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.Comment: 25 + 7 pages, 5 + 3 figure

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

Recent Insights into Manganese Oxides in Catalyzing Oxygen Reduction Kinetics

The sluggish kinetics of the oxygen reduction reaction (ORR) limit the efficiency of numerous oxygen-based energy conversion devices such as fuel cells and metal-air batteries. Among earth abundant catalysts, manganese-based oxides have the highest activities approaching that of precious metals. In this Review, we summarize and analyze literature findings to highlight key parameters that influence the catalysis of the ORR on manganese-based oxides, including the number of electrons transferred as well as specific and mass activities. These insights can help develop design guides for highly active ORR catalysts and shape future fundamental research to gain new knowledge regarding the molecular mechanism of ORR catalysis.National Science Foundation (U.S.). Materials Research Science and Engineering Centers (Program) (award number DMR- 0819762)Skoltech-MIT CenterNational Science Foundation (U.S.). Graduate Research Fellowship (Grant no. DGE-1122374)United States. Department of Energy. Office of Basic Energy Sciences (contract no. DE-AC02- 98CH10886

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

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.Comment: 25 pages, 6 figures. (38 pages with SI, 7 SI figures

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

Insights into Electrochemical Reactions from Ambient Pressure Photoelectron Spectroscopy

The understanding of fundamental processes in the bulk and at the interfaces of electrochemical devices is a prerequisite for the development of new technologies with higher efficiency and improved performance. One energy storage scheme of great interest is splitting water to form hydrogen and oxygen gas and converting back to electrical energy by their subsequent recombination with only water as a byproduct. However, kinetic limitations to the rate of oxygen-based electrochemical reactions hamper the efficiency in technologies such as solar fuels, fuel cells, and electrolyzers. For these reactions, the use of metal oxides as electrocatalysts is prevalent due to their stability, low cost, and ability to store oxygen within the lattice. However, due to the inherently convoluted nature of electrochemical and chemical processes in electrochemical systems, it is difficult to isolate and study individual electrochemical processes in a complex system. Therefore, in situ characterization tools are required for observing related physical and chemical processes directly at the places where and while they occur and can help elucidate the mechanisms of charge separation and charge transfer at electrochemical interfaces.National Science Foundation (U.S.). Materials Research Science and Engineering Centers (Program)Skoltech-MIT Center for Electrochemical Energy StorageUnited States. Department of EnergyNational Energy Technology Laboratory (U.S.)Solid State Energy Conversion Alliance. Core Technology Program (DEFE0009435

Alkali Metal Salt Interference on the Salicylate Method for Quantifying Ammonia from Nitrogen Reduction

[EN] The salicylate method has been extensively used for quantifying ammonia in the emerging field of nitrogen (electro)fixation. Alkali metal salts are widely used as supporting electrolytes for nitrogen reduction, especially in the context of electrochemical nitrogen fixation. However, these salts are known to cause interferences on the salicylate method, introducing significant uncertainties in ammonia quantification. In this work, the interference of lithium, sodium and potassium chlorides, perchlorates and sulfates on the ammonia quantification results obtained using the salicylate method was experimentally quantified, and an empirical model was developed to capture the effect of the presence of these interferents on the ammonia quantification by the salicylate method. Based on the obtained experimental interference results, the tested interferents can be ranked from stronger interferent (i.e. lower admissible concentration) to weaker interferent: Li2SO4, KClO4, LiCl, LiClO4, K2SO4, NaClO4, NaCl, Na2SO4, KCl. The developed model can be used to assess the experimental error in ammonia quantification from nitrogen reduction, in samples containing these interferents. This model can be used to correct the interferences on the ammonia quantification, when the interferent concentration in a sample is known (or measurable).This work was supported by the Toyota Research Institute through the Accelerated Materials Design and Discovery program.J.J.G.S. is very grateful to the Generalitat Valenciana and to the European Social Fund, for their economic support in the form of a Vali+d postdoctoral fellowship (APOSTD-2018-001); and to the Ministerio de Ciencia e Innovación, to the Next Generation EU, and to the Agencia Estatal de Investigación, for their support by a Juan de la Cierva-Incorporación fellowship IJC2020-044087-I funded by MCIN/AEI/10.13039/501100011033 and by the European Union NextGenerationEU/PRTR. G.M.L. was partially supported by a Natural Sciences and Engineering Research Council of Canada (NSERC) PGS-D and a Siebel Scholarship (Class of 2020). The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript.Giner-Sanz, JJ.; Leverick, G.; Giordano, L.; Pérez-Herranz, V.; Shao-Horn, Y. (2022). Alkali Metal Salt Interference on the Salicylate Method for Quantifying Ammonia from Nitrogen Reduction. ECS Advances. 1(2):1-13. https://doi.org/10.1149/2754-2734/ac6a681131

Toward the rational design of non-precious transition metal oxides for oxygen electrocatalysis

In this Review, we discuss the state-of-the-art understanding of non-precious transition metal oxides that catalyze the oxygen reduction and evolution reactions. Understanding and mastering the kinetics of oxygen electrocatalysis is instrumental to making use of photosynthesis, advancing solar fuels, fuel cells, electrolyzers, and metal–air batteries. We first present key insights, assumptions and limitations of well-known activity descriptors and reaction mechanisms in the past four decades. The turnover frequency of crystalline oxides as promising catalysts is also put into perspective with amorphous oxides and photosystem II. Particular attention is paid to electronic structure parameters that can potentially govern the adsorbate binding strength and thus provide simple rationales and design principles to predict new catalyst chemistries with enhanced activity. We share new perspective synthesizing mechanism and electronic descriptors developed from both molecular orbital and solid state band structure principles. We conclude with an outlook on the opportunities in future research within this rapidly developing field.National Science Foundation (U.S.) (DMR - 0819762)National Science Foundation (U.S.) (DGE-1122374

Virus-templated Au and Au–Pt core–shell nanowires and their electrocatalytic activities for fuel cell applications

A facile synthetic route was developed to make Au nanowires (NWs) from surfactant-mediated bio-mineralization of a genetically engineered M13 phage with specific Au binding peptides. From the selective interaction between Au binding M13 phage and Au ions in aqueous solution, Au NWs with uniform diameter were synthesized at room temperature with yields greater than 98% without the need for size selection. The diameters of Au NWs were controlled from 10 nm to 50 nm. The Au NWs were found to be active for electrocatalytic oxidation of CO molecules for all sizes, where the activity was highly dependent on the surface facets of Au NWs. This low-temperature high yield method of preparing Au NWs was further extended to the synthesis of Au–Pt core–shell NWs with controlled coverage of Pt shell layers. Electro-catalytic studies of ethanol oxidation with different Pt loading showed enhanced activity relative to a commercial supported Pt catalyst, indicative of the dual functionality of Pt for the ethanol oxidation and Au for the anti-poisoning component of Pt. These new one-dimensional noble metal NWs with controlled compositions could facilitate the design of new alloy materials with tunable properties.United States. Army Research Office (Institute for Collaborative Biotechnologies, grant W911NF-09-0001)National Science Foundation (U.S.) (MRSEC Program, award no. DMR–0819762)Samsung (Firm) (Samsung Foundation of Culture, Samsung Scholarship