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Dr Amin Alibakhshi

Amin

Email: a.alibakhshi@qmul.ac.uk
Room Number: Joseph Priestley Building, Room G.06

Profile

Dr Amin Alibakhshi is a Lecturer (Assistant Professor) in Computational Chemistry at Department of Chemistry. He leads a research group developing next-generation methods at the intersection of quantum chemistry, atomistic simulation, and machine learning, with a particular focus on building reliable, high-accuracy molecular models for biomolecular modellig and computational material design. The methods applied include transformer-based models, neural-network interatomic potentials, and methods to treat long-range interactions such as dispersion and polarisability. A key goal is to create models that are accurate and transferable, supported by careful validation and uncertainty-aware predictions, to enable trustworthy simulations.

Before joining QMUL, Dr Alibakhshi held a Marie Skłodowska-Curie postdoctoral fellowship in the group of Prof Alessandro Laio at SISSA (2025), a postdoctoral position at Ruhr University Bochum in the research group of Prof Jörg Behler (2024) and Prof Lars Schäfer (2022-2024) and a Marie Skłodowska-Curie PhD fellowship at the University of Kiel.

Teaching

CHE701P

Research

Research Interests:

See Amin Alibakshi’s research profile pages including details of research interests, publications, and live grants.

Publications

  1. Steffen J., Alibakhshi A.*, ”Hydrogen diffusion on Ni (100): A Combined Machine-Learning, Ring Polymer Molecular Dynamics, and Kinetic Monte Carlo Study”, Journal of Chemical Physics, 161 (184116) 2024
  2. Alibakhshi A.*, Schaefer, L., ”Electron iso-density surfaces provide a thermodynamically consistent representation of atomic and molecular surfaces”, Nature Communications, 15 (1), 2024
  3. Alibakhshi A.*, Hartke, B., “Dependence of Vaporization Enthalpy on Molecular Surfaces and Temperature: Thermodynamically Effective Molecular Surfaces”, Physical Review Letters, 129 (20), 206001,2022
  4. Alibakhshi A.*, Hartke, B., “Implicitly perturbed Hamiltonian as a class of versatile and general-purpose molecular representations for machine learning”, Nature Communications, 13 (1), 1-10,2022
  5. Alibakhshi A.*, Hartke, B., “Improved prediction of solvation free energies by machine-learning polarizable continuum solvation model”, Nature Communications, 12 (1), 1-7,2022
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