APS March Meeting 2022
Volume 67, Number 3
Monday–Friday, March 14–18, 2022;
Chicago
Session Q46: Emerging Trends in Molecular Dynamics Simulations and Machine Learning II
3:00 PM–5:48 PM,
Wednesday, March 16, 2022
Room: McCormick Place W-470A
Sponsoring
Units:
DCOMP GDS DSOFT DPOLY
Chair: Thomas Linker, University of Southern California
Abstract: Q46.00001 : Bio-inspired machine learning towards mechanistic insights, generative design, and discovery from the bottom up
3:00 PM–3:36 PM
Abstract
Presenter:
Markus Buehler
(MIT)
Author:
Markus Buehler
(MIT)
Nature produces a variety of tough materials with many functions, often out of simple and abundant materials, and at low energy. Such systems - examples of which include spider silk, conch shells, nacre or bone - provide broad inspiration for engineering. Here we explore the translation of biomaterials to engineering designs, using a variety of tools including molecular modeling, AI and machine learning, and experimental synthesis using 3D printing, and characterization. We review a series of bottom-up studies focused on the mechanical behavior of bio-inspired composite materials, especially fracture , compression and impact, and how these phenomena can be modeled using a combination of molecular dynamics and machine learning. We present examples that involve deep convolutional neural networks, graph neural networks, transformers, and game theoretical approaches towards analysis and design of atomic-level material structures. One case study will cover a recent example that realizes a text-to-material design approach, developing new architected multimaterial composite designs based on human readable description and subsequent 3D printing - from word to matter, using transformer neural networks. Another case study will explore the use of deep learning to design synthetic diatom geometries, which are manufactured using multimaterial 3D printing. We conclude the talk with a series of case studies of material optimization using genetic algorithms focused on grain boundary architectures and gradients, novel 3D printed composites, as well as a translation of molecular structures to music and back to assess universal patterns through vibrational patterning. A particular case study will include an analysis of Bach's Goldberg variations and translation to proteins using deep learning, and new musical composition. The approach used in this example, the Deep Aria (https://soundcloud.com/user-275864738/aria-inceptionism-in-protein) will be explained.