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Boris Kozinsky
Researcher at Harvard University
Publications - 190
Citations - 7166
Boris Kozinsky is an academic researcher from Harvard University. The author has contributed to research in topics: Electrode & Electrochemical cell. The author has an hindex of 31, co-authored 165 publications receiving 5225 citations. Previous affiliations of Boris Kozinsky include Massachusetts Institute of Technology & University of Houston System.
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A Critical Review of Li/Air Batteries
Jake Christensen,Paul Albertus,Roel S. Sánchez-Carrera,Timm Lohmann,Boris Kozinsky,Ralf Liedtke,Jasim Ahmed,Aleksandar Kojic +7 more
TL;DR: In this paper, the authors discuss the most critical challenges to developing robust, high-energy Li/air batteries and suggest future research directions to understand and overcome these challenges and predict that Li-air batteries will primarily remain a research topic for the next several years.
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AiiDA: automated interactive infrastructure and database for computational science
TL;DR: The paradigm sustaining such vision is illustrated, based around the four pillars of Automation, Data, Environment, and Sharing, and it is believed that AiiDA's design and its sharing capabilities will encourage the creation of social ecosystems to disseminate codes, data, and scientific workflows.
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Secular Evolution of Hierarchical Triple Star Systems
TL;DR: In this paper, the authors derived octupole-level perturbation equations for hierarchical triple systems, using classical Hamiltonian perturbations techniques, which are applicable to a much wider range of parameters.
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Role of Disorder and Anharmonicity in the Thermal Conductivity of Silicon-Germanium Alloys: A First-Principles Study
TL;DR: Calculated thermal conductivity of disordered silicon-germanium alloys is computed from density-functional perturbation theory and with relaxation times that include both harmonic and anharmonic scattering terms, and mass disorder is found to increase the an Harmonic scattering of phonons through a modification of their vibration eigenmodes.
Posted ContentDOI
SE(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials
Simon Batzner,Tess Smidt,Lixin Sun,Jonathan P. Mailoa,Mordechai Kornbluth,Nicola Molinari,Boris Kozinsky +6 more
TL;DR: The NequIP method achieves state-of-the-art accuracy on a challenging set of diverse molecules and materials while exhibiting remarkable data efficiency, challenging the widely held belief that deep neural networks require massive training sets.