840 citations · 4.2k across the 66 of their papers we have counts for
31 papers · 1 filter
Statistical methods for resolving poor uncertainty quantification in machine learning interatomic potentials
Emil Annevelink, Venkatasubramanian Viswanathan
Machine learning interatomic potentials (MLIPs) are promising surrogates for quantum mechanics evaluations in ab-initio molecular dynamics simulations due to their ability to repro…
Accurate Surface and Finite Temperature Bulk Properties of Lithium Metal at Large Scales using Machine Learning Interaction Potentials
Mgcini Keith Phuthi, Archie Mingze Yao, Simon Batzner +4
The properties of lithium metal are key parameters in the design of lithium ion and lithium metal batteries. They are difficult to probe experimentally due to the high reactivity a…
Nonequilibrium Electrochemical Phase Maps: Beyond Butler-Volmer Kinetics
Rachel C. Kurchin, Dhairya Gandhi, Venkatasubramanian Viswanathan
Electrochemical kinetics at electrode-electrolyte interfaces are crucial to understand high-rate behavior of energy storage devices. Phase transformation of electrodes is typically…
By how much can closed-loop frameworks accelerate computational materials discovery?
Lance Kavalsky, Vinay I. Hegde, Eric Muckley +3
The implementation of automation and machine learning surrogatization within closed-loop computational workflows is an increasingly popular approach to accelerate materials discove…
Effect of disorder and doping on electronic structure and diffusion properties of LiVO
Mohammad Babar, Hasnain Hafiz, Zeeshan Ahmad +3
VO in its phase (LiVO) with excess lithium is a potential alternative to the graphite anode for lithium-ion batteries at low temperature and fast…
AutoMat: Accelerated Computational Electrochemical systems Discovery
Emil Annevelink, Rachel Kurchin, Eric Muckley +17
Large-scale electrification is vital to addressing the climate crisis, but several scientific and technological challenges remain to fully electrify both the chemical industry and…
MeltNet: Predicting alloy melting temperature by machine learning
Pin-Wen Guan, Venkatasubramanian Viswanathan
Thermodynamics is fundamental for understanding and synthesizing multi-component materials, while efficient and accurate prediction of it still remain urgent and challenging. As a…
Robust Active Site Design of Single Atom Catalysts for Electrochemical Ammonia Synthesis
Lance Kavalsky, Venkatasubramanian Viswanathan
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…
: Combining pressure and electrochemistry to synthesize superhydrides
Pin-Wen Guan, Russell J. Hemley, Venkatasubramanian Viswanathan
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…
Uncertainty quantification in first-principles predictions of phonon properties and lattice thermal conductivity
Holden L. Parks, Hyun-Young Kim, Venkatasubramanian Viswanathan +1
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 conducti…
Machine Learning Enabled Discovery of Application Dependent Design Principles for Two-dimensional Materials
Victor Venturi, Holden Parks, Zeeshan Ahmad +1
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 calcul…
Uncertainty Quantification of First Principles Computational Phase Diagram Predictions of Li-Si System Via Bayesian Sampling
Ying Yuan, Gregory Houchins, Pin-Wen Guan +1
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…
Uncertainty Quantification of DFT-predicted Finite Temperature Thermodynamic Properties within the Debye Model
Pinwen Guan, Gregory Houchins, Venkatasubramanian Viswanathan
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…
Computational Screening of Current Collectors for Enabling Anode-free Lithium Metal Batteries
Vikram Pande, Venkatasubramanian Viswanathan
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…
Engineering Three-Dimensional (3D) Out-of-Plane Graphene Edge Sites for Highly-Selective Two-Electron Oxygen Reduction Electrocatalysis
Daniel San Roman, Dilip Krishnamurthy, Raghav Garg +4
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 e…
Universal Chemomechanical Design Rules for Solid-Ion Conductors to Prevent Dendrite Formation in Lithium Metal Batteries
Chengyin Fu, Victor Venturi, Zeeshan Ahmad +3
Dendrite formation during electrodeposition while charging lithium metal batteries compromises their safety. While high shear modulus solid-ion conductors (SICs) have been prioriti…
Phase-field simulations of lithium dendrite growth with open-source software
Zijian Hong, Venkatasubramanian Viswanathan
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-…
Towards Ultra Low Cobalt Cathodes: A High Fidelity Computational Phase Search of Layered Li-Ni-Mn-Co Oxides
Gregory Houchins, Venkatasubramanian Viswanathan
Layered Li(Ni,Mn,Co,)O (NMC) presents an intriguing ternary alloy design space for optimization as a cathode material in Li-ion batteries. Recently, the high cost and resource…
Machine Learning Enabled Computational Screening of Inorganic Solid Electrolytes for Dendrite Suppression with Li Metal Anode
Zeeshan Ahmad, Tian Xie, Chinmay Maheshwari +2
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 circui…
Quantifying Confidence in DFT Predicted Surface Pourbaix Diagrams and Associated Reaction Pathways for Chlorine Evolution
Vaidish Sumaria, Dilip Krishnamurthy, Venkatasubramanian Viswanathan
Density functional theory calculations can be used to identify dominant reaction mechanisms. However, the dominant reaction mechanism is sensitive to choice of the exchange correla…
Quantifying Confidence in DFT Predicted Surface Pourbaix Diagrams of Transition Metal Electrode-Electrolyte Interfaces
Olga Vinogradova, Dilip Krishnamurthy, Vikram Pande +1
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 t…
Maximal predictability approach for identifying the right descriptors for electrocatalytic reactions
Dilip Krishnamurthy, Vaidish Sumaria, Venkatasubramanian Viswanathan
Density Functional Theory (DFT) calculations are being routinely used to identify new material candidates that approach activity near fundamental limits imposed by thermodynamics o…
Role of anisotropy in determining stability of electrodeposition at solid-solid interfaces
Zeeshan Ahmad, Venkatasubramanian Viswanathan
We investigate the stability of electrodeposition at solid-solid interfaces for materials exhibiting an anisotropic mechanical response. The stability of electrodeposition or resis…
Surface Restructuring of Nickel Sulfide Generates Optimally-Coordinated Active Sites for ORR Catalysis
Bing Yan, Dilip Krishnamurthy, Christopher H. Hendon +3
First-row transition metal oxides and chalcogenides have been found to rival the performance of precious metal-based catalysts for the interconversion of water and O. The high…