Haley K. Beech

Bio: Haley K. Beech is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Polymer & Medicine. The author has an hindex of 6, co-authored 12 publications receiving 170 citations. Previous affiliations of Haley K. Beech include Duke University & University of Minnesota.

BigSMILES: A Structurally-Based Line Notation for Describing Macromolecules

Abstract: Having a compact yet robust structurally based identifier or representation system is a key enabling factor for efficient sharing and dissemination of research results within the chemistry communit...

Molecular Characterization of Polymer Networks.

Abstract: Polymer networks are complex systems consisting of molecular components. Whereas the properties of the individual components are typically well understood by most chemists, translating that chemical insight into polymer networks themselves is limited by the statistical and poorly defined nature of network structures. As a result, it is challenging, if not currently impossible, to extrapolate from the molecular behavior of components to the full range of performance and properties of the entire polymer network. Polymer networks therefore present an unrealized, important, and interdisciplinary opportunity to exert molecular-level, chemical control on material macroscopic properties. A barrier to sophisticated molecular approaches to polymer networks is that the techniques for characterizing the molecular structure of networks are often unfamiliar to many scientists. Here, we present a critical overview of the current characterization techniques available to understand the relation between the molecular properties and the resulting performance and behavior of polymer networks, in the absence of added fillers. We highlight the methods available to characterize the chemistry and molecular-level properties of individual polymer strands and junctions, the gelation process by which strands form networks, the structure of the resulting network, and the dynamics and mechanics of the final material. The purpose is not to serve as a detailed manual for conducting these measurements but rather to unify the underlying principles, point out remaining challenges, and provide a concise overview by which chemists can plan characterization strategies that suit their research objectives. Because polymer networks cannot often be sufficiently characterized with a single method, strategic combinations of multiple techniques are typically required for their molecular characterization.

Toughening hydrogels through force-triggered chemical reactions that lengthen polymer strands.

Abstract: The utility and lifetime of materials made from polymer networks, including hydrogels, depend on their capacity to stretch and resist tearing. In gels and elastomers, those mechanical properties ar...

Synthesis and Self-Assembly of ABn Miktoarm Star Polymers

Abstract: The stability of tetrahedrally close-packed (TCP) phases in block copolymer melts is predicted by theory to depend on molecular architecture, yet no experimental studies to date have probed its eff...

Role of Chain Length in the Formation of Frank-Kasper Phases in Diblock Copolymers

Abstract: The phase behavior of poly(styrene)-$b$-poly(1,4-butadiene) diblock copolymers with a polymer block invariant degree of polymerization ${\overline{N}}_{b}\ensuremath{\approx}800$ shows no evidence of Frank-Kasper phases, in contrast to low molar mass diblock copolymers (${\overline{N}}_{b}l100$) with the same conformational asymmetry. A universal self-concentration crossover parameter ${\overline{N}}_{x}\ensuremath{\approx}400$ is identified, directly related to the crossover to entanglement dynamics in polymer melts. Mean-field behavior is recovered when ${\overline{N}}_{b}g{\overline{N}}_{x}$, while complex low symmetry phase formation is attributed to fluctuations and space-filling constraints, which dominate when ${\overline{N}}_{b}l{\overline{N}}_{x}$.

Machine Learning of Molecular Electronic Properties in Chemical Compound Space

Abstract: The combination of modern scientific computing with electronic structure theory can lead to an unprecedented amount of data amenable to intelligent data analysis for the identification of meaningful, novel, and predictive structure-property relationships. Such relationships enable high-throughput screening for relevant properties in an exponentially growing pool of virtual compounds that are synthetically accessible. Here, we present a machine learning (ML) model, trained on a data base of \textit{ab initio} calculation results for thousands of organic molecules, that simultaneously predicts multiple electronic ground- and excited-state properties. The properties include atomization energy, polarizability, frontier orbital eigenvalues, ionization potential, electron affinity, and excitation energies. The ML model is based on a deep multi-task artificial neural network, exploiting underlying correlations between various molecular properties. The input is identical to \emph{ab initio} methods, \emph{i.e.} nuclear charges and Cartesian coordinates of all atoms. For small organic molecules the accuracy of such a "Quantum Machine" is similar, and sometimes superior, to modern quantum-chemical methods---at negligible computational cost.

Molecular representations in AI-driven drug discovery: a review and practical guide

Abstract: The technological advances of the past century, marked by the computer revolution and the advent of high-throughput screening technologies in drug discovery, opened the path to the computational analysis and visualization of bioactive molecules. For this purpose, it became necessary to represent molecules in a syntax that would be readable by computers and understandable by scientists of various fields. A large number of chemical representations have been developed over the years, their numerosity being due to the fast development of computers and the complexity of producing a representation that encompasses all structural and chemical characteristics. We present here some of the most popular electronic molecular and macromolecular representations used in drug discovery, many of which are based on graph representations. Furthermore, we describe applications of these representations in AI-driven drug discovery. Our aim is to provide a brief guide on structural representations that are essential to the practice of AI in drug discovery. This review serves as a guide for researchers who have little experience with the handling of chemical representations and plan to work on applications at the interface of these fields.

Polymer Networks: From Plastics and Gels to Porous Frameworks.

Abstract: Polymer networks, which are materials composed of many smaller components-referred to as "junctions" and "strands"-connected together via covalent or non-covalent/supramolecular interactions, are arguably the most versatile, widely studied, broadly used, and important materials known. From the first commercial polymers through the plastics revolution of the 20th century to today, there are almost no aspects of modern life that are not impacted by polymer networks. Nevertheless, there are still many challenges that must be addressed to enable a complete understanding of these materials and facilitate their development for emerging applications ranging from sustainability and energy harvesting/storage to tissue engineering and additive manufacturing. Here, we provide a unifying overview of the fundamentals of polymer network synthesis, structure, and properties, tying together recent trends in the field that are not always associated with classical polymer networks, such as the advent of crystalline "framework" materials. We also highlight recent advances in using molecular design and control of topology to showcase how a deep understanding of structure-property relationships can lead to advanced networks with exceptional properties.

Atomic Mechanism of Fracture of Solid Polymers

Abstract: Macromolecular chain scission under mechanical stress has been studied by infrared spectroscopy. The dependence of accumulation of chemical bond scissions on temperature T and uniaxial tensile stress σ has been investigated. The rate constant K for bond dissociation under mechanical stress has been found to obey the modified Arrhenius equation: K = K0 exp{ − (EA − ασ)/RA}. The quantitative connection between the rate constant for bond dissociation and mechanical lifetime τ has been established. Analysis of the experimental data indicates that the strength and mechanical lifetime of polymers is determined by the kinetics of mechanochemical scission of the main chains of polymer molecules.