Achieving high catalytic performance with the lowest possible amount of platinum is critical for fuel cell cost reduction. Here we describe a method of preparing highly active yet stable ...electrocatalysts containing ultralow-loading platinum content by using cobalt or bimetallic cobalt and zinc zeolitic imidazolate frameworks as precursors. Synergistic catalysis between strained platinum-cobalt core-shell nanoparticles over a platinum-group metal (PGM)-free catalytic substrate led to excellent fuel cell performance under 1 atmosphere of O
or air at both high-voltage and high-current domains. Two catalysts achieved oxygen reduction reaction (ORR) mass activities of 1.08 amperes per milligram of platinum (A mg
) and 1.77 A mg
and retained 64% and 15% of initial values after 30,000 voltage cycles in a fuel cell. Computational modeling reveals that the interaction between platinum-cobalt nanoparticles and PGM-free sites improves ORR activity and durability.
Machine learning (ML) techniques with the genetic algorithm (GA) have been applied to determine a polarizable force field parameters using only ab initio data from quantum mechanics (QM) calculations ...of molecular clusters at the MP2/6-31G(d,p), DFMP2(fc)/jul-cc-pVDZ, and DFMP2(fc)/jul-cc-pVTZ levels to predict experimental condensed phase properties (i.e., density and heat of vaporization). The performance of this ML/GA approach is demonstrated on 4943 dimer electrostatic potentials and 1250 cluster interaction energies for methanol. Excellent agreement between the training data set from QM calculations and the optimized force field model was achieved. The results were further improved by introducing an offset factor during the machine learning process to compensate for the discrepancy between the QM calculated energy and the energy reproduced by optimized force field, while maintaining the local “shape” of the QM energy surface. Throughout the machine learning process, experimental observables were not involved in the objective function, but were only used for model validation. The best model, optimized from the QM data at the DFMP2(fc)/jul-cc-pVTZ level, appears to perform even better than the original AMOEBA force field (amoeba09.prm), which was optimized empirically to match liquid properties. The present effort shows the possibility of using machine learning techniques to develop descriptive polarizable force field using only QM data. The ML/GA strategy to optimize force fields parameters described here could easily be extended to other molecular systems.
The ever-increasing power of modern supercomputers, along with the availability of highly scalable atomistic simulation codes, has begun to revolutionize predictive modeling of materials. In ...particular, molecular dynamics (MD) has led to breakthrough advances in diverse fields, including tribology, catalysis, sensing, and nanoparticle self-assembly. Furthermore, recent integration of MD simulations with X-ray characterization has demonstrated promise in real-time 3-D characterization of materials on the atomic scale. The popularity of MD is driven by its applicability at disparate length/time scales, ranging from ab initio MD (hundreds of atoms and tens of picoseconds) to all-atom classical MD (millions of atoms over microseconds), and coarse-grained (CG) models (micrometers and tens of microseconds). Nevertheless, a substantial gap persists between AIMD, which is highly accurate but restricted to extremely small sizes, and those based on classical force fields (atomistic and CG) with limited accuracy but access to larger length/time scales. The accuracy and predictive power of classical MD simulations is dictated by the empirical force fields, and their capability to capture the relevant physics. Here, we discuss some of our recent work on the use of machine learning (ML) to combine the accuracy and flexibility of electronic structure calculations with the speed of classical potentials. Our ML framework attempts to bridge the significant gulf that exists between the handful of research groups that develop new interatomic potential models (often requiring several years of effort), and the increasingly large user community from academia and industry that applies these models. Our data-driven approach represents significant departure from the status quo and involves several steps including generation and manipulation of extensive training data sets through electronic structure calculations, defining novel potential functional forms, employing state-of-the-art ML algorithms to formulate highly optimized training procedures, and subsequently developing user-friendly workflow tools integrating these algorithms on high-performance computers (HPCs). Our ML approach shows marked success in developing force fields for a wide range of materials from metals, oxides, nitrides, and heterointerfaces to two-dimensional (2D) materials.
This study investigates the temperature-dependent frictional behavior of MoS2 in humid environments within the context of a long-standing debate over increased friction due to oxidation processes or ...molecular adsorption. By combining sliding friction experiments and density functional theory (DFT)-based first-principles simulations, it aims to clarify the role of water molecule adsorption in influencing frictional properties of MoS2, addressing the challenge of identifying interfacial bonding behavior responsible for friction in such conditions. Sliding experiments revealed that magnetron-sputtered MoS2 exhibits a reduction in the coefficient of friction (COF) with an increase in temperature from 25 to 100 °C under 20 and 40% relative humidity. This change in the COF obeys the Arrhenius law, presenting an energy barrier of 0.165 eV, indicative of the temperature-dependent nature of these frictional changes and suggests a consistent frictional mechanism. DFT simulations showed that H2O molecules are adsorbed at MoS2 vacancy defects with adsorption energies ranging from −0.56 to −0.17 eV, which align with the experimentally determined energy barrier. Adsorptive interactions, particularly the formation of stable H···S interfacial hydrogen bonds at defect sites, increase the interlayer adhesion and impede layer shearing. TEM analysis confirms that although MoS2 layers align parallel to the sliding direction in humid conditions, the COF remains at 0.12, as opposed to approximately 0.02 in dry air. This demonstrates that parallel layer alignment does not notably decrease the COF, underscoring humidity’s significant role in MoS2’s COF values, a result also supported by the Arrhenius analysis. The reversibility of the physisorption process, demonstrated by the recovery of the COF in high-temperature humid environments, suggests its dynamic nature. This study yields fundamental insights into MoS2 interfaces for environments with variable humidity and temperature, crucial for demanding tribological applications.
This study investigates the temperature-dependent frictional behavior of MoS
in humid environments within the context of a long-standing debate over increased friction due to oxidation processes or ...molecular adsorption. By combining sliding friction experiments and density functional theory (DFT)-based first-principles simulations, it aims to clarify the role of water molecule adsorption in influencing frictional properties of MoS
, addressing the challenge of identifying interfacial bonding behavior responsible for friction in such conditions. Sliding experiments revealed that magnetron-sputtered MoS
exhibits a reduction in the coefficient of friction (COF) with an increase in temperature from 25 to 100 °C under 20 and 40% relative humidity. This change in the COF obeys the Arrhenius law, presenting an energy barrier of 0.165 eV, indicative of the temperature-dependent nature of these frictional changes and suggests a consistent frictional mechanism. DFT simulations showed that H
O molecules are adsorbed at MoS
vacancy defects with adsorption energies ranging from -0.56 to -0.17 eV, which align with the experimentally determined energy barrier. Adsorptive interactions, particularly the formation of stable H···S interfacial hydrogen bonds at defect sites, increase the interlayer adhesion and impede layer shearing. TEM analysis confirms that although MoS
layers align parallel to the sliding direction in humid conditions, the COF remains at 0.12, as opposed to approximately 0.02 in dry air. This demonstrates that parallel layer alignment does not notably decrease the COF, underscoring humidity's significant role in MoS
's COF values, a result also supported by the Arrhenius analysis. The reversibility of the physisorption process, demonstrated by the recovery of the COF in high-temperature humid environments, suggests its dynamic nature. This study yields fundamental insights into MoS
interfaces for environments with variable humidity and temperature, crucial for demanding tribological applications.
Sliding contact experiments and first-principles calculations were performed to elucidate the roles of structural defects and water dissociative adsorption process on the tribo-chemical mechanisms ...responsible for low friction of graphene. Sliding friction tests conducted in ambient air and under a dry N
atmosphere showed that in both cases a high running-in coefficient of friction (COF) occurred initially but a low steady-state COF was reached only when the sliding was continued in air with moisture. Density functional theory (DFT) calculations indicated that the energy barrier (E
) for dissociative adsorption of H
O was significantly lower in case of reconstructed graphene with a monovacancy compared to pristine graphene. Cross-sectional transmission electron microscopy of graphene transferred to the counterface revealed a partly amorphous structure incorporating damaged graphene layers with d-spacings larger than that of the original layers. DFT calculations on the reconstructed bilayer AB graphene systems revealed an increase of d-spacing due to the chemisorption of H, O, and OH at the vacancy sites and a reduction in the interlayer binding energy (E
) between the bilayer graphene interfaces compared to pristine graphene. Thus, sliding induced defects facilitated dissociative adsorption of water molecules and reduced COF of graphene for sliding tests under ambient and humid environments but not under an inert atmosphere.
Oxidation can drastically change mechanical properties of nanostructures that typically have large surface-to-volume ratios. However, the underlying mechanisms describing the effect oxidation has on ...the mechanical properties of nanostructures have yet to be characterized. Here we use reactive molecular dynamics and show that the oxidation enhances the aluminium nanowire ductility, and the oxide shell exhibits superplastic behaviour. The oxide shell decreases the aluminium dislocation nucleation stress by increasing the activation volume and the number of nucleation sites. Superplasticity of the amorphous oxide shell is due to viscous flow as a result of healing of the broken aluminium-oxygen bonds by oxygen diffusion, below a critical strain rate. The interplay between the strain rate and oxidation rate is not only essential for designing nanodevices in ambient environments, but also controls interface properties in large-scale deformation processes.
The ability to predict the likelihood of impurity incorporation and their electronic energy levels in semiconductors is crucial for controlling its conductivity, and thus the semiconductor’s ...performance in solar cells, photodiodes, and optoelectronics. The difficulty and expense of experimental and computational determination of impurity levels makes a data-driven machine learning approach appropriate. In this work, we show that a density functional theory-generated dataset of impurities in Cd-based chalcogenides CdTe, CdSe, and CdS can lead to accurate and generalizable predictive models of defect properties. By converting any semiconductor + impurity system into a set of numerical descriptors, regression models are developed for the impurity formation enthalpy and charge transition levels. These regression models can subsequently predict impurity properties in mixed anion CdX compounds (where X is a combination of Te, Se and S) fairly accurately, proving that although trained only on the end points, they are applicable to intermediate compositions. We make machine-learned predictions of the Fermi-level-dependent formation energies of hundreds of possible impurities in 5 chalcogenide compounds, and we suggest a list of impurities which can shift the equilibrium Fermi level in the semiconductor as determined by the dominant intrinsic defects. Machine learning predictions for the dominating impurities compare well with DFT predictions, revealing the power of machine-learned models in the quick screening of impurities likely to affect the optoelectronic behavior of semiconductors.
CdTe is the second most-widely deployed photovoltaic (PV) material due to its high efficiency and low manufacturing costs. Currently, polycrystalline CdTe (poly-CdTe) has a record efficiency of ~22%, ...which is still well below the theoretical limit (~30%). Polycrystalline CdTe films that have incorporated Cl or Se show higher efficiency, possibly due to the segregation of these ions to the grain boundaries (GBs), where they may passivate the dangling bonds. However, the efficiency enhancement mechanisms of passivants in CdTe GBs, and the feasibility of employing alternative passivants, have not been well explored. Here, we present a systematic computational study of CdTe GBs with potential passivants, namely S, P, As, Se, and Sb on Te sites, and Na, Mg, Al, Sc, Cu, and Zn on Cd sites. Density functional theory (DFT) calculations were performed on GB dislocation core structures derived from scanning transmission electron microscopy (STEM) images of a model GB (bicrystal). We computed the segregation thermodynamics, electronic density of states, and charge variations near doped CdTe GBs. We find that segregation of impurities to GBs is thermodynamically favorable. For a Te-terminated core, Se, S, and P on Te sites effectively reduce midgap states. For both Cd- and Te- terminated dislocation cores, Sc and Al reduce midgap states when substituted for Cd atoms. The greatest improvement was achieved with co-doping, i.e. simultaneously substituting Te with Se and substituting Cd with Cu or Al. The elimination of midgap states is predicted to increase the photovoltaic efficiency of CdTe by reducing the recombination at grain boundaries.
•CdTe solar cell efficiency depends highly on grain boundary properties.•Large scale first principles computational study was performed on realistic grain boundary models.•For a Te-terminated core, Se, S, P, and As on Te sites effectively reduce midgap states and improve efficiency.•For both Cd- and Te- terminated dislocation cores, Sc and Al on Cd sites reduce the midgap states and improve efficiency.
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