Remembering the work of Phillip L. Geissler: A coda to his scientific trajectory

Phillip L. Geissler made important contributions to the statistical mechanics of biological polymers, heterogeneous materials, and chemical dynamics in aqueous environments. He devised analytical and computational methods that revealed the underlying organization of complex systems at the frontiers of biology, chemistry, and materials science. In this retrospective, we celebrate his work at these frontiers

Monte-Carlo Simulations of Soft Matter Using SIMONA: A Review of Recent Applications

Molecular simulations such as Molecular Dynamics (MD) and Monte Carlo (MC) have gained increasing importance in the explanation of various physicochemical and biochemical phenomena in soft matter and help elucidate processes that often cannot be understood by experimental techniques alone. While there is a large number of computational studies and developments in MD, MC simulations are less widely used, but they offer a powerful alternative approach to explore the potential energy surface of complex systems in a way that is not feasible for atomistic MD, which still remains fundamentally constrained by the femtosecond timestep, limiting investigations of many essential processes. This paper provides a review of the current developments of a MC based code, SIMONA, which is an efficient and versatile tool to perform large-scale conformational sampling of different kinds of (macro)molecules. We provide an overview of the approach, and an application to soft-matter problems, such as protocols for protein and polymer folding, physical vapor deposition of functional organic molecules and complex oligomer modeling. SIMONA offers solutions to different levels of programming expertise (basic, expert and developer level) through the usage of a designed Graphical Interface pre-processor, a convenient coding environment using XML and the development of new algorithms using Python/C++. We believe that the development of versatile codes which can be used in different fields, along with related protocols and data analysis, paves the way for wider use of MC methods

Stochastic Modeling and Optimal Control for Colloidal Organization, Navigation, and Machines

Colloidal suspensions consisting of particles undergoing Brownian motion are ubiquitous in scientific research and emerging technologies. Longstanding challenges in strategic control of complex colloidal systems are to investigate the principle of optimal control, overcome the curse of dimensionality, design efficient algorithms, and develop generalizable control strategies. In the first part of this dissertation, we present methods and results from three case studies to illustrate how these challenges are addressed from the perspectives of modeling and optimal control. Single-agent optimal navigation in complex mazes. We investigate the optimal navigation principle of a self-propelled colloidal particle in complex mazes. We construct approximate Markov chain model and use the Markov decision process framework to obtain the general principle of optimal navigation. Multiple-agent cooperation and coordination for colloidal machines. Using self-propelled Janus motors as the model system, we illustrate a new paradigm for cargo capture and transport based on multiple-agent feedback control. The control algorithm can coordinate multiple motors to cooperate on forming a reconfigurable machine for cargo capture and transport. Low-dimensional modeling and ensemble control. Optimal control in a high dimensional self-assembly processes with limited actuations presents a challenge in both modelling and controller design. We use colloidal crystallization in an electric field as a model system to illustrate the methodologies of low-dimensional modeling and control for self-assembly processes. We use a nonlinear machine learning algorithm to characterize the dimensionality and parametrize the low-dimension manifold on which the system evolves. A low-dimensional Smoluchowski model is constructed and calibrated to illustrate the dynamic pathways of the assembly process. The resulting model is further leveraged to perform optimal control of the assembly process. In the second part of dissertation, we report three additional relevant research projects on colloidal interaction, dynamics, and control. The first project extends ensemble control from finite-size systems to infinite-size systems using feedback control in sedimentation. The second project develops a computational method to model depletion interactions between general geometric objects The third project develops modified Stokesian dynamics methods to investigate the colloidal rod motion near a planar wall with hydrodynamic interactions

Modeling and design of heterogeneous hierarchical bioinspired spider web structures using generative deep learning and additive manufacturing

Spider webs are incredible biological structures, comprising thin but strong silk filament and arranged into complex hierarchical architectures with striking mechanical properties (e.g., lightweight but high strength, achieving diverse mechanical responses). While simple 2D orb webs can easily be mimicked, the modeling and synthesis of 3D-based web structures remain challenging, partly due to the rich set of design features. Here we provide a detailed analysis of the heterogenous graph structures of spider webs, and use deep learning as a way to model and then synthesize artificial, bio-inspired 3D web structures. The generative AI models are conditioned based on key geometric parameters (including average edge length, number of nodes, average node degree, and others). To identify graph construction principles, we use inductive representation sampling of large experimentally determined spider web graphs, to yield a dataset that is used to train three conditional generative models: 1) An analog diffusion model inspired by nonequilibrium thermodynamics, with sparse neighbor representation, 2) a discrete diffusion model with full neighbor representation, and 3) an autoregressive transformer architecture with full neighbor representation. All three models are scalable, produce complex, de novo bio-inspired spider web mimics, and successfully construct graphs that meet the design objectives. We further propose algorithm that assembles web samples produced by the generative models into larger-scale structures based on a series of geometric design targets, including helical and parametric shapes, mimicking, and extending natural design principles towards integration with diverging engineering objectives. Several webs are manufactured using 3D printing and tested to assess mechanical properties

Nonlinear machine learning of macromolecular folding and self-assembly

High performance computation and sophisticated machine learning algorithms have emerged as new tools for studying biological, physical and chemical systems at the atomistic scale. In this thesis, I report several applications of molecular dynamics simulation and machine learning in the study of the macromolecular folding and assembly. In the first aspect, I employ molecular simulation and non-linear manifold learning to explore the dynamics and configuration of linear and ring polymers. Integrating statistical mechanics with dynamical systems theory, I establish a means to determine single molecule folding funnels from univariate time series in experimentally accessible observables. In the second aspect, I utilize coarse grained molecular simulation to explore the self-assembly of hundreds of asphaltene molecules over micro-second time scales to reveal the aggregation phase behavior as a function of temperature, pressure and solvent conditions. I then employ graph matching and non-linear manifold learning to obtain asphaltene folding and assembly free energy landscapes. This thesis establishes new fundamental understanding of the folding and assembly of macromolecules, builds connections between computer simulation and experimental measurements, and provides new routes to the rational design of functional molecular materials

Two decades of Martini:Better beads, broader scope

The Martini model, a coarse-grained force field for molecular dynamics simulations, has been around for nearly two decades. Originally developed for lipid-based systems by the groups of Marrink and Tieleman, the Martini model has over the years been extended as a community effort to the current level of a general-purpose force field. Apart from the obvious benefit of a reduction in computational cost, the popularity of the model is largely due to the systematic yet intuitive building-block approach that underlies the model, as well as the open nature of the development and its continuous validation. The easy implementation in the widely used Gromacs software suite has also been instrumental. Since its conception in 2002, the Martini model underwent a gradual refinement of the bead interactions and a widening scope of applications. In this review, we look back at this development, culminating with the release of the Martini 3 version in 2021. The power of the model is illustrated with key examples of recent important findings in biological and material sciences enabled with Martini, as well as examples from areas where coarse-grained resolution is essential, namely high-throughput applications, systems with large complexity, and simulations approaching the scale of whole cells. This article is categorized under: Software > Molecular Modeling Molecular and Statistical Mechanics > Molecular Dynamics and Monte-Carlo Methods Structure and Mechanism > Computational Materials Science Structure and Mechanism > Computational Biochemistry and Biophysics

Phase behaviour of macromolecular liquid crystlline materials: computational studies at the molecular level

Molecular simulations provide an increasingly useful insight into the static and dynamic characteristics of materials. In this thesis molecular simulations of macro-molecular liquid crystalline materials are reported. The first liquid crystalline material that has been investigated is a side chain liquid crystal polymer (SCLCP). In this study semi-atomistic molecular dynamics simulations have been conducted at a range of temperatures and an aligning potential has been applied to mimic the effect of a magnetic field. In cooling the SCLCP from an isotropic melt, microphase separation was observed yielding a domain structure. The application of a magnetic field to this structure aligns the domains producing a stable smectic mesophase. This is the first study in which mesophases have been observed using an off-lattice model of a SCLCP. The second material that has been investigated is a dendrimer with terminal mesogenic functionalization. Here, a multi-scale approach has been taken with Monte Carlo studies of a single dendrimer molecule in the gas phase at the atomistic level, semi-atomistic molecular dynamics of a single molecule in liquid crystalline solvents and a coarse-grained molecular dynamics study of the dendrimer in the bulk. The coarse-grained model has been developed and parameterized using the results of the atomistic and semi-atomistic work. The single molecule studies showed that the liquid crystalline dendrimer was able to change its structure by conformational changes in the flexible chains that link the mesogenic groups to the core. Structural change was seen under the application of a mean field ordering potential in the gas phase, and in the presence of liquid crystalline solvents. No liquid crystalline phases were observed for the bulk phase studies of the coarse-grained model. However, when the length of the mesogenic units was increased there was some evidence for microphase separation in these systems

Multiscale Methods for Random Composite Materials

Simulation of material behaviour is not only a vital tool in accelerating product development and increasing design efficiency but also in advancing our fundamental understanding of materials. While homogeneous, isotropic materials are often simple to simulate, advanced, anisotropic materials pose a more sizeable challenge. In simulating entire composite components such as a 25m aircraft wing made by stacking several 0.25mm thick plies, finite element models typically exceed millions or even a billion unknowns. This problem is exacerbated by the inclusion of sub-millimeter manufacturing defects for two reasons. Firstly, a finer resolution is required which makes the problem larger. Secondly, defects introduce randomness. Traditionally, this randomness or uncertainty has been quantified heuristically since commercial codes are largely unsuccessful in solving problems of this size. This thesis develops a rigorous uncertainty quantification (UQ) framework permitted by a state of the art finite element package \texttt{dune-composites}, also developed here, designed for but not limited to composite applications. A key feature of this open-source package is a robust, parallel and scalable preconditioner \texttt{GenEO}, that guarantees constant iteration counts independent of problem size. It boasts near perfect scaling properties in both, a strong and a weak sense on over $15,000$ cores. It is numerically verified by solving industrially motivated problems containing upwards of 200 million unknowns. Equipped with the capability of solving expensive models, a novel stochastic framework is developed to quantify variability in part performance arising from localized out-of-plane defects. Theoretical part strength is determined for independent samples drawn from a distribution inferred from B-scans of wrinkles. Supported by literature, the results indicate a strong dependence between maximum misalignment angle and strength knockdown based on which an engineering model is presented to allow rapid estimation of residual strength bypassing expensive simulations. The engineering model itself is built from a large set of simulations of residual strength, each of which is computed using the following two step approach. First, a novel parametric representation of wrinkles is developed where the spread of parameters defines the wrinkle distribution. Second, expensive forward models are only solved for independent wrinkles using \texttt{dune-composites}. Besides scalability the other key feature of \texttt{dune-composites}, the \texttt{GenEO} coarse space, doubles as an excellent multiscale basis which is exploited to build high quality reduced order models that are orders of magnitude smaller. This is important because it enables multiple coarse solves for the cost of one fine solve. In an MCMC framework, where many solves are wasted in arriving at the next independent sample, this is a sought after quality because it greatly increases effective sample size for a fixed computational budget thus providing a route to high-fidelity UQ. This thesis exploits both, new solvers and multiscale methods developed here to design an efficient Bayesian framework to carry out previously intractable (large scale) simulations calibrated by experimental data. These new capabilities provide the basis for future work on modelling random heterogeneous materials while also offering the scope for building virtual test programs including nonlinear analyses, all of which can be implemented within a probabilistic setting

Composition, thermodynamics, and morphology: A multi-scale computational approach for the design of self-assembling peptides

Peptide self-assembly has generated significant interest as a means for the bottom-up fabrication of highly tunable biocompatible nanoaggregates. Individual peptides can be synthesized to include non-natural π-conjugated subunits, endowing assembled aggregates with a range of optical and electronic properties that render them useful in applications as biocompatible organic electronics. The immense number of possible peptides, however, causes the exhaustive traversal of sequence space to be intractable. This massive composition space lends itself toward the use of computer simulation and data science tools to understand molecular aggregation and guide experimental synthesis and design. In this dissertation, I present work employing a hierarchy of molecular modeling techniques to identify self-assembling peptides with specific photophysical properties by probing thermodynamic and structural characteristics of peptide aggregation. We employ classical molecular dynamics simulation to probe the key molecular forces governing the morphology and free energy of oligomerization, time dependent density functional theory to predict photophysical properties as a function of aggregate morphology, and data-driven quantitative structure property models to perform high-throughput virtual screening of chemical space to identify promising peptide chemistries. This work establishes a multi-scale framework for the principled computational design of self-assembling π-conjugated peptides with engineered photophysical properties