arXiv subjects

Yang Shao-Horn

Publications and source records attributed to Yang Shao-Horn.

16 recordsLinked to original sources

arXiv2025

Correlated Terahertz phonon-ion interactions control ion conduction in a solid electrolyte

Ionic conduction in solids that exceeds 1 mS/cm is predicted to involve coupled phonon-ion interactions in the crystal lattice. Here, we use theory and experiment to measure the possible contribution of coupled phonon-ion hopping modes which enhance Li+ migration in Li0.5La0.5TiO3 (LLTO). The ab initio calculations predict that the targeted excitation of individual TiO6 rocking modes greatly increases the Li+ jump rate as compared to the excitation of vibrational modes associated with heating. Experimentally, coherently driving TiO6 rocking modes via terahertz (THz) illumination leads to a ten-fold decrease in the differential impedance compared to the excitation of acoustic and optical phonons. Additionally, we differentiate the ultrafast responses of LLTO due to ultrafast heating and THz-range vibrations using laser-driven spectroscopy (LUIS), finding a unique long-lived response for the THz-range excitation. These findings provide new insights into coupled ion migration mechanisms, indicating the important role of THz-range coupled phonon-ion hopping modes in enabling fast ion conduction at room temperature.

cond-mat.mtrl-sci↗

arXiv2024

Dynamic Optimization of Proton Exchange Membrane Water Electrolyzers Considering Usage-Based Degradation

We present a techno-economic optimization model for evaluating the design and operation of proton exchange membrane (PEM) electrolyzers, crucial for hydrogen production powered by variable renewable electricity. This model integrates a 0-D physics representation of the electrolyzer stack, complete mass and energy balances, operational constraints, and empirical data on use-dependent degradation. Utilizing a decomposition approach, the model predicts optimal electrolyzer size, operation, and necessary hydrogen storage to satisfy baseload demands across various technology and electricity price scenarios. Analysis for 2022 shows that including degradation effects raises the levelized cost of hydrogen from \$4.56/kg to \$6.60/kg and decreases stack life to two years. However, projections for 2030 anticipate a significant reduction in costs to approximately \$2.50/kg due to lower capital expenses, leading to larger stacks, extended lifetimes, and less hydrogen storage. This approach is adaptable to other electrochemical systems relevant for decarbonization.

eess.SY↗

arXiv2023

Decarbonization of aviation via hydrogen propulsion: technology performance targets and energy system impacts

The aviation sector is challenging to decarbonize since aircraft require high power and energy per unit of weight. Liquid hydrogen is an interesting solution due to its high gravimetric energy density, minimal warming impact, and low-carbon production potential. We quantify the performance targets for fuel cell systems and on-board storage to enable hydrogen-powered regional aviation. We then explore the energy infrastructure impacts of meeting this additional H2 demand in the European context under deep decarbonization scenarios. We find that minimal payload reduction would be needed for powering regional aviation up to 1000 nmi if fuel cell system specific power of 2 kW/kg and tank gravimetric index of 50% can be achieved. The energy systems analysis highlights the importance of utilizing multiple technology options: such as nuclear expansion and natural gas reforming with CCS for hydrogen production. Levelized cost of liquid hydrogen as low as 3.5 Euros/kg demonstrates pathways for Europe to achieve cost-competitive production.

cs.CE↗

arXiv2023

Multimodal machine learning for materials science: composition-structure bimodal learning for experimentally measured properties

The widespread application of multimodal machine learning models like GPT-4 has revolutionized various research fields including computer vision and natural language processing. However, its implementation in materials informatics remains underexplored, despite the presence of materials data across diverse modalities, such as composition and structure. The effectiveness of machine learning models trained on large calculated datasets depends on the accuracy of calculations, while experimental datasets often have limited data availability and incomplete information. This paper introduces a novel approach to multimodal machine learning in materials science via composition-structure bimodal learning. The proposed COmposition-Structure Bimodal Network (COSNet) is designed to enhance learning and predictions of experimentally measured materials properties that have incomplete structure information. Bimodal learning significantly reduces prediction errors across distinct materials properties including Li conductivity in solid electrolyte, band gap, refractive index, dielectric constant, energy, and magnetic moment, surpassing composition-only learning methods. Furthermore, we identified that data augmentation based on modal availability plays a pivotal role in the success of bimodal learning.

cond-mat.mtrl-sci↗

arXiv2023

Atom-by-atom design of metal oxide catalysts for the oxygen evolution reaction with machine learning

Green hydrogen production is crucial for a sustainable future, but current catalysts for the oxygen evolution reaction (OER) suffer from slow kinetics, despite many efforts to produce optimal designs, particularly through the calculation of descriptors for activity. In this study, we develop a dataset of density functional theory calculations of bulk and surface perovskite oxides, and adsorption energies of OER intermediates, which includes compositions up to quaternary and facets up to (555). We demonstrate that per-site properties of perovskite oxides such as Bader charge or band center can be tuned through element substitution and faceting, and develop a machine learning model that accurately predicts these properties directly from the local chemical environment. We leverage these per-site properties to identify promising perovskites with high theoretical OER activity. The identified design principles and promising new materials provide a roadmap for closing the gap between current artificial catalysts and biological enzymes.

cond-mat.mtrl-sci↗

arXiv2023

Examining graph neural networks for crystal structures: limitations and opportunities for capturing periodicity

Historically, materials informatics has relied on human-designed descriptors of materials structures. In recent years, graph neural networks (GNNs) have been proposed for learning representations of crystal structures from data end-to-end producing vectorial embeddings that are optimized for downstream prediction tasks. However, a systematic scheme is lacking to analyze and understand the limits of GNNs for capturing crystal structures. In this work, we propose to use human-designed descriptors as a bank of human knowledge to test whether black-box GNNs can capture the knowledge of crystal structures. We find that current state-of-the-art GNNs cannot capture the periodicity of crystal structures well, and we analyze the limitations of the GNN models that result in this failure from three aspects: local expressive power, long-range information, and readout function. We propose an initial solution, hybridizing descriptors with GNNs, to improve the prediction of GNNs for materials properties, especially phonon internal energy and heat capacity with 90% lower errors, and we analyze the mechanisms for the improved prediction. All the analysis can be extended easily to other deep representation learning models, human-designed descriptors, and systems such as molecules and amorphous materials.

cond-mat.mtrl-sci↗

arXiv2022

A cloud platform for automating and sharing analysis of raw simulation data from high throughput polymer molecular dynamics simulations

Open material databases storing hundreds of thousands of material structures and their corresponding properties have become the cornerstone of modern computational materials science. Yet, the raw outputs of the simulations, such as the trajectories from molecular dynamics simulations and charge densities from density functional theory calculations, are generally not shared due to their huge size. In this work, we describe a cloud-based platform to facilitate the sharing of raw data and enable the fast post-processing in the cloud to extract new properties defined by the user. As an initial demonstration, our database currently includes 6286 molecular dynamics trajectories for amorphous polymer electrolytes and 5.7 terabytes of data. We create a public analysis library at https://github.com/TRI-AMDD/htp_md to extract multiple properties from the raw data, using both expert designed functions and machine learning models. The analysis is run automatically with computation in the cloud, and results then populate a database that can be accessed publicly. Our platform encourages users to contribute both new trajectory data and analysis functions via public interfaces. Newly analyzed properties will be incorporated into the database. Finally, we create a front-end user interface at https://www.htpmd.matr.io for browsing and visualization of our data. We envision the platform to be a new way of sharing raw data and new insights for the computational materials science community.

cond-mat.mtrl-sci↗

arXiv2022

Accelerating amorphous polymer electrolyte screening by learning to reduce errors in molecular dynamics simulated properties

Polymer electrolytes are promising candidates for the next generation lithium-ion battery technology. Large scale screening of polymer electrolytes is hindered by the significant cost of molecular dynamics (MD) simulation in amorphous systems: the amorphous structure of polymers requires multiple, repeated sampling to reduce noise and the slow relaxation requires long simulation time for convergence. Here, we accelerate the screening with a multi-task graph neural network that learns from a large amount of noisy, unconverged, short MD data and a small number of converged, long MD data. We achieve accurate predictions of 4 different converged properties and screen a space of 6247 polymers that is orders of magnitude larger than previous computational studies. Further, we extract several design principles for polymer electrolytes and provide an open dataset for the community. Our approach could be applicable to a broad class of material discovery problems that involve the simulation of complex, amorphous materials.

cond-mat.mtrl-sci↗

arXiv2021

Electronic Polarizability Induced Cooper-like Pairing and Energy Gap in High-Tc superconductors

High temperature superconducting materials have been known since the pioneering work of Bednorz and Mueller in 1986. While the microscopic mechanism responsible for high Tc superconductivity is still debated, most materials showing high Tc contain highly electronic polarizable ions, suggesting that the mechanism driving high Tc superconductivity can be related to the ion electronic polarizability in high Tc materials. Here we show that a free charge carrier polarizes the ions surrounding it and the total electrical potential generated by the charge carrier itself and the polarized ions becomes attractive in some regions of space. Our results on bulk FeSe, monolayer FeSe on SrTiO3 and La2CuO4 are in excellent agreement with the experiments. The fact that the electronic polarizability explains correctly and quantitatively the superconductivity parameters: Tc, gap and paring energies of both pnictides and cuprates with similar polarizability parameters, suggests that the same model may be applicable to other material systems within these groups as well as other high Tc groups.

cond-mat.supr-con↗

arXiv2020

Enhancement of Ion Diffusion by Targeted Phonon Excitation

Ion diffusion is important in a variety of applications, yet fundamental understanding of the diffusive process in solids is still missing, especially considering the interaction of lattice vibrations (phonons) and the mobile species. In this work, we introduce two formalisms that determine the individual contributions of normal modes of vibration (phonons) to the diffusion of ions through a solid, based on (i) Nudged Elastic Band (NEB) calculations and (ii) molecular dynamics (MD) simulations. The results for a model ion conductor of $\rm{Ge}$-substituted $\rm{Li_3PO_4}$ ($\rm{Li_{3.042}Ge_{0.042}P_{0.958}O_4}$) revealed that more than 87% of the $\rm{Li^+}$ ion diffusion in the lattice originated from a subset of less than 10% of the vibrational modes with frequencies between 8 and 20 THz. By deliberately exciting a small targeted subset of these contributing modes (less than 1%) to a higher temperature and still keeping the lattice at low temperature, we observed an increase in diffusivity by several orders of magnitude, consistent with what would be observed if the entire material (i.e., all modes) were excited to the same high temperature. This observation suggests that an entire material need not be heated to elevated temperatures to increase diffusivity, but instead only the modes that contribute to diffusion, or more generally a reaction/transition pathway, need to be excited to elevated temperatures. This new understanding identifies new avenues for increasing diffusivity by engineering the vibrations in a material, and/or increasing diffusivity by external stimuli/excitation of phonons (e.g., via photons or other interactions) without necessarily changing the compound chemistry.

cond-mat.mtrl-sci↗

arXiv2020

Theory of coupled ion-electron transfer kinetics

The microscopic theory of chemical reactions is based on transition state theory, where atoms or ions transfer classically over an energy barrier, as electrons maintain their ground state. Electron transfer is fundamentally different and occurs by tunneling in response to solvent fluctuations. Here, we develop the theory of coupled ion-electron transfer, in which ions and solvent molecules fluctuate cooperatively to facilitate electron transfer. We derive a general formula of the reaction rate that depends on the overpotential, solvent properties, the electronic structure of the electron donor/acceptor, and the excess chemical potential of ions in the transition state. For Faradaic reactions, the theory predicts curved Tafel plots with a concentration-dependent reaction-limited current. For moderate overpotentials, our formula reduces to the Butler-Volmer equation and explains its relevance, not only in the well-known limit of large electron-transfer (solvent reorganization) energy, but also in the opposite limit of large ion-transfer energy. The rate formula is applied to Li-ion batteries, where reduction of the electrode host material couples with ion insertion. In the case of lithium iron phosphate, the theory accurately predicts the concentration dependence of the exchange current measured by {\it in operando} X-Ray microscopy without any adjustable parameters. These results pave the way for interfacial engineering to enhance ion intercalation rates, not only for batteries, but also for ionic separations and neuromorphic computing.

physics.chem-ph↗

arXiv2020

Stern Layers on Surfaces of RuO2(100), RuO2(110), and Pt(111): Surface X-ray Scattering Studies

Surface X-ray scattering studies of electrochemical Stern layer are reported. The Stern layers formed at the interfaces of RuO2 (110) and (100) in 0.1 M CsF electrolyte are compared to the previously reported Stern layer on Pt(111) [Liu et al., J. Phys. Chem. Lett., 9 (2018) 1265]. While the Cs+ density profiles at the potentials close to hydrogen evolution reaction are similar, the hydration layers intervening the surface and the Cs+ layer on RuO2 surfaces are significantly denser than the hydration layer on Pt(111) surface possibly due to the oxygen termination of RuO2 surfaces. We also discuss in-plane ordering in the Stern layer on Pt(111) surface.

cond-mat.mtrl-sci↗

arXiv2019

Graph Dynamical Networks for Unsupervised Learning of Atomic Scale Dynamics in Materials

Understanding the dynamical processes that govern the performance of functional materials is essential for the design of next generation materials to tackle global energy and environmental challenges. Many of these processes involve the dynamics of individual atoms or small molecules in condensed phases, e.g. lithium ions in electrolytes, water molecules in membranes, molten atoms at interfaces, etc., which are difficult to understand due to the complexity of local environments. In this work, we develop graph dynamical networks, an unsupervised learning approach for understanding atomic scale dynamics in arbitrary phases and environments from molecular dynamics simulations. We show that important dynamical information can be learned for various multi-component amorphous material systems, which is difficult to obtain otherwise. With the large amounts of molecular dynamics data generated everyday in nearly every aspect of materials design, this approach provides a broadly useful, automated tool to understand atomic scale dynamics in material systems.

cond-mat.mtrl-sci↗

arXiv2019

An In Situ Surface-Enhanced Infrared Absorption Spectroscopy Study of Electrochemical CO2 Reduction: Selectivity Dependence on Surface C-Bound and O-Bound Reaction Intermediates

The CO_{2} electro-reduction reaction (CORR) is a promising avenue to convert greenhouse gases into high-value fuels and chemicals, in addition to being an attractive method for storing intermittent renewable energy. Although polycrystalline Cu surfaces have long known to be unique in their capabilities of catalyzing the conversion of CO_{2} to higher-order C1 and C2 fuels, such as hydrocarbons (CH_{4}, C_{2}H_{4} etc.) and alcohols (CH_{3}OH, C_{2}H_{5}OH), product selectivity remains a challenge. In this study, we select three metal catalysts (Pt, Au, Cu) and apply in situ surface enhanced infrared absorption spectroscopy (SEIRAS) and ambient-pressure X-ray photoelectron spectroscopy (APXPS), coupled to density-functional theory (DFT) calculations, to get insight into the reaction pathway for the CORR. We present a comprehensive reaction mechanism for the CORR, and show that the preferential reaction pathway can be rationalized in terms of metal-carbon (M-C) and metal-oxygen (M-O) affinity. We show that the final products are determined by the configuration of the initial intermediates, C-bound and O-bound, which can be obtained from CO_{2} and (H)CO_{3}, respectively. C1 hydrocarbons are produced via OCH_{3, ad} intermediates obtained from O-bound CO_{3, ad} and require a catalyst with relatively high affinity for O-bound intermediates. Additionally, C2 hydrocarbon formation is suggested to result from the C-C coupling between C-bound CO_{ad} and (H)CO_{ad}, which requires an optimal affinity for the C-bound species, so that (H)CO_{ad} can be further reduced without poisoning the catalyst surface. Our findings pave the way towards a design strategy for CORR catalysts with improved selectivity, based on this experimental/theoretical reaction mechanisms that have been identified.

cond-mat.mtrl-sci↗

arXiv2019

Assessing correlations of perovskite catalytic performance with electronic structure descriptors

Electronic structure descriptors are computationally efficient quantities used to construct qualitative correlations for a variety of properties. In particular, the oxygen p-band center has been used to guide material discovery and fundamental understanding of an array of perovskite compounds for use in catalyzing the oxygen reduction and evolution reactions. However, an assessment of the effectiveness of the oxygen p-band center at predicting key measures of perovskite catalytic activity has not been made, and would be highly beneficial to guide future predictions and codify best practices. Here, we have used Density Functional Theory at the PBE, PBEsol, PBE+U, SCAN and HSE06 levels to assess the correlations of numerous measures of catalytic performance for a series of technologically relevant perovskite oxides, using the bulk oxygen p-band center as an electronic structure descriptor. We have analyzed correlations of the calculated oxygen p-band center for all considered functionals with the experimentally measured X-ray emission spectroscopy oxygen p-band center and multiple measures of catalytic activity, including high temperature oxygen reduction surface exchange rates, aqueous oxygen evolution current densities, and binding energies of oxygen evolution intermediate species. Our results show that the best correlations for all measures of catalytic activity considered here are made with PBE-level calculations, with strong observed linear correlations with the bulk oxygen p-band center (R2 = 0.81-0.87). This study shows that strong linear correlations between numerous important measures of catalytic activity and the oxygen p-band bulk descriptor can be obtained under a consistent computational framework, and these correlations can serve as a guide for future experiments and simulations for development of perovskite and related oxide catalysts.

cond-mat.mtrl-sci↗

arXiv2016

Oxygen Point Defect Chemistry in Ruddlesden-Popper Oxides (La$_{1-x}$Sr$_{x}$)$_{2}$MO$_{4{\pm}δ}$ (M = Co, Ni, Cu)

Stability of oxygen point defects in Ruddlesden-Popper oxides (La$_{1-x}$Sr$_{x}$)$_{2}$MO$_{4{\pm}δ}$ (M = Co, Ni, Cu) is studied with density functional theory calculations to determine their stable sites, charge states, and energetics as functions of Sr content ($x$), transition metal (M) and defect concentration (δ). We demonstrate that the dominant O point defects can change between oxide interstitials, peroxide interstitials, and vacancies. Generally, increasing $x$ and atomic number of M stabilizes peroxide over oxide interstitials, as well as vacancies over both peroxide and oxide interstitials; increasing δ destabilizes both oxide interstitials and vacancies, but affects little peroxide interstitials. We also demonstrate that the O 2$p$-band center is a powerful descriptor for these materials and correlates linearly with the formation energy of all the defects. The trends of formation energy versus $x$, M and δ and the correlation with O 2$p$-band center are explained in terms of oxidation chemistry and electronic structure.

cond-mat.mtrl-sci↗