材料基因工程的研發理念深刻變革了材料研發范式，提高了新材料的研發效率，降低了研發成本。材料基因工程研發理念的核心是材料信息學，人工智能技術是材料信息學的核心工具。

來自上海大學材料基因組工程研究院的張統一院士團隊，開發了MLMD (matdesign.top)，一個面向材料設計的無需編程AI平臺，平臺可以實現材料的高通量篩選和代理優化，進行單目標或者多目標的材料設計。同時可以針對材料領域小數據問題，開發了基于貝葉斯的主動學習和基于遷移學習的無編程材料在線設計流程。

Fig. 2 | Flowcharts of materials design inMLMD platform.

MLMD平臺包含了三個主要的材料設計流程：模型推理、代理優化和主動學習。模型推理和代理優化的效率取決于預測模型的魯棒性，而模型性能則受限于可用數據的質量。在代理優化中，訓練好的預測模型被集成到隨機優化算法中，以加速材料設計。主動學習模塊利用貝葉斯理論，平衡探索和開發，以制定最優的材料設計策略，推薦當前最優的材料參數。針對推薦參數開展新的實驗或計算，不僅可以驗證ML預測，還為數據集提供新數據，用于主動學習新一輪循環。

MLMD平臺通過對6類材料數據開展的示例性研究顯示，平臺可以僅通過鼠標點擊式操作的方式，完成材料的性能預測和優化設計。例如，在代理優化模塊中，作者使用MLMD成功地設計出在300?℃環境下強塑性優異的RAFM鋼，其中抗拉強度723.1 MPa，總伸長率20.7%，抗拉強度與初始數據集相比提高了12.5%，總伸長率提高了41.4%。并且通過簡單的超參數設置，可發現位于Pareto邊界上的其他具有優異特性的材料，根據具體要求應用于多種場景。在主動學習模塊中，作者基于自研的主動學習庫（Bgolearn），使用了EI，REI和UCB等效能函數對高硬度的AlCoCrCuFeNi高熵合金進行了成分設計，所得的成分與原始工作中的成分相近，并提供了更多候選成分。

Fig. 5 | The RAFM steels design process through surrogate optimization module?in MLMD.

Editorial Summary

Improving the efficiency of materials discovery is crucial for advancing modern industry. However, researchers often face challenges in navigating complex experimental processes, which can be time-consuming and labor-intensive. The emergence of artificial intelligence (AI) offers a promising solution to streamline this process. Despite numerous AI tools and platforms developed for materials science, they often have limitations, such as focusing solely on property prediction and being difficult to use without programming expertise, especially when dealing with limited data sets.

Fig. 6 | The atomic percentage distribution of novel AlxCoyCrzCuuFevNiw HEAs?designed by Wen and MLMD.

A research team led by Prof. Tong-Yi Zhang from the Materials Genome Institute at Shanghai University has developed a programming-free AI platform called MLMD for materials design. This platform enables materials design by optimizing single or multiple properties through high-throughput screening and/or surrogate optimization. The challenge of limited data was overcome by leveraging active learning with Bayesian methods and surrogate optimization based on transfer learning.

Using the surrogate optimization module, the work successfully designed an advanced RAFM steel with an ultimate tensile strength (UTS) of 723.1 MPa and a total elongation (TE) of 20.7%. These properties represent improvements of 12.5% for UTS and 41.4% for TE compared to the original dataset. The MLMD platform can also identify other advanced materials on the Pareto frontier by adjusting hyperparameters. These new materials can be tailored for specific applications.

Within the active learning module, the team has developed a tool called Bgolearn, which is specifically designed for materials design. Using this tool, they have discovered a high-hardness AlCoCrCuFeNi alloy (HEAs) with a composition similar to that of previous work. This finding demonstrates the effectiveness of their approach in identifying new materials with desired properties.

MLMD is designed to integrate materials experimentation/computation with AI-driven design, providing researchers with a cutting-edge tool for programming-free materials discovery. This platform can accelerate the identification of new materials with specific or multiple properties. MLMD is poised to become an indispensable resource for materials scientists and will significantly advance the field of materials informatics.

The first authors of this study are Jiaxuan Ma from Shanghai University and Bin Cao from Hong Kong University of Science and Technology (Guangzhou). The corresponding authors are Prof. Sheng Sun and Assistant Professor Jie Xiong, both from Shanghai University.

This article was recentlypublished in npj Computational Materials 10: 59 (2024).

原文Abstract及其翻譯

MLMD: A programming-free AI platform to predict and design materials (MLMD：一個無需編程的AI平臺，用于材料性能預測和材料設計)

Jiaxuan Ma, Bin Cao, Shuya Dong, Yuan Tian, Menghuan Wang, Jie Xiong, & Sheng Sun?

Abstract Accelerating the discovery of advanced materials is crucial for modern industries, aerospace, biomedicine, and energy. Nevertheless, only a small fraction of materials are currently under experimental investigation within the vast chemical space. Materials scientists are plagued by time-consuming and labor-intensive experiments due to lacking efficient material discovery strategies. Artificial intelligence (AI) has emerged as a promising instrument to bridge this gap. Although numerous AI toolkits or platforms for material science have been developed, they suffer from many shortcomings. These include primarily focusing on material property prediction and being unfriendly to material scientists lacking programming experience, especially performing poorly with limited data. Here, we developed MLMD, an AI platform for materials design. It is capable of effectively discovering novel materials with high-potential advanced properties end-to-end, utilizing model inference, surrogate optimization, and even working in situations of data scarcity based on active learning. Additionally, it integrates data analysis, descriptor refactoring, hyper-parameters auto-optimizing, and properties prediction. It also provides a web-based friendly interface without need programming and can be used anywhere, anytime. MLMD is dedicated to the integration of material experiment/computation and design, and accelerate the new material discovery with desired one or multiple properties. It demonstrates the strong power to direct experiments on various materials (perovskites, steel, high-entropy alloy, etc). MLMD will be an essential tool for materials scientists and facilitate the advancement of materials informatics.

摘要先進材料的加速研發對包括航空航天、生物醫藥和能源問題在內的現代工業發展至關重要。然而材料研發的搜索空間巨大，所研發的材料占比極小。材料科學家們常常因為缺乏高效的新材料發現策略而陷入費時費力的試驗過程。人工智能（AI）技術有望成為突破這一壁壘的關鍵工具，展現出巨大潛力。盡管目前已經出現了多個針對材料科學的AI工具包和平臺，但它們仍存在不少局限性，例如過分側重于材料性能的預測、對沒有編程背景的材料研究人員不夠友好，以及在數據量較少的情況下表現不盡人意等。為了解決這些問題，我們開發了MLMD——一個專注于材料設計的AI平臺。MLMD整合了模型推理和代理優化技術，能夠在數據稀缺的環境中通過主動學習和遷移學習的方法進行材料設計，能夠高效地發掘性能優越的新材料。此外，MLMD還具有數據分析、描述符重構、超參數自動優化和性能預測等多項功能，提供了一個用戶友好的、無需編程的Web界面。MLMD致力于將材料試驗/計算與設計相結合，對各類材料（如鈣鈦礦、鋼、高熵合金等）均具有強大的試驗指導能力。我們堅信它將成為材料研究人員的重要工具，推動材料信息學的發展。