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properties of Li-rich three-dimensional materials for lithium battery cathodes using density functional theory (DFT), molecular dynamics, cluster expansion, machine learning computational techniques. This work
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density functional theory (DFT) Evidence of independent funding Downloading a copy of our Job Description Full details of the role and the skills, knowledge and experience required can be found in the Job
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of enantiomeric excess. Experience in using/applying density functional theory (DFT) Evidence of independent funding Downloading a copy of our Job Description Full details of the role and the skills, knowledge and
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. Experience in asymmetric synthesis and the determination of enantiomeric excess. 2. Experience in using/applying density functional theory (DFT) 3. Evidence of independent funding
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behavior within the storage system to optimize design and performance. Demonstrated proficiency in Density Functional Theory (DFT) and/or Molecular Dynamics (MD) simulations, enabling the computational
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advanced characterization methods of inorganic materials and their assemblies, ideally with a focus on battery materials. Demonstrated proficiency in Density Functional Theory (DFT) and/or Molecular Dynamics
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assemblies, ideally with a focus on battery materials. Demonstrated proficiency in Density Functional Theory (DFT) and/or Molecular Dynamics (MD) simulations, enabling the computational investigation
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proficiency in Density Functional Theory (DFT) and/or Molecular Dynamics (MD) simulations, enabling the computational investigation of material properties, electronic structure, and atomic-scale behavior
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using molecular mechanics and hybrid QM/MM methods. Applying density functional theory, correlated wavefunction methods (e.g., MP2, CCSD(T)), and multiconfigurational approaches (e.g., CASSCF, CASPT2
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the development of hierarchical computational materials discovery schemes combining random structure searching, machine learning, atomistic, and density functional theory (DFT) calculations to accurately and