Manifold learning in atomistic simulations: a conceptual review

J Rydzewski, M Chen, O Valsson - Machine Learning: Science …, 2023 - iopscience.iop.org
Analyzing large volumes of high-dimensional data requires dimensionality reduction: finding
meaningful low-dimensional structures hidden in their high-dimensional observations. Such …

Estimating position-dependent and anisotropic diffusivity tensors from molecular dynamics trajectories: Existing methods and future outlook

TS Domingues, R Coifman… - Journal of Chemical …, 2024 - ACS Publications
Confinement can substantially alter the physicochemical properties of materials by breaking
translational isotropy and rendering all physical properties position-dependent. Molecular …

Reweighted manifold learning of collective variables from enhanced sampling simulations

J Rydzewski, M Chen, TK Ghosh… - Journal of Chemical …, 2022 - ACS Publications
Enhanced sampling methods are indispensable in computational chemistry and physics,
where atomistic simulations cannot exhaustively sample the high-dimensional configuration …

On committor functions in milestoning

X Ji, R Wang, H Wang, W Liu - The Journal of Chemical Physics, 2023 - pubs.aip.org
As an optimal one-dimensional reaction coordinate, the committor function not only
describes the probability of a trajectory initiated at a phase space point first reaching the …

Optimal control for sampling the transition path process and estimating rates

J Yuan, A Shah, C Bentz, M Cameron - Communications in Nonlinear …, 2024 - Elsevier
Many processes in nature such as conformal changes in biomolecules and clusters of
interacting particles, genetic switches, mechanical or electromechanical oscillators with …

ScMiles2: A script to conduct and analyze Milestoning trajectories for Long Time Dynamics

AE Cardenas, A Hunter, H Wang… - Journal of chemical …, 2022 - ACS Publications
Milestoning is a theory and an algorithm that computes kinetics and thermodynamics at long
time scales. It is based on partitioning the (phase) space into cells and running a large …

Committor guided estimates of molecular transition rates

AR Mitchell, GM Rotskoff - Journal of Chemical Theory and …, 2024 - ACS Publications
The probability that a configuration of a physical system reacts, or transitions from one
metastable state to another, is quantified by the committor function. This function contains …

A finite expression method for solving high-dimensional committor problems

Z Song, MK Cameron, H Yang - arXiv preprint arXiv:2306.12268, 2023 - arxiv.org
Transition path theory (TPT) is a mathematical framework for quantifying rare transition
events between a pair of selected metastable states $ A $ and $ B $. Central to TPT is the …

Deep Learning Method for Computing Committor Functions with Adaptive Sampling

B Lin, W Ren - arXiv preprint arXiv:2404.06206, 2024 - arxiv.org
The committor function is a central object for quantifying the transitions between metastable
states of dynamical systems. Recently, a number of computational methods based on deep …

Advanced simulations with PLUMED: OPES and Machine Learning Collective Variables

E Trizio, A Rizzi, PM Piaggi, M Invernizzi… - arXiv preprint arXiv …, 2024 - arxiv.org
Many biological processes occur on time scales longer than those accessible to molecular
dynamics simulations. Identifying collective variables (CVs) and introducing an external …