End-to-End Differentiable Molecular Mechanics Force Field Construction

Molecular mechanics (MM) potentials have long been a workhorse of computational chemistry. Leveraging accuracy and speed, these functional forms find use in a wide variety of applications from rapid virtual screening to detailed free energy calculations. Traditionally, MM potentials have relied on human-curated, inflexible, and poorly extensible discrete chemical perception rules (atom types) for applying parameters to molecules or biopolymers, making them difficult to optimize to fit quantum chemical or physical property data. Here, we propose an alternative approach that uses graph nets to perceive chemical environments, producing continuous atom embeddings from which valence and nonbonded parameters can be predicted using a feed-forward neural network. Since all stages are built using smooth functions, the entire process of chemical perception and parameter assignment is differentiable end-to-end with respect to model parameters, allowing new force fields to be easily constructed, extended, and applied to arbitrary molecules. We show that this approach has the capacity to reproduce legacy atom types and can be fit to MM and QM energies and forces, among other targets

Minimization and Eulerian Formulation of Differential Geometry Based Nonpolar Multiscale Solvation Models

In this work, the existence of a global minimizer for the previous Lagrangian formulation of nonpolar solvation model proposed in [1] has been proved. One of the proofs involves a construction of a phase field model that converges to the Lagrangian formulation. Moreover, an Eulerian formulation of nonpolar solvation model is proposed and implemented under a similar parameterization scheme to that in [1]. By doing so, the connection, similarity and difference between the Eulerian formulation and its Lagrangian counterpart can be analyzed. It turns out that both of them have a great potential in solvation prediction for nonpolar molecules, while their decompositions of attractive and repulsive parts are different. That indicates a distinction between phase field models of solvation and our Eulerian formulation

A functional group oxidation model (FGOM) for SOA formation and aging

Secondary organic aerosol (SOA) formation from a volatile organic compound (VOC) involves multiple generations of oxidation that include functionalization and fragmentation of the parent carbon backbone and likely particle-phase oxidation and/or accretion reactions. Despite the typical complexity of the detailed molecular mechanism of SOA formation and aging, a relatively small number of functional groups characterize the oxidized molecules that constitute SOA. Given the carbon number and set of functional groups, the volatility of the molecule can be estimated. We present here a functional group oxidation model (FGOM) that represents the process of SOA formation and aging. The FGOM contains a set of parameters that are to be determined by fitting of the model to laboratory chamber data: total organic aerosol concentration, and O : C and H : C atomic ratios. The sensitivity of the model prediction to variation of the adjustable parameters allows one to assess the relative importance of various pathways involved in SOA formation. An analysis of SOA formation from the high- and low-NOx photooxidation of four C12 alkanes (n-dodecane, 2-methylundecane, hexylcyclohexane, and cyclododecane) using the FGOM is presented, and comparison with the statistical oxidation model (SOM) of Cappa et al. (2013) is discussed

NEXAFS Spectroscopy of Condensed N-alkanes

The carbon 1s Near-Edge X-ray Absorption Fine Structure (NEXAFS) spectra of alkanes vary with chain length, substitution, and phase change. For short gaseous alkanes, the carbon 1s NEXAFS spectra of alkanes are dominated by Rydberg transitions with distinctive vibronic features. In the case of condensed alkanes, the characteristic C-H feature appears at 287-288 eV. Computational models can effectively reproduce and interpret NEXAFS spectra of simple gaseous alkanes, provided that vibronic transitions are neglected. However, computational methods have been ineffective in reproducing and interpreting the NEXAFS spectra of condensed alkanes. We hypothesize that this shortcoming is due to computational limitations in modeling effect such as structural variations and disorder. An objective of this thesis is to study the effect of structural changes such as chain length on the NEXAFS spectra of n-alkanes. This objective involves computational modelling as well as experimental studies of the spectra of liquid n-alkanes. It should be noted that the NEXAFS spectra of liquid n-alkanes are entirely unexplored. The second objective of this thesis is to study the role of disorder caused by nuclear motion on the NEXAFS spectra of n-alkanes. The effect of nuclear motion refers to the contribution of the range of thermally accessible structures to the average NEXAFS spectrum of a material. In liquids, this effect can give a distribution of molecular structures rather than the single lowest energy structure. These thermally accessible structures include geometry defects such as gauche defects, thermally populated vibrational states, as well as zero-point motion. This thesis will characterize the role of disorder such geometry defects and nuclear motion on the NEXAFS spectra of n-alkanes using a Density Functional Theory approach. As part of this objective, the effect of temperature change on the NEXAFS spectra of n-alkanes will be studied

Towards predicting liquid fuel physicochemical properties using molecular dynamics guided machine learning models

Accurate determination of fuel properties of complex mixtures over a wide range of pressure and temperature conditions is essential to utilizing alternative fuels. The present work aims to construct cheap-to-compute machine learning (ML) models to act as closure equations for predicting the physical properties of alternative fuels. Those models can be trained using the database from MD simulations and/or experimental measurements in a data-fusion-fidelity approach. Here, Gaussian Process (GP) and probabilistic generative models are adopted. GP is a popular non-parametric Bayesian approach to build surrogate models mainly due to its capacity to handle the aleatory and epistemic uncertainties. Generative models have shown the ability of deep neural networks employed with the same intent. In this work, ML analysis is focused on two particular properties, the fuel density and diffusion, but it can also be extended to other physicochemical properties. This study explores the versatility of the ML models to handle multi-fidelity data. The results show that ML models can predict accurately the fuel properties of a wide range of pressure and temperature conditions.The research leading to these results had received funding from the Brazilian National Agency of Petroleum, Natural Gas and Biofuels (ANP) through Programa de Recursos Humanos (PRH) under the PRH 8 - Mechanical Engineering for the Efficient Use of Biofuels, grant agreement numbers F0A5.EDDE.B5C0.3BCB and 2B61.4F5C.A83B.A713.Peer ReviewedPostprint (published version

Force Field Optimization, Advanced Sampling, And Free Energy Methods With Gpu-Optimized Monte Carlo (gomc) Software

In this work, to address the sampling problem for systems at high densities and low temperatures, a generalized identity exchange algorithm is developed for grand canonical Monte Carlo simulations. The algorithm, referred to as Molecular Exchange Monte Carlo (MEMC), is implemented in the GPU-Optimized Monte Carlo (GOMC) software and may be applied to multicomponent systems of arbitrary molecular topology, and provides significant enhancements in the sampling of phase space over a wide range of compositions and temperatures. Three different approaches are presented for the insertion/deletion of the large molecules, and the pros and cons of each method are discussed. Next, the MEMC method is extended to Gibbs ensemble Monte Carlo (GEMC). The utility of the MEMC method is demonstrated through the calculation of the free energies of transfer of n-alkanes from vapor into liquid 1-octanol, n-hexadecane, and 2,2,4-trimethylpentane, using isobaric-isothermal GEMC simulations. Alternatively, for system with strong inter-molecular interaction (e.g. hydrogen bonds), it’s more efficient to calculate the free energies of transfer, using standard thermodynamic integration (TI) and free energy perturbation (FEP) methods. The TI and FEP free energy calculation methods are implemented in GOMC and utility of these methods are demonstrated by calculating the hydration and solvation free energies of fluorinated 1-octanol, to understand the role of fluorination on the interactions and partitioning of alcohols in aqueous and organic environments. Additionally, using GOMC, a transferable united-atom (UA) force field, based on Mie potentials, is optimized for alkynes to accurately reproduce experimental phase equilibrium properties. The performance of the optimized Mie potential parameters is assessed for 1-alkynes and 2-alkynes using grand canonical histogram-reweighting Monte Carlo simulations. For each compound, vapor-liquid coexistence curves, vapor pressures, heats of vaporization, critical properties, and normal boiling points are predicted and compared to experiment