AI Music Lecture Series｜AI-Driven Molecular Music Generation: From Chemical Structures to Chord Progressions
Theme: AI-Driven Molecular Music Generation: From Chemical Structures to Chord Progressions
Language: Chinese
Speaker: Prof. Zhu Xi (SSE)
Host: Prof. Jin Ping
Date&Time: Sep. 10, 2025 (Wed.) 17:00
Venue: Lecture Theatre 101, Music Teaching Building (MUS) (2 International University Park Road, Longgang District, Shenzhen)
Admission Free.
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Notice: Children under 1.2m are not admitted
Abstract
The lecture will introduce an innovative interdisciplinary approach that maps molecular structures—such as amino acids and small organic molecules—onto musical chord progressions using AI. We will examine how molecules can be represented through SMILES strings and transformed into chord sequences guided by PLR chord transformation theory and energy stability principles. By integrating the QM9 molecular database with the HookTheory music database, we demonstrate how machine learning models can classify molecules into musical genres (e.g., pop, electronic, country) and generate "sonifiable" molecular music. Furthermore, the talk will highlight how optimizing molecular structures can be used to "repair" musically dissonant progressions, reflecting a profound synthesis of science and art.
About the Speaker
Zhu Xi
Associate Professor, School of Science and Engineering, CUHK-Shenzhen
Chair of the AI Education Innovation Committee, School of Artificial Intelligence, CUHK-Shenzhen
Professor Zhu Xi earned his Bachelor's degree in Physics from the University of Science and Technology of China and a Ph.D. in Materials Science and Engineering from Nanyang Technological University in Singapore. Since establishing his independent research career at The Chinese University of Hong Kong, Shenzhen, in 2017, he has been dedicated to integrating AI and robotics in the field of chemical materials. He has developed an AI-Supervisor system and various intelligent experimental robots that cover processes from idea-generation to idea-execution, with workflow supervision. Focusing on the context of AI and robotics as emerging social infrastructure, he explores the restructuring of scientific research methodologies and talent development approaches in the traditional discipline of chemical experimentation. As an expert in the field of "AI + experimental science," he has also published a book (with Wiley) that systematically discusses the principles and scenario analyses of AI robotics technology applied in materials science.
