Free Energy Computations: A Mathematical Perspective

Tinker 8: Software Tools for Molecular Design

Rackers, J. A., Wang, Z., Lu, C. T., Laury, M. L., Lagardère, L., Schnieders, M. J., Piquemal, J., Ren, P. Y., & Ponder, J. W. (2018) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.8b00529 · Cited by 1,214

Equalities and Inequalities: Irreversibility and the Second Law of Thermodynamics at the Nanoscale

Jarzynski, C. (2011) · Annual Review of Condensed Matter Physics · doi:10.1146/annurev-conmatphys-062910-140506 · Cited by 995

The Adaptive Biasing Force Method: Everything You Always Wanted To Know but Were Afraid To Ask

Comer, J. R., Gumbart, J. C., Hénin, J., Tony Lelièvre, A., Pohorille, A., & Chipot, C. J. (2014) · The Journal of Physical Chemistry B · doi:10.1021/jp506633n · Cited by 492

Enhanced Sampling Methods for Molecular Dynamics Simulations [Article v1.0]

Hénin, J., Tony Lelièvre, A., Shirts, M. R., Valsson, Ó., & Delemotte, L. (2022) · Living Journal of Computational Molecular Science · doi:10.33011/livecoms.4.1.1583 · Cited by 460

Rethinking Metadynamics: From Bias Potentials to Probability Distributions

Invernizzi, M., & Parrinello, M. (2020) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.0c00497 · Cited by 355

Best Practices for Alchemical Free Energy Calculations [Article v1.0]

Mey, A. S. J. S., Allen, B. K., Macdonald, H. E. B., Chodera, J. D., Hahn, D. F., Kuhn, M., Michel, J., Mobley, D. L., Naden, L. N., Prasad, S., et al. (2019) · Living Journal of Computational Molecular Science · doi:10.33011/livecoms.2.1.18378 · Cited by 325

A Toolkit for the Analysis of Free-Energy Perturbation Calculations

Liu, P., Dehez, F., Cai, W., & Chipot, C. J. (2012) · Journal of Chemical Theory and Computation · doi:10.1021/ct300242f · Cited by 188

Partial differential equations and stochastic methods in molecular dynamics

Tony Lelièvre, A., & Stoltz, G. (2016) · Acta Numerica · doi:10.1017/s0962492916000039 · Cited by 171

Frontiers in free‐energy calculations of biological systems

Chipot, C. J. (2013) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1157 · Cited by 170

Smoothed Biasing Forces Yield Unbiased Free Energies with the Extended-System Adaptive Biasing Force Method

Lesage, A., Tony Lelièvre, A., Stoltz, G., & Hénin, J. (2016) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.6b10055 · Cited by 164

Couplings and quantitative contraction rates for Langevin dynamics

Eberle, A., Guillin, A., & Zimmer, R. (2019) · The Annals of Probability · doi:10.1214/18-aop1299 · Cited by 158

Extended Adaptive Biasing Force Algorithm. An On-the-Fly Implementation for Accurate Free-Energy Calculations

Fu, H., Shao, X., Chipot, C. J., & Cai, W. (2016) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.6b00447 · Cited by 160

A Benchmark Test Set for Alchemical Free Energy Transformations and Its Use to Quantify Error in Common Free Energy Methods

Paliwal, H., & Shirts, M. R. (2011) · Journal of Chemical Theory and Computation · doi:10.1021/ct2003995 · Cited by 149

Zooming across the Free-Energy Landscape: Shaving Barriers, and Flooding Valleys

Fu, H., Zhang, H., Chen, H., Shao, X., Chipot, C. J., & Cai, W. (2018) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.8b01994 · Cited by 149

The sample size required in importance sampling

Chatterjee, S., & Diaconis, P. W. (2018) · The Annals of Applied Probability · doi:10.1214/17-aap1326 · Cited by 144

CHARMM at 45: Enhancements in Accessibility, Functionality, and Speed

Hwang, W., Austin, S. L., Blondel, A., Boittier, E. D., Boresch, S., Buck, M., Buckner, J., Caflisch, A., Chang, H.-T., Cheng, X., et al. (2024) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.4c04100 · Cited by 136

Generalisation of the Eyring–Kramers Transition Rate Formula to Irreversible Diffusion Processes

Bouchet, F., & Reygner, J. (2016) · Annales Henri Poincaré · doi:10.1007/s00023-016-0507-4 · Cited by 125

Many-Body Coarse-Grained Interactions Using Gaussian Approximation Potentials

John, S. T., & Cśanyi, G. (2017) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.7b09636 · Cited by 124

Optimal non-reversible linear drift for the convergence to equilibrium of a diffusion

Lelièvre, T., Nier, F., & Pavliotis, G. A. (2013) · Journal of Statistical Physics · doi:10.1007/s10955-013-0769-x · Cited by 123

The computation of averages from equilibrium and nonequilibrium Langevin molecular dynamics

Leimkuhler, B., Matthews, C., & Stoltz, G. (2015) · IMA Journal of Numerical Analysis · doi:10.1093/imanum/dru056 · Cited by 119

Evolution of Alchemical Free Energy Methods in Drug Discovery

Song, L. F., & Merz, K. M. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00547 · Cited by 119

Nonequilibrium candidate Monte Carlo is an efficient tool for equilibrium simulation

Nilmeier, J. P., Crooks, G. E., Minh, D. D. L., & Chodera, J. D. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1106094108 · Cited by 120

Modern Alchemical Free Energy Methods for Drug Discovery Explained

York, D. M. (2023) · ACS Physical Chemistry Au · doi:10.1021/acsphyschemau.3c00033 · Cited by 114

Variance Reduction Using Nonreversible Langevin Samplers

Duncan, A., Tony Lelièvre, A., & Pavliotis, G. A. (2016) · Journal of Statistical Physics · doi:10.1007/s10955-016-1491-2 · Cited by 106

Exploration, Sampling, And Reconstruction of Free Energy Surfaces with Gaussian Process Regression

Mones, L., Bernstein, N., & Csányi, G. (2016) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.6b00553 · Cited by 91

Geometric integrators and the Hamiltonian Monte Carlo method

Bou-Rabee, N. M., & Sanz-Serna, J. M. (2018) · Acta Numerica · doi:10.1017/s0962492917000101 · Cited by 88

Potential of Mean Force Calculations: A Multiple-Walker Adaptive Biasing Force Approach

Minoukadeh, K., Chipot, C. J., & Tony Lelièvre, A. (2010) · Journal of Chemical Theory and Computation · doi:10.1021/ct900524t · Cited by 87

Chasing Collective Variables Using Autoencoders and Biased Trajectories

Belkacemi, Z., Gkeka, P., Tony Lelièvre, A., & Stoltz, G. (2021) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.1c00415 · Cited by 89

Multiple-Replica Strategies for Free-Energy Calculations in NAMD: Multiple-Walker Adaptive Biasing Force and Walker Selection Rules

Comer, J. R., Phillips, J. C., Schulten, K. J., & Chipot, C. J. (2014) · Journal of Chemical Theory and Computation · doi:10.1021/ct500874p · Cited by 82

Subdiffusion in Membrane Permeation of Small Molecules

Chipot, C. J., & Comer, J. R. (2016) · Scientific Reports · doi:10.1038/srep35913 · Cited by 85

Binding Modes of Ligands Using Enhanced Sampling (BLUES): Rapid Decorrelation of Ligand Binding Modes via Nonequilibrium Candidate Monte Carlo

Gill, S. C., Lim, N. M., Grinaway, P., Rustenburg, A. S., Fass, J., Ross, G. A., Chodera, J. D., & Mobley, D. L. (2018) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.7b11820 · Cited by 80

Binding free energy of protein/ligand complexes calculated using dissociation Parallel Cascade Selection Molecular Dynamics and Markov state model

Hata, H., Tran, D. P., Sobeh, M., & Kitao, A. (2021) · Biophysics and Physicobiology · doi:10.2142/biophysico.bppb-v18.037 · Cited by 82

Calculating Position-Dependent Diffusivity in Biased Molecular Dynamics Simulations

Comer, J. R., Chipot, C. J., & González-Nilo, F. D. (2013) · Journal of Chemical Theory and Computation · doi:10.1021/ct300867e · Cited by 78

Efficient and transferable machine learning potentials for the simulation of crystal defects in bcc Fe and W

Goryaeva, A. M., Dérès, J., Lapointe, C., Grigorev, P., Swinburne, T. D., Kermode, J. R., Ventelon, L., Baima, J., & Marinica, M. (2021) · Physical Review Materials · doi:10.1103/physrevmaterials.5.103803 · Cited by 76

New Coarse Variables for the Accurate Determination of Standard Binding Free Energies

Fu, H., Cai, W., Hénin, J., Roux, B., & Chipot, C. (2017) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.7b00791 · Cited by 74

Free Energy Methods in Drug Discovery—Introduction

Cournia, Z., Chipot, C. J., Roux, B., York, D. M., & Sherman, W. (2021) · ACS symposium series · doi:10.1021/bk-2021-1397.ch001 · Cited by 78

Electrophilic Aromatic Sulfonation with SO3: Concerted or Classic SEAr Mechanism?

Koleva, G., Galabov, B., Kong, J., Schaefer, H. F., & Schleyer, P. V. R. (2011) · Journal of the American Chemical Society · doi:10.1021/ja201866h · Cited by 74

Time Step Rescaling Recovers Continuous-Time Dynamical Properties for Discrete-Time Langevin Integration of Nonequilibrium Systems

Sivak, D. A., Chodera, J. D., & Crooks, G. E. (2014) · The Journal of Physical Chemistry B · doi:10.1021/jp411770f · Cited by 76

Free Energy Methods for the Description of Molecular Processes

Chipot, C. J. (2023) · Annual Review of Biophysics · doi:10.1146/annurev-biophys-062722-093258 · Cited by 76

Non-Reversible Parallel Tempering: A Scalable Highly Parallel MCMC Scheme

Syed, S., Bouchard-Côté, A., Deligiannidis, G., & Doucet, A. (2022) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/rssb.12464 · Cited by 68

Effective dynamics along given reaction coordinates, and reaction rate theory

Zhang, W., Hartmann, C., & Schütte, C. (2016) · Faraday Discussions · doi:10.1039/c6fd00147e · Cited by 73

Discovering Reaction Pathways, Slow Variables, and Committor Probabilities with Machine Learning

Chen, H., Roux, B., & Chipot, C. J. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.3c00028 · Cited by 70

Using Nonequilibrium Fluctuation Theorems to Understand and Correct Errors in Equilibrium and Nonequilibrium Simulations of Discrete Langevin Dynamics

Sivak, D. A., Chodera, J. D., & Crooks, G. E. (2013) · Physical Review X · doi:10.1103/physrevx.3.011007 · Cited by 60

Unsupervised Calculation of Free Energy Barriers in Large Crystalline Systems

Swinburne, T. D., & Marinica, M. (2018) · Physical Review Letters · doi:10.1103/physrevlett.120.135503 · Cited by 59

Efficient rare event simulation by optimal nonequilibrium forcing

Hartmann, C., & Schütte, C. (2012) · Journal of Statistical Mechanics Theory and Experiment · doi:10.1088/1742-5468/2012/11/p11004 · Cited by 58

Thermodynamics of Deca-alanine Folding in Water

Hazel, A. J., Chipot, C. J., & Gumbart, J. C. (2014) · Journal of Chemical Theory and Computation · doi:10.1021/ct5002076 · Cited by 55

Ice Ih–Water Interfacial Free Energy of Simple Water Models with Full Electrostatic Interactions

Davidchack, R. L., Handel, R., Anwar, J., & Brukhno, A. V. (2012) · Journal of Chemical Theory and Computation · doi:10.1021/ct300193e · Cited by 55

Reinforcing materials modelling by encoding the structures of defects in crystalline solids into distortion scores

Goryaeva, A. M., Lapointe, C., Dai, C., Dérès, J., Maillet, J., & Marinica, M. (2020) · Nature Communications · doi:10.1038/s41467-020-18282-2 · Cited by 52

Weak backward error analysis for Langevin process

Kopec, M. (2015) · BIT Numerical Mathematics · doi:10.1007/s10543-015-0546-0 · Cited by 52

Using Multistate Reweighting to Rapidly and Efficiently Explore Molecular Simulation Parameters Space for Nonbonded Interactions

Paliwal, H., & Shirts, M. R. (2013) · Journal of Chemical Theory and Computation · doi:10.1021/ct4005068 · Cited by 48

Jump Markov models and transition state theory: the quasi-stationary distribution approach

Gesù, G. D., Tony Lelièvre, A., Peutrec, D. L., & Nectoux, B. (2016) · Faraday Discussions · doi:10.1039/c6fd00120c · Cited by 48

String Method with Swarms-of-Trajectories, Mean Drifts, Lag Time, and Committor

Roux, B. (2021) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.1c04110 · Cited by 48

Quantifying Configuration-Sampling Error in Langevin Simulations of Complex Molecular Systems

Fass, J., Sivak, D. A., Crooks, G. E., Beauchamp, K. A., Leimkuhler, B., & Chodera, J. D. (2018) · Entropy · doi:10.3390/e20050318 · Cited by 49

The Extended Generalized Adaptive Biasing Force Algorithm for Multidimensional Free-Energy Calculations

Zhao, T., Fu, H., Tony Lelièvre, A., Shao, X., Chipot, C. J., & Cai, W. (2017) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.7b00032 · Cited by 48

Unbiased Hamiltonian Monte Carlo with couplings

Heng, J., & Jacob, P. E. (2018) · Biometrika · doi:10.1093/biomet/asy074 · Cited by 46

Editorial Guidelines for Computational Studies of Ligand Binding Using MM/PBSA and MM/GBSA Approximations Wisely

Roux, B., & Chipot, C. J. (2024) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.4c06614 · Cited by 47

Diffusive Models of Membrane Permeation with Explicit Orientational Freedom

Comer, J. R., Schulten, K. J., & Chipot, C. J. (2014) · Journal of Chemical Theory and Computation · doi:10.1021/ct500209j · Cited by 44

Protein–Ligand Dissociation Simulated by Parallel Cascade Selection Molecular Dynamics

Tran, D. P., Takemura, K., Kuwata, K., & Kitao, A. (2017) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.7b00504 · Cited by 42

Hybrid Monte Carlo methods for sampling probability measures on submanifolds

Tony Lelièvre, A., Rousset, M., & Stoltz, G. (2019) · Numerische Mathematik · doi:10.1007/s00211-019-01056-4 · Cited by 40

A Companion Guide to the String Method with Swarms of Trajectories: Characterization, Performance, and Pitfalls

Chen, H., Ogden, D. S., Pant, S., Cai, W., Tajkhorshid, E., Moradi, M., Roux, B., & Chipot, C. J. (2022) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.1c01049 · Cited by 43

Quantification of coarse-graining error in Langevin and overdamped Langevin dynamics

Duong, M. H., Lamacz, A., Peletier, M. A., Schlichting, A., & Sharma, U. (2018) · Nonlinearity · doi:10.1088/1361-6544/aaced5 · Cited by 36

Local and global perspectives on diffusion maps in the analysis of molecular systems

Trstanova, Z., Leimkuhler, B., & Tony Lelièvre, A. (2020) · Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rspa.2019.0036 · Cited by 35

Using Perturbed Underdamped Langevin Dynamics to Efficiently Sample from Probability Distributions

Duncan, A. B., Nüsken, N., & Pavliotis, G. A. (2017) · Journal of Statistical Physics · doi:10.1007/s10955-017-1906-8 · Cited by 34

Convergence rates for nonequilibrium Langevin dynamics

Iacobucci, A., Olla, S., & Stoltz, G. (2017) · Annales mathématiques du Québec · doi:10.1007/s40316-017-0091-0 · Cited by 32

Nested Transition Path Sampling

Bolhuis, P. G., & Cśanyi, G. (2018) · Physical Review Letters · doi:10.1103/physrevlett.120.250601 · Cited by 32

High-dimensional MCMC with a standard splitting scheme for the underdamped Langevin diffusion.

Monmarché, P. (2021) · Electronic Journal of Statistics · doi:10.1214/21-ejs1888 · Cited by 31

On two possible definitions of the free energy for collective variables

Hartmann, C., Latorre, J. C., & Ciccotti, G. (2011) · The European Physical Journal Special Topics · doi:10.1140/epjst/e2011-01519-7 · Cited by 30

Computation of Standard Binding Free Energies of Polar and Charged Ligands to the Glutamate Receptor GluA2

Heinzelmann, G., Chen, P., & Kuyucak, S. (2014) · The Journal of Physical Chemistry B · doi:10.1021/jp412195m · Cited by 30

How Biased Is Your Model? Concentration Inequalities, Information and Model Bias

Gourgoulias, K., Katsoulakis, M. A., Rey-Bellet, L., & Wang, J. (2020) · IEEE Transactions on Information Theory · doi:10.1109/tit.2020.2977067 · Cited by 30

Approaching a parameter-free metadynamics

Dickson, B. M. (2011) · Physical Review E · doi:10.1103/physreve.84.037701 · Cited by 31

Exploring high‐dimensional free energy landscapes of chemical reactions

Awasthi, S., & Nair, N. N. (2018) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1398 · Cited by 29

Statistically Optimal Continuous Free Energy Surfaces from Biased Simulations and Multistate Reweighting

Shirts, M. R., & Ferguson, A. L. (2020) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.0c00077 · Cited by 28

Optimal linear drift for the speed of convergence of an hypoelliptic diffusion

Guillin, A., & Monmarché, P. (2016) · Electronic Communications in Probability · doi:10.1214/16-ecp25 · Cited by 27

Fuel Cell Reactors for the Clean Cogeneration of Electrical Energy and Value-Added Chemicals

Si, F., Liu, S., Liang, Y., Fu, X., Zhang, J. J., & Luo, J. (2022) · Electrochemical Energy Reviews · doi:10.1007/s41918-022-00168-0 · Cited by 27

Two Mathematical Tools to Analyze Metastable Stochastic Processes

Tony Lelièvre, A. (2012) · Springer eBooks · doi:10.1007/978-3-642-33134-3_83 · Cited by 26

How Do α-Cyclodextrins Self-Organize on a Polymer Chain?

Liu, P., Chipot, C. J., Shao, X., & Cai, W. (2012) · The Journal of Physical Chemistry C · doi:10.1021/jp304035q · Cited by 27

Surface Energy-Mediated Multistep Pathways for Heterogeneous Ice Nucleation

Li, C., Gao, X., & Li, Z. (2018) · The Journal of Physical Chemistry C · doi:10.1021/acs.jpcc.8b01358 · Cited by 25

Adaptively Restrained Particle Simulations

Artemova, S., & Redon, S. (2012) · Physical Review Letters · doi:10.1103/physrevlett.109.190201 · Cited by 31

Long-time convergence of an adaptive biasing force method: Variance reduction by Helmholtz projection

Alrachid, H., & Tony Lelièvre, A. (2016) · SMAI Journal of Computational Mathematics · doi:10.5802/smai-jcm.4 · Cited by 23

Methodology for the Simulation of Molecular Motors at Different Scales

Singharoy, A., & Chipot, C. J. (2016) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.6b09350 · Cited by 23

Free energy simulations for protein ligand binding and stability

Hage, K. E., Mondal, P., & Meuwly, M. (2018) · Molecular Simulation · doi:10.1080/08927022.2017.1416115 · Cited by 23

A Rigorous Framework for Calculating Protein–Protein Binding Affinities in Membranes

Blazhynska, M., Gumbart, J. C., Chen, H., Tajkhorshid, E., Roux, B., & Chipot, C. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.3c00941 · Cited by 22

Collapse of superhydrophobicity on nanopillared surfaces

Amabili, M., Giacomello, A., Meloni, S., & Casciola, C. M. (2017) · Physical Review Fluids · doi:10.1103/physrevfluids.2.034202 · Cited by 22

Mechanism and Kinetics of Aztreonam Hydrolysis Catalyzed by Class-C β-Lactamase: A Temperature-Accelerated Sliced Sampling Study

Awasthi, S., Gupta, S., Tripathi, R., & Nair, N. N. (2018) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.8b01287 · Cited by 22

A Hypocoercivity Related Ergodicity Method for Singularly Distorted Non-Symmetric Diffusions

Grothaus, M., & Stilgenbauer, P. (2015) · Integral Equations and Operator Theory · doi:10.1007/s00020-015-2254-1 · Cited by 22

Accelerated dynamics: Mathematical foundations and algorithmic improvements

Tony Lelièvre, A. (2015) · The European Physical Journal Special Topics · doi:10.1140/epjst/e2015-02420-1 · Cited by 21

Revisiting the Exact Relation between Potential of Mean Force and Free-Energy Profile

den Otter, W. K. (2013) · Journal of Chemical Theory and Computation · doi:10.1021/ct400134d · Cited by 21

Sensitivity analysis for rare events based on Rényi divergence

Dupuis, P. G., Katsoulakis, M. A., Pantazis, Y., & Rey-Bellet, L. (2020) · The Annals of Applied Probability · doi:10.1214/19-aap1468 · Cited by 21

Machine learning surrogate models for prediction of point defect vibrational entropy

Lapointe, C., Swinburne, T. D., Thiry, L., Mallat, S., Proville, L., Becquart, C., & Marinica, M. (2020) · Physical Review Materials · doi:10.1103/physrevmaterials.4.063802 · Cited by 21

Free energy calculation of crystalline solids using normalizing flows

Ahmad, R., & Wei Cai, A. (2022) · Modelling and Simulation in Materials Science and Engineering · doi:10.1088/1361-651x/ac7f4b · Cited by 20

Predictions from First-Principles of Membrane Permeability to Small Molecules: How Useful Are They in Practice?

Chipot, C. J. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c00686 · Cited by 21

A Hybrid Monte Carlo Scheme for Multibackbone Protein Design

Druart, K., Bigot, J., Audit, E., & Simonson, T. (2016) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.6b00421 · Cited by 20

Improving dynamical properties of metropolized discretizations of overdamped Langevin dynamics

Fathi, M., & Stoltz, G. (2016) · Numerische Mathematik · doi:10.1007/s00211-016-0849-3 · Cited by 20

GEOMETRIC LANGEVIN EQUATIONS ON SUBMANIFOLDS AND APPLICATIONS TO THE STOCHASTIC MELT-SPINNING PROCESS OF NONWOVENS AND BIOLOGYPreprint

Grothaus, M., & Stilgenbauer, P. (2016) · Cited by 20

Leveraging the Information from Markov State Models To Improve the Convergence of Umbrella Sampling Simulations

Jo, S., Suh, D., He, Z., Chipot, C. J., & Roux, B. (2016) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.6b05125 · Cited by 19

A probabilistic study of the kinetic Fokker–Planck equation in cylindrical domains

Tony Lelièvre, A., Ramil, M., & Reygner, J. (2022) · Journal of Evolution Equations · doi:10.1007/s00028-022-00796-5 · Cited by 19

Fast and Accurate Multidimensional Free Energy Integration

Hénin, J. (2021) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.1c00593 · Cited by 19

Error Analysis of Modified Langevin Dynamics

Redon, S., Stoltz, G., & Trstanova, Z. (2016) · Journal of Statistical Physics · doi:10.1007/s10955-016-1544-6 · Cited by 18

Convergence of unadjusted Hamiltonian Monte Carlo for mean-field models

Bou-Rabee, N. M., & Schuh, K. (2023) · Electronic Journal of Probability · doi:10.1214/23-ejp970 · Cited by 18

Capabilities and limits of autoencoders for extracting collective variables in atomistic materials science

Baima, J., Goryaeva, A. M., Swinburne, T. D., Maillet, J., Nastar, M., & Marinica, M. (2022) · Physical Chemistry Chemical Physics · doi:10.1039/d2cp01917e · Cited by 18

Free Energy Calculations

(2006) · Springer eBooks · doi:10.1007/1-4020-3587-x_14 · Cited by 74

Numerical Computations

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Free Energy Simulations

Straatsma, T. P. (1998) · Wiley eBooks · doi:10.1002/0470845015.cfa012 · Cited by 3

Energy

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Free energy

Ryabov, V. A. (2018) · Institute of Physics eBooks · doi:10.1088/978-0-7503-1341-4ch11