A Variational Approach to Enhanced Sampling and Free Energy Calculations
arXiv:1407.0477 · doi:10.1103/PhysRevLett.113.090601
Abstract
The ability of widely used sampling methods, such as molecular dynamics or Monte Carlo, to explore complex free energy landscapes is severely hampered by the presence of kinetic bottlenecks. A large number of solutions have been proposed to alleviate this problem. Many are based on the introduction of a bias potential which is a function of a small number of collective variable. However constructing such a bias is not simple. Here we introduce a functional of the bias potential and an associated variational principle. The bias that minimizes the functional relates in a simple way to the free energy surface. This variational principle can be turned into a practical, efficient and flexible sampling method. A number of numerical examples are presented which include the determination of a three dimensional free energy surface. We argue that, beside being numerically advantageous, our variational approach provides a convenient standpoint for looking with novel eyes at the sampling problem.
4 pages, 2 figures
References in corpus (5)
Cited by in corpus (85)
- Machine learning for molecular simulation
- Rethinking Metadynamics: from bias potentials to probability distributions
- Enhanced sampling methods for molecular dynamics simulations
- Caliber based spectral gap optimization of order parameters (SGOOP) for sampling complex molecular systems
- Data-Driven Collective Variables for Enhanced Sampling
- Machine learning force fields and coarse-grained variables in molecular dynamics: application to materials and biological systems
- Nonlinear Discovery of Slow Molecular Modes using State-Free Reversible VAMPnets
- A Unified Approach to Enhanced Sampling
- Neural networks-based variationally enhanced sampling
- A variational conformational dynamics approach to the selection of collective variables in metadynamics
- Using the Maximum Entropy Principle to Combine Simulations and Solution Experiments
- Enhancing entropy and enthalpy fluctuations to drive crystallization in atomistic simulations
- Combining simulations and solution experiments as a paradigm for RNA force field refinement
- Enhanced Conformational Sampling using Replica Exchange with Collective-Variable Tempering
- Phase equilibrium of water with hexagonal and cubic ice using the SCAN functional
- Homogeneous nucleation of ice
- A unified framework for machine learning collective variables for enhanced sampling simulations:
- Analyzing and biasing simulations with PLUMED
- Efficient sampling of high-dimensional free energy landscapes using adaptive reinforced dynamics
- Computing the Committor with the Committor: an Anatomy of the Transition State Ensemble
- Learning Free Energy Landscapes Using Artificial Neural Networks
- Calculation of phase diagrams in the multithermal-multibaric ensemble
- Folding a Small Protein Using Harmonic Linear Discriminant Analysis
- Recognizing Local and Global Structural Motifs at the Atomic Scale
- Empirical corrections to the Amber RNA force field with Target Metadynamics
- Deep Learning Collective Variables from Transition Path Ensemble
- A Perspective on Deep Learning for Molecular Modeling and Simulations
- Gaussian Mixture Based Enhanced Sampling
- A microscopic description of acid-base equilibrium
- Variationally Optimized Free Energy Flooding for Rate Calculation
- Finding Efficient Collective Variables: The Case of Crystallization
- Enhanced Sampling in the Age of Machine Learning: Algorithms and Applications
- Multiscale reweighted stochastic embedding (MRSE): Deep learning of collective variables for enhanced sampling
- Making the best of a bad situation: a multiscale approach to free energy calculation
- Finding Multiple Reaction Pathways of Ligand Unbinding
- Combining Metadynamics and Integrated Tempering Sampling
- Deep Reinforcement Learning of Transition States
- Predictive Collective Variable Discovery with Deep Bayesian Models
- Multithermal-multibaric molecular simulations from a variational principle
- Variational control forces for enhanced sampling of nonequilibrium molecular dynamics simulations
- Dynamical reweighting methods for Markov models
- Riemann metric approach to optimal sampling of multidimensional free-energy landscapes
- Statistically optimal continuous free energy surfaces from biased simulations and multistate reweighting
- Enhanced sampling of transition states
- Assessing the accuracy of direct-coupling analysis for RNA contact prediction
- Temporally coherent backmapping of molecular trajectories from coarse-grained to atomistic resolution
- Neural Canonical Transformation with Symplectic Flows
- Recent Advances in Maximum Entropy Biasing Techniques for Molecular Dynamics
- Chemical Potential Calculations In Dense Liquids Using Metadynamics
- Phase equilibrium of liquid water and hexagonal ice from enhanced sampling molecular dynamics simulations
- Conformational Entropy as Collective Variable for Proteins
- Variational design principles for nonequilibrium colloidal assembly
- Sparse inference and active learning of stochastic differential equations from data
- Reweighted Manifold Learning of Collective Variables from Enhanced Sampling Simulations
- Infinite Switch Simulated Tempering in Force (FISST)
- Ab Initio Generalized Langevin Equation
- Manifold Learning in Atomistic Simulations: A Conceptual Review
- A variational approach to nucleation simulation
- A perturbative solution to metadynamics ordinary differential equation
- Deep learning the slow modes for rare events sampling
- Quantifying the impact of vibrational nonequilibrium in plasma catalysis: Insights from a molecular dynamics model of dissociative chemisorption
- Chemical Potential Calculations in Non-Homogeneous Liquids
- Tautomeric equilibrium in condensed phases
- maze: Heterogeneous Ligand Unbinding along Transient Protein Tunnels
- A Variational Approach to Monte Carlo Renormalization Group
- Implementing Dimer Metadynamics using GROMACS
- Enhancing the formation of ionic defects to study the ice Ih/XI transition with molecular dynamics simulations
- Generalization and Memorization: The Bias Potential Model
- Determination of the Critical Manifold Tangent Space and Curvature with Monte Carlo Renormalization Group
- Metashooting: A Novel Tool for Free Energy Reconstruction from Polymorphic Phase Transition Mechanisms
- Relative Resolution: A Multipole Approximation at Appropriate Distances
- Exploration vs Convergence Speed in Adaptive-bias Enhanced Sampling
- First-Passage Approach to Optimizing Perturbations for Improved Training of Machine Learning Models
- Determining Free Energy Differences Through Variational Morphing
- A Mathematical Framework for Learning Probability Distributions
- Monte Carlo Renormalization Group for Systems with Quenched Disorder
- Ab Initio bulk free energy surface of proper ferroelectrics
- A Sinking Approach to Explore Arbitrary Areas in Free Energy Landscapes
- Sampling Rare Event Energy Landscapes via Birth-Death Augmented Dynamics
- Multi-Hill Strategy in Metadynamics for Interstitial Diffusion in Crystals
- Quantum Momentum Distribution and Quantum Entanglement in the Deep Tunneling Regime
- Machine learning approaches for analyzing and enhancing molecular dynamics simulations
- Reweighted Jarzynski sampling: Acceleration of rare events and free energy calculation with a bias potential learned from nonequilibrium work
- Global free energy landscapes as a smoothly joined collection of local maps
- Improving the Efficiency of Variationally Enhanced Sampling with Wavelet-Based Bias Potentials