimate size limit in the quest for miniaturizing magnetic materials. Several bottlenecks currently hindering breakthroughs in quantum information and communication technologies could be alleviated by new generations of SIMs displaying multifunctionality. Here, ultrafast optical absorption spectroscopy and X‐ray emission spectroscopy are employed to track the photoinduced spin‐state switching of the prototypical complex [Co(terpy) 2 ] 2+ (terpy = 2,2′:6′,2″‐terpyridine) in solution phase. The combined measurements and their analysis supported by density functional theory (DFT), time‐dependent‐DFT (TD‐DFT) and multireference quantum chemistry calculations reveal that the complex undergoes a spin‐state transition from a tetragonally elongated doublet state to a tetragonally compressed quartet state on the femtosecond timescale, i.e., it sustains ultrafast Jahn‐Teller (JT) photoswitching between two different spin multiplicities. Adding new Co‐based complexes as possible contenders in the search for JT photoswitching SIMs will greatly widen the possibilities for implementing magnetic multifunctionality and eventually controlling ultrafast magnetization with optical photons.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Integrated Experimental and Computational K-Edge X-ray Absorption Near-Edge Structure Analysis of Vanadium Catalysts

X-ray Absorption Near-Edge Structure (XANES) spectroscopy is a powerful tool to reveal key structural and electronic features of isolated catalytic sites, yet insights into molecular structure and more detailed orbital analysis through a combination of experimental and computed XANES analysis are necessary for accurate interpretation of the spectra, especially when significant heterogeneity exits among the catalytic sites. Herein, we present an integrated computational and experimental strategy to determine both primary and secondary bonding interactions within the XANES pre-edge region for organovanadium complexes, which was developed using a series of well-defined molecular vanadium complexes and then applied to the characterization of a supported organovanadium olefin hydrogenation catalyst. Time-dependent density functional theory is used to predict the energy of pre-edge XANES features for a series of vanadium complexes with a variety of oxidation states and local coordination environments. Further, a calibration scheme incorporating different density functionals and basis sets is established, resulting in an optimized scheme that accurately predicts pre-edge energies with a mean absolute error of 0.40 eV. Second-shell coordination (e.g., V---V) effects within XANES are identified through the analysis of the computed dominant orbital contributions for multi-vanadium complexes. Orbital analysis also provided confirmation that the vanadium-hydride formation combined with the heterogeneity of the catalytic active species in ole-fin hydrogenation caused the energy shift and broadening of the pre-edge peak after hydrogen treatment of the silica-supported organovanadium pre-catalyst. This work further elucidates computational XANES simulations and techniques potentially guiding characterization in surface organometallic chemistry.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

A Theory of Ultrafast Charge Transfer Relaxation with Non-Innocent Solvent Molecules

In this work we revisit the photodynamics of tetracyanoethylene-hexamethylbenzene (TCNE- HMB), the molecular complex studied by Hochstrasser et al. [J. Chem. Phys. 100, 4797–4810, 1994] that has long challenged the applicability of Marcus’s theory of elec- tron transfer for predicting photochemical reactions. Using a novel black-box electronic structure algorithm (time-dependent density functional theory with one double, TD- DFT-1D) to efficiently run molecular dynamics that can treat charge recombination, we run ab initio surface hopping molecular dynamics and confirm that, for a polar solvent, charge recombination rates can be incredibly fast (indeed faster than the sol- vent relaxation time); for non-polar solvents, the rate is much slower. We demonstrate that, although Marcus theory cannot be directly applied, these nonequilibrium (and sometimes incredibly fast) photoexcited dynamics can be effectively explained within a two-state model without any evidence of a transition through a conical intersection. Most importantly, for this paradigmatic model system, we are able to identify two nuclear coordinates of interest (rather than the single coordinate predicted by Marcus or a full set of internal quantum modes studied by Bixon and Jortner): the solvent relaxation in the first shell (that strongly modulates the energies of the charge trans- fer state and differentiates time scales for relaxation) and a nuclear displacement in the TCNE-HMB complex arising from a handful of vibrations that induces non-Born Oppenheimer motion and eventually facilitates an abrupt electronic transition to the ground state. Altogether, these findings suggest a tractable generalization of Marcus theory for future simulations of photochemistry with non-innocent solvent environ- ments in the spirit of a Hamiltonian suggested by Stuchebrukhov (J. Chem. Phys. 107, 3821, 1997).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Stability Analysis of Substituted Cobaltocenium [Bis(cyclopentadienyl)cobalt(III)] Employing Chemistry-Informed Neural Networks

Cobaltocenium derivatives are promising components of the anion exchange membranes due to their excellent thermal and alkaline stability under the operating conditions of a fuel cell. Here we present an efficient modeling approach of assessing the chemical stability of substituted cobaltocenium CoCp 2 + based on the computed electronic structure enhanced by machine learning techniques. Within the aqueous environment, the positive charge of the metal cation is balanced by the hydroxide anion through formation of the CoCp 2 + OH¯ complexes, whose dissociation is studied within the implicit solvent employing density functional theory. The data set of about 118 species based on 42 substituent groups characterized by a range of electron- donating (ED) and electron-withdrawing (EW) properties is constructed and analyzed. Given 12 carefully chosen chemistry-informed descriptors of the complexes and relevant fragments, the stability of the complexes is found to strongly correlate with the energies of the highest occupied and lowest unoccupied molecular orbitals, modulated by a switching function of the Hirshfeld charge. The latter is used as a measure of the electron withdrawing-donating character of the substituents. Based on this observation from the conventional regression analysis, two fully connected, feed-forward neural network (FNN) models with different unit structures, called the chemistry-informed (CINN) and the quadratic (QNN) neural networks, are developed. Both models predict the bond dissociation energies of the cobaltocenium complexes with mean relative errors less than 5.40% and average absolute errors less than 0.94 kcal/mol. The results show the potential of QNN to efficiently capture more complex relationships. Here, the concept of incorporating the domain (chemical) knowledge/insight into the neural network structure paves the way to applications of machine learning techniques with small data sets, ultimately leading to better predictive models compared to the conventional regression analysis.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Turbulence theories and statistical closure approaches

When discussing research in physics and in science more generally, it is common to ascribe equal importance to the three components of the scientific trinity: theoretical, experimental, and computational studies. This review will explore the future of modern turbulence theory by tracing its history, which began in earnest with Kolmogorov’s 1941 analysis of turbulence cascade and inertial range [A.N. Kolmogorov, Dokl. Akad. Nauk SSSR, 30, 299, (1941); 32, 19, (1941)]. The 80th Anniversary of Kolmogorov’s landmark study is a welcome opportunity to survey the achievements and evaluate the future of the theoretical approach of turbulence research. Over the years, turbulence theories have been critically important in laying the foundation of our understanding of the nature of turbulent flows. In particular, the Direct Interaction Approximation (DIA) [R.H. Kraichnan, J. Fluid Mech., 5, 497 (1959)] and its subsequent development, known as the statistical closure approach, can be identified as perhaps the most profound single advancement. The remarkable success of the statistical closure has furnished a platform to study such essential concepts as the energy transfer process and interacting scales, and the roles of the straining and sweeping motions. More recently, the quasi-Lagrangian formulation of V. L’vov & I. Procaccia and Kraichnan’s solvable passive scalar model provided powerful ways to explore another fundamental aspect of turbulent flows, the phenomena of intermittency, and the associated anomalous scaling exponents. In the meantime, the theory of fluid equilibria has been developed to describe the large-scale structures that can emerge from turbulent cascades of two-dimensional and geophysical flows at a later time. And yet, despite all these successes, analytical treatments suffer from mathematical complexities. As a result, the utility of theoretical approaches has been limited to relatively idealized flows. On the other hand, in recent decades, computational abilities and experimental facilities have reached an unprecedented scale. Looking beyond the horizon, the imminent deployment of exascale supercomputers will generate complete datasets of the entire flow field of key benchmark flows, allowing researchers to extract additional measurements concerning fully developed, complex turbulent flow fields far beyond those available from the statistical closure theories. Some other developments that could potentially influence the future course of turbulence theories include the advancement of machine learning, artificial intelligence, and data science; likely disruptions arising from the advent of quantum computation; and the increasingly prominent role of turbulence research in providing more accurate climate scientific data. Finally, turbulence theorists can leverage these developments by asking the right questions and developing advanced, sophisticated frameworks that will be able to predict and correlate vast amounts of data from the other two components of the trinity.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Atomistic theories and Simulations of Multiferroics

The goal of this award was to build a research program aimed at investigating puzzling complex phenomena in multiferroics, that are systems possessing electric and magnetic dipoles. We first emphasized the study of multiferroic nanostructures, that are 0D, 1D, 2D systems as well as superlattices and then also study multiferroics as a whole, i.e., bulks as well. In order to fully understand such complex systems, we also decided to study magnetic systems and ferroelectrics, both in their nanostructure and bulk forms. For that and in addition to use standard Density Functional Theory codes, we developed and used ab-initio tools that are able to model properties of large supercells (about a million atoms nowadays) at finite temperature and for any mechanical and boundary conditions. We also collaborated with experimentalists to fully understand the systems under study. The whole program resulted in 108 publications that are listed at the end of this report. In the following, I will put an emphasis on some publications before the last award period and also indicate a description of the last award period.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Intermetallic Nanocatalysts from Heterobimetallic Group 10–14 Pyridine-2-thiolate Precursors

Intermetallic compounds are atomically ordered inorganic materials containing two or more transition metals and main-group elements in unique crystal structures. Intermetallics based on group 10 and group 14 metals have shown enhanced activity, selectivity, and durability in comparison to simple metals and alloys in many catalytic reactions. While high-temperature solid-state methods to prepare intermetallic compounds exist, softer synthetic methods can provide key advantages, such as enabling the preparation of metastable phases or of smaller particles with increased surface areas for catalysis. In this work, we study a generalized family of heterobimetallic precursors to binary intermetallics, each containing a group 10 metal and a group 14 tetrel bonded together and supported by pincer-like pyridine-2-thiolate ligands. Upon thermal decomposition, these heterobimetallic complexes form 10–14 binary intermetallic nanocrystals. Experiments and density functional theory (DFT) computations help in better understanding the reactivity of these precursors toward the synthesis of specific intermetallic binary phases. Using Pd 2 Sn as an example, we demonstrate that nanoparticles made in this way can act as uniquely selective catalysts for the reduction of nitroarenes to azoxyarenes, which highlights the utility of the intermetallics made by our method. Employing heterobimetallic pincer complexes as precursors toward binary nanocrystals and other metal-rich intermetallics provides opportunities to explore the fundamental chemistry and applications of these materials.

10-14 intermetallics↗
DOE OSTI2020

Hybrid Carbon Dioxide Reduction Photocatalysts Consisting of Macrocyclic Cobalt(III) Complexes Deposited on Semiconductor Surfaces

Hybrid photocatalysts can be prepared by coupling metal ligand complexes with light-harvesting semiconductors. It is often challenging and time consuming to derivatize ligands with anchoring groups to effectively attach onto surfaces. In this study, we synthesized hybrid carbon dioxide reduction photocatalysts by directly depositing two macrocyclic Co(III) complexes on three different semiconductor surfaces (TiO 2 , N-Ta 2 O 5 and C 3 N 4 ). The resulting hybrid photocatalysts were characterized with various techniques and tested in CO 2 reduction reactions under different light conditions. Excellent visible-light CO 2 -reduction activity was obtained using C 3 N 4 as the light-harvesting semiconductor. Furthermore, density functional theory calculations were conducted to help understand interactions between the cobalt complexes with a model TiO 2 surface.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Mean Field Theory of Self‐Organizing Memristive Connectomes

Abstract Biological neuronal networks are characterized by nonlinear interactions and complex connectivity. Given the growing impetus to build neuromorphic computers, understanding physical devices that exhibit structures and functionalities similar to biological neural networks is an important step toward this goal. Self‐organizing circuits of nanodevices are at the forefront of the research in neuromorphic computing, as their behavior mimics synaptic plasticity features of biological neuronal circuits. However, an effective theory to describe their behavior is lacking. This study provides for the first time an effective mean field theory for the emergent voltage‐induced polymorphism of circuits of a nanowire connectome, showing that the behavior of these circuits can be explained by a low‐dimensional dynamical equation. The equation can be derived from the microscopic dynamics of a single memristive junction in analytical form. The effective model is tested on experiments of nanowire networks and show that it fits both the potentiation and depression of these synapse‐mimicking circuits. It is shown that this theory applies beyond the case of nanowire networks by formulating a general mean‐field theory of conductance transitions in self‐organizing memristive connectomes.

Caravelli, Francesco↗
DOE OSTI2024

Extending GPU-accelerated Gaussian integrals in the TeraChem software package to f type orbitals: Implementation and applications

Here, the increasing availability of graphics processing units (GPUs) for scientific computing has prompted interest in accelerating quantum chemical calculations through their use. However, the complexity of integral kernels for high angular momentum basis functions often limits the utility of GPU implementations with large basis sets or for metal containing systems. In this work, we report the implementation of f function support in the GPU-accelerated TeraChem software package through the development of efficient kernels for the evaluation of Hamiltonian integrals. The high efficiency of the resulting code is demonstrated through density functional theory (DFT) calculations on increasingly large organic molecules and transition metal complexes, as well as coupled cluster singles and doubles calculations on water clusters. Preliminary investigations into Ni(I) catalysis with DFT and the photochemistry of MnH(CH 3 ) with complete active space self-consistent field are also carried out. Overall, our GPU-accelerated software appears to be well-suited for fast simulation of large transition metal containing systems, as well as organic molecules.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Simulation of Inelastic Neutron Scattering Spectra Directly from Molecular Dynamics Trajectories

Inelastic neutron scattering (INS) is a widely used technique to study atomic and molecular vibrations. With the increasing complexity of materials and thus the INS spectra, being able to simulate the spectra from various atomistic models becomes an essential step and also a major bottleneck for INS data analysis. The conventional approach using density functional theory and lattice dynamics often falls short when the materials of interest are complex (e.g., defective, disordered, heterogeneous, amorphous, large-scale), for which molecular dynamics driven by an interatomic force field is a more common approach. In this paper, we demonstrate a method to directly convert molecular dynamics trajectories into simulated INS spectra, including not only fundamental but also higher order excitations. In this work, the results are compared with data collected on various representative samples from different neutron spectrometers. This development will open great opportunities by providing the key tool to perform in-depth analysis of INS data and to validate and optimize computer models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Non–linear bonding trends in maleonitrile-1,2–dithiolate complexes of the transuranium actinides

The trivalent actinides are produced in the nuclear fuel cycle during power production and provide the largest long-term radiation dose in used nuclear fuel. It is ideal for these elements to be removed from used nuclear fuel for disposal and a necessity for fuel recycling. A key challenge to this is the similarity of chemical behavior of the trivalent actinides to the lanthanides that are also present as fission products in used fuel. Thus far, some of the most effective separations of actinides from lanthanides utilise chelating agents containing sulfur moieties such as dithiophosphinates that selectively bind to actinide ions because of a greater bond covalency relative to lanthanide ions. Typically, greater differences between actinide and lanthanide ions are observable the more ligands and chelators bonds have a covalent character. Here, a series of complexes of the trivalent actinides Np(III) through Cf(III) (excluding Bk(III)) with maleonitrile-1,2-dithiolate (mnt 2– ) are synthesized along with their lanthanide counterparts (La(III) – Nd(III), Sm(III) – Gd(III), Dy(III)), in order to characterize the nature of chemical bonds with these metal ions and a polarizable, non-innocent, sulfur-donor ligand. The metal-sulfur bonds in these complexes trend shorter than measured for lanthanides with equivalent ionic radii. However, particularly large deviations are observed in the neptunium and plutonium complexes in both structure and bonding, resulting in a nonlinear bond length trendline for the actinide series. Density Functional Theory (DFT) calculations with Quantum Theory of Atoms in Molecules (QTAIM) and Natural Bond Order (NBO) analyses indicate that for the neptunium and plutonium complexes, the presence of increased 5f-orbital participation, energy degeneracy of the metal and ligand orbitals, and the structure packing result in shortened M–S bonds. The stabilization of the energy of the 5f-orbitals and the decrease in f-contribution to bonding orbitals in the later actinides results in structural properties more similar to the lanthanide complexes.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2025

Defect Thermodynamics and Transport Properties of Proton Conducting Perovskite Electrode and Electrolyte Materials Evaluated Based on Density Functional Theory Modeling

Both electron-rich and electron-poor perovskite oxides have been used in solid oxide cell applications as electrode and electrolyte materials. The rich oxygen defect chemistry and its coupling to temperature, hydrogen-steam or oxygen-steam gas pressure, or to the applied potentials creates enormous complexities for modeling performance and degradation of the materials. Herein, density functional theory-based thermodynamic modeling was carried out to describe the defect chemistry and transport properties of the proton-conducting electrolyte BaZr1-xYxO3-δ (x≤0.1) and of the triple-conducting perovskite (La,Ba)(Fe,M)O3-δ (M=Y and Zr). The defect thermodynamics of intrinsic point defects and the hydrogen-related defect reactions were solved in integrated defect models and further used to predict the Brouwer diagram and the transport properties of the functional perovskites. For the electron-poor electrolytes BaZr0.9Y0.1O3-δ, the developed model has been used to describe the experimental transport properties in the SOC operating conditions. Specifically, the roles played by the acceptor-bound holes and the intrinsic and hydrogen point defects upon the conductivities of holes, protons, and oxygen vacancies under the hydrogen-rich and oxygen-rich conditions at various humidity levels were demonstrated. A defect modeling tool was also developed for the triple-conducting perovskite (La,Ba)(Fe,M)O3-δ (M=Y and Zr) to examine magnetic effects and hydride defects in defect equilibria.

defect thermodynamics↗
DOE OSTI2025

Quantum magic and multipartite entanglement in the structure of nuclei

Motivated by the Gottesman-Knill theorem, we present a detailed study of the quantum complexity of p -shell and s d -shell nuclei. Valence-space nuclear shell-model wave functions generated by the code are mapped to qubit registers using the Jordan-Wigner mapping (12 qubits for the p shell and 24 qubits for the s d shell), from which measures of the many-body entanglement ( n -tangles) and magic (nonstabilizerness) are determined. While exact evaluations of these measures are possible for nuclei with a modest number of active nucleons, Monte Carlo simulations are required for the more complex nuclei. The broadly applicable Pauli-string I ̂ Z ̂ exact (PSIZe) Markov chain Monte Carlo (MCMC) technique is introduced to accelerate the evaluation of measures of magic in deformed nuclei (with hierarchical wave functions), by factors of ≈ 8 for some nuclei. Significant multinucleon entanglement is found in the s d shell, dominated by proton-neutron configurations, along with significant measures of magic. This is evident not only for the deformed states, but also for nuclei on the path to instability via regions of shape coexistence and level inversion. These results indicate that quantum-computing resources will accelerate precision simulations of such nuclei and beyond. Published by the American Physical Society 2025

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Development of neural network force fields for corrosion studies

To fully understand the chemistry and physics of corrosion, novel methods of simulation must be developed. One approach is designing machine learning (ML) algorithms integrated with density functional theory to develop adaptive force fields to gain insight into corrosion behavior namely at the surface of metal oxides. Current methods of modeling corrosion are slow due to the computational cost of resolving both reaction mechanics and mass transport processes. Machine learning methods can be implemented to obtain structure-activity relationships at both the molecular and bulk scale while still retaining the accuracy of density functional theory (DFT) and significantly decreasing the time needed for simulations of complex chemical processes in the various environments of corrosion. Multiscale models are needed for corrosion studies to fully understand its processes not only at the atomic length scale (chemical bonding, energies, and forces), but also at the nano and meso length scales (solid-state physics and material science processes). Current methods of study include DFT, molecular dynamics, and Monte Carlo. The limitation of DFT is that only a small number of atoms or molecules can be simulated at that level of theory. Density functional theory is used to study the electronic structure of atoms and molecules, and calculate the force component of each atom. However, these calculations are limited to about 1000 atoms. Custom periodic boundary conditions (PBC) can be used to describe the various environments and defects that affect the atomic forces to produce a large data set from which a training set can be derived. Machine learning can be utilized to overcome the barrier of modeling macroscopic and multi-scale processes from ab initio calculations through the development of adaptive force fields. Local environments determine the atomic forces of a given system, therefore adaptive force fields must be created to produce reliable quantum mechanical calculations. This can be achieved by developing a learning algorithm that uses the mapped atomic forces or fingerprint as an input to produce energies and magnetic moments as output. A systematic approach was used to begin to build a data set in order to accurately describe the atomic forces in various environments. In Figure 4 below, a simple PBC cell of Fe{sub 2}O{sub 3} was first optimized. A surface optimization was performed next, followed by a hydroxylated surface optimization. Once this calculation has converged, the adsorption of halide species to the hydroxylated surface will be investigated. TensorFlow is an open source platform for machine learning developed by Google. Using a high level application program interface (API) such as Keras allows for building and training ML models easily in a number of different environments and languages. For this project, a neural network was developed within Anaconda in Python. Future Work: Further development of reference data set; Refining neural network and learning algorithm; Fingerprinting atomic environment to enable mapping of atomic force components; Choosing appropriate training set from reference data; Learning from training set and enabling non-linear mapping of training set fingerprints and the atomic forces; Estimation of uncertainty to identify ranges of outside applicability; Testing and analysis of molecular dynamic simulations.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Unveiling the Electrocatalytic Hydrogen Evolution Reaction Pathway on RuP 2 through Ab Initio Grand Canonical Monte Carlo

In this study, the high catalytic reactivity of ruthenium phosphide (RuP 2 ) has been identified by first-principles density functional theory (DFT) calculations for the electrocatalytic hydrogen evolution reaction (HER). Complex surface reconstructions are considered by applying the ab initio grand canonical Monte Carlo (ai-GCMC) algorithm, efficiently providing a sufficient phase-space exploration of possible surfaces. Combined with surface-phase Pourbaix diagrams, we are able to identify the actual surfaces that obtained under specific experimental environments, thus leading to a more accurate understanding of the nature of the active sites and the binding strength of adsorbates. Specifically, through hundreds of surface reconstructions and hydrogenation states generated with ai-GCMC, we identify the most favorable surface phases of RuP 2 under aqueous acidic conditions. We discover that the HER activity is determined by multiple surfaces with different stoichiometries within a narrow electrode potential window. Low HER overpotential (η) has been found for each of the identified surfaces, as low as 0.04 V. High H-coverage reconstructed surfaces have been discovered under acidic conditions, and the surface Ru sites introduced by additional Ru adatoms or exposed by P-vacancies serve as the active sites for HER based on their nearly reversible H binding. Furthermore, this work provides atomistic insights into the origin of high HER activity on RuP 2 by exploring the dynamic surface phases of electrocatalysts and features a generalizable method to explore the reconstructed/hydrogenated surface space as a function of experimental conditions.

25 ENERGY STORAGE↗