海歸學者發起的公益學術平臺
分享信息，整合資源
交流學術，偶爾風月
高性能功能材料的發現對於克服現代工業中的技術問題至關重要。從實驗和材料設計的角度看，人們對加速和促進材料發現過程作了廣泛而大量的努力。機器學習輔助材料發現被廣泛關注，是因其能為材料的有效探索提供合理指導，既能避免耗時的迭代又無需前人的知識積累。但如何實現完全數據驅動的機器學習和新材料發現，尚需探索。
來自韓國三星和成均館大學的KyungdocKim及其同事，夢想著想要有什麼屬性的材料，只要告訴計算機，計算機就能幫他設計出所需的材料分子。為此，他們基於深度編碼器-解碼器架構的逆設計模型，由神經機器語言翻譯的啟發，開發了兩種計算機算法，用於目標分子設計，兩種計算方法能為同一設計目的而共同工作。第一種算法是深度神經網絡編碼器，能查看已知有機分子及其屬性的數據庫，提取分子結構與其材料性能之間的隱藏特徵，找到描述結構/性能關係的抽象規則；第二種算法為遞歸神經網絡解碼器，能將提取的特徵重建為具有目標性質的新分子結構。他們用其所構建的完全數據驅動方法，成功地從給定的數據庫中學習了設計規則，並通過創建新的配體和組合規則，設計了能夠吸收所需波長的分子，有希望實現高效、穩定的藍光有機顯示器材料。該技術可用於發現應用範圍更廣的相關全新分子和設計規則。
該文近期發表於npj Computational Materials 4: 67 (2018)，英文標題與摘要如下，點擊左下角“閱讀原文”可以自由獲取論文PDF。
Deep-learning-based inverse design model for intelligent discovery of organic molecules
Kyungdoc Kim, Seokho Kang, Jiho Yoo, Youngchun Kwon, Youngmin Nam, Dongseon Lee, Inkoo Kim, Youn-Suk Choi, Yongsik Jung, Sangmo Kim, Won-Joon Son, Jhunmo Son, Hyo Sug Lee, Sunghan Kim, Jaikwang Shin & Sungwoo Hwang
The discovery of high-performance functional materials is crucial for overcoming technical issues in modern industries. Extensive efforts have been devoted toward accelerating and facilitating this process, not only experimentally but also from the viewpoint of materials design. Recently, machine learning has attracted considerable attention, as it can provide rational guidelines for efficient material exploration without time-consuming iterations or prior human knowledge. In this regard, here we develop an inverse design model based on a deep encoder-decoder architecture for targeted molecular design. Inspired by neural machine language translation, the deep neural network encoder extracts hidden features between molecular structures and their material properties, while the recurrent neural network decoder reconstructs the extracted features into new molecular structures having the target properties. In material design tasks, the proposed fully data-driven methodology successfully learned design rules from the given databases and generated promising light-absorbing molecules and host materials for a phosphorescent organic light-emitting diode by creating new ligands and combinatorial rules.
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