87 citations · 410 across the 29 of their papers we have counts for
29 papers
Using Diffusion Models to Estimate Uncertainties in Analytic Continuation
Sagi Meir, Daniel Freedman, Barak Hirshberg
Inverse problems are ubiquitous in physics, chemistry, and engineering, arising when reconstructing hidden quantities from indirect measurements. A key example is the analytic cont…
Bosonic Condensed Phase Real-time Dynamics from Ring Polymer Molecular Dynamics
Yotam M. Y. Feldman, Sourav Karmakar, Sutirtha Paul +3
We present a method for approximating real-time correlation functions and quantum transport coefficients of bosonic condensed phases. The direct evaluation of quantum real-time cor…
Stochastic Resetting: A Non-Equilibrium Framework for Prediction, Inference and Design
Tommer D. Keidar, Sagi Meir, Nir Sherf +4
Stochastic resetting has evolved from a simple model of diffusive search acceleration into a general framework for predicting, inferring, and controlling stochastic dynamics far fr…
Generalized Suzuki-Chin Factorization in Bosonic Path Integral Molecular Dynamics
Jacob Higer, Barak Hirshberg
Modern implementations of path integral molecular dynamics (PIMD) simulations of distinguishable particles frequently make use of high order factorization schemes for the Boltzmann…
fix pimd/langevin: An Efficient Implementation of Path Integral Molecular Dynamics in LAMMPS
Yifan Li, Axel Gomez, Kehan Cai +10
Path integral molecular dynamics (PIMD), which maps a quantum particle onto a fictitious classical system of ring polymers and propagates the "beads" of this extended classical sys…
More Bang for the Buck: Improving the Inference of Large Language Models at a Fixed Budget using Reset and Discard (ReD)
Sagi Meir, Tommer D. Keidar, Noam Levi +2
The performance of large language models (LLMs) on verifiable tasks is usually measured by pass@k, the probability of answering a question correctly at least once in k trials. At a…
Combining Harmonic Sampling with the Worm Algorithm to Improve the Efficiency of Path Integral Monte Carlo
Sourav Karmakar, Sutirtha Paul, Adrian Del Maestro +1
We propose an improved Path Integral Monte Carlo (PIMC) algorithm called Harmonic PIMC (H-PIMC) and its generalization, Mixed PIMC (M-PIMC). PIMC is a powerful tool for studying qu…
Stochastic Resetting vs. Thermal Equilibration: Faster Relaxation, Different Destination
Nir Sherf, Remi Goerlich, Barak Hirshberg +1
Stochastic resetting is known for its ability to accelerate search processes and induce non-equilibrium steady states. Here, we compare the relaxation times and resulting steady st…
Machine Learning the Entropy to Estimate Free Energy Differences without Sampling Transitions
Yamin Ben-Shimon, Barak Hirshberg, Yohai Bar-Sinai
Thermodynamic phase transitions, a central concept in physics and chemistry, are typically controlled by an interplay of enthalpic and entropic contributions. In most cases, the es…
Have you tried turning it off and on again? Stochastic resetting for enhanced sampling
Ofir Blumer, Barak Hirshberg
Molecular dynamics simulations are widely used across chemistry, physics, and biology, providing quantitative insight into complex processes with atomic detail. However, their limi…
Periodic Boundary Conditions for Bosonic Path Integral Molecular Dynamics
Jacob Higer, Yotam M. Y. Feldman, Barak Hirshberg
We develop an algorithm for bosonic path integral molecular dynamics (PIMD) simulations with periodic boundary conditions (PBC) that scales quadratically with the number of particl…
First-Passage Approach to Optimizing Perturbations for Improved Training of Machine Learning Models
Sagi Meir, Tommer D. Keidar, Shlomi Reuveni +1
Machine learning models have become indispensable tools in applications across the physical sciences. Their training is often time-consuming, vastly exceeding the inference timesca…
Inference of non-exponential kinetics through stochastic resetting
Ofir Blumer, Shlomi Reuveni, Barak Hirshberg
We present an inference scheme of long timescale, non-exponential kinetics from Molecular Dynamics simulations accelerated by stochastic resetting. Standard simulations provide val…
Adaptive Resetting for Informed Search Strategies and the Design of Non-equilibrium Steady-states
Tommer D. Keidar, Ofir Blumer, Barak Hirshberg +1
Stochastic resetting, the procedure of stopping and re-initializing random processes, has recently emerged as a powerful tool for accelerating processes ranging from queuing system…
Accelerating Molecular Dynamics through Informed Resetting
Jonathan R. Church, Ofir Blumer, Tommer D. Keidar +3
We present a procedure for enhanced sampling of molecular dynamics simulations through informed stochastic resetting. Many phenomena, such as protein folding and crystal nucleation…
i-PI 3.0: a flexible and efficient framework for advanced atomistic simulations
Yair Litman, Venkat Kapil, Yotam M. Y. Feldman +13
Atomic-scale simulations have progressed tremendously over the past decade, largely due to the availability of machine-learning interatomic potentials. These potentials combine the…
Unveiling a crystal's entropy of disorder via electron diffraction. A statistical mechanics approach
Dongxin Liu, Oren Elishav, Jiarui Fu +5
Upon melting, the molecules in the crystal explore numerous configurations, reflecting an increase in disorder. The molar entropy of disorder can be defined by Bolzmann's formula d…
The effect of ligands on the size distribution of copper nanoclusters: insights from molecular dynamics simulations
Oren Elishav, Ofir Blumer, T. Kyle Vanderlick +1
Controlling the size distribution in the nucleation of copper particles is crucial for achieving nanocrystals with desired physical and chemical properties. However, their synthesi…
Short-Time Infrequent Metadynamics for Improved Kinetics Inference
Ofir Blumer, Shlomi Reuveni, Barak Hirshberg
Infrequent Metadynamics is a popular method to obtain the rates of long timescale processes from accelerated simulations. The inference procedure is based on rescaling the first-pa…
Fermionic physics from ab initio path integral Monte Carlo simulations of fictitious identical particles
Tobias Dornheim, Panagiotis Tolias, Simon Groth +3
The \emph{ab initio} path integral Monte Carlo (PIMC) method is one of the most successful methods in statistical physics, quantum chemistry and related fields, but its application…
Resetting Metadynamics
Ofir Blumer, Shlomi Reuveni, Barak Hirshberg
Metadynamics is a powerful method to accelerate molecular dynamics simulations, but its efficiency critically depends on the identification of collective variables that capture the…
Quadratic Scaling Bosonic Path Integral Molecular Dynamics
Yotam M. Y. Feldman, Barak Hirshberg
Bosonic exchange symmetry leads to fascinating quantum phenomena, from exciton condensation in quantum materials to the superfluidity of liquid Helium-4. Unfortunately, path integr…
Collective Variables for Conformational Polymorphism in Molecular Crystals
Oren Elishav, Roy Podgaetsky, Olga Meikler +1
Controlling polymorphism in molecular crystals is crucial in the pharmaceutical, dye, and pesticide industries. However, its theoretical description is extremely challenging, due t…
Stochastic Resetting for Enhanced Sampling
Ofir Blumer, Shlomi Reuveni, Barak Hirshberg
We present a method for enhanced sampling of molecular dynamics simulations using stochastic resetting. Various phenomena, ranging from crystal nucleation to protein folding, occur…