Bulletin of the American Physical Society
APS March Meeting 2017
Volume 62, Number 4
Monday–Friday, March 13–17, 2017; New Orleans, Louisiana
Session B29: Frontiers in Computational Materials ScienceInvited
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Sponsoring Units: COM Chair: Alfredo Alexander-Katz, Massachusetts Institute of Technology Room: 292 |
Monday, March 13, 2017 11:15AM - 11:51AM |
B29.00001: Nonlinear machine learning in soft materials engineering and design Invited Speaker: Andrew Ferguson The inherently many-body nature of molecular folding and colloidal self-assembly makes it challenging to identify the underlying collective mechanisms and pathways governing system behavior, and has hindered rational design of soft materials with desired structure and function. Fundamentally, there exists a predictive gulf between the architecture and chemistry of individual molecules or colloids and the collective many-body thermodynamics and kinetics. Integrating machine learning techniques with statistical thermodynamics provides a means to bridge this divide and identify emergent folding pathways and self-assembly mechanisms from computer simulations or experimental particle tracking data. We will survey a few of our applications of this framework that illustrate the value of nonlinear machine learning in understanding and engineering soft materials: the non-equilibrium self-assembly of Janus colloids into pinwheels, clusters, and archipelagos; engineering reconfigurable "digital colloids" as a novel high-density information storage substrate; probing hierarchically self-assembling π-conjugated asphaltenes in crude oil; and determining macromolecular folding funnels from measurements of single experimental observables. We close with an outlook on the future of machine learning in soft materials engineering, and share some personal perspectives on working at this disciplinary intersection. [Preview Abstract] |
Monday, March 13, 2017 11:51AM - 12:27PM |
B29.00002: Using density functional theory to solve complex problems: from liquid water to dark matter Invited Speaker: Marivi Fernandez Serra In this talk I will review our current efforts on on understanding the physics of liquid water and the interaction of water with functional semiconductor surfaces using ab initio molecular dynamics methods. I will present the state of the art of current simulations and the challenges we face, focusing on two specific problems: the description of electron-electron interactions using semilocal density functionals and the role of nuclear quantum effects. I will finish the talk introducing our work in the field of dark matter detection, showing how electronic structure theory is a tool that can easily be used by high energy theorists to evaluate their predictions about the interactions of dark matter particles with electrons in solids, opening a bridge between two otherwise very distant communities. [Preview Abstract] |
Monday, March 13, 2017 12:27PM - 1:03PM |
B29.00003: Quantum Monte Carlo in Materials Science: Electronic Structure Invited Speaker: William Lester, Jr Background on quantum Monte Carlo for the electronic structure of molecular systems will be presented. Aspects of the computational algorithm will be discussed. Selected applications will be described to provide insight on the capability of the method. [Preview Abstract] |
Monday, March 13, 2017 1:03PM - 1:39PM |
B29.00004: Pushing the Envelope Beyond Standard Density Functional Theory for Simulations of Zero Emission Energy Materials Invited Speaker: Emily Carter This talk will provide an update into two quantum mechanics techniques that my group has been developing over the past 20 years: embedded correlated wavefunction theory and orbital-free density functional theory (OF-DFT). The first technique locally refines the electronic structure beyond standard DFT and can be used to study localized phenomena such as charge transfer or excited states where standard DFT approximations are inaccurate. The correlated wavefunction methods treat electron exchange exactly and electron correlation systematically, leading to very accurate predictions. The embedding potential is derived from optimized effective potential theory and is formally unique and exact. Examples will be given from our recent efforts to design plasmonic nanocatalysts that can use visible light to break chemical bonds that conventionally use energy from fossil fuels to do so. The second technique is aimed at much larger sample sizes, in order to compute properties involving larger-length-scale features. OF-DFT solves directly for the electron density -- no wavefunctions -- and therefore can be made (quasi)linear scaling with a small prefactor. Because of the lack of wavefunctions, electron kinetic energy must be evaluated using a density functional; we have developed many over the years that obey exact limits for certain classes of materials. Here we will give examples of our work studying properties of (i) complex lightweight metal alloys, which could improve fuel efficiency if used in vehicle construction, and (ii) liquid metals under consideration as first wall materials in fusion reactors. [Preview Abstract] |
Monday, March 13, 2017 1:39PM - 2:15PM |
B29.00005: Machine Learning for Materials and Chemicals Discovery. Invited Speaker: Alan Aspuru-Guzik In this talk, I will discuss the recent progress of my research group in machine learning. In particular, I will discuss the use of regression and generative models for the discovery of novel materials in a variety of spaces including organic light emitting diodes and organic flow batteries. I will review the challenges and opportunities for the design of materials using high-throughput methods. [Preview Abstract] |
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