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nanoparticles to uncover atomistic mechanisms for sustainable catalysis. 3. Visualizing Chemical Dynamics in Real Time You will apply advanced atomic-resolution imaging and image analysis to uncover surface
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nanoparticles to uncover atomistic mechanisms for sustainable catalysis. (3) Visualizing Chemical Dynamics in Real Time You will apply advanced atomic-resolution imaging and image analysis to uncover surface
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structure–property relationships in metal halide perovskites at an atomistic level”. This collaborative project will establish structure-property relationships in hybrid metal-halide semiconductors
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for candidates with interests in multiscale simulations of complex physical phenomena, from the atomistic/electronic scale to mesocopics and beyond. Of particular interest is the development and application
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learning in chemistry would be advantageous, as would familiarity with ML approaches for atomistic modelling (e.g., MACE, ACE, NequIP, PhysNet, reactive MD). Prior contributions to scientific code
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states, charge density waves, superconductivity, and quantum magnetism - Kagome materials and superconducting hydrides - Machine learning interatomic potentials (MLIPs) and data-driven atomistic
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. Working within an interdisciplinary team, you will develop frameworks that connect atomistic features, mesoscale dynamics, and device-level performance. The effort will integrate heterogeneous data from
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at ORNL, along with computational tools for integrated atomistic modeling in support of materials research for extreme environments. The candidates will develop and apply advanced experimental
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atomistic tight-binding and multi-bands k.p models for the electronic structure of the materials. Using TB_Sim, CEA has made significant progress in the understanding of various aspects of the physics of spin
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Max Planck Institute for Multidisciplinary Sciences, Göttingen | Gottingen, Niedersachsen | Germany | 2 months ago
the structure from such data is challenging, and new theoretical methods and algorithms are required. The research project aims at deriving priors for Bayesian methods from atomistic simulations and machine