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Available for new position
I am seeking a Postdoctoral/Researcher/R&D position specializing in atomistic simulations (DFT/MD) and application of machine learning in computational materials science.
My current research focuses on developing Machine Learning Interatomic Potentials (MLIPs) for 2D materials using Graph Neural Networks (GNNs). As part of this work, I developed ALFF: Automated Frameworks for Active-Learning Machine Learning Interatomic Potentials and Material Properties Calculation. This package automates the generation of training data and the refinement of GNN-based force fields (such as MACE, SevenNet,...) through active learning cycles.
Designed for extensibility, ALFF allows for the seamless integration of new MLIP architectures, MD engines, and DFT codes. It also streamlines the high-throughput computation of material properties, including elastic constants, phonon dispersions, PES scanning, and more.
I would appreciate any information regarding open opportunities in related fields. Get my full CV here.