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We are an independent research group focusing on computational chemistry and the development of machine-learned interatomic potentials to accelerate computationally demanding calculations and molecular simulations. Our long-term research initiative, the ATLAS Project, focuses on developing a machine-learning pipeline integrated with first-principles calculations to enable fast and automated learning. We are particularly interested in metal clusters because of their fascinating properties and potential applications in areas such as catalysis, photochemistry, and polariton chemistry.


Amir Mahdi Zardoshti

> B.Sc. Applied Chemistry — University of Tehran

> M.Sc. Physical Chemistry — Sharif University of Technology


Supervisor: 

Prof. Zahra Jamshidi (Link)(Link)

Professor of Physical Chemistry Department of Chemistry Sharif University of Technology


E-mail: amzardoshti.edu@gmail.com

CV: [Download Link]

LinkedIn: [Link]

GitHub: [Link]

 

Amir Reza Zardoshti

> M.Sc. Biochemistry —  University of Tehran


Supervisor: 

Reza Yousefi (Link)

Professor of Biochemistry at University of Tehran


E-mail: Reza.zardoshti@at.uc.ir

CV: [Download Link]

LinkedIn: [Link]



 


Research Interests                                              

>>> Machine-Learned Interatomic Potentials (MLIPs)

>>> Ab Initio Molecular Dynamics (Born–Oppenheimer Molecular Dynamics, BOMD)
>>> Molecular Quantum Dynamics (Multiconfiguration Time-Dependent Hartree, MCTDH)

>>> Long-Range Interactions (Many-Body Dispersion, MBD)


Highlighted Research Projects

CP2K–MACE Package: Automated Active Learning for Fast Construction of Potential Energy Surfaces and Dynamical Analysis of Metal Clusters 


Hartree-Fock Study of Two-Electron Systems

Point Cloud-based Deep Learning to Predict Drug Efficacy on the VEGFR2 Protein


Generative Point Cloud Model with Transformer-Based SMILES Captioning for De Novo Drug Design

Machine Learning–enhanced Molecular Dynamics for Infrared spectral Simulation


Our GPUs: NVIDIA RTX 3090 & NVIDIA Tesla K80


Headline

NVIDIA GeForce RTX 3090

  • VRAM: 24 GB GDDR6X

  • CUDA Cores: 10,496

  • Release Year: 2020


  • Headline

    NVIDIA Tesla K80

  • VRAM: 24 GB GDDR5 (12 GB per GPU, dual-GPU card)

  • CUDA Cores: 4,992 (2,496 per GPU)

  • Release Year: 2014