Welcome to our website!
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 Learning 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)

Conference Presentation:

Machine-Learning Interatomic Potentials for Structural and Dynamical Properties of Ag20 Clusters. Presented at the workshop (General-Purpose Machine-Learned Force Fields in Theory and Practice), University of Luxembourg, Luxembourg (link).

Conference Publication:

Enhancing Drug Design for VEGFR2: Integrating Quantum Mechanics-Driven 3D QSAR with Deep Learning to Predict Drug Efficacy (The 12th National Conference and the 3rd International Bioinformatics Conference, Behshahr) (link).