arXiv subjects

Venkatasubramanian Viswanathan

Publications and source records attributed to Venkatasubramanian Viswanathan.

At least 73 records · Page 4Linked to original sources

arXiv2019

Engineering Three-Dimensional (3D) Out-of-Plane Graphene Edge Sites for Highly-Selective Two-Electron Oxygen Reduction Electrocatalysis

Selective two-electron oxygen reduction reaction (ORR) offers a promising route for hydrogen peroxide synthesis, and defective sp2 carbon-based materials are attractive, low-cost electrocatalysts for this process. However, due to a wide range of possible defect structures formed during material synthesis, identification and fabrication of precise active sites remain a challenge. Here, we report a graphene edgebased electrocatalyst for two-electron ORR - nanowire-templated three-dimensional fuzzy graphene (NT3DFG). NT-3DFG exhibits excellent efficiency (onset potential of 0.79 $\pm$ 0.01 V versus RHE), selectivity (93 $\pm$ 3 % H2O2), and tunable ORR activity as a function of graphene edge site density. Using spectroscopic surface characterization and density functional theory calculations, we find that NT-3DFG edge sites are readily functionalized by carbonyl (C=O) and hydroxyl (C-OH) groups under alkaline ORR conditions. Our calculations indicate that multiple site configurations at both armchair and zigzag edges may achieve a local coordination environment that allows selective, two-electron ORR. We derive a general geometric descriptor based on the local coordination environment that provides activity predictions of graphene surface sites within ~0.1 V of computed values. Herein we combine synthesis, spectroscopy, and simulations to improve active site characterization and accelerate carbon-based electrocatalyst discovery.

cond-mat.mtrl-sci↗

arXiv2019

Vulnerabilities of Electric Vehicle Battery Packs to Cyberattacks

Electric Vehicles (EVs), like all modern vehicles, are entirely controlled by electronic devices embedded within networks that are exposed to the threat of cyberattacks. Cyber vulnerabilities are magnified with EVs due to unique risks associated with EV battery packs. Current batteries have well-known issues with specific energy, cost and fire-related safety risks. In this study, we develop a systematic framework to assess the impact of cyberattacks on EVs. While the current focus of automotive cyberattacks is on short-term physical safety, it is crucial to consider long-term cyberattacks that aim to cause financial losses through accrued impact, especially in the context of EVs. Faulty components of battery management systems such as a compromised voltage regulator could lead to cyberattacks that can overdischarge or overcharge the battery. Overdischarge could lead to failures such as internal shorts in the timescale of minutes through cyberattacks that compromise energy-intensive EV subsystems like auxiliary components. Attacks that overcharge the pack could shorten the lifetime of a new battery pack to less than a year. Further, such attacks also pose physical safety risks via the triggering of thermal (fire) events. Attacks on auxiliary components lead to battery drain, which could be up to 20% of the state-of-charge per hour. Lastly, we develop a heuristic for the stealthiness of a cyberattack to augment traditional threat models. The methodology presented here will help in building the foundational principles of electric vehicle cybersecurity: a nascent but critical topic in the coming years.

cs.CR↗

arXiv2019

Computational Screening of Current Collectors for Enabling Anode-free Lithium Metal Batteries

Lithium metal cells are key towards achieving high specific energy and energy density for electrification of transportation and aviation. Anode-free cells are the limiting case of lithium metal cells involving no excess lithium and the highest possible specific energy. In addition, anode-free cells are easier, cheaper and safer as they avoid handling and manufacturing of lithium metal foils. Issues related to dendrite growth and poor cycling are magnified in anode-free cells due to lack of excess lithium. Electrolyte and current collector surface play a crucial role in affecting the cycling performance of anode-free cells. In this work, we have computationally screened for candidate current collectors that can nucleate lithium effectively and allow uniform growth. These are determined by the free energy of lithium adsorption and lithium surface diffusion barrier on candidate current collectors. Using density functional theory calculations, we show that Li-alloys possess ideal characteristics for Li nucleation and growth. These can lead to vastly improved specific energy compared to current transition metal current collectors.

cond-mat.mtrl-sci↗

arXiv2019

Towards Ultra Low Cobalt Cathodes: A High Fidelity Computational Phase Search of Layered Li-Ni-Mn-Co Oxides

Layered Li(Ni,Mn,Co,)O$_2$ (NMC) presents an intriguing ternary alloy design space for optimization as a cathode material in Li-ion batteries. Recently, the high cost and resource limitations of Co have added a new design constraint and high Ni-containing NMC alloys have gained enormous attention despite possible performance trade-offs. It is not fully understood if this material space is a disordered solid solution at room temperature and any arbitrary combination can be used or if there exist distinct transition metal orderings to which meta-stable solid solutions will decay during cycling and affect performance. Here, we present a high fidelity computational search of the ternary phase diagram with an emphasis on high-Ni, and thus low Co, containing compositional phases to understand the room temperature stability of the ordered and disordered solid solution phases. This is done through the use of density functional theory training data fed into a reduced order model Hamiltonian that accounts for effective electronic and spin interactions of neighboring transition metal atoms at various lengths in a background of fixed composition and position lithium and oxygen atoms. This model can then be solved to include finite temperature thermodynamics into a convex hull analysis to understand the regions of ordered and disordered solid solution as well the transition metal orderings within the ordered region of the phase diagram. We find that for the majority of transition metal compositions of the layered material, specifically medium to high-Ni content, prefer transition metal ordering and predict the collection of preferred compositions in the ordered region.

cond-mat.mtrl-sci↗

arXiv2019

Descriptors for Electrolyte-Renormalized Oxidative Stability of Solvents in Lithium-ion Batteries

Electrolyte stability against oxidation is one of the important factors limiting the development of high energy density batteries. HOMO level of solvent molecules has been successfully used for understanding trends in their oxidative stability but assumes a non-interacting environment. However, solvent HOMO levels are renormalized due to molecules in their solvation shells. In this work, we first demonstrate an inexpensive and accurate method to determine the HOMO level of solvent followed by simple descriptors for renormalization of HOMO level due to different electrolyte components. The descriptors are based on Gutmann Donor and Acceptor numbers of solvent and other components. The method uses fast GGA-level DFT calculations compared to previously used expensive, experimental data dependent methods. This method can be used to screen for unexplored stable solvents among the large number of known organic compounds to design novel high voltage stable electrolytes.

physics.chem-ph↗

arXiv2019

Benchmarking conductivity predictions of the Advanced Electrolyte Model (AEM) for aqueous systems

High-concentration aqueous electrolytes have shown promise as candidates for a safer, lower-cost battery system. Ionic conductivity is a key property required in high performing electrolytes; the Advanced Electrolyte Model (AEM) has previously shown great accuracy in predicting ionic conductivity in highly-concentrated non-aqueous electrolytes. This work provides extensive experimental data for mixed and highly concentrated aqueous electrolyte systems, rapidly generated via a robotic electrolyte testing apparatus. These data demonstrate exceptional accuracy from AEM in predicting conductivity in aqueous systems, with the accuracy being maintained even in highly-concentrated and mixed-salt regimes.

physics.chem-ph↗

arXiv2019

Design Principles for Self-forming Interfaces Enabling Stable Lithium Metal Anodes

The path toward Li-ion batteries with higher energy-densities will likely involve use of thin lithium metal (Li) anode (<50 $μ$m in thickness), whose cyclability today remains limited by dendrite formation and low Coulombic efficiency. Previous studies have shown that the solid-electrolyte-interface (SEI) of Li metal plays a crucial role in Li electrodeposition and stripping. However, design rules for optimal SEIs on lithium metal are not well-established. Here, using integrated experimental and modeling studies on a series of structurally-similar SEI-modifying compounds as model systems, we reveal the relationship between SEI compositions, Li deposition morphology and coulombic efficiency, and identify two key descriptors (ionicity and compactness) for high performance SEIs through integrated experimental and modeling studies. Using this understanding, we design a highly ionic and compact SEI that shows excellent cycling performance in LiCoO$_2$-Li full cells at practical current densities. Our results provide guidance for the rational selection and optimization of SEI modifiers to further improve Li metal anodes.

physics.app-ph↗

arXiv2019

Theoretical Characterization of Structural Disorder in the Tetramer Model Structure of Eumelanin

Eumelanin is regarded to be an attractive candidate material for biomedical applications. Despite many theoretical studies exploring the structure of eumelanin, an exact mapping of the energetic landscape of the very large phase space of eumelanin is still elusive. In this work, we implement a piecewise Ising Model to predict formation enthalpies of Eumelanin single and double tetramers, and demonstrate its superior predictive and generalizable capabilities. We believe this model will prove very useful in theoretically characterizing the many unique properties attributed to its disorder. The modular nature of the predictive Ising model built up in this work is well-suited for analysis and characterization of a larger phase space of eumelanin polymers, including hexamers and octomers, as well as larger stacked structures, such as potential triple and quadruple eumelanin tetramers. Absorbance data can be incorporated with population-wide predictions of polymer abundance to produce weighted-average predictions of broadband absorbance of bulk eumelanin.

physics.chem-ph↗

arXiv2019

Universal Chemomechanical Design Rules for Solid-Ion Conductors to Prevent Dendrite Formation in Lithium Metal Batteries

Dendrite formation during electrodeposition while charging lithium metal batteries compromises their safety. While high shear modulus solid-ion conductors (SICs) have been prioritized to resolve pressure-driven instabilities that lead to dendrite propagation and cell shorting, it is unclear whether these or alternatives are needed to guide uniform lithium electrodeposition, which is intrinsically density-driven. Here, we show that SICs can be designed within a universal chemomechanical paradigm to access either pressure-driven dendrite-blocking or density-driven dendrite-suppressing properties, but not both. This dichotomy reflects the competing influence of the SICs mechanical properties and partial molar volume of Li+ relative to those of the lithium anode on plating outcomes. Within this paradigm, we explore SICs in a previously unrecognized dendrite-suppressing regime that are concomitantly soft, as is typical of polymer electrolytes, but feature atypically low Li+ partial molar volume, more reminiscent of hard ceramics. Li plating mediated by these SICs is uniform, as revealed using synchrotron hard x-ray microtomography. As a result, cell cycle-life is extended, even when assembled with thin Li anodes and high-voltage NMC-622 cathodes, where 20 percent of the Li inventory is reversibly cycled.

cond-mat.mtrl-sci↗

arXiv2018

Uncertainty Quantification in First-Principles Predictions of Harmonic Vibrational Frequencies of Molecules and Molecular Complexes

Accurate prediction of molecular vibrational frequencies is important to identify spectroscopic signatures and reaction thermodynamics. In this work, we develop a method to quantify uncertainty associated with density functional theory predicted harmonic vibration frequencies utilizing the built-in error estimation capabilities of the BEEF-vdW exchange-correlation functional. The method is computationally efficiency by estimating the uncertainty at nearly the same computational cost as a single vibrational frequency calculation. We demonstrate the utility and robustness of the method by showing that the uncertainty estimates bounds the self-consistent calculations of six exchange correlation functionals for small molecules, rare gas dimers, and molecular complexes from the S22 dataset. Ten rare-gas dimers and the S22 dataset of molecular complexes provide a rigorous test as they are systems with complicated vibrational motion and non-covalent interactions. Using coefficient of variation as a uncertainty metric, we find that modes involving bending or torsional motion and those dominated by non-covalent interactions are found to have higher uncertainty in their predicted frequencies than covalent stretching modes. Given the simplicity of the method, we believe that this method can be easily adopted and should form a routine part of DFT-predicted harmonic frequency analysis.

physics.chem-ph↗

arXiv2018

Robust high-fidelity DFT study of the lithium-graphite phase diagram

Graphite is the most widely used and among the most widely-studied anode materials for lithium-ion batteries. With increasing demands on lithium batteries to operate at lower temperatures and higher currents, it is crucial to understand lithium intercalation in graphite due to issues associated with lithium plating. Lithium intercalation into graphite has been extensively studied theoretically using density functional theory (DFT) calculations, complemented by experimental studies through X-ray diffraction, spectroscopy, optical imaging and other techniques. In this work, we present a first principles based model using DFT calculations, employing the BEEF-vdW as the exchange correlation functional, and Ising model to determine the phase transformations and subsequently, the thermodynamic intercalation potential diagram. We explore a configurational phase space of about 1 billion structures by accurately determining the important interactions for the Ising model. The BEEF-vdW exchange correlation functional employed accurately captures a range of interactions including vdW, covalent and ionic interactions. We incorporate phonon contributions at finite temperatures and configurational entropy to get high accuracy in free energy and potentials. We utilize the built-in error estimation capabilities of the BEEF-vdW exchange correlation functional and to develop a methodological framework for determining the uncertainty associated with DFT calculated phase diagrams and intercalation potentials. The framework also determines the confidence of each predicted stable phase. The confidence value of a phase can help us to identify regions of solid solutions and phase transformations accurately.

cond-mat.mtrl-sci↗

arXiv2018

Quantifying Confidence in DFT Predicted Surface Pourbaix Diagrams of Transition Metal Electrode-Electrolyte Interfaces

Density Functional Theory (DFT) calculations have been widely used to predict the activity of catalysts based on the free energies of reaction intermediates. The incorporation of the state of the catalyst surface under the electrochemical operating conditions while constructing the free energy diagram is crucial, without which even trends in activity predictions could be imprecisely captured. Surface Pourbaix diagrams indicate the surface state as a function of the pH and the potential. In this work, we utilize error-estimation capabilities within the BEEF-vdW exchange correlation functional as an ensemble approach to propagate the uncertainty associated with the adsorption energetics in the construction of Pourbaix diagrams. Within this approach, surface-transition phase boundaries are no longer sharp and are therefore associated with a finite width. We determine the surface phase diagram for several transition metals under reaction conditions and electrode potentials relevant for the Oxygen Reduction Reaction (ORR). We observe that our surface phase predictions for most predominant species are in good agreement with cyclic voltammetry experiments and prior DFT studies. We use the OH$^*$ intermediate for comparing adsorption characteristics on Pt(111), Pt(100), Pd(111), Ir(111), Rh(111), and Ru(0001) since it has been shown to have a higher prediction efficiency relative to O$^*$, and find the trend Ru>Rh>Ir>Pt>Pd for (111) metal facets, where Ru binds OH$^*$ the strongest. We robustly predict the likely surface phase as a function of reaction conditions by associating c-values to quantifying the confidence in predictions within the Pourbaix diagram. We define a confidence quantifying metric using which certain experimentally observed surface phases and peak assignments can be better rationalized.

cond-mat.mtrl-sci↗

arXiv2018

Phase-field simulations of lithium dendrite growth with open-source software

Dendrite growth is a long-standing challenge that has limited the applications of rechargeable lithium metal electrodes. Here, we developed a grand potential based nonlinear phase-field model to study the electrodeposition of lithium as relevant for a lithium metal anode, using open-source software package MOOSE. The dynamic morphological evolution under large/small overpotential is studied in 2-dimensions, revealing important dendrite growth/stable deposition patterns. The corresponding temporal-spatial distributions of ion concentration, overpotential and driving force are studied, which demonstrate an intimate, dynamic competition between ion transport and electrochemical reactions, resulting in vastly different growth patterns. Given the importance of morphological evolution for lithium metal electrodes, wide-spread applications of phase-field models have been limited in part due to in-house or proprietary software. In order to spur growth of this field, we utilize an open-source software package and make all files available to enable future studies to study the many unsolved aspects related to morphology evolution for lithium metal electrodes.

cond-mat.mtrl-sci↗

arXiv2018

Quantifying the Economic Case for Electric Semi-Trucks

There has been considerable interest in the electrification of freight transport, particularly heavy-duty trucks to downscale the greenhouse-gas (GHG) emissions from the transportation sector. However, the economic competitiveness of electric semi-trucks is uncertain as there are substantial additional initial costs associated with the large battery packs required. In this work, we analyze the trade-off between the initial investment and the operating cost for realistic usage scenarios to compare a fleet of electric semi-trucks with a range of 500 miles with a fleet of diesel trucks. For the baseline case with 30% of fleet requiring battery pack replacements and a price differential of US\$50,000, we find a payback period of about 3 years. Based on sensitivity analysis, we find that the fraction of the fleet that requires battery pack replacements is a major factor. For the case with 100% replacement fraction, the payback period could be as high as 5-6 years. We identify the price of electricity as the second most important variable, where a price of US$0.14/kWh, the payback period could go up to 5 years. Electric semi-trucks are expected to lead to savings due to reduced repairs and magnitude of these savings could play a crucial role in the payback period as well. With increased penetration of autonomous vehicles, the annual mileage of semi-trucks could substantially increase and this heavily sways in favor of electric semi-trucks, bringing down the payback period to around 2 years at an annual mileage of 120,000 miles. There is an undeniable economic case for electric semi-trucks and developing battery packs with longer cycle life and higher specific energy would make this case even stronger.

econ.EM↗

arXiv2018

Machine Learning Enabled Computational Screening of Inorganic Solid Electrolytes for Dendrite Suppression with Li Metal Anode

Next generation batteries based on lithium (Li) metal anodes have been plagued by the dendritic electrodeposition of Li metal on the anode during cycling, resulting in short circuit and capacity loss. Suppression of dendritic growth through the use of solid electrolytes has emerged as one of the most promising strategies for enabling the use of Li metal anodes. We perform a computational screening of over 12,000 inorganic solids based on their ability to suppress dendrite initiation in contact with Li metal anode. Properties for mechanically isotropic and anisotropic interfaces that can be used in stability criteria for determining the propensity of dendrite initiation are usually obtained from computationally expensive first-principles methods. In order to obtain a large dataset for screening, we use machine learning models to predict the mechanical properties of several new solid electrolytes. We train a convolutional neural network on the shear and bulk moduli purely on structural features of the material. We use AdaBoost, Lasso and Bayesian ridge regression to train the elastic constants, where the choice of the model depended on the size of the training data and the noise that it can handle. Our models give us direct interpretability by revealing the dominant structural features affecting the elastic constants. The stiffness is found to increase with a decrease in volume per atom, increase in minimum anion-anion separation, and increase in sublattice (all but Li) packing fraction. Cross-validation/test performance suggests our models generalize well. We predict over 20 mechanically anisotropic interfaces between Li metal and 6 solid electrolytes which can be used to suppress dendrite growth. Our screened candidates are generally soft and highly anisotropic, and present opportunities for simultaneously obtaining dendrite suppression and high ionic conductivity in solid electrolytes.

cond-mat.mtrl-sci↗

arXiv2018

Quantifying Confidence in DFT Predicted Surface Pourbaix Diagrams and Associated Reaction Pathways for Chlorine Evolution

Density functional theory calculations can be used to identify dominant reaction mechanisms. However, the dominant reaction mechanism is sensitive to choice of the exchange correlation functional. Here, we demonstrate using the example case of chlorine evolution reaction on rutile oxides, which can occur through at least three reaction mechanisms each mediated by different surface intermediates and active sites. We utilize Bayesian error estimation capabilities within the BEEF-vdW exchange correlation (XC) functional to quantify the uncertainty associated with predictions of the operative reaction mechanism by systematically propagating the uncertainty originating from DFT-computed adsorption free energies. We construct surface Pourbaix diagrams based on the calculated adsorption free energies for rutile oxides of Ru, Ir, Ti, Pt, V, Sn and Rh. We utilize confidence-value (c-value) to determine the degree of confidence in the predicted surface phase diagrams. Using the scaling relations between the adsorption energies of intermediates we construct a generalized Pourbaix diagram showing the stable surface composition as a function of potential and the oxygen binding energy on the cus site. This is used to consistently determine activity volcano relationships. We incorporate the uncertainty in linear scaling relations to quantify the confidence in generalized Pourbaix diagram and the associated activity. This allows us to compute the expectation limiting potential as a function of $ΔE_{O^c}$, which provides a more appropriate activity measure incorporating DFT uncertainty. We show that the confidence in the classification problem of identifying the active reaction mechanism is much better than the prediction problem of determining catalytic activity.

cond-mat.mtrl-sci↗

arXiv2018

Understanding Ion Pairing in High Salt Concentration Electrolytes using Classical Molecular Dynamics Simulations and its Implications for Nonaqueous Li-O$_2$ Batteries

A precise understanding of solvation is essential for rational search and design of electrolytes that can meet performance demands in Li-ion and beyond Li-ion batteries. In the context of Li-O$_2$ batteries, ion pairing is decisive in determining battery capacity via the solution mediated discharge mechanism without compromising heavily on electrolyte stability. We argue that models based on coordination numbers of the counterion in the first solvation shell are inadequate at describing the extent of ion pairing, especially at higher salt concentrations, and are often not consistent with experimental observations. In this study, we use classical molecular dynamics simulations for several salt anions (NO$_3^-$, BF$_4^-$, CF$_3$SO$_3^-$, (CF$_3$SO$_2$)$_2$N$^-$) and nonaqueous solvent (DMSO, DME, ACN, THF, DMA) combinations to improve the understanding of ion paring with the help of a new metric of ion-pairing. We proposed a metric that defines the degree of clustering of a cation by its counterions and solvent molecules on a continuous scale, the limits if which are based on a simple and intuitive condition of charge neutrality. Using these metrics, we identify the extent of ion pairing in good agreement with experimental solvation phase diagrams and further discuss its usefulness in understanding commonly employed measures of salt and solvent donicity such as the Gutmann donor number.

physics.chem-ph↗

arXiv2017

Stability of Electrodeposition at Solid-Solid Interfaces and Implications for Metal Anodes

We generalize the conditions for stable electrodeposition at isotropic solid-solid interfaces using a kinetic model which incorporates the effects of stresses and surface tension at the interface. We develop a stability diagram that shows two regimes of stability: previously known pressure-driven mechanism and a new density-driven stability mechanism that is governed by the relative density of metal in the two phases. We show that inorganic solids and solid polymers generally do not lead to stable electrodeposition, and provide design guidelines for achieving stable electrodeposition.

cond-mat.mtrl-sci↗