Studying polymer physics by machine learning.

Recently, machine learning becomes a computational method that burst in popularity. Many disciplines, such as condensed matter physics, quantum chemistry, chemical engineering as well as polymer physics have incorporate machine learning into their studies. This thesis mainly focuses on applying machine learning methods into the study of polymer physics. More specifically, two computational methods are studied: 1. how to classify polymer states by supervised or unsupervised learning methods, 2. how to use FNNs to search for structures of diblock copolymer under self-consistent field theory scheme. In the first topic, polymer samples that consist of both vastly different structures, such as gas-like random coil, liquid-like globular and subtly different structures, such as crystalline anti-Mackay, Mackay, are generated by Monte Carlo method. We then explored the capability of a FNN on the classification of different polymer configurations systematically. Base on a series of numerical experiments, we find that a FNN, after appropriate training, is able to not only identify all these structures, but also accurately locate the transition points between multiple states. The location given by the FNN has a good agreement with that provided by specific-heat calculations from the traditional method, which shows that the FNN offers a new tool for further studies of the polymeric phase transitions. We also studied these states with principal component analysis (PCA). When polymer samples only contain coil and globular states, PCA can distinguish these states, and offer insights to understand the relation between features and order parameters of these states. However, PCA itself is not powerful enough to distinguish globular, anti-Mackay, Mackay states. Then, a hybrid scheme combining PCA and supervised learning is utilized to identify and precisely detect the critical point of phase transitions between these polymer configurations. Compared with traditional methods, our studies demonstrate machine learning based methods have some distinct advantages. Firstly, these methods directly and only use molecular coordinates, which indicates its high compatibility with multiple sampling methods. In addition, the trained FNN has high transferability. In terms of identify transition points, our approaches requires much fewer samples, which indicates they are computationally faster than the traditional methods. In the second topic, we start from using the universal approximation theorem of FNN to build a machine learning based PDE solver. Our work mainly focuses on diffusion equations. This algorithm utilizes the function generated by the FNN as a trial function and adjusts the weights and biases of the FNN to search for the solution of a given PDE. The trial function will have a good match with the solution, when the weights and biases are optimal. Our approach is important to high dimensional diffusion equations. We discovered that the growth of the computational time obeys a power law with respect to the dimensionality, which indicates that the machine learning based solver offers a candidate algorithm that may not suffer from the ``curse of dimensionality''. We then demonstrated that this machine learning PDE solver can be conveniently adopted to deal with multi-variable, coupled integrodifferential equations in the self-consistent field theory for predicting polymer self-assembly structures. We observed all known three-dimensional classical structures, and our solutions have an excellent agreement with traditional solutions

100th Anniversary of Macromolecular Science Viewpoint: Opportunities in the Physics of Sequence-Defined Polymers

Polymer science has been driven by ever-increasing molecular complexity, as polymer synthesis expands an already-vast palette of chemical and architectural parameter space. Copolymers represent a key example, where simple homopolymers have given rise to random, alternating, gradient, and block copolymers. Polymer physics has provided the insight needed to explore this monomer sequence parameter space. The future of polymer science, however, must contend with further increases in monomer precision, as this class of macromolecules moves ever closer to the sequence-monodisperse polymers that are the workhorses of biology. The advent of sequence-defined polymers gives rise to opportunities for material design, with increasing levels of chemical information being incorporated into long-chain molecules; however, this also raises questions that polymer physics must address. What properties uniquely emerge from sequence-definition? Is this circumstance-dependent? How do we define and think about sequence dispersity? How do we think about a hierarchy of sequence effects? Are more sophisticated characterization methods, as well as theoretical and computational tools, needed to understand this class of macromolecules? The answers to these questions touch on many difficult scientific challenges, setting the stage for a rich future for sequence-defined polymers in polymer physics

SciTech News Volume 71, No. 1 (2017)

Columns and Reports From the Editor 3 Division News Science-Technology Division 5 Chemistry Division 8 Engineering Division Aerospace Section of the Engineering Division 9 Architecture, Building Engineering, Construction and Design Section of the Engineering Division 11 Reviews Sci-Tech Book News Reviews 12 Advertisements IEEE

ToScA North America (6 – 8 June 2017, The University of Texas, Austin, TX) Program

ToScA North America will address key areas of science, including Multi-modal Imaging, Geosciences, Forensics, Increasing Contrast, Educational Outreach, Data, Materials Science and Medical and Biological Science.University of Texas High-Resolution X-ray CT Facility (UTCT); Jackson School of Geosciences, The University of Texas at Austin; Natural History Museum (London); Royal Microscopical Society (Oxford, UK)Geological Science

Graph Dynamical Networks for Unsupervised Learning of Atomic Scale Dynamics in Materials

Understanding the dynamical processes that govern the performance of functional materials is essential for the design of next generation materials to tackle global energy and environmental challenges. Many of these processes involve the dynamics of individual atoms or small molecules in condensed phases, e.g. lithium ions in electrolytes, water molecules in membranes, molten atoms at interfaces, etc., which are difficult to understand due to the complexity of local environments. In this work, we develop graph dynamical networks, an unsupervised learning approach for understanding atomic scale dynamics in arbitrary phases and environments from molecular dynamics simulations. We show that important dynamical information can be learned for various multi-component amorphous material systems, which is difficult to obtain otherwise. With the large amounts of molecular dynamics data generated everyday in nearly every aspect of materials design, this approach provides a broadly useful, automated tool to understand atomic scale dynamics in material systems.Comment: 25 + 7 pages, 5 + 3 figure