Engineering topics
Kozinsky, Boris
Publications and source records attributed to Kozinsky, Boris.
Complexity of many-body interactions in transition metals via machine-learned force fields from the TM23 data set
Abstract This work examines challenges associated with the accuracy of machine-learned force fields (MLFFs) for bulk solid and liquid phases ofd-block elements. In exhaustive detail, we contrast the performance of force, energy, and stress predictions across the transition metals for two leading MLFF models: a kernel-based atomic cluster expansion method implemented using sparse Gaussian processes (FLARE), and an equivariant message-passing neural network (NequIP). Early transition metals present higher relative errors and are more difficult to learn relative to late platinum- and coinage-group elements, and this trend persists across model architectures. Trends in complexity of interatomic interactions for different metals are revealed via comparison of the performance of representations with different many-body order and angular resolution. Using arguments based on perturbation theory on the occupied and unoccupieddstates near the Fermi level, we determine that the large, sharpddensity of states both above and below the Fermi level in early transition metals leads to a more complex, harder-to-learn potential energy surface for these metals. Increasing the fictitious electronic temperature (smearing) modifies the angular sensitivity of forces and makes the early transition metal forces easier to learn. This work illustrates challenges in capturing intricate properties of metallic bonding with current leading MLFFs and provides a reference data set for transition metals, aimed at benchmarking the accuracy and improving the development of emerging machine-learned approximations.
Low-index mesoscopic surface reconstructions of Au surfaces using Bayesian force fields
Metal surfaces have long been known to reconstruct, significantly influencing their structural and catalytic properties. Many key mechanistic aspects of these subtle transformations remain poorly understood due to limitations of previous simulation approaches. Using active learning of Bayesian machine-learned force fields trained from ab initio calculations, we enable large-scale molecular dynamics simulations to describe the thermodynamics and time evolution of the low-index mesoscopic surface reconstructions of Au (e.g., the Au(111)-‘Herringbone,’ Au(110)-(1 × 2)-‘Missing-Row,’ and Au(100)-‘Quasi-Hexagonal’ reconstructions). This capability yields direct atomistic understanding of the dynamic emergence of these surface states from their initial facets, providing previously inaccessible information such as nucleation kinetics and a complete mechanistic interpretation of reconstruction under the effects of strain and local deviations from the original stoichiometry. We successfully reproduce previous experimental observations of reconstructions on pristine surfaces and provide quantitative predictions of the emergence of spinodal decomposition and localized reconstruction in response to strain at non-ideal stoichiometries. A unified mechanistic explanation is presented of the kinetic and thermodynamic factors driving surface reconstruction. Furthermore, we study surface reconstructions on Au nanoparticles, where characteristic (111) and (100) reconstructions spontaneously appear on a variety of high-symmetry particle morphologies.
Design & Assembly of Atomically-Precise Quantum Materials & Devices (Final Report)
Atomic-layer honeycomb materials (Xene, where “X” may stand for a number of different elements) are a promising platform for new quantum states. When X is a heavier element such as tin (Sn) or bismuth (Bi), the edges of the honeycomb may conduct electron charge or spin without resistance - an effect that could persist up to room temperature in some cases. The type of edge states, and their robustness at room temperature, depend sensitively on (a) the substrate material that the Xene is sitting on; (b) out-of-plane buckling of the honeycomb structure; (c) the pattern of atoms or molecules sitting atop the honeycomb. This sensitivity suggests that Xene edge states could be tuned and rearranged to construct nanoscale electronic or spintronic devices. We report on theoretical progress in modeling the assembly and structure of Xenes, and their underlying substrates and overlaying atoms. Specifically, we model the stability of Sn atoms forming a single atomic layer (“stanene”), the stability and phase transitions
Accurate Surface and Finite-Temperature Bulk Properties of Lithium Metal at Large Scales Using Machine Learning Interaction Potentials
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Fast uncertainty estimates in deep learning interatomic potentials
Deep learning has emerged as a promising paradigm to give access to highly accurate predictions of molecular and material properties. A common short-coming shared by current approaches, however, is that neural networks only give point estimates of their predictions and do not come with predictive uncertainties associated with these estimates. Existing uncertainty quantification efforts have primarily leveraged the standard deviation of predictions across an ensemble of independently trained neural networks. This incurs a large computational overhead in both training and prediction, resulting in order-of-magnitude more expensive predictions. Here, we propose a method to estimate the predictive uncertainty based on a single neural network without the need for an ensemble. This allows us to obtain uncertainty estimates with virtually no additional computational overhead over standard training and inference. We demonstrate that the quality of the uncertainty estimates matches those obtained from deep ensembles. We further examine the uncertainty estimates of our methods and deep ensembles across the configuration space of our test system and compare the uncertainties to the potential energy surface. Finally, we study the efficacy of the method in an active learning setting and find the results to match an ensemble-based strategy at order-of-magnitude reduced computational cost.
Medium-Range Ordering in the Ionic Glass Electrolytes LiPON and LiSiPON
In this work, we provide an in-depth structural characterization of the amorphous ionic glasses LiPON and LiSiPON with high Li content. Based on ab initio molecular dynamics simulations, the structure of these materials is an inverted structure with either isolated polyanion tetrahedra or polyanion dimers in a Li+ matrix. Based on neutron scattering data, this type of inverted structure leads to a significant amount of medium-range ordering in the structure, as demonstrated by two sharp diffraction peaks and a periodic structural oscillation in the density function G(r). While this medium-range ordering is commonly observed in liquids and metallic glasses, it has not previously been observed in oxides. On a local scale, adding N and Si increases the number of anion bridges and polyanion dimer structures, leading to higher ionic conductivity. In the medium-range ordering, the addition of Si leads to more disorder in the polyanion substructure but a significant increase in the ordering of the O substructure. Finally, we demonstrate that this inverted structure with medium-range ordering results in a glassy material that is both mechanically stiff and ductile on the nanoscale.
Decoding reactive structures in dilute alloy catalysts
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Atomic-Scale STEM Analysis Shows Structural Changes of Au–Pd Nanoparticles in Various Gaseous Environments
We report dynamical restructuring effects in free-standing Au 0.75 Pd 0.25 nanoparticles occurring in gaseous environments at elevated temperatures. The freshly prepared sample was found to have a core–shell structure with a Pd-rich phase on the surface. The evolution of sample composition and morphology under exposure to 1 bar of pure gases, namely O 2 , H 2 , air, CO, and CO 2 , at different temperatures was studied using in situ scanning transmission electron microscopy (STEM) and energy-dispersive X-ray spectroscopy (EDS). We observed sharper facets on the surface of the particles under O 2 or air at 400 °C. Small islands of Pd were present on the surface, although some Pd was redistributed inside the bulk when the temperature was increased under O 2 . Subtle changes in surface roughness were noted when O 2 was substituted with H 2 at 400 °C, an observation correlated to density functional theory (DFT) calculations. The particles lost clean surface facets when CO was introduced at room temperature and at 200 °C. No substantial changes could be observed after exposure to CO 2 at 250 °C. The adsorption of CO molecules on the surface modifies the surface of the particles and decreases the facet prevalence. Furthermore, these in situ observations show how gases can induce subtle modification of the surface of nanocatalysts, potentially impacting their chemical properties.
Phoebe: a high-performance framework for solving phonon and electron Boltzmann transport equations
Understanding the electrical and thermal transport properties of materials is critical to the design of electronics, sensors, and energy conversion devices. Computational modeling can accurately predict material properties but, in order to be reliable, requires accurate descriptions of electron and phonon states and their interactions. While first-principles methods are capable of describing the energy spectrum of each carrier, using them to compute transport properties is still a formidable task, both computationally demanding and memory intensive, requiring integration of fine microscopic scattering details for estimation of macroscopic transport properties. To address this challenge, we present Phoebe—a newly developed software package that includes the effects of electron–phonon, phonon–phonon, boundary, and isotope scattering in computations of electrical and thermal transport properties of materials with a variety of available methods and approximations. This open source C++ code combines MPI-OpenMP hybrid parallelization with GPU acceleration and distributed memory structures to manage computational cost, allowing Phoebe to effectively take advantage of contemporary computing infrastructures. We demonstrate that Phoebe accurately and efficiently predicts a wide range of transport properties, opening avenues for accelerated computational analysis of complex crystals.
Dynamical Study of Adsorbate-Induced Restructuring Kinetics in Bimetallic Catalysts Using the PdAu(111) Model System
The dynamic restructuring of bimetallic catalysts plays a crucial role in their catalytic activity and selectivity. In particular, catalyst pretreatment with species such as carbon monoxide and oxygen has been shown to be an effective strategy for tuning the surface composition and morphology. Mechanistic and kinetic understanding of such restructuring are fundamental to the chemistry and engineering of surface active sites but have remained challenging due to the large structural, chemical, and temporal degrees of freedom. Here, we combine time-resolved temperature-programmed infrared reflection absorption spectroscopy, ab initio thermodynamics, and machine-learning molecular dynamics to uncover previously unidentified timescale and kinetic parameters of in situ restructuring in Pd/Au(111), a highly relevant model system for dilute Pd-in-Au nanoparticle catalysts. The key innovation lies in utilizing CO not only as a chemically sensitive probe of surface Pd, but also as an agent that induces restructuring of the surface. Upon annealing in vacuum, as-deposited Pd islands became encapsulated by Au and partially dissolved into the subsurface, leaving behind isolated Pd monomers on the surface. Subsequent exposure to 0.1 mbar CO enabled Pd monomers to repopulate the surface up to 373 K, above which complete Pd dissolution occurred by 473 K, with apparent activation energies of 0.14 and 0.48 eV, respectively. Furthermore, these restructuring processes occurred over the span of ~1000 s at a given temperature. Such a minute-timescale dynamics not only elucidates the fluxional nature of alloy catalysts but also presents an opportunity to fine-tune the surface at moderate temperature and pressure conditions.
Replication Data for: Decoding reactive structures in dilute alloy catalysts
The data underlying this published work have been made publicly available in this repository as part of the IMASC Data Management Plan. This work was supported as part of the Integrated Mesoscale Architectures for Sustainable Catalysis (IMASC), an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences under Award # DE-SC0012573.
Replication Data for: Multitask Machine Learning of Collective Variables for Enhanced Sampling of Rare Events
The data underlying this published work have been made publicly available in this repository as part of the IMASC Data Management Plan. This work was supported as part of the Integrated Mesoscale Architectures for Sustainable Catalysis (IMASC), an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences under Award # DE-SC0012573.
Multitask Machine Learning of Collective Variables for Enhanced Sampling of Rare Events
Computing accurate reaction rates is a central challenge in computational chemistry and biology because of the high cost of free energy estimation with unbiased molecular dynamics. In this work, a data-driven machine learning algorithm is devised to learn collective variables with a multitask neural network, where a common upstream part reduces the high dimensionality of atomic configurations to a low dimensional latent space and separate downstream parts map the latent space to predictions of basin class labels and potential energies. Here, the resulting latent space is shown to be an effective low-dimensional representation, capturing the reaction progress and guiding effective umbrella sampling to obtain accurate free energy landscapes. This approach is successfully applied to model systems including a 5D Müller Brown model, a 5D three-well model, the alanine dipeptide in vacuum, and an Au(110) surface reconstruction unit reaction. It enables automated dimensionality reduction for energy controlled reactions in complex systems, offers a unified and data-efficient framework that can be trained with limited data, and outperforms single-task learning approaches, including autoencoders.
Dilute Alloys Based on Au, Ag, or Cu for Efficient Catalysis: From Synthesis to Active Sites
The development of new catalyst materials for energy-efficient chemical synthesis is critical as over 80% of industrial processes rely on catalysts, with many of the most energy-intensive processes specifically using heterogeneous catalysis. Catalytic performance is a complex interplay of phenomena involving temperature, pressure, gas composition, surface composition and structure over multiple length and time scales. In response to this complexity, the integrated approach to heterogeneous dilute-alloy catalysis reviewed here brings together materials synthesis, mechanistic surface chemistry, reaction kinetics, in-situ and operando characterization, and theoretical calculations in a coordinated effort to develop design principles to predict and improve catalytic selectivity. Dilute alloy catalysts—in which isolated atoms or small ensembles of the minority metal on the host metal lead to enhanced reactivity while retaining selectivity—are particularly promising as selective catalysts. Several dilute alloy materials using Au, Ag and Cu as the majority host element, including more recently introduced support-free nanoporous metals and oxide-supported nanoparticle "raspberry colloid templated (RCT)" materials, are reviewed for selective oxidation and hydrogenation reactions. Progress in understanding how such dilute alloy catalysts can be used to enhance selectivity of key synthetic reactions is reviewed, including quantitative scaling from model studies to catalytic conditions. The dynamic evolution of catalyst structure and composition studied in surface science and catalytic conditions and their relationship to catalytic function are also discussed, followed by advanced characterization and theoretical modeling that have been developed to determine the distribution of minority metal atoms at or near the surface. Furthermore, the integrated approach demonstrates the success of bridging the divide between fundamental knowledge and design of catalytic processes in complex catalytic systems, which can accelerate the development of new and efficient catalytic processes.
Understanding Relationships between Free Volume and Oxygen Absorption in Ionic Liquids
In this report understanding the factors that govern gas absorption in ionic liquids is critical to the development of high-capacity solvents for catalysis, electrochemistry, and gas separations. Here, we report experimental probes of liquid structure that provide insights into how free volume impacts the O 2 absorption properties of ionic liquids. Specifically, we establish that isothermal compressibility - measured rapidly and accurately through small-angle X-ray scattering - reports on the size distribution of transient voids within a representative series of ionic liquids and is correlated with O 2 absorption capacity. Additionally, O 2 absorption capacities are correlated with thermal expansion coefficients, reflecting the beneficial effect of weak intermolecular interactions in ionic liquids on free volume and gas absorption capacity. Molecular dynamics simulations show that the void size distribution - in particular, the probability of forming larger voids within an ionic liquid - has a greater impact on O 2 absorption than the total free volume. These results establish relationships between the ionic liquid structure and gas absorption properties that offer design strategies for ionic liquids with high gas solubilities.