Dilip Krishnamurthy
Affiliations: | 2015- | Mechanical Engineering | Carnegie Mellon University, Pittsburgh, PA |
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Publications
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| Krishnamurthy D, Lazouski N, Gala ML, et al. (2021) Closed-Loop Electrolyte Design for Lithium-Mediated Ammonia Synthesis. Acs Central Science. 7: 2073-2082 |
| Zhang Y, Krishnamurthy D, Viswanathan V. (2020) Engineering Solid Electrolyte Interphase Composition by Assessing Decomposition Pathways of Fluorinated Organic Solvents in Lithium Metal Batteries Journal of the Electrochemical Society. 167: 070554 |
| San Roman D, Krishnamurthy D, Garg R, et al. (2020) Engineering Three-Dimensional (3D) Out-of-Plane Graphene Edge Sites for Highly Selective Two-Electron Oxygen Reduction Electrocatalysis Acs Catalysis. 10: 1993-2008 |
| Krishnamurthy D, Sumaria V, Viswanathan V. (2019) Quantifying robustness of DFT predicted pathways and activity determining elementary steps for electrochemical reactions. The Journal of Chemical Physics. 150: 041717 |
| Houchins G, Krishnamurthy D, Viswanathan V. (2019) The role of uncertainty quantification and propagation in accelerating the discovery of electrochemical functional materials Mrs Bulletin. 44: 204-212 |
| Vinogradova O, Krishnamurthy D, Pande V, et al. (2018) Quantifying Confidence in DFT Predicted Surface Pourbaix Diagrams of Transition Metal Electrode-Electrolyte Interfaces. Langmuir : the Acs Journal of Surfaces and Colloids |
| Lee A, Krishnamurthy D, Viswanathan V. (2018) Exploring MXenes as Cathodes for Non-Aqueous Lithium-Oxygen Batteries: Design Rules for Selectively Nucleating Li2O2. Chemsuschem |
| Khetan A, Krishnamurthy D, Viswanathan V. (2018) Towards Synergistic Electrode-Electrolyte Design Principles for Nonaqueous Li-O[Formula: see text] batteries. Topics in Current Chemistry (Cham). 376: 11 |
| Krishnamurthy D, Sumaria V, Viswanathan V. (2018) Maximal Predictability Approach for Identifying the Right Descriptors for Electrocatalytic Reactions. The Journal of Physical Chemistry Letters |
| Krishnamurthy D, Weiland H, Barati Farimani A, et al. (2018) Machine Learning Based Approaches to Accelerate Energy Materials Discovery and Optimization Acs Energy Letters. 4: 187-191 |