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Welcome to the

Hirshberg Lab

A research group in theoretical chemistry

School of Chemistry, Tel Aviv University.

Simulating the Interface between Chemistry and Physics,
One Atom at a Time.

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Path Integral Molecular Dynamics

​We develop classical molecular dynamics (MD) simulations that provide quantum expectation values for condensed phase systems.

Enhanced Sampling

We develop methods to investigate slow chemical transformations, such as protein folding or crystal nucleation and growth using molecular simulations.

Machine Learning and Molecular Simulations

We apply powerful machine learning (ML) tools, such as artificial neural networks, to push the boundaries of molecular simulations.

Latest Publications

Combining stochastic resetting with Metadynamics to speed-up molecular dynamics simulations

Blumer O.; Reuveni S.; Hirshberg B.

Nature Communications

2024

Collective Variables for Conformational Polymorphism in Molecular Crystals

Elishav O.; Podgaetsky R.; Meikler O.; Hirshberg B.

Journal of Physical Chemistry Letters

2023

Quadratic scaling bosonic path integral molecular dynamics

Feldman Y.M.Y.; Hirshberg B.

Journal of Chemical Physics

2023


Stochastic Resetting for Enhanced Sampling

Blumer O.; Reuveni S.; Hirshberg B.

Journal of Physical Chemistry Letters

2022

Research spotlight:
Path integral molecular dynamics including exchange effects. 

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Being bosons or fermions is one of the most fundamental properties of quantum particles. PIMD simulations allow us to study thermodynamic properties of quantum systems using classical molecular dynamics - a great computational advantage. However, they completely neglect the effects of exchange. We have recently solved this important problem by developing a new PIMD method for bosons and fermions.

Research spotlight:

Stochastic Resetting for Enhanced Sampling

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Molecular dynamics simulations provide insights into various processes and have different technological applications, including drug design. However, these simulations are limited to processes slower than one-millionth of a second, and cannot describe slower processes such as protein folding and crystal nucleation. Counter-intuitively, we showed that we can accelerate simulations through randomly stopping, and restarting them.

We are hiring!

Several positions are available at all levels (Postdoc, Ph.D., MSc, undergraduate interns).
If you are interested in discovering how machine learning can improve molecular simulations, how to study quantum condensed phase systems using classical simulations, or how to apply these tools to exotic quantum materials or chemical reactions on water surfaces - get in touch!
To apply, please send your CV and a short description of your research interests to Barak.

Contact Us

School of Chemistry

Faculty of Exact Sciences

Tel Aviv University 

Offices: Ornstein 404, 403, 411

hirshb@tauex.tau.ac.il

+972-(0)73-380-4460

30 Haim Levanon st., Ramat Aviv

Tel Aviv 6997801

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