在不同長度尺度上具有特定建構的分級材料在自然界中隨處可見，如骨骼、木材等。在結構中增添建構可以增強材料的機械性能，是對原子級微觀結構和宏觀級零件維度進行設計的又一手段。因此，對分級建構材料的研究，包括控制材料的疲勞耐受性、能量吸收、剛度和強度等，引起了人們濃厚的興趣。蜂窩狀結構材料由于其極低的重量和優異的機械性能，在汽車、鐵路、航空航天工業中均有著廣泛的應用。Fig. 1 Data representation of MD simulations.近年來，人工智能的發展增強了建構化設計的能力，在實現受生物啟發的分級復合材料、使用圖神經網絡的半監督方法以及自然語言輸入生成設計架構化材料等方面均取得了成功。同時，機器學習模型也常常已被應用于其他材料性能的研究，如預測斷裂、柔度和屈曲等多種力學性能。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.來自麻省理工學院原子和分子力學實驗室的Andrew J. Lew等，提出了一個建構化蜂窩狀材料壓縮設計的完整工作流程。他們使用分子動力學模擬確定了分級蜂窩狀晶格空間，使用機器學習和遺傳算法生成了目標行為的候選結構，并利用增材制造技術對頂級候選結構進行快速測試。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.訓練后的機器學習模型為解決正向設計問題提供了一個有效的工具：對于給定的蜂窩狀超結構，能夠直接快速預測其壓縮行為，而無需建立、運行和分析物理模擬過程。他們通過模擬和實驗，驗證了遺傳算法搜索的有效性，可高效解決逆向設計問題。Fig. 4 Inverse design of stiffness and ultimate stress.作者的報道展示了一個從設想的性能需求到實際的材料結構的“端到端”壓縮設計過程。該過程可以推廣到多種材料性質，并且無需知道基材的特征。這為未來使用計算模擬、人工智能和實驗手段協同增強材料設計提供了另一種途徑。該文近期發布于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及其翻譯Designing architected materials for mechanical compression via simulation, deep learning, and experimentation (機械壓縮建構化材料設計：計算模擬、深度學習和實驗驗證)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.與普通材料相比，建構化材料能夠實現增強的性能。材料中特定的建構可以作為一種有力的設計手段，在不改變基材的情況下實現目標行為。因此，材料建構和對應性能之間的聯系仍然是航空航天、民用工業、汽車應用等眾多領域所感興趣的開放話題。這里，我們關注與機械壓縮相關的性能，設計了一種分級蜂窩狀結構，以滿足特定剛度和壓縮應力的需要。為此，我們在單個工作流程中采用組合策略：從分子動力學模擬出發解決正向設計問題，增加數據驅動人工智能模型以解決逆向設計問題，并通過實驗上制造的樣品驗證了新結構的機械行為。由此，我們給出了一種建構化設計方法，該方法可推廣到多種材料性質，并且無需知道基材的特征。【做計算 找華算】華算科技專注DFT代算服務、正版商業軟件版權、全職海歸計算團隊，10000+成功案例！Nature Catalysis、JACS、Angew.、AM、AEM、AFM等狂發頂刊，好評如潮！計算內容涉及OER、HER、ORR、CO2RR、NRR自由能臺階圖、火山理論、d帶中心、反應路徑、摻雜、缺陷、表面能、吸附能等。添加下方微信好友，立即咨詢：
