在不同长度尺度上具有特定建构的分级材料在自然界中随处可见，如骨骼、木材等。在结构中增添建构可以增强材料的机械性能，是对原子级微观结构和宏观级零件维度进行设计的又一手段。因此，对分级建构材料的研究，包括控制材料的疲劳耐受性、能量吸收、刚度和强度等，引起了人们浓厚的兴趣。蜂窝状结构材料由于其极低的重量和优异的机械性能，在汽车、铁路、航空航天工业中均有着广泛的应用。Fig. 1 Data representation of MD simulations.近年来，人工智能的发展增强了建构化设计的能力，在实现受生物启发的分级复合材料、使用图神经网络的半监督方法以及自然语言输入生成设计架构化材料等方面均取得了成功。同时，机器学习模型也常常已被应用于其他材料性能的研究，如预测断裂、柔度和屈曲等多种力学性能。Fig. 2 LSTM model training. a An ensemble of 1445 MD simulations were used to train the convolutional LSTM network. b Predicted ML stresses align well with real MD stresses, with an r2 = 0.95 and (c) validation loss = 0.00058. d Predicted curves across a range of stress behaviors align well with MD, with the samples from Fig. 1b provided as example.来自麻省理工学院原子和分子力学实验室的Andrew J. Lew等，提出了一个建构化蜂窝状材料压缩设计的完整工作流程。他们使用分子动力学模拟确定了分级蜂窝状晶格空间，使用机器学习和遗传算法生成了目标行为的候选结构，并利用增材制造技术对顶级候选结构进行快速测试。Fig. 3 Inverse design procedure. The stress prediction ML model directly solves the forward design problem, where we input an arbitrary structure vector and rapidly receive its stress strain curve. Here, we solve the inverse design problem via genetic algorithm, which comprises an iterative two stage process of generation and evaluation, to obtain structures given a desired stress behavior as input.训练后的机器学习模型为解决正向设计问题提供了一个有效的工具：对于给定的蜂窝状超结构，能够直接快速预测其压缩行为，而无需建立、运行和分析物理模拟过程。他们通过模拟和实验，验证了遗传算法搜索的有效性，可高效解决逆向设计问题。Fig. 4 Inverse design of stiffness and ultimate stress.作者的报道展示了一个从设想的性能需求到实际的材料结构的“端到端”压缩设计过程。该过程可以推广到多种材料性质，并且无需知道基材的特征。这为未来使用计算模拟、人工智能和实验手段协同增强材料设计提供了另一种途径。该文近期发布于npj Computational Materials 9: 80 (2023)。Fig. 5 Experimental verification of stiffness design.Editorial SummaryArchitected materials design: Simulation, Deep learning and ExperimentationHierarchical materials with specific architecture at different length scales are observed everywhere in nature, like in bone and wood. Adding architecture to structures can enhance mechanical properties and provides an extra design lever on top of atomic-level microstructure and macroscopic-level part dimensions. Investigations into hierarchically architected materials have thus been of great interest, with efforts to control fatigue tolerance, energy absorption, and stiffness and strength, among many others. Honeycomb structures are of particular interest due to their ultra-low weight and outstanding mechanical properties, with a variety of applications across automotive, railway, and aerospace industries. Recent advances in artificial intelligence have afforded emerging capabilities for architectural design. For example, there have been successes in achieving bioinspired hierarchical composites, in using semi-supervised approaches with graph neural networks, and in implementing natural language inputs for generative design of architected materials. Concurrently, machine learning (ML) models have been used in other material platforms for the prediction of a multitude of mechanical properties including fracture, compliance, and buckling. Fig. 6 Experimental verification of stress design.Andrew J. Lew et al. from the Laboratory for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology,demonstrated a full workflow to tackle compression design of architected honeycomb materials. They used molecular dynamics simulations to determine initial insights into the space of hierarchical honeycomb lattices, machine learning and genetic algorithms to generate candidates for desired behavior, and additive manufacturing to rapidly test top structural candidates. The trained ML model provides an effective tool for the forward design problem, in which a given super-honeycomb structure can have its compressive behavior directly and rapidly predicted without having to set up, run, and analyze a physics-based simulation. A genetic algorithm search validated by simulation and experimentation enables effective interrogation of the inverse design problem. This work demonstrates a successful end-to-end process for compression design from ideated property requirements to actualized material structures. This process is generalizable to multiple material properties and agnostic to the identity of the base material, which can provide alternative avenues at the intersection of simulation, artificial intelligence, and experiment that can synergistically empower materials design in the future. This article was recently published in npj Computational Materials 9: 80 (2023).原文Abstract及其翻译Designing architected materials for mechanical compression via simulation, deep learning, and experimentation (机械压缩建构化材料设计：计算模拟、深度学习和实验验证)Andrew J. Lew,Kai Jin & Markus J. Buehler Abstract Architected materials can achieve enhanced properties compared to their plain counterparts. Specific architecting serves as a powerful design lever to achieve targeted behavior without changing the base material. Thus, the connection between architected structure and resultant properties remains an open field of great interest to many fields, from aerospace to civil to automotive applications. Here, we focus on properties related to mechanical compression, and design hierarchical honeycomb structures to meet specific values of stiffness and compressive stress. To do so, we employ a combination of techniques in a singular workflow, starting with molecular dynamics simulation of the forward design problem, augmenting with data-driven artificial intelligence models to address the inverse design problem, and verifying the behavior of de novo structures with experimentation of additively manufactured samples. We thereby demonstrate an approach for architected design that is generalizable to multiple material properties and agnostic to the identity of the base material.与普通材料相比，建构化材料能够实现增强的性能。材料中特定的建构可以作为一种有力的设计手段，在不改变基材的情况下实现目标行为。因此，材料建构和对应性能之间的联系仍然是航空航天、民用工业、汽车应用等众多领域所感兴趣的开放话题。这里，我们关注与机械压缩相关的性能，设计了一种分级蜂窝状结构，以满足特定刚度和压缩应力的需要。为此，我们在单个工作流程中采用组合策略：从分子动力学模拟出发解决正向设计问题，增加数据驱动人工智能模型以解决逆向设计问题，并通过实验上制造的样品验证了新结构的机械行为。由此，我们给出了一种建构化设计方法，该方法可推广到多种材料性质，并且无需知道基材的特征。【做计算 找华算】华算科技专注DFT代算服务、正版商业软件版权、全职海归计算团队，10000+成功案例！Nature Catalysis、JACS、Angew.、AM、AEM、AFM等狂发顶刊，好评如潮！计算内容涉及OER、HER、ORR、CO2RR、NRR自由能台阶图、火山理论、d带中心、反应路径、掺杂、缺陷、表面能、吸附能等。添加下方微信好友，立即咨询：
