Uncertainty Quantification in MD Simulations. Part II: Bayesian Inference of Force-Field Parameters

The differential evolution (DE) algorithm is applied for obtaining the optimized intermolecular interaction parameters between CH4and 2-methylimidazolate ([C4N2H5]−) using quantum binding energies of CH4-[C4N2H5]−complexes. The initial parameters and their upper/lower bounds are obtained from the general AMBER force field. The DE optimized and the AMBER parameters are then used in the molecular dynamics (MD) simulations of CH4molecules in the frameworks of ZIF-8. The results show that the DE parameters are better for representing the quantum interaction energies than the AMBER parameters. The dynamical and structural behaviors obtained from MD simulations with both sets of parameters are also of notable differences.

Download Full-text

Development of Cu2+-Based Distance Methods and Force Field Parameters for the Determination of PNA Conformations and Dynamics by EPR and MD Simulations

The Journal of Physical Chemistry B ◽

10.1021/acs.jpcb.0c05509 ◽

2020 ◽

Vol 124 (35) ◽

pp. 7544-7556

Author(s):

Austin Gamble Jarvi ◽

Artur Sargun ◽

Xiaowei Bogetti ◽

Junmei Wang ◽

Catalina Achim ◽

...

Keyword(s):

Force Field ◽

Md Simulations ◽

Distance Methods ◽

Force Field Parameters

Download Full-text

An ab-initio study of pyrrole and imidazole arylamides

Journal of the Serbian Chemical Society ◽

10.2298/jsc130929104a ◽

2013 ◽

Vol 78 (11) ◽

pp. 1789-1795

Author(s):

Ara Abramyan ◽

Zhiwei Liu ◽

Vojislava Pophristic

Keyword(s):

Dna Binding ◽

Force Field ◽

Dna Sequences ◽

Density Functional ◽

Md Simulations ◽

Building Blocks ◽

Amide Bond ◽

Amide Bonds ◽

Force Field Parameters ◽

Arylamide Foldamers

Arylamide foldamers have been shown to have a number of biological and medicinal applications. For example, a class of pyrrole-imidazole polyamide foldamers is capable of binding specific DNA sequences and preventing development of various gene disorders, most importantly cancer. Molecular dynamics (MD) simulations can provide crucial details in understanding the atomic level events related to foldamer/DNA binding. An important first step in the accurate simulation of these foldamer/DNA systems is the reparametrization of force field parameters for torsion around the aryl-amide bonds. Here we highlight our Density Functional Theory (DFT) potential energy profiles and derived force field parameters for four aryl-amide bond types for the pyrrole and imidazole building blocks extensively used in foldamer design for the DNA-binding polyamides. These results contribute to developing of computational tools for an appropriate molecular modeling of pyrrole-imidazole polyamide/DNA binding, and provide an insight into the chemical factors that influence the flexibility of the pyrrole-imidazole polyamides, and their binding to DNA.

Download Full-text

Empirical optimization of molecular simulation force fields by Bayesian inference

The European Physical Journal B ◽

10.1140/epjb/s10051-021-00234-4 ◽

2021 ◽

Vol 94 (12) ◽

Author(s):

Jürgen Köfinger ◽

Gerhard Hummer

Keyword(s):

Bayesian Inference ◽

Force Field ◽

Force Fields ◽

Chem Phys ◽

Exascale Computing ◽

Wide Range ◽

Force Field Parameters ◽

Dynamics Simulations ◽

High Level ◽

Predictor Corrector

Abstract The demands on the accuracy of force fields for classical molecular dynamics simulations are steadily growing as larger and more complex systems are studied over longer times. One way to meet these growing demands is to hand over the learning of force fields and their parameters to machines in a systematic (semi)automatic manner. Doing so, we can take full advantage of exascale computing, the increasing availability of experimental data, and advances in quantum mechanical computations and the calculation of experimental observables from molecular ensembles. Here, we discuss and illustrate the challenges one faces in this endeavor and explore a way forward by adapting the Bayesian inference of ensembles (BioEn) method [Hummer and Köfinger, J. Chem. Phys. (2015)] for force field parameterization. In the Bayesian inference of force fields (BioFF) method developed here, the optimization problem is regularized by a simplified prior on the force field parameters and an entropic prior acting on the ensemble. The latter compensates for the unavoidable over simplifications in the parameter prior. We determine optimal force field parameters using an iterative predictor–corrector approach, in which we run simulations, determine the reference ensemble using the weighted histogram analysis method (WHAM), and update the force field according to the BioFF posterior. We illustrate this approach for a simple polymer model, using the distance between two labeled sites as the experimental observable. By systematically resolving force field issues, instead of just reweighting a structural ensemble, the BioFF corrections extend to observables not included in ensemble reweighting. We envision future force field optimization as a formalized, systematic, and (semi)automatic machine-learning effort that incorporates a wide range of data from experiment and high-level quantum chemical calculations, and takes advantage of exascale computing resources. Graphic abstract

Download Full-text

Uncertainty Quantification of Artificial Neural Network Based Machine Learning Potentials

Volume 12: Materials: Genetics to Structures ◽

10.1115/imece2018-88071 ◽

2018 ◽

Cited By ~ 2

Author(s):

Yumeng Li ◽

Weirong Xiao ◽

Pingfeng Wang

Keyword(s):

Machine Learning ◽

Force Field ◽

Uncertainty Quantification ◽

Material Design ◽

Md Simulations ◽

Machine Learning Techniques ◽

Field Potentials ◽

Analysis And Design ◽

Learning Techniques ◽

Study Results

Atomistic simulations play an important role in the material analysis and design by being rooted in the accurate first principles methods that free from empirical parameters and phenomenological models. However, successful applications of MD simulations largely depend on the availability of efficient and accurate force field potentials used for describing the interatomic interactions. As a powerful tool revolutionizing many areas in science and technology, machine learning techniques have gained growing attentions in the field of material science and engineering due to their potentials to accelerate the material discovery through their applications in surrogate model assisted material design. Despite tremendous advantages of employing machine learning techniques for the development of force field potentials as compared to conventional approaches, the uncertainty involved in the machine learning interpolated atomic potential energy surface has not drew much attention although it is an important issue. In this paper, the uncertainty quantification study is performed for the machine learning interpolated atomic potentials, and applied to the titanium dioxide (TiO2), an industrially relevant and well-studies material. The study results indicated that quantifying uncertainties is an indispensable task that must be performed along with the atomistic simulation process for a successful application of the machine learning based force field potentials.

Download Full-text

QUBEKit: Automating the Derivation of Force Field Parameters from Quantum Mechanics

10.26434/chemrxiv.7247045.v2 ◽

2019 ◽

Cited By ~ 1

Author(s):

Joshua Horton ◽

Alice Allen ◽

Leela Dodda ◽

Daniel Cole

Keyword(s):

Quantum Mechanics ◽

Force Field ◽

Organic Molecules ◽

Force Fields ◽

Liquid Density ◽

Small Organic Molecules ◽

Automated Generation ◽

Energy Of Hydration ◽

Novel Method ◽

Force Field Parameters

<div><div><div><p>Modern molecular mechanics force fields are widely used for modelling the dynamics and interactions of small organic molecules using libraries of transferable force field parameters. For molecules outside the training set, parameters may be missing or inaccurate, and in these cases, it may be preferable to derive molecule-specific parameters. Here we present an intuitive parameter derivation toolkit, QUBEKit (QUantum mechanical BEspoke Kit), which enables the automated generation of system-specific small molecule force field parameters directly from quantum mechanics. QUBEKit is written in python and combines the latest QM parameter derivation methodologies with a novel method for deriving the positions and charges of off-center virtual sites. As a proof of concept, we have re-derived a complete set of parameters for 109 small organic molecules, and assessed the accuracy by comparing computed liquid properties with experiment. QUBEKit gives highly competitive results when compared to standard transferable force fields, with mean unsigned errors of 0.024 g/cm3, 0.79 kcal/mol and 1.17 kcal/mol for the liquid density, heat of vaporization and free energy of hydration respectively. This indicates that the derived parameters are suitable for molecular modelling applications, including computer-aided drug design.</p></div></div></div>

Download Full-text

QUBEKit: Automating the Derivation of Force Field Parameters from Quantum Mechanics

10.26434/chemrxiv.7247045 ◽

2019 ◽

Cited By ~ 1

Author(s):

Joshua Horton ◽

Alice Allen ◽

Leela Dodda ◽

Daniel Cole

Keyword(s):

Quantum Mechanics ◽

Force Field ◽

Organic Molecules ◽

Force Fields ◽

Liquid Density ◽

Small Organic Molecules ◽

Automated Generation ◽

Energy Of Hydration ◽

Novel Method ◽

Force Field Parameters

<div><div><div><p>Modern molecular mechanics force fields are widely used for modelling the dynamics and interactions of small organic molecules using libraries of transferable force field parameters. For molecules outside the training set, parameters may be missing or inaccurate, and in these cases, it may be preferable to derive molecule-specific parameters. Here we present an intuitive parameter derivation toolkit, QUBEKit (QUantum mechanical BEspoke Kit), which enables the automated generation of system-specific small molecule force field parameters directly from quantum mechanics. QUBEKit is written in python and combines the latest QM parameter derivation methodologies with a novel method for deriving the positions and charges of off-center virtual sites. As a proof of concept, we have re-derived a complete set of parameters for 109 small organic molecules, and assessed the accuracy by comparing computed liquid properties with experiment. QUBEKit gives highly competitive results when compared to standard transferable force fields, with mean unsigned errors of 0.024 g/cm3, 0.79 kcal/mol and 1.17 kcal/mol for the liquid density, heat of vaporization and free energy of hydration respectively. This indicates that the derived parameters are suitable for molecular modelling applications, including computer-aided drug design.</p></div></div></div>

Download Full-text