scholarly journals Combining experimental and simulation data of molecular processes via augmented Markov models

2017 ◽  
Vol 114 (31) ◽  
pp. 8265-8270 ◽  
Author(s):  
Simon Olsson ◽  
Hao Wu ◽  
Fabian Paul ◽  
Cecilia Clementi ◽  
Frank Noé
Keyword(s):  
Force Field ◽  
Structural Changes ◽  
Markov Models ◽  
Force Fields ◽  
Divide And Conquer ◽  
Simulation Data ◽  
Wide Range ◽  
Simulation And Experiment ◽  
Kinetics Of

Accurate mechanistic description of structural changes in biomolecules is an increasingly important topic in structural and chemical biology. Markov models have emerged as a powerful way to approximate the molecular kinetics of large biomolecules while keeping full structural resolution in a divide-and-conquer fashion. However, the accuracy of these models is limited by that of the force fields used to generate the underlying molecular dynamics (MD) simulation data. Whereas the quality of classical MD force fields has improved significantly in recent years, remaining errors in the Boltzmann weights are still on the order of a few kT, which may lead to significant discrepancies when comparing to experimentally measured rates or state populations. Here we take the view that simulations using a sufficiently good force-field sample conformations that are valid but have inaccurate weights, yet these weights may be made accurate by incorporating experimental data a posteriori. To do so, we propose augmented Markov models (AMMs), an approach that combines concepts from probability theory and information theory to consistently treat systematic force-field error and statistical errors in simulation and experiment. Our results demonstrate that AMMs can reconcile conflicting results for protein mechanisms obtained by different force fields and correct for a wide range of stationary and dynamical observables even when only equilibrium measurements are incorporated into the estimation process. This approach constitutes a unique avenue to combine experiment and computation into integrative models of biomolecular structure and dynamics.

scholarly journals Benchmark Assessment of Molecular Geometries and Energies from Small Molecule Force Fields

2020 ◽  
Author(s):  
Victoria T. Lim ◽  
David F. Hahn ◽  
Gary Tresadern ◽  
Christopher I. Bayly ◽  
David Mobley
Keyword(s):  
Force Field ◽  
Small Molecules ◽  
Force Fields ◽  
Molecular Structures ◽  
Property Prediction ◽  
Benchmark Assessment ◽  
Heavy Atoms ◽  
Comparable Level ◽  
Optimized Geometries

<div>Force fields are used in a wide variety of contexts for classical molecular simulation, including studies on protein-ligand binding, membrane permeation, and thermophysical property prediction. The quality of these studies relies on the quality of the force fields used to represent the systems. </div><div>Focusing on small molecules of fewer than 50 heavy atoms, our aim in this work is to compare nine force fields: GAFF, GAFF2, MMFF94, MMFF94S, OPLS3e, SMIRNOFF99Frosst, and the Open Force Field Parsley, versions 1.0, 1.1 and 1.2. On a dataset comprising 22,675 molecular structures of 3,271 molecules, we analyzed force field-optimized geometries and conformer energies compared these to reference quantum mechanical (QM) data. We show that while OPLS3e performs best, the latest Open Force Field Parsley release is approaching a comparable level of accuracy in reproducing QM geometries and energetics for this set of molecules. Meanwhile, the performance of established force fields such as MMFF94s and GAFF2 is generally somewhat worse. We also find that the series of recent Open Force Field versions provide significant increases in accuracy. Our molecule set and results are available for other researchers to use in testing.</div>

scholarly journals Benchmark assessment of molecular geometries and energies from small molecule force fields

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 1390
Author(s):  
Victoria T. Lim ◽  
David F. Hahn ◽  
Gary Tresadern ◽  
Christopher I. Bayly ◽  
David L. Mobley
Keyword(s):  
Force Field ◽  
Small Molecules ◽  
Force Fields ◽  
Molecular Structures ◽  
Benchmark Assessment ◽  
Heavy Atoms ◽  
Extensive Test ◽  
Better Than ◽  
Optimized Geometries

Background: Force fields are used in a wide variety of contexts for classical molecular simulation, including studies on protein-ligand binding, membrane permeation, and thermophysical property prediction. The quality of these studies relies on the quality of the force fields used to represent the systems. Methods: Focusing on small molecules of fewer than 50 heavy atoms, our aim in this work is to compare nine force fields: GAFF, GAFF2, MMFF94, MMFF94S, OPLS3e, SMIRNOFF99Frosst, and the Open Force Field Parsley, versions 1.0, 1.1, and 1.2. On a dataset comprising 22,675 molecular structures of 3,271 molecules, we analyzed force field-optimized geometries and conformer energies compared to reference quantum mechanical (QM) data. Results: We show that while OPLS3e performs best, the latest Open Force Field Parsley release is approaching a comparable level of accuracy in reproducing QM geometries and energetics for this set of molecules. Meanwhile, the performance of established force fields such as MMFF94S and GAFF2 is generally somewhat worse. We also find that the series of recent Open Force Field versions provide significant increases in accuracy. Conclusions: This study provides an extensive test of the performance of different molecular mechanics force fields on a diverse molecule set, and highlights two (OPLS3e and OpenFF 1.2) that perform better than the others tested on the present comparison. Our molecule set and results are available for other researchers to use in testing.

scholarly journals Benchmark Assessment of Molecular Geometries and Energies from Small Molecule Force Fields

2020 ◽  
Author(s):  
Victoria T. Lim ◽  
David Mobley
Keyword(s):  
Force Field ◽  
Small Molecules ◽  
Gas Phase ◽  
Force Fields ◽  
Molecular Structures ◽  
Benchmark Assessment ◽  
Heavy Atoms ◽  
Chemical Groups ◽  
Optimized Geometries

<div>Force fields are used in a wide variety of contexts for classical molecular simulation, including studies on protein-ligand binding, membrane permeation, and thermophysical property prediction. The quality of these studies relies on the quality of the force fields used to represent the systems. </div><div>Focusing on small molecules of fewer than 50 heavy atoms, our aim in this work is to compare six force fields: GAFF, GAFF2, MMFF94, MMFF94S, SMIRNOFF99Frosst, and the Open Force Field version 1.0 (Parsley) force field. On a dataset comprising over 26,000 molecular structures, we analyzed their force field-optimized geometries and conformer energies compared to reference quantum mechanical (QM) data. We show that most of these force fields are comparable in accuracy at reproducing gas-phase QM geometries and energetics, but that GAFF/GAFF2/Parsley do slightly better in reproducing QM energies and that MMFF94/MMFF94S perform slightly better in geometries. Parsley shows considerable improvement over its predecessor SMIRNOFF99Frosst, and we identify particular outlying chemical groups for further force field improvement.</div>

scholarly journals Neural network reactive force field for C, H, N, and O systems

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Pilsun Yoo ◽  
Michael Sakano ◽  
Saaketh Desai ◽  
Md Mahbubul Islam ◽  
Peilin Liao ◽  
...  
Keyword(s):  
Neural Network ◽  
Force Field ◽  
Density Functional ◽  
Force Fields ◽  
High Energy ◽  
Reactive Force ◽  
Dispersion Interactions ◽  
Reactive Force Field ◽  
Wide Range ◽  
Reactive Force Fields

AbstractReactive force fields have enabled an atomic level description of a wide range of phenomena, from chemistry at extreme conditions to the operation of electrochemical devices and catalysis. While significant insight and semi-quantitative understanding have been drawn from such work, the accuracy of reactive force fields limits quantitative predictions. We developed a neural network reactive force field (NNRF) for CHNO systems to describe the decomposition and reaction of the high-energy nitramine 1,3,5-trinitroperhydro-1,3,5-triazine (RDX). NNRF was trained using energies and forces of a total of 3100 molecules (11,941 geometries) and 15 condensed matter systems (32,973 geometries) obtained from density functional theory calculations with semi-empirical corrections to dispersion interactions. The training set is generated via a semi-automated iterative procedure that enables refinement of the NNRF until a desired accuracy is attained. The root mean square (RMS) error of NNRF on a testing set of configurations describing the reaction of RDX is one order of magnitude lower than current state of the art potentials.

Artificial intelligence in the modeling of chemical reactions kinetics

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Maciej Staszak
Keyword(s):  
Artificial Intelligence ◽  
Chemical Processes ◽  
Rate Equations ◽  
Network Systems ◽  
Current State ◽  
Wide Range ◽  
Kinetics Of ◽  
Fast Evolution ◽  
Selection Of

AbstractThe work presents a selection of recent papers in the field of modeling chemical kinetics by the use of artificial intelligence methods. Due to the fact that kinetics of the chemical reaction is the key element of industrial reactor design and analysis, the work is focused on the presentation of the quality of modeling, the assembly of neural network systems and methods of training required to achieve acceptable results. The work covers a wide range of classes of chemical processes and modeling approaches presented by several authors. Because of the fact that the methods of neural networks training require huge amounts of data, many approaches proposed are intrinsically based on classical kinetics modeling like Monte Carlo methods, quantum ab initio models or classical Arrhenius-like approaches using mass balance rate equations. The work does not fully exhaust the area of artificial intelligence because of its very broad scope and very fast evolution, which has been greatly accelerated recently. However, it is a contribution to describing the current state of science in this field.

scholarly journals RNA force field with accuracy comparable to state-of-the-art protein force fields

2018 ◽  
Vol 115 (7) ◽  
pp. E1346-E1355 ◽  
Author(s):  
Dazhi Tan ◽  
Stefano Piana ◽  
Robert M. Dirks ◽  
David E. Shaw
Keyword(s):  
Force Field ◽  
Md Simulation ◽  
State Of The Art ◽  
Force Fields ◽  
Md Simulations ◽  
Physical Models ◽  
Rna Structures ◽  
Rna Molecules ◽  
Wide Range ◽  
Protein Force Fields

Molecular dynamics (MD) simulation has become a powerful tool for characterizing at an atomic level of detail the conformational changes undergone by proteins. The application of such simulations to RNA structures, however, has proven more challenging, due in large part to the fact that the physical models (“force fields”) available for MD simulations of RNA molecules are substantially less accurate in many respects than those currently available for proteins. Here, we introduce an extensive revision of a widely used RNA force field in which the parameters have been modified, based on quantum mechanical calculations and existing experimental information, to more accurately reflect the fundamental forces that stabilize RNA structures. We evaluate these revised parameters through long-timescale MD simulations of a set of RNA molecules that covers a wide range of structural complexity, including single-stranded RNAs, RNA duplexes, RNA hairpins, and riboswitches. The structural and thermodynamic properties measured in these simulations exhibited dramatically improved agreement with experimentally determined values. Based on the comparisons we performed, this RNA force field appears to achieve a level of accuracy comparable to that of state-of-the-art protein force fields, thus significantly advancing the utility of MD simulation as a tool for elucidating the structural dynamics and function of RNA molecules and RNA-containing biological assemblies.

scholarly journals Molecular simulation of the thermophysical properties and phase behaviour of impure CO2 relevant to CCS

2016 ◽  
Vol 192 ◽  
pp. 415-436 ◽  
Author(s):  
Alexander J. Cresswell ◽  
Richard J. Wheatley ◽  
Richard D. Wilkinson ◽  
Richard S. Graham
Keyword(s):  
Physical Properties ◽  
Force Field ◽  
Ab Initio ◽  
Molecular Simulation ◽  
Quantum Chemical Calculations ◽  
Quantum Chemical ◽  
Force Fields ◽  
Phase Behaviour ◽  
Suitable Model ◽  
Wide Range

Impurities from the CCS chain can greatly influence the physical properties of CO2. This has important design, safety and cost implications for the compression, transport and storage of CO2. There is an urgent need to understand and predict the properties of impure CO2 to assist with CCS implementation. However, CCS presents demanding modelling requirements. A suitable model must both accurately and robustly predict CO2 phase behaviour over a wide range of temperatures and pressures, and maintain that predictive power for CO2 mixtures with numerous, mutually interacting chemical species. A promising technique to address this task is molecular simulation. It offers a molecular approach, with foundations in firmly established physical principles, along with the potential to predict the wide range of physical properties required for CCS. The quality of predictions from molecular simulation depends on accurate force-fields to describe the interactions between CO2 and other molecules. Unfortunately, there is currently no universally applicable method to obtain force-fields suitable for molecular simulation. In this paper we present two methods of obtaining force-fields: the first being semi-empirical and the second using ab initio quantum-chemical calculations. In the first approach we optimise the impurity force-field against measurements of the phase and pressure–volume behaviour of CO2 binary mixtures with N2, O2, Ar and H2. A gradient-free optimiser allows us to use the simulation itself as the underlying model. This leads to accurate and robust predictions under conditions relevant to CCS. In the second approach we use quantum-chemical calculations to produce ab initio evaluations of the interactions between CO2 and relevant impurities, taking N2 as an exemplar. We use a modest number of these calculations to train a machine-learning algorithm, known as a Gaussian process, to describe these data. The resulting model is then able to accurately predict a much broader set of ab initio force-field calculations at comparatively low numerical cost. Although our method is not yet ready to be implemented in a molecular simulation, we outline the necessary steps here. Such simulations have the potential to deliver first-principles simulation of the thermodynamic properties of impure CO2, without fitting to experimental data.

scholarly journals Multi-Walled Carbon Nanotubes-Based Sensors for Strain Sensing Applications

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1261 ◽  
Author(s):  
Anindya Nag ◽  
Md. Eshrat E Alahi ◽  
Subhas Chandra Mukhopadhyay ◽  
Zhi Liu
Keyword(s):  
Structural Changes ◽  
Mechanical And Thermal Properties ◽  
Strain Sensing ◽  
Sensing Applications ◽  
Multi Walled Carbon Nanotube ◽  
Wide Range ◽  
Multi Walled Carbon Nanotubes ◽  
Conductive Nanomaterials ◽  
Walled Carbon Nanotubes

The use of multi-walled carbon nanotube (MWCNT)-based sensors for strain–strain applications is showcased in this paper. Extensive use of MWCNTs has been done for the fabrication and implementation of flexible sensors due to their enhanced electrical, mechanical, and thermal properties. These nanotubes have been deployed both in pure and composite forms for obtaining highly efficient sensors in terms of sensitivity, robustness, and longevity. Among the wide range of applications that MWCNTs have been exploited for, strain-sensing has been one of the most popular ones due to the high mechanical flexibility of these carbon allotropes. The MWCNT-based sensors have been able to deduce a broad spectrum of macro- and micro-scaled tensions through structural changes. This paper highlights some of the well-approved conjugations of MWCNTs with different kinds of polymers and other conductive nanomaterials to form the electrodes of the strain sensors. It also underlines some of the measures that can be taken in the future to improve the quality of these MWCNT-based sensors for strain-related applications.

Laser Crystallization of Deposited Silicon Films

1980 ◽  
Vol 1 ◽  
Author(s):  
G. K. Celler ◽  
H. J. Leamy ◽  
D. E. Aspnes ◽  
C. J. Doherty ◽  
T. T. Sheng ◽  
...  
Keyword(s):  
Laser Energy ◽  
Small Diameter ◽  
Laser Crystallization ◽  
Wide Range ◽  
Grain Distribution ◽  
Si Films ◽  
Kinetics Of ◽  
Melting And Solidification ◽  
Dense Films

ABSTRACTSilicon layers evaporated on crystalline Si have been crystallized by Q-switched Nd:YAG laser irradiation. A strong correlation was observed between the density of a-Si films and the quality of the epitaxial regrowth from the liquid phase. Dense films crystallized epitaxially in a wide range of laser energy densities. Layers with 20% lower density, as determined by spectroscopic ellipsometry, had higher crystallization thresholds and suffered from severe pitting of the surface. Coalescence of the excess void volume into microbubbles, stabilized by gaseous contaminants, is responsible for the surface degradation.In polycrystalline films on amorphous insulating substrates laser melting changes the grain distribution. Rapid melting and solidification of small diameter spots creates concentric rings of large crystallites. This characteristic pattern is explained by a simple model, based on the kinetics of crystallization.

scholarly journals Empirical optimization of molecular simulation force fields by Bayesian inference

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

Sign in / Sign up

Export Citation Format

Share Document