Genetic Algorithm Optimization of Point Charges in Force Field Development: Challenges and Insights

Evolutionary methods, such as genetic algorithms (GAs), provide powerful tools for optimization of the force field parameters, especially in the case of simultaneous fitting of the force field terms against extensive reference data. However, GA fitting of the nonbonded interaction parameters that includes point charges has not been explored in the literature, likely due to numerous difficulties with even a simpler problem of the least-squares fitting of the atomic point charges against a reference molecular electrostatic potential (MEP), which often demonstrates an unusually high variation of the fitted charges on buried atoms. Here, we examine the performance of the GA approach for the least-squares MEP point charge fitting, and show that the GA optimizations suffer from a magnified version of the classical buried atom effect, producing highly scattered yet correlated solutions. This effect can be understood in terms of the linearly independent, natural coordinates of the MEP fitting problem defined by the eigenvectors of the least-squares sum Hessian matrix, which are also equivalent to the eigenvectors of the covariance matrix evaluated for the scattered GA solutions. GAs quickly converge with respect to the high-curvature coordinates defined by the eigenvectors related to the leading terms of the multipole expansion, but have difficulty converging with respect to the low-curvature coordinates that mostly depend on the buried atom charges. The performance of the evolutionary techniques dramatically improves when the point charge optimization is performed using the Hessian or covariance matrix eigenvectors, an approach with a significant potential for the evolutionary optimization of the fixed-charge biomolecular force fields

An aerothermodynamic design optimization framework for hypersonic vehicles

In the aviation field great interest is growing in passengers transportation at hypersonic speed. This requires, however, careful study of the enabling technologies necessary for the optimal design of hypersonic vehicles. In this framework, the present work reports on a highly integrated design environment that has been developed in order to provide an optimization loop for vehicle aerothermodynamic design. It includes modules for geometrical parametrization, automated data transfer between tools, automated execution of computational analysis codes, and design optimization methods. This optimization environment is exploited for the aerodynamic design of an unmanned hypersonic cruiser flying at M∞=8 and 30 km altitude. The original contribution of this work is mainly found in the capability of the developed optimization environment of working simultaneously on shape and topology of the aircraft. The results reported and discussed highlight interesting design capabilities, and promise extension to more challenging and realistic integrated aerothermodynamic design problems

First-principles molecular structure search with a genetic algorithm

The identification of low-energy conformers for a given molecule is a fundamental problem in computational chemistry and cheminformatics. We assess here a conformer search that employs a genetic algorithm for sampling the low-energy segment of the conformation space of molecules. The algorithm is designed to work with first-principles methods, facilitated by the incorporation of local optimization and blacklisting conformers to prevent repeated evaluations of very similar solutions. The aim of the search is not only to find the global minimum, but to predict all conformers within an energy window above the global minimum. The performance of the search strategy is: (i) evaluated for a reference data set extracted from a database with amino acid dipeptide conformers obtained by an extensive combined force field and first-principles search and (ii) compared to the performance of a systematic search and a random conformer generator for the example of a drug-like ligand with 43 atoms, 8 rotatable bonds and 1 cis/trans bond

Development of a Transferable Reactive Force Field of P/H Systems: Application to the Chemical and Mechanical Properties of Phosphorene

ReaxFF provides a method to model reactive chemical systems in large-scale molecular dynamics simulations. Here, we developed ReaxFF parameters for phosphorus and hydrogen to give a good description of the chemical and mechanical properties of pristine and defected black phosphorene. ReaxFF for P/H is transferable to a wide range of phosphorus and hydrogen containing systems including bulk black phosphorus, blue phosphorene, edge-hydrogenated phosphorene, phosphorus clusters and phosphorus hydride molecules. The potential parameters were obtained by conducting unbiased global optimization with respect to a set of reference data generated by extensive ab initio calculations. We extend ReaxFF by adding a 60{\deg} correction term which significantly improves the description of phosphorus clusters. Emphasis has been put on obtaining a good description of mechanical response of black phosphorene with different types of defects. Compared to nonreactive SW potential [1], ReaxFF for P/H systems provides a huge improvement in describing the mechanical properties the pristine and defected black phosphorene and the thermal stability of phosphorene nanotubes. A counterintuitive phenomenon is observed that single vacancies weaken the black phosphorene more than double vacancies with higher formation energy. Our results also show that mechanical response of black phosphorene is more sensitive to defects for the zigzag direction than for the armchair direction. Since ReaxFF allows straightforward extensions to the heterogeneous systems, such as oxides, nitrides, ReaxFF parameters for P/H systems build a solid foundation for the reactive force field description of heterogeneous P systems, including P-containing 2D van der Waals heterostructures, oxides, etc

Development of Atomistic Potentials for Silicate Materials and Coarse-Grained Simulation of Self-Assembly at Surfaces

This thesis is composed of two parts. The first is a study of evolutionary strategies for parametrization of empirical potentials, and their application in development of a charge-transfer potential for silica. An evolutionary strategy was meta-optimized for use in empirical potential parametrization, and a new charge-transfer empirical model was developed for use with isobaric-isothermal ensemble molecular dynamics simulations. The second is a study of thermodynamics and self-assembly in a particular class of athermal two-dimensional lattice models. The effects of shape on self-assembly and thermodynamics for polyominoes and tetrominoes were examined. Many interesting results were observed, including complex clustering, non-ideal mixing, and phase transitions. In both parts, computational efficiency and performance were important goals, and this was reflected in method and program development

TopologyNet: Topology based deep convolutional neural networks for biomolecular property predictions

Although deep learning approaches have had tremendous success in image, video and audio processing, computer vision, and speech recognition, their applications to three-dimensional (3D) biomolecular structural data sets have been hindered by the entangled geometric complexity and biological complexity. We introduce topology, i.e., element specific persistent homology (ESPH), to untangle geometric complexity and biological complexity. ESPH represents 3D complex geometry by one-dimensional (1D) topological invariants and retains crucial biological information via a multichannel image representation. It is able to reveal hidden structure-function relationships in biomolecules. We further integrate ESPH and convolutional neural networks to construct a multichannel topological neural network (TopologyNet) for the predictions of protein-ligand binding affinities and protein stability changes upon mutation. To overcome the limitations to deep learning arising from small and noisy training sets, we present a multitask topological convolutional neural network (MT-TCNN). We demonstrate that the present TopologyNet architectures outperform other state-of-the-art methods in the predictions of protein-ligand binding affinities, globular protein mutation impacts, and membrane protein mutation impacts.Comment: 20 pages, 8 figures, 5 table

Addressing current challenges in cancer immunotherapy with mathematical and computational modeling

The goal of cancer immunotherapy is to boost a patient's immune response to a tumor. Yet, the design of an effective immunotherapy is complicated by various factors, including a potentially immunosuppressive tumor microenvironment, immune-modulating effects of conventional treatments, and therapy-related toxicities. These complexities can be incorporated into mathematical and computational models of cancer immunotherapy that can then be used to aid in rational therapy design. In this review, we survey modeling approaches under the umbrella of the major challenges facing immunotherapy development, which encompass tumor classification, optimal treatment scheduling, and combination therapy design. Although overlapping, each challenge has presented unique opportunities for modelers to make contributions using analytical and numerical analysis of model outcomes, as well as optimization algorithms. We discuss several examples of models that have grown in complexity as more biological information has become available, showcasing how model development is a dynamic process interlinked with the rapid advances in tumor-immune biology. We conclude the review with recommendations for modelers both with respect to methodology and biological direction that might help keep modelers at the forefront of cancer immunotherapy development.Comment: Accepted for publication in the Journal of the Royal Society Interfac

ReaxFF parameter optimization with Monte-Carlo and evolutionary algorithms : guidelines and insights

ReaxFF is a computationally efficient force field to simulate complex reactive dynamics in extended molecular models with diverse chemistries, if reliable force-field parameters are available for the chemistry of interest. If not, they must be optimized by minimizing the error ReaxFF makes on a relevant training set. Because this optimization is far from trivial, many methods, in particular, genetic algorithms (GAs), have been developed to search for the global optimum in parameter space. Recently, two alternative parameter calibration techniques were proposed, that is, Monte-Carlo force field optimizer (MCFF) and covariance matrix adaptation evolutionary strategy (CMA-ES). In this work, CMA-ES, MCFF, and a GA method (OGOLEM) are systematically compared using three training sets from the literature. By repeating optimizations with different random seeds and initial parameter guesses, it is shown that a single optimization run with any of these methods should not be trusted blindly: nonreproducible, poor or premature convergence is a common deficiency. GA shows the smallest risk of getting trapped into a local minimum, whereas CMA-ES is capable of reaching the lowest errors for two-third of the cases, although not systematically. For each method, we provide reasonable default settings, and our analysis offers useful guidelines for their usage in future work. An important side effect impairing parameter optimization is numerical noise. A detailed analysis reveals that it can be reduced, for example, by using exclusively unambiguous geometry optimization in the training set. Even without this noise, many distinct near-optimal parameter vectors can be found, which opens new avenues for improving the training set and detecting overfitting artifacts

Theoretische Untersuchungen Kovalenter Mechanochemie

This thesis is concerned with computational-chemistry investigations of mechanoresponsive molecules which feature predetermined breaking points (PBPs). The mechanophoric systems have been approached at different levels of theory. Reactive molecular dynamics (rMD), density functional theory (DFT), second order Møller-Plesset perturbation theory (MP2) and multireference methods were employed to obtain a complete picture of the mechanochemical reactions.Die vorliegende Arbeit befasst sich mit der Untersuchung von mechanoresponsiven Molekülen, die molekulare Sollbruchstellen (PBPs) enhalten, mit Methoden der Computerchemie. In Abhängigkeit von den jeweiligen Fragestellungen kamen dabei unterschiedliche Methoden zum Einsatz. Reaktive molekulare Dynamik (rMD), Dichtefunktionaltheorie (DFT), Møller-Plesset Störungstheorie zweiter Ordnung (MP2) und Multireferenzverfahren wurden verwendet, um ein vollständiges Bild der mechanochemischen Reaktionen zu erhalten

Realistic and verifiable coherent control of excitonic states in a light harvesting complex

We explore the feasibility of coherent control of excitonic dynamics in light harvesting complexes, analyzing the limits imposed by the open nature of these quantum systems. We establish feasible targets for phase and phase/amplitude control of the electronically excited state populations in the Fenna-Mathews-Olson (FMO) complex and analyze the robustness of this control with respect to orientational and energetic disorder, as well as decoherence arising from coupling to the protein environment. We further present two possible routes to verification of the control target, with simulations for the FMO complex showing that steering of the excited state is experimentally verifiable either by extending excitonic coherence or by producing novel states in a pump-probe setup. Our results provide a first step toward coherent control of these complex biological quantum systems in an ultrafast spectroscopy setup.Comment: 12 pages, 8 figure