Established Force Fields Research Articles

Hybrid Quantum Mechanical, Molecular Mechanical, and Machine Learning Potential for Computing Aqueous-Phase Adsorption Free Energies on Metal Surfaces.

Performing reliable computer simulations of elementary processes occurring at metal-water interfaces is pivotal for novel catalyst design in sustainable energy applications. Computational catalyst design hinges on the ability to reliably and efficiently compute the potential energy surface (PES) of the system. Due to the large system sizes needed for studying processes at liquid water-metal interfaces, these systems can currently not be described using density functional theory (DFT). In this work, we used a hybrid quantum mechanical, molecular mechanical, and machine learning potential for studying the adsorption behavior of phenol, atomic hydrogen, 2-butanol, and 2-butanone on the (0001) facet of Ru under reducing conditions when Ru is not oxidized. Specifically, we describe the adsorbate and the surrounding metal atoms at the DFT level of theory. Here, we also considered the electrostatic field effect of the water molecules on adsorbate-metal interactions. Next, for the water-water and water-adsorbate interactions, we used established classical force fields. Finally, for the water-Ru surface interaction, for which no reliable force fields have been published, we used Behler-Parrinello high-dimensional neural network potentials (HDNNPs). Employing this setup, we used our explicit solvation for metal surface (eSMS) approach to compute the aqueous-phase effect on the low-coverage adsorption of selected molecules and atoms on the (0001) facet of Ru. In agreement with previous experimental and computational studies of oxygenated molecules over transition metal facets, we found that liquid water destabilizes the tested adsorbates on Ru(0001). Interestingly, our findings indicate that adsorbates on Ru are less affected by the presence of an aqueous phase than on other transition metals (e.g., Pt), highlighting the necessity of experimental investigations of Ru-based catalytic systems in liquid water.

Exploiting Metal-Organic Frameworks for Vinylidene Fluoride Adsorption: From Force Field Development, Computational Screening to Machine Learning.

Metal-organic frameworks (MOFs) represent a distinctive class of nanoporous materials with considerable potential across a wide range of applications. Recently, a handful of MOFs has been explored for the storage of environmentally hazardous fluorinated gases (Keasler et al. Science 2023, 381, 1455), yet the potential of over 100,000 MOFs for this specific application has not been thoroughly investigated, particularly due to the absence of an established force field. In this study, we develop an accurate force field for nonaversive hydrofluorocarbon vinylidene fluoride (VDF) and conduct high-throughput computational screening to identify top-performing MOFs with high VDF adsorption capacities. Quantitative structure-property relationships are analyzed via machine learning models on the combinations of geometric, chemical, and topological features, followed by feature importance analysis to probe the effects of these features on VDF adsorption. Finally, from detailed structural analysis via radial distribution functions and spatial densities, we elucidate the significance of different interaction modes between VDF and metal nodes in top-performing MOFs. By synergizing force-field development, computational screening, and machine learning, our findings provide microscopic insights into VDF adsorption in MOFs that will advance the development of new nanoporous materials for high-performance VDF storage or capture.

Machine learning aided computational exploration of metal–organic frameworks with open Cu sites for the effective separation of hydrogen isotopes

Efficient separation of hydrogen isotopes is of vital importance to develop nuclear energy industry, while it remains a significant challenge to separate D2 from H2 due to their identical physicochemical properties. As one of the efficient alternatives to conventional techniques, the thermodynamic quantum sieving technology using metal–organic frameworks (MOFs) featuring open metal sites (OMSs) has shown a great potential. However, the lack of transferable force fields in conventional molecular simulations and high expense of brute-force screening hinder the quick discovery of MOFs targeted for D2/H2 separation. Herein, based on the established force field with high accuracy and transferability, machine learning and feature engineering are applied to address these challenges. Machine learning comprehensively assesses different descriptors that influence the separation performance of 929 experimentally-reported MOFs bearing Cu(II)-OMS. By employing the same metal nodes, new Cu MOF database (6,748 MOFs) is constructed, in which 45 hypothetical MOFs are firstly identified out through feature engineering that exhibiting high performance. Furthermore, grand canonical Monte Carlo simulations are performed on these MOFs, among which the optimal one exhibits comparable selectivity (36.9) and high adsorbent performance score (315.9) that surpasses the state-of-the-art materials do. This work not only presents a cost-effective approach firstly applying in the separation of hydrogen isotopes, but also provides experimental guidance for the design of high-performance adsorbents.

VMD as a Platform for Interactive Small Molecule Preparation and Visualization in Quantum and Classical Simulations.

Modeling and simulation of small molecules such as drugs and biological cofactors have been both a major focus of computational chemistry for decades and a growing need among computational biophysicists who seek to investigate the interaction of different types of ligands with biomolecules. Of particular interest in this regard are quantum mechanical (QM) calculations that are used to more accurately describe such small molecules, which can be of heterogeneous structures and chemistry, either in purely QM calculations or in hybrid QM/molecular mechanics (MM) simulations. QM programs are also used to develop MM force field parameters for small molecules to be used along with established force fields for biomolecules in classical simulations. With this growing need in mind, here we report a set of software tools developed and closely integrated within the broadly used molecular visualization/analysis program, VMD, that allow the user to construct, modify, and parametrize small molecules and prepare them for QM, hybrid QM/MM, or classical simulations. The tools also provide interactive analysis and visualization capabilities in an easy-to-use and integrated environment. In this paper, we briefly report on these tools and their major features and capabilities, along with examples of how they can facilitate molecular research in computational biophysics that might be otherwise prohibitively complex.

Collaborative Assessment of Molecular Geometries and Energies from the Open Force Field.

Force fields form the basis for classical molecular simulations, and their accuracy is crucial for the quality of, for instance, protein-ligand binding simulations in drug discovery. The huge diversity of small-molecule chemistry makes it a challenge to build and parameterize a suitable force field. The Open Force Field Initiative is a combined industry and academic consortium developing a state-of-the-art small-molecule force field. In this report, industry members of the consortium worked together to objectively evaluate the performance of the force fields (referred to here as OpenFF) produced by the initiative on a combined public and proprietary dataset of 19,653 relevant molecules selected from their internal research and compound collections. This evaluation was important because it was completely blind; at most partners, none of the molecules or data were used in force field development or testing prior to this work. We compare the Open Force Field "Sage" version 2.0.0 and "Parsley" version 1.3.0 with GAFF-2.11-AM1BCC, OPLS4, and SMIRNOFF99Frosst. We analyzed force-field-optimized geometries and conformer energies compared to reference quantum mechanical data. We show that OPLS4 performs best, and the latest Open Force Field release shows a clear improvement compared to its predecessors. The performance of established force fields such as GAFF-2.11 was generally worse. While OpenFF researchers were involved in building the benchmarking infrastructure used in this work, benchmarking was done entirely in-house within industrial organizations and the resulting assessment is reported here. This work assesses the force field performance using separate benchmarking steps, external datasets, and involving external research groups. This effort may also be unique in terms of the number of different industrial partners involved, with 10 different companies participating in the benchmark efforts.

PyFoldX: enabling biomolecular analysis and engineering along structural ensembles.

SummaryRecent years have seen an increase in the number of structures available, not only for new proteins but also for the same protein crystallized with different molecules and proteins. While protein design software has proven to be successful in designing and modifying proteins, they can also be overly sensitive to small conformational differences between structures of the same protein. To cope with this, we introduce here pyFoldX, a python library that allows the integrative analysis of structures of the same protein using FoldX, an established forcefield and modelling software. The library offers new functionalities for handling different structures of the same protein, an improved molecular parametrization module and an easy integration with the data analysis ecosystem of the python programming language.Availability and implementationpyFoldX rely on the FoldX software for energy calculations and modelling, which can be downloaded upon registration in http://foldxsuite.crg.eu/ and its licence is free of charge for academics. The pyFoldX library is open-source. Full details on installation, tutorials covering the library functionality and the scripts used to generate the data and figures presented in this paper are available at https://github.com/leandroradusky/pyFoldX.Supplementary information Supplementary data are available at Bioinformatics online.

Extremely High Thermal Conductivity of Aligned Polyacetylene Predicted using First-Principles-Informed United-Atom Force Field

Molecular simulations of polymer rely on accurate force fields to describe the inter-atomic interactions. In this work, we use first-principles density functional theory (DFT) calculations to parameterize a united-atom force field for polyacetylene (PA), a conjugated polymer potentially of high thermal conductivity. Different electron correlation functionals in DFT have been tested. Bonding interactions for the alternating single and double bonds in the conjugated polymer backbone are explicitly described and Class II anharmonic functions are separately parameterized. Bond angle and dihedral interactions are also anharmonic and parameterized against DFT energy surfaces. The established force field is then used in molecular dynamics (MD) simulations to calculate the thermal conductivity of single PA chains and PA crystals with different simulation domain lengths. It is found that the thermal conductivity values of both PA single chains and crystals are very high and are length-dependent. At 790 nm, their respective thermal conductivity values are ~480 Wm-1K-1 and ~320 Wm-1K-1, which are comparatively higher than those of polyethylene (PE), the most thermally conductive polymer fiber measured up-to-date.

Topology Automated Force-Field Interactions (TAFFI): A Framework for Developing Transferable Force Fields.

Force-field development has undergone a revolution in the past decade with the proliferation of quantum chemistry based parametrizations and the introduction of machine learning approximations of the atomistic potential energy surface. Nevertheless, transferable force fields with broad coverage of organic chemical space remain necessary for applications in materials and chemical discovery where throughput, consistency, and computational cost are paramount. Here, we introduce a force-field development framework called Topology Automated Force-Field Interactions (TAFFI) for developing transferable force fields of varying complexity against an extensible database of quantum chemistry calculations. TAFFI formalizes the concept of atom typing and makes it the basis for generating systematic training data that maintains a one-to-one correspondence with force-field terms. This feature makes TAFFI arbitrarily extensible to new chemistries while maintaining internal consistency and transferability. As a demonstration of TAFFI, we have developed a fixed-charge force-field, TAFFI-gen, from scratch that includes coverage for common organic functional groups that is comparable to established transferable force fields. The performance of TAFFI-gen was benchmarked against OPLS and GAFF for reproducing several experimental properties of 87 organic liquids. The consistent performance of these force fields, despite their distinct origins, validates the TAFFI framework while also providing evidence of the representability limitations of fixed-charge force fields.

Development of Mg/Al/Si/O ReaxFF Parameters for Magnesium Aluminosilicate Glass Using an Artificial Neural Network-Assisted Genetic Algorithm

Commercial high-strength S-glass fiber used in structural composites mainly consists of SiO2, Al2O3, and MgO. There is no established reactive force field to characterize S-glass fiber. In this study, a newly developed artificial neural network (ANN)-assisted genetic algorithm (GA) is applied to optimize a new ReaxFF parameter set to describe Mg/Al/Si/O interactions in S-glass and other magnesium aluminosilicate (MAS) glass compositions. The training set includes the density functional theory data of the energy response of various Mg/Al/Si/O crystals during volumetric expansion and compression and Mg migration inside Mg/Al/Si/O crystals. Test molecular dynamics simulations showed the characteristics of tectosilicate MAS glasses. Different structural properties, including oxide coordination, density, structural factors, and mechanical properties, showed fair agreement with references from experiments and other simulations. A newly developed GA-ANN parametrization algorithm assisted the training process. This force field can be used for virtual composition mapping to develop new glass fiber materials. We also believe our force field would support computational studies of mechanical properties of amorphous materials used in geochemistry, construction, and protective material applications.

CONI-Net: Machine Learning of Separable Intermolecular Force Fields.

Noncovalent interactions (NCIs) play an essential role in soft matter and biomolecular simulations. The ab initio method symmetry-adapted perturbation theory allows a precise quantitative analysis of NCIs and offers an inherent energy decomposition, enabling a deeper understanding of the nature of intermolecular interactions. However, this method is limited to small systems, for instance, dimers of molecules. Here, we present a scale-bridging approach to systematically derive an intermolecular force field from ab initio data while preserving the energy decomposition of the underlying method. We apply the model in molecular dynamics simulations of several solvents and compare two predicted thermodynamic observables-mass density and enthalpy of vaporization-to experiments and established force fields. For a data set limited to hydrocarbons, we investigate the extrapolation capabilities to molecules absent from the training set. Overall, despite the affordable moderate quality of the reference ab initio data, we find promising results. With the straightforward data set generation procedure and the lack of target data in the fitting process, we have developed a method that enables the rapid development of predictive force fields with an extra dimension of insights into the balance of NCIs.

Atomic permutationally invariant polynomials for fitting molecular force fields

We introduce and explore an approach for constructing force fields for small molecules, which combines intuitive low body order empirical force field terms with the concepts of data driven statistical fits of recent machine learned potentials. We bring these two key ideas together to bridge the gap between established empirical force fields that have a high degree of transferability on the one hand, and the machine learned potentials that are systematically improvable and can converge to very high accuracy, on the other. Our framework extends the atomic permutationally invariant polynomials (aPIP) developed for elemental materials in (2019 Mach. Learn.: Sci. Technol. 1 015004) to molecular systems. The body order decomposition allows us to keep the dimensionality of each term low, while the use of an iterative fitting scheme as well as regularisation procedures improve the extrapolation outside the training set. We investigate aPIP force fields with up to generalised 4-body terms, and examine the performance on a set of small organic molecules. We achieve a high level of accuracy when fitting individual molecules, comparable to those of the many-body machine learned force fields. Fitted to a combined training set of short linear alkanes, the accuracy of the aPIP force field still significantly exceeds what can be expected from classical empirical force fields, while retaining reasonable transferability to both configurations far from the training set and to new molecules.

Establishment of a Reax force field to study SF6 gas over-thermal decomposition

This paper focuses on the problem of missing parameters in the Reax force field containing S and F elements. First, density functional theory was used to scan SF6 and low-fluoride sulfide molecules to obtain the basic dataset for the Reax force field. The Monte Carlo method was then used to perform fitting optimization and quality verification of the established force field. Based on the established force field, molecular dynamics studies were carried out on the over-thermal decomposition of a SF6 gas-insulating medium, and infrared spectra were obtained, describing the vibration characteristics of SF6 and low-fluoride sulfide molecules. According to the vibration modes revealed by the infrared spectra, a potential-energy surface scan was performed. This paper focuses on the analysis of the total energy, potential energy and kinetic energy of SF6 and low-fluoride sulfide molecules moving at different temperatures. The results show that the energy error of the established force field is about 10% when describing the bonding and breaking processes of SF6, SF5, SF4, SF3 and SF2 molecules, which verifies the reliability of the reactive force field method when used to describe molecular behavior. The research work detailed in this paper lays the foundation for the next systematic study of the microscopic physical mechanisms of SF6 over-thermal decomposition.

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

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.

Systematic Evaluation of ReaxFF Reactive Force Fields for Biochemical Applications

Four established ReaxFF force fields, trained on biochemical systems, have been systematically benchmarked on 20 proteinogenic amino acids and 11 dipeptides. The force fields were compared with respect to geometries, energetics, and atomic charges of conformers for the amino acids. To assess the performance with respect to reactivity, the condensation reactions for the formation of dipeptides were investigated by calculating the reaction energetics and pathways. We found systematic errors in the torsion angles for the amino acids, with deviations over 100°, and a generally incorrect account of relative energies for amino acid conformers. In describing the reactivity, only one of the force fields could reproduce the reaction energies of amino acid condensations quantitatively. All four force fields predict unphysical mechanisms for these reactions, involving highly unstable intermediate structures, proton transfers involving aliphatic protons, and even five-coordinate carbon atoms. The corresponding energy landscapes exhibit fluctuations on small length scales and artificial minima.

Conduction and Gating Properties of the TRAAK Channel from Molecular Dynamics Simulations with Different Force Fields.

In recent years, the K2P family of potassium channels has been the subject of intense research activity. Owing to the complex function and regulation of this family of ion channels, it is common practice to complement experimental findings with the atomistic description provided by computational approaches such as molecular dynamics (MD) simulations, especially, in light of the unprecedented timescales accessible at present. However, despite recent substantial improvements, the accuracy of MD simulations is still undermined by the intrinsic limitations of force fields. Here, we systematically assessed the performance of the most popular force fields employed to study ion channels at timescales that are orders of magnitude greater than the ones accessible when these energy functions were first developed. Using 32 μs of trajectories, we investigated the dynamics of a member of the K2P ion channel family, the TRAAK channel, using two established force fields in simulations of biological systems: AMBER and CHARMM. We found that while results are comparable on the nanosecond timescales, significant inconsistencies arise at microsecond timescales.

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

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.

Salinity-dependent alterations of static and dynamic contact angles in oil/brine/calcite systems: A molecular dynamics simulation study

In this study, classic Molecular Dynamics (MD) simulations with established force fields were first performed to investigate the salinity effects on the static contact angle of a n-decane droplet immersing in water atmosphere within a calcite nanochannel to advance our microscopic understanding on low salinity flooding. By applying an external body force, dynamic contact angle of n-decane in water phase was also studied in the presence of various salt concentrations based on Non-Equilibrium MD simulation. The predicted n-decane static contact angles are around 59.68° ± 0.26°, which agree well with experimental results in previous studies. A reduction of the static contact angle of the nanodrop is observed with the increase of salinity, which implies an enhancement of surface hydrophilicity. Under flow conditions, the deformation of nanodrop, as evidenced by the centre of mass analysis, becomes faster by increasing the salt concentration. The recovery/mobility of the n-decane nanodrop is, however, still significantly restricted by the adsorption interaction between the substrate and n-decane phase, which may lead to droplet snapping off and/or breaking up into small droplets.

Theoretical study of interface thermodynamic properties of 1,3,5-triamino-2,4,6-trinitrobenzene based polymer bonded explosives

The thermodynamic properties of insensitive high explosive 1,3,5-triamino-2,4,6-trinitrobenzene (TATB) based polymer bonded explosives (PBXs) are investigated by using first principle calculation and molecular dynamics simulation. The results include the phonon dispersion relations, interface thermal conductances, and thermal conductivities of TATB based PBXs. Both TATB and PVDF structures are optimized, in which the optimized lattice constants accord with previous results. The phonon dispersion relation of TATB and PVDF are calculated based on lattice dynamics. All interatomic force constants are calculated by the finite displacement method (numerical derivatives from perturbed supercells). The calculated phonon dispersion relation of TATB and heat capacity are in general agreement with experimental and theoretical results. The imaginary frequencies are observed in both TATB and PVDF dispersion relation. The imaginary frequencies are mainly due to the smaller calculated supercell size and temperature effect. The phonon mode of TATB and PVDF are assigned at Γ point. Based on the calculated phonon dispersion, some information including heat capacity, phonon density of states and phonon mode assignment is derived. The TATB possesses 144 phonon modes including 3 acoustic-phonon modes and 141 optical phonon modes. The anylized phonon mode of TATB shows that -NO<sub>2</sub> dominates the phonon DOS in low frequency zone, phenyl rings dominate in middle frequency zone and -NH<sub>2</sub> dominates in high frequency zone. By analyzing the phonon density of states and capacity, both TATB and PVDF imply that low-frequency vibration dominates the thermal conductivity. The thermal conductivity is determined for TATB by using the equlibrium molecular dynamics method and an established TATB force field. The TATB model is built with 2880 atoms. The structure of TATB is optimized by using molecular mechanics, then this system is relaxed by using a Nose-Hoover thermostat and barostat with a damping factor of 50 fs cin time steps of 0.1 fs. The calcultated thermal conductivity at room temperature shows good agreement with experimental result. The interface thermal conductance of TATB-PVDF is calculated by using a diffusive mismatch model. The interface thermal transport still follows Fourier’s law of heat conduction, and ballistic thermal transport mechanism is not involved. By using the above results, the thermal conductivity of mixture TATB-PVDF system is analized with a simple series model. The particle size smaller than 100 nm significantly suppresses the mixture system thermal conductivity.

Computing osmotic permeabilities of aquaporins AQP4, AQP5, and GlpF from near-equilibrium simulations

Measuring or computing the single-channel permeability of aquaporins/aquaglyceroporins (AQPs) has long been a challenge. The measured values scatter over an order of magnitude but the corresponding Arrhenius activation energies converge in the current literature. Osmotic flux through an AQP was simulated as water current forced through the channel by kilobar hydraulic pressure or theoretically approximated as single-file diffusion. In this paper, we report large scale simulations of osmotic current under sub M gradient through three AQPs (water channels AQP4 and AQP5 and glycerol-water channel GlpF) using the mature particle mesh Ewald technique (PME) for which the established force fields have been optimized with known accuracy. These simulations were implemented with hybrid periodic boundary conditions devised to avoid the artifactitious mixing across the membrane in a regular PME simulation. The computed single-channel permeabilities at 5°C and 25°C are in agreement with recently refined experiments on GlpF. The Arrhenius activation energies extracted from our simulations for all the three AQPs agree with the in vitro measurements. The single-file diffusion approximations from our large-scale simulations are consistent with the current literature on smaller systems. From these unambiguous agreements among the in vitro and in silico studies, we observe the quantitative accuracy of the all-atom force fields of the current literature for water-channel biology. We also observe that AQP4, that is particularly rich in the central nervous system, is more efficient in water conduction and more temperature-sensitive than other water-only channels (excluding glycerol channels that also conduct water when not inhibited by glycerol).

Elongation affinity, activation barrier, and stability of Aβ42 oligomers/fibrils in physiological saline

Amyloid-beta (Aβ) peptides, Aβ40 and the more neurotoxic Aβ42, have been the subject of many research efforts for Alzheimer's disease. In two recent independent investigations, the atomistic structure of Aβ42 fibril has been clearly established in the S-shaped conformation consisting of three β-sheets stabilized by salt bridges formed between the Lys28 sidechain and the C-terminus of Ala42. This structure distinctively differs from the long-known structure of Aβ40 in the β-hairpin shaped conformation consisting of two β-sheets. Recent in silico investigations based on all-atom models have reached closer agreement with the in vitro measurements of Aβ40 thermodynamics. In this study, we present an in silico investigation of Aβ42 thermodynamics. Using the established force field parameters in seven sets of all-atom simulations, we examined the stability of small Aβ42 oligomers in physiological saline. We computed the elongation affinity of the S-shaped Aβ42 fibril, reaching agreement with the experimental data. We also estimated the Arrhenius activation barrier along the elongation pathway (from the disordered conformation of a free Aβ42 peptide to its S-shaped conformation on a fibril) that amounts to about 16 kcal/mol, which is consistent with the experimental data. Based on these quantitative agreements, we conclude that aggregation of Aβ42 peptides into fibrils is thermodynamically slow without precipitation by extrinsic factors such as heparan sulfate proteoglycan and highlight the possibility to prevent Aβ42 aggregation by eliminating some precipitation factors or by increasing competitive agents to capture and transport free Aβ42 peptides from the cerebrospinal fluid.