Related papers: Mean-Field Ring Polymer Rates Using a Population D…
We consider cooperative reactions and we study the effects of the interaction strength among the system components on the reaction rate, hence realizing a connection between microscopic and macroscopic observables. Our approach is based on…
Centroid molecular dynamics (CMD) incorporates nuclear quantum statistics into the calculation of vibrational spectra. However, when performed in Cartesian coordinates, CMD shows unphysical artifacts in certain vibrational bands, known as…
We apply Dynamical Mean-Field Theory to strongly interacting fermions in an inhomogeneous environment. With the help of this Real-Space Dynamical Mean-Field Theory (R-DMFT) we investigate antiferromagnetic states of repulsively interacting…
The enormous structural and chemical diversity of metal-organic frameworks (MOFs) forces researchers to actively use simulation techniques on an equal footing with experiments. MOFs are widely known for outstanding adsorption properties, so…
Semiclassical instanton theory captures nuclear quantum effects such as tunnelling in chemical reactions. It was originally derived from two different starting points, the flux correlation function and the ImF premise. In pursuit of a…
This paper studies the rate of convergence of a family of continuous-time Markov chains (CTMC) to a mean-field model. When the mean-field model is a finite-dimensional dynamical system with a unique equilibrium point, an analysis based on…
In this paper, we investigate the role of a high-frequency magnetic field in the resonant behavior displayed by a spin-$1/2$ particle under the influence of a rotating magnetic field. We propose two alternative methods for analyzing the…
Active plasma resonance spectroscopy (APRS) is a process-compatible plasma diagnostic method which utilizes the natural ability of plasmas to resonate on or near the electron plasma frequency. The Multipole Resonance Probe (MRP) is a…
Comparing conditional distributions is a fundamental challenge in statistics and machine learning, with applications across a wide range of domains. While proposed methods for measuring discrepancies using kernel embeddings of distributions…
Mean Field inference is central to statistical physics. It has attracted much interest in the Computer Vision community to efficiently solve problems expressible in terms of large Conditional Random Fields. However, since it models the…
Recent advances in experimental techniques enable the simultaneous recording of activity from thousands of neurons in the brain, presenting both an opportunity and a challenge: to build meaningful, scalable models of large neural…
It is now established that nuclear quantum motion plays an important role in determining water's hydrogen bonding, structure, and dynamics. Such effects are important to include in density functional theory (DFT) based molecular dynamics…
Internal coordinate molecular dynamics (ICMD) is an efficient method for studying biopolymers, but it is readily applicable only to molecules with tree topologies, that is with no internal flexible rings. Common examples violating this…
An efficient sampling method, the pmmLang+RBM, is proposed to compute the quantum thermal average in the interacting quantum particle system. Benefiting from the random batch method (RBM), the pmmLang+RBM reduces the complexity due to the…
The conformational statistics of ring polymers in melts or dense solutions is strongly affected by their quenched microscopic topological state. The effect is particularly strong for non-concatenated unknotted rings, which are known to…
Magnetic Resonance Fingerprinting (MRF) leverages transient-state signal dynamics generated by the tunable acquisition parameters, making the design of an optimal, robust sequence a complex, high-dimensional sequential decision problem,…
This study evaluates the accuracy of nuclear fragmentation simulations using a quantum molecular dynamics (QMD) model based on relativistic mean field (RMF) theory for an energy range of 50-400 MeV/u, relevant to hadron therapy. A total of…
There has been recent progress on the problem of inferring the structure of interactions in complex networks when they are in stationary states satisfying detailed balance, but little has been done for non-equilibrium systems. Here we…
Reinforcement learning (RL) is a technique to learn the control policy for an agent that interacts with a stochastic environment. In any given state, the agent takes some action, and the environment determines the probability distribution…
We present a mean-field approach to simulating merging processes of two spherical collisionless stellar systems. This approach is realized with a self-consistent field (SCF) method in which the full spatial dependence of the density and…