Elastic Strain Engineering (ESE) utilizes all six components of the strain tensor to guide the interactions of material structures with electrons, phonons, etc. and control energy, mass and information flows. The success of Strained Silicon technology today harbingers what deep ESE (>5% elastic strain in a large enough space–time volume) may accomplish for civilization, with likely breakthroughs in electronics, photonics, superconductivity, quantum information processing, etc. In this webinar I give examples of exploiting the strain and strain-gradient design space of nanostructured materials. Inhomogeneous elastic strain patterns lead to dynamically tunable artificial atoms and pseudo-heterostructures to regulate quasiparticle energetics and motion. Strain also governs crystal defect charging levels, carrier effective mass, direct-to-indirect bandgap and band topology transitions, etc. which can be efficiently sampled by quantum mechanical calculations and represented by machine-learning models such as neural network (NN) representations. Technology computer-aided design (TCAD) finite-element simulations with first-principles machine-learned (FPML) constitutive relations coupled to topology optimization (TO) tools are being developed to guide the design of freely suspended, micro-electromechanical system (MEMS) devices in fin-field-effect transistor (FinFET) like geometries. Productions of kilogram-scale nanowire composites and metallic glasses under large tensile elastic strain have also been demonstrated for energy technology applications, that can lead to better superconductors, catalysts and magnets. By controlling the strain patterns, one opens up a much larger parameter space - on par with chemical alloying - for optimizing the functional properties of materials, thus fulfilling Feynman’s vision “There’s Plenty of Room at the Bottom”. EML webinar speakers, videos, and overviews can be found at https://imechanica.org/node/24098.

弹性应变工程 (ESE) 利用应变张量的所有六个分量来引导材料结构与电子、声子等的相互作用，并控制能量、质量和信息流。今天应变硅技术的成功预示着深度 ESE（在足够大的时空体积中 >5% 的弹性应变）可能会为文明带来什么，可能会在电子、光子学、超导、量子信息处理等方面取得突破。 在本次网络研讨会上我给出了利用纳米结构材料的应变和应变梯度设计空间的例子。不均匀的弹性应变模式导致动态可调的人造原子和伪异质结构，以调节准粒子的能量和运动。应变还控制着晶体缺陷带电水平、载流子有效质量、直接到间接的带隙和带拓扑转换等可以通过量子力学计算有效采样并由机器学习模型表示，例如神经网络 (NN) 表示。技术计算机辅助设计 (TCAD) 有限元模拟与第一性原理机器学习 (FPML) 本构关系耦合拓扑优化 (TO) 工具正在开发中，以指导自由悬挂的微机电系统 (MEMS) 的设计鳍式场效应晶体管 (FinFET) 中的器件，如几何形状。在大拉伸弹性应变下生产千克级纳米线复合材料和金属玻璃也已被证明可用于能源技术应用，这可以产生更好的超导体、催化剂和磁铁。通过控制应变模式，一个打开了一个更大的参数空间 - 与化学合金化相当 - 用于优化材料的功能特性，从而实现费曼的愿景“底部有很多空间”。EML 网络研讨会演讲者、视频和概述可在 https://imechanica.org/node/24098 上找到。