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, with machine-learned interatomic potentials fine-tuned on in-house DFT calculations. The aim is to identify the elementary steps that control catalytic activity, and to predict how it responds to changes
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into databases such as AFLOW). The postdoc will extend PAOFLOW, our software suite to build tight-binding Hamiltonians from projections of DFT wavefunctions on atomic orbitals and compute a plethora of properties
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electrocatalysis to contribute to the externally funded GETCO2 projects and collaborate with experimental researchers. Your key responsibilities will be to: perform first-principles/DFT studies of electrocatalytic
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density functional theory (DFT), to investigate catalytic conversion pathways on promising SAAs. You will also employ statistical mechanics methods, including kinetic Monte Carlo (kMC), to predict
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electronic-structure methods beyond standard DFT, molecular dynamics, machine-learning interatomic potentials, or computational method development. Experience studying electron–phonon interactions, charge
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functional theory (DFT) calculations of NMR parameters, powder X-ray diffraction, and other complementary analytical techniques. The postdoctoral researcher will be responsible for planning and conducting
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Responsibilities Theory Quantum transport modeling using NEGF; first-principles materials and interface calculations using DFT (VASP, Quantum ESPRESSO, or equivalent). Atomistic spin dynamics
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error estimate, and a full record of how it was computed. Build and scale MLIP/MD/DFT workflows (e.g., atomate2, MACE/CHGNet/UMA-class potentials) to hundreds of compositions on HPC. Validate predictions
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position within a Research Infrastructure? No Offer Description Activities and context: The fellow will develop machine-learning interatomic potentials (MLPs), trained on density functional theory-DFT data
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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