arXiv subjects

Venkatasubramanian Viswanathan

Publications and source records attributed to Venkatasubramanian Viswanathan.

At least 55 records · Page 3Linked to original sources

arXiv2021

Tunable electrochemistry with moiré flat bands and topological defects at twisted bilayer graphene

Tailoring electron transfer dynamics across solid-liquid interfaces is fundamental to the interconversion of electrical and chemical energy. Stacking atomically thin layers with a very small azimuthal misorientation to produce moiré superlattices enables the controlled engineering of electronic band structures and the formation of extremely flat electronic bands. Here, we report a strong twist angle dependence of heterogeneous charge transfer kinetics at twisted bilayer graphene electrodes with the greatest enhancement observed near the 'magic angle' (~1.1 degrees). This effect is driven by the angle-dependent tuning of moiré-derived flat bands that modulate electron transfer processes with the solution-phase redox couple. Combined experimental and computational analysis reveals that the variation in electrochemical activity with moiré angle is controlled by atomic reconstruction of the moiré superlattice at twist angles <2 degrees, and topological defect AA stacking regions produce a large anomalous local electrochemical enhancement that cannot be accounted for by the elevated local density of states alone. Our results introduce moiré flat band materials as a distinctively tunable paradigm for mediating electrochemical transformations.

cond-mat.mes-hall↗

arXiv2021

The promise of energy-efficient battery-powered urban aircraft

Improvements in rechargeable batteries are enabling several electric urban air mobility (UAM) aircraft designs with up to 300 miles of range with payload equivalents of up to 7 passengers. We find that novel UAM aircraft consume between 130 Wh/passenger-mile up to ~1,200 Wh/passenger-mile depending on the design and utilization, relative to an expected consumption of over 220 Wh/passenger-mi for terrestrial electric vehicles and 1,000 Wh/passenger-mile for combustion engine vehicles. We also find that several UAM aircraft designs are approaching technological viability with current Li-ion batteries, based on the specific power-and-energy while rechargeability and lifetime performance remain uncertain. These aspects highlight the technological readiness of a new segment of transportation.

eess.SY↗

arXiv2021

Universal Battery Performance and Degradation Model for Electric Aircraft

Development of Urban Air Mobility (UAM) concepts has been primarily focused on electric vertical takeoff and landing aircraft (eVTOLs), small aircraft which can land and takeoff vertically, and which are powered by rechargeable (typically lithium-ion) batteries. Design, analysis, and operation of eVTOLs requires fast and accurate prediction of Li-ion battery performance throughout the lifetime of the battery. eVTOL battery performance modeling must be particularly accurate at high discharge rates to ensure accurate simulation of the high power takeoff and landing portions of the flight. In this work, we generate a battery performance and thermal behavior dataset specific to eVTOL duty cycles. We use this dataset to develop a battery performance and degradation model (Cellfit) which employs physics-informed machine learning in the form of Universal Ordinary Differential Equations (U-ODE's) combined with an electrochemical cell model and degradation models which include solid electrolyte interphase (SEI) growth, lithium plating, and charge loss. We show that Cellfit with U-ODE's is better able to predict battery degradation than a mechanistic battery degradation model. We show that the improved accuracy of the degradation model improves the accuracy of the performance model. We believe that Cellfit will prove to be a valuable tool for eVTOL designers.

physics.app-ph↗

arXiv2021

Thermodynamics of Lithium Stripping and Limits for Fast Discharge in Lithium Metal Batteries

Lithium metal batteries are seen as a critical piece towards electrifying aviation. During charging, plating of lithium metal, a critical failure mechanism, has been studied and mitigation strategies have been proposed. For electric aircraft, high discharge power requirements necessitate stripping of lithium metal in an uniform way and recent studies have identified the evolution of surface voids and pits as a potential failure mechanism. In this work, using density functional theory calculations and thermodynamic analysis, we investigate the discharge process on lithium metal surfaces. In particular, we calculate the tendency for vacancy congregation on lithium metal surfaces, which constitutes the first step in the formation of voids and pits. We find that among the low Miller index surfaces, the (111) surface is the least likely to exhibit pitting issues. Our analysis suggests that faceting control during electrodeposition could be a key pathway towards simultaneously enabling both fast charge and fast discharge.

physics.chem-ph↗

arXiv2020

Interfacial Effects on Solid Electrolyte Interphase in Lithium-ion Batteries

The existence of passivating layers at the interfaces is a major factor enabling modern lithium-ion (Li-ion) batteries. Their properties determine the cycle life, performance, and safety of batteries. A special case is the solid electrolyte interphase (SEI), a heterogeneous multi-component film formed due to the instability and subsequent decomposition of the electrolyte at the surface of the anode. The SEI acts as a passivating layer that hinders further electrolyte disintegration, which is detrimental to the Coulombic efficiency. In this work, we use first-principles simulations to investigate the kinetic and electronic properties of the interface between lithium fluoride (LiF) and lithium carbonate (Li$_2$CO$_3$), two common SEI components present in Li-ion batteries with organic liquid electrolytes. We find a coherent interface between these components that restricts the strain in each of them to below 3%. We find that the interface causes a large increase in the formation energy of the Frenkel defect, generating Li vacancies in LiF and Li interstitials in Li$_2$CO$_3$ responsible for transport. On the other hand, the Li interstitial hopping barrier is reduced from $0.3$ eV in bulk Li$_2$CO$_3$ to $0.10$ or $0.22$ eV in the interfacial structure considered, demonstrating the favorable role of the interface. Controlling these two effects in a heterogeneous SEI is crucial for maintaining fast ion transport in the SEI. We further perform Car-Parrinello molecular dynamics simulations to explore Li ion conduction in our interfacial structure, which reveal an enhanced Li ion diffusion in the vicinity of the interface. Understanding the interfacial properties of the multiphase SEI represents an important frontier to enable next-generation batteries.

physics.chem-ph↗

arXiv2020

MeltNet: Predicting alloy melting temperature by machine learning

Thermodynamics is fundamental for understanding and synthesizing multi-component materials, while efficient and accurate prediction of it still remain urgent and challenging. As a demonstration of the "Divide and conquer" strategy decomposing a phase diagram into different learnable features, quantitative prediction of melting temperature of binary alloys is made by constructing the machine learning (ML) model "MeltNet" in the present work. The influences of model hyperparameters on the prediction accuracy is systematically studied, and the optimal hyperparameters are obtained by Bayesian optimization. A comprehensive error analysis is made on various aspects including training duration, chemistry and input features. It is found that except a few discrepancies mainly caused by less satisfactory treatment of metalloid/semimetal elements and large melting point difference with poor liquid mixing ability between constituent elements, MeltNet achieves overall success in prediction, especially capturing subtle composition-dependent features in the unseen chemical systems for the first time. The reliability, robustness and accuracy of MeltNet is further largely boosted by introducing the ensemble method with uncertainty quantification. Based on the state-of-the-art underlying techniques, MeltNet achieves a prediction mean average error (MAE) as low as about 120 K, at a minimal computational cost. We believe the present work has a general value for significant acceleration of predicting thermodynamics of complicated multi-component systems.

cond-mat.mtrl-sci↗

arXiv2020

$\mathcal{P}^2$: Combining pressure and electrochemistry to synthesize superhydrides

Recently, superhydrides have been computationally identified and subsequently synthesized with a variety of metals at very high pressures. In this work, we evaluate the possibility of synthesizing superhydrides by uniquely combining electrochemistry and applied pressure. We perform computational searches for palladium superhydrides using density functional theory and particle swarm optimization calculations over a broad range of pressures and electrode potentials. We incorporate exchange-correlation functional uncertainty using the Bayesian error estimation formalism to quantify the uncertainty associated with the identified stable phases. Based on a thermodynamic analysis, we construct pressure-potential phase diagrams and provide an alternate synthesis concept, $\mathcal{P}^2$ (pressure-potential), to accessing novel phases having high hydrogen content. Palladium-hydrogen is a widely-studied material system with the highest hydride phase being Pd$_3$H$_4$. Most strikingly for this system, at potentials above hydrogen evolution and $\sim$300 MPa pressure, we find the possibility to make palladium superhydrides (e.g., PdH$_{10}$). We demonstrate the generalizability of this approach for La-H, Y-H and Mg-H with 10-100 fold reduction in required pressure for stabilizing phases. In addition, the $\mathcal{P}^2$ strategy allows stabilizing new phases that cannot be done purely by either pressure or potential and is a general approach that is likely to work for synthesizing other superhydrides at modest pressures.

cond-mat.mtrl-sci↗

arXiv2020

Closed-Loop Design of Proton Donors for Lithium-Mediated Ammonia Synthesis with Interpretable Models and Molecular Machine Learning

In this work, we experimentally determined the efficacy of several classes of proton donors for lithium-mediated electrochemical nitrogen reduction in a tetrahydrofuran-based electrolyte, an attractive alternative method for producing ammonia. We then built an interpretable data-driven classification model which identified solvatochromic Kamlet-Taft parameters as important for distinguishing between active and inactive proton donors. After curating a dataset for the Kamlet-Taft parameters, we trained a deep learning model to predict the Kamlet-Taft parameters. The combination of classification model and deep learning model provides a predictive mapping from a given proton donor to the ability to produce ammonia. We demonstrate that this combination of classification model with deep learning is superior to a purely mechanistic or data-driven approach in accuracy and experimental data efficiency.

physics.chem-ph↗

arXiv2020

Kinetics of lithium electrodeposition and stripping

Electrodeposition and stripping are fundamental electrochemical processes for metals and have gained importance in rechargeable Li-ion batteries due to lithium metal electrodes. The electrode kinetics associated with lithium metal electrodeposition and stripping is crucial in determining performance at fast discharge and charge which is important for electric vertical take-off and landing (eVTOL) aircraft and electric vehicles (EV). In this work, we show the use of Marcus-Hush-Chidsey (MHC) kinetics to accurately predict the Tafel curve data from the work of Boyle et al. We discuss the differences in predictions of reorganization energies from the Marcus-Hush and the MHC models for lithium metal electrodes in four solvents. The MHC kinetic model is implemented and open-sourced within Cantera. Using the reaction kinetic model in a pseudo-2D battery model with a lithium anode paired with a LiFePO$_4$ cathode, we show the importance of accounting for the MHC kinetics and compare it to the use of Butler-Volmer (BV) and Marcus-Hush kinetic models. We find that significant deviation in the overpotentials associated with reaction kinetics for the two different rate laws for conditions of fast discharge and charge relevant for eVTOL and EV respectively.

physics.chem-ph↗

arXiv2020

Marcus-Hush-Chidsey Kinetics at Electrode-Electrolyte Interfaces

Electrochemical kinetics at electrode-electrolyte interfaces limit performance of devices including fuel cells and batteries. While the importance of moving beyond Butler-Volmer kinetics and incorporating the effect of electronic density of states of the electrode have been recognized, a unified framework that incorporates these aspects directly into electrochemical performance models is still lacking. In this work, we explicitly account for the DFT-calculated density of states numerically in calculating electrochemical reaction rates for a variety of electrode-electrolyte interfaces. We first show the utility of this for two cases related to Li metal electrodeposition and stripping on a Li surface and a Cu surface (anode-free configuration). The deviation in reaction rates is minor for cases with flat densities of states such as Li, but is significant for Cu due to nondispersive d-bands creating large variation. Finally, we consider a semiconducting case of a solid-electrolyte interphase (SEI) consisting of LiF and Li$_2$CO$_3$ and note the importance of the Fermi level at the interface, pinned by the redox reaction occuring there. We identify the asymmetry in reaction rates as a function of discharge/charge naturally within this approach. The analysis code used in this work is available open-source on Github.

physics.chem-ph↗

arXiv2020

Robust Active Site Design of Single Atom Catalysts for Electrochemical Ammonia Synthesis

In this work, we provide a computational methodological framework using the single-atom systems as an example material class for ammonia synthesis that is robust towards parameter selection. Applying this to Pt$_1$/g-C$_3$N$_4$, Ru$_1$/g-C$_3$N$_4$, and Fe$_1$/g-C$_3$N$_4$, we generate ensembles of limiting potentials, using the ensemble of functionals collected via Bayesian Error Estimation Functionals (BEEF), to robustly predict catalytic activity. We then extend this to study the scaling between NRR reaction intermediates and use it to identify that NNH* as the best descriptor for these relations. In addition, a procedure to investigate selectivity is outlined, and a more robust way to analyze the selectivity-activity trade-off is presented. For this single-atom material class, we find choosing catalysts that lie on the strong binding leg of the activity volcano are worth further exploration. Given the ease of integration of the proposed method with minimal additional computational cost, we believe this should become a routine part of analysis workflow for multi-electron electrochemical reactions.

cond-mat.mtrl-sci↗

arXiv2020

Uncertainty quantification in first-principles predictions of phonon properties and lattice thermal conductivity

We present a framework for quantifying the uncertainty that results from the choice of exchange-correlation (XC) functional in predictions of phonon properties and thermal conductivity that use density functional theory (DFT) to calculate the atomic force constants. The energy ensemble capabilities of the BEEF-vdW XC functional are first applied to determine an ensemble of interatomic force constants, which are then used as inputs to lattice dynamics calculations and a solution of the Boltzmann transport equation. The framework is applied to isotopically-pure silicon. We find that the uncertainty estimates bound property predictions (e.g., phonon dispersions, specific heat, thermal conductivity) from other XC functionals and experiments. We distinguish between properties that are correlated with the predicted thermal conductivity [e.g., the transverse acoustic branch sound speed ($R^2=0.89$) and average Grüneisen parameter ($R^2=0.85$)] and those that are not [e.g., longitudinal acoustic branch sound speed ($R^2=0.23$) and specific heat ($R^2=0.00$)]. We find that differences in ensemble predictions of thermal conductivity are correlated with the behavior of phonons with mean free paths between $100$ and $300$ nm. The framework systematically accounts for XC uncertainty in phonon calculations and should be used whenever it is suspected that the choice of XC functional is influencing physical interpretations.

cond-mat.mtrl-sci↗

arXiv2020

Design rules for liquid crystalline electrolytes for enabling dendrite-free lithium metal batteries

Dendrite free electrodeposition of lithium metal is necessary for the adoption of high energy density rechargeable lithium metal batteries. Here, we demonstrate a new mechanism of using a liquid crystalline electrolyte to suppress dendrite growth with a lithium metal anode. A nematic liquid crystalline electrolyte modifies the kinetics of electrodeposition by introducing additional overpotential due to its bulk distortion and anchoring free energy. By extending the phase-field model, we simulate the morphological evolution of the metal anode and explore the role of bulk distortion and anchoring strengths on the electrodeposition process. We find that adsorption energy of liquid crystalline molecules on lithium surface can be a good descriptor for the anchoring energy and obtain it using first-principles density functional theory calculations. Unlike other extrinsic mechanisms, we find that liquid crystals with high anchoring strengths can ensure smooth electrodeposition of lithium metal, thus paving the way for practical applications in rechargeable batteries based on metal anodes.

physics.app-ph↗

arXiv2020

Machine Learning Enabled Discovery of Application Dependent Design Principles for Two-dimensional Materials

The large-scale search for high-performing candidate 2D materials is limited to calculating a few simple descriptors, usually with first-principles density functional theory calculations. In this work, we alleviate this issue by extending and generalizing crystal graph convolutional neural networks to systems with planar periodicity, and train an ensemble of models to predict thermodynamic, mechanical, and electronic properties. To demonstrate the utility of this approach, we carry out a screening of nearly 45,000 structures for two largely disjoint applications: namely, mechanically robust composites and photovoltaics. An analysis of the uncertainty associated with our methods indicates the ensemble of neural networks is well-calibrated and has errors comparable with those from accurate first-principles density functional theory calculations. The ensemble of models allows us to gauge the confidence of our predictions, and to find the candidates most likely to exhibit effective performance in their applications. Since the datasets used in our screening were combinatorically generated, we are also able to investigate, using an innovative method, structural and compositional design principles that impact the properties of the structures surveyed and which can act as a generative model basis for future material discovery through reverse engineering. Our approach allowed us to recover some well-accepted design principles: for instance, we find that hybrid organic-inorganic perovskites with lead and tin tend to be good candidates for solar cell applications.

cond-mat.mtrl-sci↗

arXiv2020

Uncertainty Quantification of First Principles Computational Phase Diagram Predictions of Li-Si System Via Bayesian Sampling

In this work, an assessment of the CALPHAD method trained on only density functional theory (DFT) data is performed for the Li-Si binary system, as a case study. Using a parameter sampling approach based on the Bayesian Error Estimation Functional (BEEF-vdW) exchange-correlation potential. By using built-in ensemble of functionals from BEEF-vdW, the uncertainties of the Gibbs Free Energy fitting parameters are obtained and can be propagated to the resulting phase diagram. To find the best fitting form of the CALPHAD model, we implement a model selection step using the Bayesian information criterion (BIC). Applying the best selected CALPHAD model from the DFT calculation, to other sampled BEEF functionals, an ensemble of CALPHAD models is generated leading to an ensemble of phase diagram predictions. The resulting phase diagrams are then compiled into a single-phase diagram representing the most probable phase predicted as well as a quantitative metric of confidence for the prediction. This treatment of uncertainty resulting from DFT provides a rigorous way to ensure the correlated errors of DFT is accounted for in the estimation of uncertainty. From the phase diagram, we have determined intercalation voltages for lithiated silicon. In combination, we can generate a better understanding of the phase transitions and voltage profile to make a more analysis-informed prediction for experiments and the performance of Si-anodes within batteries.

cond-mat.mtrl-sci↗

arXiv2020

Automation is no barrier to light vehicle electrification

Weight, computational load, sensor load, and possibly higher drag may increase the energy use of automated electric vehicles (AEVs) relative to human-driven electric vehicles (EVs), although this increase may be offset by smoother driving. We use a vehicle dynamics model to show that automation is likely to impose a minor penalty on EV range and have negligible effect on battery longevity. As such, while some commentators have suggested that the power and energy requirements of automation mean that the first automated vehicles (AVs) will be gas-electric hybrids, we conclude that this need not be the case. We also find that drivers need to place only a modest value on the time saved by automation for its benefits to exceed direct costs.

cs.CY↗

arXiv2019

Autonomous discovery of battery electrolytes with robotic experimentation and machine-learning

Innovations in batteries take years to formulate and commercialize, requiring extensive experimentation during the design and optimization phases. We approached the design and selection of a battery electrolyte through a black-box optimization algorithm directly integrated into a robotic test-stand. We report here the discovery of a novel battery electrolyte by this experiment completely guided by the machine-learning software without human intervention. Motivated by the recent trend toward super-concentrated aqueous electrolytes for high-performance batteries, we utilize Dragonfly - a Bayesian machine-learning software package - to search mixtures of commonly used lithium and sodium salts for super-concentrated aqueous electrolytes with wide electrochemical stability windows. Dragonfly autonomously managed the robotic test-stand, recommending electrolyte designs to test and receiving experimental feedback in real time. In 40 hours of continuous experimentation over a four-dimensional design space with millions of potential candidates, Dragonfly discovered a novel, mixed-anion aqueous sodium electrolyte with a wider electrochemical stability window than state-of-the-art sodium electrolyte. A human-guided design process may have missed this optimal electrolyte. This result demonstrates the possibility of integrating robotics with machine-learning to rapidly and autonomously discover novel battery materials.

physics.app-ph↗

arXiv2019

Uncertainty Quantification of DFT-predicted Finite Temperature Thermodynamic Properties within the Debye Model

Finite-temperature effects can be included by calculating the vibrations properties and this can greatly improve the fidelity of computational screening. An important challenge for DFT-based screening is the sensitivity of the predictions to the choice of the exchange correlation function. In this work, we rigorously explore the sensitivity of finite temperature thermodynamic properties to the choice of the exchange correlation functional using the built-in error estimation capabilities within the Bayesian Error Estimation Functional. The vibrational properties are estimated using the Debye model and we quantify the uncertainty associated with finite-temperature properties for a diverse collection of materials. We find good agreement with experiment and small spread in predictions over different exchange correlation functionals for Mg, Al$_2$O$_3$, Al, Ca, and GaAs. In the case of Li, Li$_2$O, and NiO, however, we find a large spread in predictions as well as disagreement between experiment and functionals due to complex bonding environments. While the energetics generated by BEEF-vdW ensemble is typically normal, the complex mapping through the Debye model leads to the derived finite temperature properties having non-Gaussian behavior. We test a wide variety of probability distributions that best represent the finite temperature distribution and find that properties such as specific heat, Gibbs free energy, entropy, and the thermal expansion coefficient are well described by normal or transformed normal distributions, while the prediction spread of volume at a given temperature does not appear to be drawn from a single distribution.

cond-mat.mtrl-sci↗