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DOE OSTI2020

Active Learning A Neural Network Model For Gold Clusters & Bulk From Sparse First Principles Training Data

Small metal clusters are of fundamental scientific interest and of tremendous significance in catalysis. These nanoscale clusters display diverse geometries and structural motifs depending on the cluster size; a knowledge of this size-dependent structural motifs and their dynamical evolution has been of longstanding interest. Given the high computational cost of first-principles calculations, molecular modeling and atomistic simulations such as molecular dynamics (MD) has proven to be an important complementary tool to aid this understanding. Classical MD typically employ predefined functional forms which limits their ability to capture such complex size-dependent structural and dynamical transformation. Neural Network (NN) based potentials represent flexible alternatives and in principle, well-trained NN potentials can provide high level of flexibility, transferability and accuracy on-par with the reference model used for training. A major challenge, however, is that NN models are interpolative and requires large quantities (similar to 10 4 or greater) of training data to ensure that the model adequately samples the energy landscape both near and far-from-equilibrium. A highly desirable goal is minimize the number of training data, especially if the underlying reference model is first-principles based and hence expensive. In this work, we introduce an active learning (AL) scheme that trains a NN model on-the-fly with minimal amount of first-principles based training data. Our AL workflow is initiated with a sparse training dataset (similar to 1 to 5 data points) and is updated on-the-fly via a Nested Ensemble Monte Carlo scheme that iteratively queries the energy landscape in regions of failure and updates the training pool to improve the network performance. Using a representative system of gold clusters, we demonstrate that our AL workflow can train a NN with similar to 500 total reference calculations. Using an extensive DFT test set of similar to 1100 configurations, we show that our AL-NN is able to accurately predict both the DFT energies and the forces for clusters of a myriad of different sizes. Our NN predictions are within 30 meV/atom and 40 meV/angstrom of the reference DFT calculations. Moreover, our AL-NN model also adequately captures the various size-dependent structural and dynamical properties of gold clusters in excellent agreement with DFT calculations and available experiments. We finally show that our AL-NN model also captures bulk properties reasonably well, even though they were not included in the training data.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

FAST.Farm Development and Validation of Structural Load Prediction Against Large Eddy Simulations

FAST.Farm is a mid-fidelity engineering tool developed by the National Renewable Energy Laboratory targeted at accurately and efficiently predicting wind turbine power production and structural loading in wind farm settings, including wake interactions between turbines. FAST.Farm is based on several principles of the dynamic wake meandering (DWM) model, but also addresses limitations of previous DWM implementations. Previous FAST.Farm studies have shown the similarities and differences between FAST.Farm and large eddy simulations for rigid turbine cases. The objective of this work is to quantify the ability of FAST.Farm to accurately predict turbine structural response in a small wind farm. This is done by comparing FAST.Farm structural response to SOWFA-OpenFAST results for three laterally-aligned turbines. The purpose of this study is to characterize the similarities and differences between FAST.Farm and a higher fidelity model for predicting turbine structural response in a wind farm for differing atmospheric inflows. Strong statistical agreement was found between FAST.Farm and SOWFA-OpenFAST structural response for the non-waked upstream turbine, and good agreement was found for the downstream turbines for most structural quantities. Higher differences were seen for downstream turbines with low ambient turbulence intensity or yawed turbines, suggesting areas for FAST.Farm wake dynamics modeling improvements. For all cases and turbines, small statistical differences were seen between blade deflections and bending moments, with larger differences for tower-top and tower-base bending moments. Overall, the results establish confidence for applying FAST.Farm to wind farm power and loads analyses and identify areas where further model validations and model improvements should be targeted.

49 EE - Wind and Water Power Program - Wind (EE-4W↗

DOE OSTI2021

Molecular fingerprint and machine learning to accelerate design of high-performance homochiral metal–organic frameworks

In this report computational screening was employed to calculate the enantioseparation capabilities of 45 functionalized homochiral metal–organic frameworks (FHMOFs), and machine learning (ML) and molecular fingerprint (MF) techniques were used to find new FHMOFs with high performance. With increasing temperature, the enantioselectivities for (R,S)-1,3-dimethyl-1,2-propadiene are improved. The “glove effect” in the chiral pockets was proposed to explain the correlations between the steric effect of functional groups and performance of FHMOFs. Moreover, the neighborhood component analysis and RDKit/MACCS MFs show the highest predictive effect on enantioselectivities among the four ML classification algorithms with nine MFs that were tested. Based on the importance of MF, 85 new FHMOFs were designed, and a newly designed FHMOF, NO 2 -NHOH-FHMOF, with high similarity to the optimal MFs achieved improved chiral separation performance, with enantioselectivities of 85%. The design principles and new chiral pockets obtained by ML and MFs could facilitate the development of new materials for chiral separation.

molecular fingerprint↗

DOE OSTI2025

Simulations of edge and SOL turbulence in diverted negative and positive triangularity plasmas

Optimizing the performance of magnetic confinement fusion devices is critical to achieving an attractive fusion reactor design. Negative triangularity (NT) scenarios have been shown to achieve excellent levels of energy confinement, while avoiding edge localized modes. Modeling turbulent transport in the edge and SOL is key in understanding the impact of NT on turbulence and extrapolating the results to future devices and regimes. Previous gyrokinetic turbulence studies have reported beneficial effects of NT across a broad range of parameters. However, most simulations have focused on the inner plasma region, neglecting the impact of NT on the outermost edge. In this work, we investigate the effect of NT in edge and scrape-off layer simulations, including the magnetic X-point and separatrix. For the first time, we employ a multi-fidelity approach, combining global, non-linear gyrokinetic simulations with drift-reduced fluid simulations, to gain a deeper understanding of the underlying physics at play. First-principles simulations using the GENE-X code demonstrate that in comparable NT and PT geometries, similar profiles are achieved, while the turbulent heat flux is reduced by more than 50% in NT. Comparisons with results from the drift-reduced fluid turbulence code GRILLIX suggest that the turbulence is driven by trapped electron modes. The parallel heat flux width on the divertor targets is reduced in NT, primarily due to a lower spreading factor S.

GENE-X↗

DOE OSTI2023

Electrolyte design for Li-ion batteries under extreme operating conditions

The ideal electrolyte for the widely used LiNi 0.8 Mn 0.1 Co 0.1 O 2 (NMC811)||graphite lithium-ion batteries is expected to have the capability of supporting higher voltages (≥4.5 volts), fast charging (≤15 minutes), charging/discharging over a wide temperature range (±60 degrees Celsius) without lithium plating, and non-flammability. No existing electrolyte simultaneously meets all these requirements and electrolyte design is hindered by the absence of an effective guiding principle that addresses the relationships between battery performance, solvation structure and solid-electrolyte-interphase chemistry. Here we report and validate an electrolyte design strategy based on a group of soft solvents that strikes a balance between weak Li+-solvent interactions, sufficient salt dissociation and desired electrochemistry to fulfil all the aforementioned requirements. Remarkably, the 4.5-volt NMC811||graphite coin cells with areal capacities of more than 2.5 milliampere hours per square centimetre retain 75 per cent (54 per cent) of their room-temperature capacity when these cells are charged and discharged at -50 degrees Celsius (-60 degrees Celsius) at a C rate of 0.1C, and the NMC811||graphite pouch cells with lean electrolyte (2.5 grams per ampere hour) achieve stable cycling with an average Coulombic efficiency of more than 99.9 per cent at -30 degrees Celsius. The comprehensive analysis further reveals an impedance matching between the NMC811 cathode and the graphite anode owing to the formation of similar lithium-fluoride-rich interphases, thus effectively avoiding lithium plating at low temperatures. Additionally, this electrolyte design principle can be extended to other alkali-metal-ion batteries operating under extreme conditions.

25 ENERGY STORAGE↗

DOE OSTI2025

The search for high-entropy fuel-cell catalysts using disorder descriptors

The transition to a hydrogen economy depends on efficient, affordable catalysts for fuel cells. Platinum—the industry standard for fuel-cell electrodes—is costly and scarce, highlighting the need for practical alternatives. High-entropy alloys offer vast compositional diversity and tunable properties that can mitigate these issues, yet their chemical complexity and configurational disorder have hindered rational discovery. Here, we introduce a data-driven framework that couples machine learning with first-principles disorder descriptors—including the entropy forming ability, disordered enthalpy-entropy descriptor, and electronic-structure similarity metrics to platinum—to predict alloy synthesizability and catalytic performance. These descriptors are applied for the first time in the context of fuel-cell catalyst discovery. The workflow rapidly screens more than 20 000 compositions and identifies several platinum-free candidates that are economically viable, readily scalable, and exhibit promising predicted activity. These results demonstrate that disorder descriptors are reliably predicted by machine learning models and can be effectively integrated into materials-discovery pipelines, accelerating innovation across complex compositional spaces.

fuel-cell catalysts↗

DOE OSTI2024

Site heterogeneity and broad surface-binding isotherms in modern catalysis: Building intuition beyond the Sabatier principle

Learning the science of heterogeneous catalysis and electrocatalysis always starts with the simple case of a flat, uniform surface with an ideal adsorbate. It has of course been recognized for a century that real catalysts are more complicated. For the increasingly complex catalysts of the 21st century, this Perspective argues that surface heterogeneity and non-ideal binding isotherms are central features, and their implications need to be incorporated in current thinking. A variety of systems are described herein where catalyst complexity leads to broad, non-Langmuirian surface isotherms for the binding of hydrogen atoms – and this occurs even for ideal, flat Pt(111) surfaces. Modern catalysis employs nanoscale materials whose surfaces have substantial step, edge, corner, impurity, and other defect sites, and they increasingly have both metallic and non-metallic elements M n X m , including metal oxides, chalcogenides, pnictides, carbides, doped carbons, etc. The surfaces of such catalysts are often not crystal facets of the bulk phase underneath, and they typically have a variety of potential active sites. Catalytic surfaces in operando are often non-stoichiometric, amorphous, dynamic, and impure, and often vary from one part of the surface to another. Understanding of the issues that arise at such nanoscale, multi-element catalysts is just beginning to emerge. Yet these catalysts are widely discussed using Brønsted/Bell-Evans-Polanyi (BEP) relations, volcano plots, Tafel slopes, the Butler-Volmer equation, and other linear free energy relations (LFERs), which all depend on the implicit assumption that the active sites are “similar” and that surface adsorption is close to ideal. These assumptions underly the ubiquitous intuition based on the Sabatier Principle, that the fastest catalysis will occur when key intermediates have free energies of adsorption that are not too strong nor too weak. Current catalysis research often aims to minimize the complexity of non-ideal isotherms through experimental and computational design (e.g., the use of single crystal surfaces), and these studies are the foundation of the field. In contrast, this Perspective argues that the heterogeneity of binding sites and binding energies is an inherent strength of these catalysts. Here, this diversity makes many nanoscale catalysts inherently a high-throughput screen wrapped in a tiny package. Only by making the heterogeneity part of the foundation of catalysis models, sorting the types of active sites and dissecting non-ideal binding isotherms, will modern catalysis learn to harness the inherent diversity of real catalysts. Controlling and exploiting diversity rather than avoiding it will help to optimize complex modern catalysts and catalytic conditions.

Mayer, James M.↗

DOE OSTI2023

Theoretical upper limits of the thermal conductivity of Si3N4

Silicon nitride (Si3N4) is a promising substrate for high-power electronics due to its superior mechanical properties and potential outstanding thermal conductivity (κ). As experiments keep pushing the upper limit of κ of Si3N4, it is believed that it can reach 450 W/mK, similar to SiC, based on classical models and molecular dynamics simulations. In this work, we reveal from first principles that the theoretical κ upper limits of β-Si3N4 are only 169 and 57 W/mK along the c and a axes at room temperature, respectively. Those of α-Si3N4 are about 116 and 87 W/mK, respectively. The predicted temperature-dependent κ matches well with the highest available experimental data, which supports the accuracy of our calculations, and suggests that the κ upper limit of Si3N4 has already been reached in the experiment. Compared to other promising semiconductors (e.g., SiC, AlN, and GaN), Si3N4 has a much lower κ than expected even though the chemical bonding and mechanical strengths are close or even stronger. We find the underlying reason is that Si3N4 has much lower phonon lifetimes and mean free paths (<0.5 μm) due to the larger three-phonon scattering phase space and stronger anharmonicity. Interestingly, we find that the larger unit cell (with more basis atoms) that leads to a smaller fraction of acoustic phonons is not the reason for lower κ. Grain size-dependent κ indicates that the grain boundary scattering plays a negligible role in most experimental samples. This work clarifies the theoretical κ upper limits of Si3N4 and can guide experimental research.

Physics↗

DOE OSTI2022

Testing Asoleyo Decorative Solar Cell and Modules: Cooperative Research and Development, CRADA Number CRD-20-16732 (Final Report)

Team Asoleyo is transforming the utilitarian rectangular grid that dominates the visual style of standard solar panels by re-designing solar cells’ silver contact grid using principles of symmetry, rhythm, and line. Our designs use well-established manufacturing equipment, techniques, and materials, achieving similar manufacturing costs but far superior aesthetic effect- without the need for additional layers or occlusion. This project will test new decorative PV Cell front contact designs, individually and interconnected within modules, for their performance characteristics.

14 SOLAR ENERGY↗

DOE OSTI2020

Charge-transfer satellites and chemical bonding in photoemission and x-ray absorption of SrTi O 3 and rutile Ti O 2 : Experiment and first-principles theory with general application to spectroscopic analysis

First-principles, real-time-cumulant, and Bethe-Salpeter-equation calculations fully capture the detailed satellite structure that occurs in response to the sudden creation of the core hole in both photoemission and x-ray absorption spectra of the transition-metal compounds SrTi O 3 and rutile Ti O 2 . Analysis of the excited-state, real-space charge-density fluctuations betrays the physical nature of these many electron excitations that are shown to reflect the materials’ solid-state electronic structure and chemical bonding. This first-principles development of the cumulant-based core hole spectral function is generally applicable to other systems and should become a standard tool for all similar spectroscopic analysis going beyond the quasiparticle physics of the photoelectric effect.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2023

Unifying principles for catalytic hydrotreating processes (Final Technical Report)

This project builds on the hypothesis that the hydrotreating processes for the removal of oxygen and sulfur are fundamentally similar at the atomic-scale and existing knowledge from the treatment of petroleum derived feedstock can be leveraged for the design of novel catalysts for the upgrade of bio-oil. We tested this hypothesis by comparing computed potential energy diagrams for hydrodesulfurization (HDS) of thiophene over MoS 2 with hydrodeoxygenation (HDO) of furan over MoO 3 and concluded that certain aspects, such as catalyst promotion with transition metals, are valid strategies for both reactions. On the other hand, we also noticed significant differences in the mechanism for hydrogen (H 2 ) activation, which requires sites with metallic character. While MoS 2 is known to have metallic edge states that can catalyze H 2 dissociation, this elementary step is prohibitively slow on defect-free oxides. Only in the presence of vacancies or by creating metal/oxide interfaces can efficient H 2 activation sites during HDO be formed. The need for bifunctional catalyst when it comes to efficient and selective HDO or dehydrogenation reactions was further corroborated in joint experimental and theoretical studies of the Guerbet reaction for the coupling of biomass derived oxygenates over PdCu alloys, nitrate reduction over In-promoted Pd nanoparticles, and ethylene dehydroaromatization over Ga-exchanged ZSM-5 zeolites. All of these catalytic systems have in common that catalytic sites with distinct functional requirements are needed to create a working catalyst. Detailed computational studies were carried out for HDO of m -cresol and phenol on Ru-modified TiO 2 surfaces, which allowed us to attribute catalytic activity to the metal/oxide interface. A surprising finding was that heterolytic cleavage of the H-H bond across the Ru/TiO 2 interface was critically important, despite lower barriers for homolytic H 2 activation on Ru metal. The explanation lies in the high barriers for hydrogen spillover from Ru to TiO 2 , which becomes unnecessary in the heterolytic activation pathway. Moreover, we also reported that proton transfer steps between metal and oxide sites are mediated by weakly adsorbed surface water. During attempts to develop and validate a kinetic Monte Carlo (kMC) model for HDO reactions at the Ru/TiO 2 interface, it became clear that lateral interactions are paramount to describe realistic surface chemistry and without these interactions, the reduction and hydroxylation behavior from our simulations was inconsistent with reported experiments. To assess the importance of lateral interactions in popular computational catalyst design strategies relying on the identification of reactivity descriptors, which can be used along with Brønsted–Evans–Polanyi (BEP) and scaling relations as input to a microkinetic model (MKM) to make predictions for activity or selectivity trends, we compared predicted trends with those obtained from descriptor-based kMC models. We critically evaluated the benefits of kMC over MKM in terms of trend predictions and computational cost when using only a small set of input parameters. After confirming that in the absence of lateral interactions the kMC and MKM approaches yield identical trends and mechanistic information, we observed substantial differences between the two kinetic models when lateral interactions were introduced. The mean-field implementation applies coverage corrections directly to the descriptors, causing an artificial overprediction of the activity of strongly binding metals. In contrast, the cluster expansion in kMC implementation can differentiate among the highly active metals but it is very sensitive to the set of included interaction parameters. Considering that computational screening relies on a minimal set of descriptors, for which MKM makes reasonable trend predictions at a ca. three orders of magnitude lower computational cost than kMC, we concluded that the MKM approach does provide an overall better entry point for computational catalyst design. Overall, this project has led to 11 peer-reviewed publications, and their scientific is impact is well illustrated by their combined 702 citations.

08 HYDROGEN↗

DOE OSTI2020

Interactions and immobilization of lanthanides with dopants in uranium-based metallic fuels

In uranium-based fuels, production and migration of lanthanide fission products and subsequent chemical interactions with cladding constituents lead to fuel–cladding chemical interaction (FCCI), limiting fuel performance. In general, dopant addition to the fuel matrix to arrest lanthanides within the fuel by forming intermetallics is found to be effective in mitigating FCCI. Recently, we proposed ab-initio based alloy-design principles, which can be useful in identifying dopant(s) that can bind a lanthanide (Nd) inside the fuel matrix. Here, we demonstrate the robustness of such principles by performing a systematic study to choose dopants to form compounds with a range of experimentally-observed lanthanide fission products. Results reveal that for a given dopant, lanthanide Ce and La exhibit similar behavior in their compound-forming tendencies compared to Nd and Pr, which is linked to lanthanide intrinsic characteristics such as the electronic configurations. Our predicted lanthanide-dopant intermetallic formation is verified experimentally in selective cases. Finally, we showed alloy design principles that accurately identify previously known dopants like Pd, and also predict new dopants As and Se, that can be effective in binding all lanthanides within the uranium matrix. Altogether, this research helps to develop generic alloy-design principles for complex multi-component systems based on lanthanide and dopant intrinsic characteristics.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2021

Photoionization from the Xe 4d orbitals of XeF 2

Here, we present a comparison of the photoionization dynamics of the 4d shell of XeF 2 from threshold to 250 eV to those of the prototypical 4d shell of atomic Xe. The new experimental data include spin–orbit and ligand-field-resolved partial cross sections, photoelectron angular distributions, branching fractions, and lifetime widths for the 4d-hole states. The spin–orbit branching fractions and angular distributions are remarkably similar to the corresponding distributions from atomic Xe across a broad energy interval that includes both the intense shape resonance in the f continuum and a Cooper minimum in the same channel. The angular distributions and branching fractions are also in reasonably good agreement with our first-principles theoretical calculations on XeF 2 . Data are also presented on the lifetime widths of the substate-resolved 4d-hole states of XeF 2 . While the trends in the widths are similar to those in the earlier experimental and theoretical work, the linewidths are considerably smaller than in the previous measurements, which may require some reinterpretation of the decay mechanism. Finally, we present new data and an analysis of the Auger electron spectra for ionization above the 4d thresholds and resonant Auger spectra for several pre-edge features.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Projecting Large Fires in the Western US With an Interpretable and Accurate Hybrid Machine Learning Method

More frequent and widespread large fires are occurring in the western United States (US), yet reliable methods for predicting these fires, particularly with extended lead times and a high spatial resolution, remain challenging. In this study, we proposed an interpretable and accurate hybrid machine learning (ML) model, that explicitly represented the controls of fuel flammability, fuel availability, and human suppression effects on fires. The model demonstrated notable accuracy with a F 1 -score of 0.846 ± 0.012, surpassing process-driven fire danger indices and four commonly used ML models by up to 40% and 9%, respectively. More importantly, the ML model showed remarkably higher interpretability relative to other ML models. Specifically, by demystifying the “black box” of each ML model using the explainable AI techniques, we identified substantial structural differences across ML fire models, even among those with similar accuracy. The relationships between fires and their drivers, identified by our model, were aligned closer with established fire physical principles. The ML structural discrepancy led to diverse fire predictions and our model predictions exhibited greater consistency with actual fire occurrence. With the highly interpretable and accurate model, we revealed the strong compound effects from multiple climate variables related to evaporative demand, energy release component, temperature, and wind speed, on the dynamics of large fires and megafires in the western US. Our findings highlight the importance of assessing the structural integrity of models in addition to their accuracy. They also underscore the critical need to address the rise in compound climate extremes linked to large wildfires.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Pressure-induced ferroelectric-like transition creates a polar metal in defect antiperovskites Hg3Te2X2 (X = Cl, Br)

Abstract Ferroelectricity is typically suppressed under hydrostatic compression because the short-range repulsions, which favor the nonpolar phase, increase more rapidly than the long-range interactions, which prefer the ferroelectric phase. Here, based on single-crystal X-ray diffraction and density-functional theory, we provide evidence of a ferroelectric-like transition from phase I 2 1 3 to R 3 induced by pressure in two isostructural defect antiperovskites Hg 3 Te 2 Cl 2 (15.5 GPa) and Hg 3 Te 2 Br 2 (17.5 GPa). First-principles calculations show that this transition is attributed to pressure-induced softening of the infrared phonon mode Γ 4 , similar to the archetypal ferroelectric material BaTiO 3 at ambient pressure. Additionally, we observe a gradual band-gap closing from ~2.5 eV to metallic-like state of Hg 3 Te 2 Br 2 with an unexpectedly stable R 3 phase even after semiconductor-to-metal transition. This study demonstrates the possibility of emergence of polar metal under pressure in this class of materials and establishes the possibility of pressure-induced ferroelectric-like transition in perovskite-related systems.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Quantum dynamics of the temporary capture of light atoms by superfluid helium nanodroplets at very low collision energies (≈1–13 meV): the case of the hydrogen atom and its isotopes

The capture dynamics of a H atom and isotopic variants [D, T and Q (hypothetical isotope of mass equal to four times the mass of H)] by a superfluid helium nanodroplet (HeND) has been investigated theoretically. The HeND (T = 0.37 K) is ( 4 He) N=400 and a mean field quantum hybrid approach [TDDFT (helium) + quantum wave packet (H, D, T or Q)] at zero angular momentum, is used to explore a rather wide range of very low initial kinetic energies (E k,0 ≈ 10–150 K). The analysis of the capture mechanism shows the existence of a dynamical barrier and a dynamical minimum that play key roles to understand the time evolution of the capture, especially the former property. In general, the H atom shows a different behavior from the other isotopes, with the behavior of T and Q being very similar to each other and the D atom behaving inbetween H and T. Besides, it is worth noting that, in principle, at the very low initial kinetic energies considered only “short” and “long” lived atom⋯HeND collision complexes are formed, i.e., in the atom-helium nanodroplet collision only the temporary capture of the atom takes place. The different behaviors observed have been interpreted considering the faster motion of the H atom when colliding with ( 4 He) N=400 and the more quantum character of the H behavior both due to its significantly lower mass. As far as we know, this is the first quantum dynamics study carried out on the collision of light atoms with HeNDs at very low energies.

Sternberg, Michael [Argonne National Laboratory (A↗

DOE OSTI2026

Ion correlations explain kinetic selectivity in diffusion-limited solid-state synthesis reactions

Establishing viable solid-state synthesis pathways for novel inorganic materials remains a major challenge in materials science. Previous pathway design methods using pairwise reaction approaches have navigated the thermodynamic landscape with first-principles data but lack kinetic information, limiting their effectiveness. This gap leads to suboptimal precursor selection and predictions, especially for reactions forming competing phases with similar formation energies, where ion diffusion is a critical influence. Here we demonstrate an inorganic synthesis framework by incorporating machine learning-derived transport properties through ‘liquid-like’ product layers into a thermodynamic cellular reaction model. In the Ba–Ti–O system, known for its competitive polymorphism, we obtain accurate predictions of phase formation with varying BaO:TiO2 ratios as a function of time and temperature. We find that diffusion–thermodynamics interplay governs phase compositions, with cross-ion transport coefficients critical for predicting diffusion-limited selectivity. This work bridges length scales and timescales by integrating solid-state reaction kinetics with first-principles thermodynamics and spatial reactivity.

Atomistic models↗

DOE OSTI2023

Optical activity of solids from first principles

Within the framework of independent particle approximation, the optical activity tensor of solids is formulated as from different contributions: the magnetic dipole, electric quadrupole, and band dispersion terms. The first two terms have similar counterparts in the theory of finite systems, whereas the last term is unique for crystals. Further, the magnetic dipole and electric quadrupole transition moments are calculated with a sum-over-states formulation. We apply the formulation to calculate and analyze the optical rotation of elemental tellurium and the circular dichroism of the (6,4) carbon nanotube. Decomposed optical activity into different contributions is discussed. The calculated spectra agree well with experiments. As a showcase of achiral crystals, we calculate the optical activity of wurtzite GaN.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗