Data-Driven Collective Variables for Enhanced Sampling

Abstract

Designing an appropriate set of collective variables is crucial to the success of several enhanced sampling methods. Here we focus on how to obtain such variables from information limited to the metastable states. We characterize these states by a large set of descriptors and employ neural networks to compress this information in a lower-dimensional space, using Fisher's linear discriminant as an objective function to maximize the discriminative power of the network. We test this method on alanine dipeptide, using the non-linearly separable dataset composed by atomic distances. We then study an intermolecular aldol reaction characterized by a concerted mechanism. The resulting variables are able to promote sampling by drawing non-linear paths in the physical space connecting the fluctuations between metastable basins. Lastly, we interpret the behavior of the neural network by studying its relation to the physical variables. Through the identification of its most relevant features, we are able to gain chemical insight into the process.

References in corpus (21)

• Adam: A Method for Stochastic Optimization
• Canonical sampling through velocity-rescaling
• Escaping free-energy minima
• PLUMED 2: New feathers for an old bird
• Well-Tempered Metadynamics: A Smoothly Converging and Tunable Free-Energy Method
• Machine learning and the physical sciences
• VAMPnets: Deep learning of molecular kinetics
• Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics
• Rethinking Metadynamics: from bias potentials to probability distributions
• A Variational Approach to Enhanced Sampling and Free Energy Calculations
• Caliber based spectral gap optimization of order parameters (SGOOP) for sampling complex molecular systems
• Molecular enhanced sampling with autoencoders: On-the-fly collective variable discovery and accelerated free energy landscape exploration
• Variational Encoding of Complex Dynamics
• Nonlinear Discovery of Slow Molecular Modes using State-Free Reversible VAMPnets
• Neural networks-based variationally enhanced sampling
• A variational conformational dynamics approach to the selection of collective variables in metadynamics
• Neural network based path collective variables for enhanced sampling of phase transformations
• Folding a Small Protein Using Harmonic Linear Discriminant Analysis
• Finding Efficient Collective Variables: The Case of Crystallization
• Predictive Collective Variable Discovery with Deep Bayesian Models
• Blind search for complex chemical pathways using Harmonic Linear Discriminant Analysis

Cited by in corpus (40)

• Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems
• A Unified Approach to Enhanced Sampling
• A unified framework for machine learning collective variables for enhanced sampling simulations: $\text{mlcolvar}$
• Rare Event Kinetics from Adaptive Bias Enhanced Sampling
• The role of water in host-guest interaction
• From enhanced sampling to reaction profiles
• Water regulates the residence time of Benzamidine in Trypsin
• Computing the Committor with the Committor: an Anatomy of the Transition State Ensemble
• Deep Learning Collective Variables from Transition Path Ensemble
• Enhanced Sampling in the Age of Machine Learning: Algorithms and Applications
• Explaining reaction coordinates of alanine dipeptide isomerization obtained from deep neural networks using Explainable Artificial Intelligence (XAI)
• Collective Variables for the Study of Crystallization
• Targeted free energy perturbation revisited: Accurate free energies from mapped reference potentials
• Stochastic Resetting for Enhanced Sampling
• Path sampling of recurrent neural networks by incorporating known physics
• Reaction Coordinates for Conformational Transitions using Linear Discriminant Analysis on Positions
• Descriptors-free Collective Variables From Geometric Graph Neural Networks
• From Free-Energy Profiles to Activation Free Energies
• Characterizing metastable states with the help of machine learning
• Everything everywhere all at once: a probability-based enhanced sampling approach to rare events
• Collective Variables for Conformational Polymorphism in Molecular Crystals
• Spectral Map: Embedding Slow Kinetics in Collective Variables
• Manifold Learning in Atomistic Simulations: A Conceptual Review
• Deep learning the slow modes for rare events sampling
• Learning Collective Variables with Synthetic Data Augmentation through Physics-Inspired Geodesic Interpolation
• Autoencoders for dimensionality reduction in molecular dynamics: collective variable dimension, biasing and transition states
• EPR-Net: Constructing non-equilibrium potential landscape via a variational force projection formulation
• Enhanced Sampling for Free Energy Profiles with Post-Transition-State Bifurcations
• Metatensor and metatomic: foundational libraries for interoperable atomistic machine learning
• Unveiling interatomic distances influencing the reaction coordinates in alanine dipeptide isomerization: An explainable deep learning approach
• A critical comparison of general-purpose collective variables for crystal nucleation
• Investigating the hyperparameter space of deep neural network models for reaction coordinates
• Exploration vs Convergence Speed in Adaptive-bias Enhanced Sampling
• A Sinking Approach to Explore Arbitrary Areas in Free Energy Landscapes
• Deep learning of committor for ion dissociation and interpretable analysis of solvent effects using atom-centered symmetry functions
• Revealing the Atomistic Mechanism of Rare Events in Molecular Dynamics
• Computing rates and understanding unbinding mechanism in host-guest systems
• Global free energy landscapes as a smoothly joined collection of local maps
• Deep learning of committor and explainable artificial intelligence analysis for identifying reaction coordinates
• Accelerated Simulations of Molecular Systems through Learning of their Effective Dynamics