The Monomer Electron Density Force Field (MEDFF) : a physically inspired model for noncovalent interactions

We propose a methodology to derive pairwise-additive noncovalent force fields from monomer electron densities without any empirical input. Energy expressions are based on the symmetry-adapted perturbation theory (SAPT) decomposition of interaction energies. This ensures a physically motivated force field featuring an electrostatic, exchange repulsion, dispersion, and induction contribution, which contains two types of parameters. First, each contribution depends on several fixed atomic parameters, resulting from a partitioning of the monomer electron density. Second, each of the last three contributions (exchange-repulsion, dispersion, and induction) contains exactly one linear fitting parameter. These three so-called interaction parameters in the model are initially estimated separately using SAPT reference calculations for the S66x8 database of noncovalent dimers. In a second step, the three interaction parameters are further refined simultaneously to reproduce CCSD(T)/CBS interaction energies for the same database. The limited number of parameters that are fitted to dimer interaction energies (only three) avoids ill-conditioned fits that plague conventional parameter optimizations. For the exchange repulsion and dispersion component, good results are obtained for all dimers in the S66x8 database using one single value for the associated interaction parameters. The values of those parameters can be considered universal and can also be used for dimers not present in the original database used for fitting. For the induction component such an approach is only viable for the dispersion dominated dimers in the S66x8 database. For other dimers (such as hydrogen-bonded complexes), we show that our methodology remains applicable. However, the interaction parameter needs to be determined on a case-specific basis. As an external validation:, the force field predicts interaction energies in good agreement with CCSD(T)/CBS values for dispersion dominated dimers extracted from an HIV-II protease crystal structure with a bound ligand (indinavir). Furthermore, experimental second virial coefficients of small alkanes and alkenes are well reproduced

Designing a machine learning potential for molecular simulation of liquid alkanes

Molecular simulation is applied to understanding the behaviour of alkane liquids with the eventual goal of being able to predict the viscosity of an arbitrary alkane mixture from first principles. Such prediction would have numerous scientific and industrial applications, as alkanes are the largest component of fuels, lubricants, and waxes; furthermore, they form the backbones of a myriad of organic compounds. This dissertation details the creation of a potential, a model for how the atoms and molecules in the simulation interact, based on a systematic approximation of the quantum mechanical potential energy surface using machine learning. This approximation has the advantage of producing forces and energies of nearly quantum mechanical accuracy at a tiny fraction of the usual cost. It enables accurate simulation of the large systems and long timescales required for accurate prediction of properties such as the density and viscosity. The approach is developed and tested on methane, the simplest alkane, and investigations are made into potentials for longer, more complex alkanes. The results show that the approach is promising and should be pursued further to create an accurate machine learning potential for the alkanes. It could even be extended to more complex molecular liquids in the future.First-year training funded by the EPSRC as part of the centre for doctoral training in computational methods for materials science (CDT CMM) under grant number EP/L015552/1. PhD studentship funding by Shell Global Solutions International B.V. Computer time provided by ARCHER (http://archer.ac.uk) under the UKCP Consortium, EPSRC grant number EP/P022596/1

Coarse-grained conformational surface hopping: Methodology and transferability

Coarse-grained (CG) conformational surface hopping (SH) adapts the concept of multisurface dynamics, initially developed to describe electronic transitions in chemical reactions, to accurately describe classical molecular dynamics at a reduced level. The SH scheme couples distinct conformational basins (states), each described by its own force field (surface), resulting in a significant improvement of the approximation to the many-body potential of mean force [Phys. Rev. Lett. 121, 256002 (2018)]. The present study first describes CG SH in more detail, through both a toy model and a three-bead model of hexane. We further extend the methodology to non-bonded interactions and report its impact on liquid properties. Finally, we investigate the transferability of the surfaces to distinct systems and thermodynamic state points, through a simple tuning of the state probabilities. In particular, applications to variations in temperature and chemical composition show good agreement with reference atomistic calculations, introducing a promising "weak-transferability regime," where CG force fields can be shared across thermodynamic and chemical neighborhoods.Comment: 15 pages, 7 figure

Hierarchical Coarse-Grained Strategy for Macromolecular Self-Assembly: Application to Hepatitis B Virus-Like Particles

Macromolecular self-assembly is at the basis of many phenomena in material and life sciences that find diverse applications in technology. One example is the formation of virus-like particles (VLPs) that act as stable empty capsids used for drug delivery or vaccine fabrication. Similarly to the capsid of a virus, VLPs are protein assemblies, but their structural formation, stability, and properties are not fully understood, especially as a function of the protein modifications. In this work, we present a data-driven modeling approach for capturing macromolecular self-assembly on scales beyond traditional molecular dynamics (MD), while preserving the chemical specificity. Each macromolecule is abstracted as an anisotropic object and high-dimensional models are formulated to describe interactions between molecules and with the solvent. For this, data-driven protein–protein interaction potentials are derived using a Kriging-based strategy, built on high-throughput MD simulations. Semi-automatic supervised learning is employed in a high performance computing environment and the resulting specialized force-fields enable a significant speed-up to the micrometer and millisecond scale, while maintaining high intermolecular detail. The reported generic framework is applied for the first time to capture the formation of hepatitis B VLPs from the smallest building unit, i.e., the dimer of the core protein HBcAg. Assembly pathways and kinetics are analyzed and compared to the available experimental observations. We demonstrate that VLP self-assembly phenomena and dependencies are now possible to be simulated. The method developed can be used for the parameterization of other macromolecules, enabling a molecular understanding of processes impossible to be attained with other theoretical models

Carbon based membranes as filtering materials for gaseous mixtures.

Treballs Finals de Grau de Química, Facultat de Química, Universitat de Barcelona, Any: 2021, Tutors: Fermín Huarte Larrañaga, Pablo Gamallo BelmonteCarbon-based membranes are a novel approach to gas separation. More precisely, new graphene-like structures are of utmost importance in this field of research. The scope of this work is to prove the effectiveness of grazyne membranes in the separation of different gaseous mixtures: carbon dioxide (CO2) with methane (CH4) and CO2 with oxygen (O2). To determine the efficiency of the membrane, a molecular dynamics simulation is carried via Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) undergoing an adaptive intermolecular reactive bond order (AIREBO) force field. Grazynes are a recently proposed family of 2D carbon allotropes consisting in graphene-like stripes bonded via acetylenic links, which allow for the design of pores of variable size, an important property for gas separation. For these simulations, the studied membrane was [1],[2]{2}-grazyne. The focus of the research was to determine their permeability and selectivity for both mixtures at different sets of pressures and constant temperature. To achieve this, a box was simulated in which a piston-like wall was set at different heights. Due to computational restraints, simulations at low pressure values (i.e. lower than 10 atm) were performed with c(2x2) supercells. The results were conclusive in determining the [1],[2]{2}-grazyne membrane as infinitely selective for CO2 over CH4 between 1 and 20 atm, meaning the membrane was impermeable for methane. For the CO2/O2 mixture, further simulations were performed with [1],[3]- and [1],[m]{1}-grazynes (m=1,2,3) as no selective separation could be carried out. No conclusive data could be obtained from such simulations, as the only selective separations occurred when only a single molecule was filtered

How Water's Properties Are Encoded in Its Molecular Structure and Energies.

How are water's material properties encoded within the structure of the water molecule? This is pertinent to understanding Earth's living systems, its materials, its geochemistry and geophysics, and a broad spectrum of its industrial chemistry. Water has distinctive liquid and solid properties: It is highly cohesive. It has volumetric anomalies-water's solid (ice) floats on its liquid; pressure can melt the solid rather than freezing the liquid; heating can shrink the liquid. It has more solid phases than other materials. Its supercooled liquid has divergent thermodynamic response functions. Its glassy state is neither fragile nor strong. Its component ions-hydroxide and protons-diffuse much faster than other ions. Aqueous solvation of ions or oils entails large entropies and heat capacities. We review how these properties are encoded within water's molecular structure and energies, as understood from theories, simulations, and experiments. Like simpler liquids, water molecules are nearly spherical and interact with each other through van der Waals forces. Unlike simpler liquids, water's orientation-dependent hydrogen bonding leads to open tetrahedral cage-like structuring that contributes to its remarkable volumetric and thermal properties

Modeling the self-assembly of functionalized fullerenes on solid surfaces using Monte Carlo simulations

Since their discovery 25 years ago, carbon fullerenes have been widely studied for their unique physicochemical properties and for applications including organic electronics and photovoltaics. For these applications it is highly desirable for crystalline fullerene thin films to spontaneously self-assemble on surfaces. Accordingly, many studies have functionalized fullerenes with the aim of tailoring their intermolecular interactions and controlling interactions with the solid substrate. The success of these rational design approaches hinges on the subtle interplay of intermolecular forces and molecule-substrate interactions. Molecular modeling is well-suited to studying these interactions by directly simulating self-assembly. In this work, we consider three different fullerene functionalization approaches and for each approach we carry out Monte Carlo simulations of the self-assembly process. In all cases, we use a coarse-grained molecular representation that preserves the dominant physical interactions between molecules and maximizes computational efficiency. The first approach we consider is the traditional gold-thiolate SAM (self-assembled monolayer) strategy which tethers molecules to a gold substrate via covalent sulfur-gold bonds. For this we study an asymmetric fullerene thiolate bridged by a phenyl group. Clusters of 40 molecules are simulated on the Au(111) substrate at different temperatures and surface coverage densities. Fullerenes and S atoms are found to compete for Au(111) surface sites, and this competition prevents self-assembly of highly ordered monolayers. Next, we investigate self-assembled monolayers formed by fullerenes with hydrogen-bonding carboxylic acid substituents. We consider five molecules with different dimensions and symmetries. Monte Carlo cooling simulations are used to find the most stable solid structures of clusters adsorbed to Au(111). The results show cases where fullerene-Au(111) attraction, fullerene close-packing, and hydrogen-bonding interactions can cooperate to guide self-assembly or compete to hinder it. Finally, we consider three bis-fullerene molecules, each with a different bridging group covalently joining two fullerenes. To effectively study the competing standing-up and lying-down morphologies, we use Monte Carlo simulations in conjunction with replica exchange and force field biasing methods. For clusters adsorbed to smooth model surfaces, we determine free energy landscapes and demonstrate their utility for rationalizing and predicting self-assembly