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will participate in conducting multiscale modeling such as quantum mechanics density functional theory calculations, molecular dynamics simulations, Monte Carlo simulations, and/or machine learning
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research techniques may involve molecular dynamics, density functional theory, and lattice Boltzmann method. Results will be validated and applied in collaborations with experimentalists. About You The
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theoretical research on magnetic and topological properties in van der Waals materials using Density Functional Theory (DFT) calculations, tight-binding and machine learning methods. Provide theoretical
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, or foundation models. Familiarity with atomistic simulations (e.g., density functional theory, molecular dynamics). Interest in developing broadly applicable machine-learning methods for physical sciences
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language models, reinforcement learning, agent learning, robotic control; (4) quantum materials simulation, density functional theory, catalysis and transition-state theory, molecular dynamics; (5
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well as with theoretical researchers specializing in atomistic simulation, density functional theory (DFT), and ab initio molecular dynamics (AIMD). The successful candidate will also engage with collaborators
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. An ideal candidate should have experience in modeling electrochemical reactions on surfaces and interfaces using first-principles density functional theory (DFT), grand canonical DFT (GC-DFT
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of Quantum Monte Carlo (QMCPACK, PYQMC) density functional theory (e.g. QE, VASP, PYSCF) and associated models to describe various properties of DOE-relevant quantum materials. The Materials Theory Group has a
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functional theory studies of nanoparticles, surfaces, or bulk materials; Chemical reaction modeling and/or enhanced sampling (NEB, metadynamics, microkinetic modeling, etc.); Molecular dynamics simulations
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journals and present research findings at professional meetings; Co-advise graduate and undergraduate researchers as appropriate. Minimum Qualifications: Demonstrated expertise in density functional theory