Mapping the Molecular Universe
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Updated: Oct 31, 2026

Mapping Molecular Diffusion in the Plasma Membrane by Multiple-Target Tracing (MTT)
Published on: May 27, 2012
Daniel Hannah1,2, Yumin Zhang1,2, Stanley Whittingham3
1SES AI Corp., Woburn, Massachusetts 01801, United States.
Abstract:
The success of large-language-models highlighted the importance of datasets, whose quantity, quality, and diversity dictate the accuracy and creativity of artificial intelligence (AI) systems in learning and inferring. However, in scientific domains, such datasets required for domain-specific AI/machine-learning (ML) models do not exist most of the time. In this perspective, by examining the logics and rationales behind the construction and mapping of a universe for small organic molecules (108-1012), we explore what new capabilities AI can bring to the materials discoveries. We identify the elusive structure-property relationship as a core challenge for new materials design and explore the possibility AI tools could enable us to design based on targeted properties instead of the structural derivation. In a broader context, we further discuss what challenges and limitations AI-driven material sciences still face and envision possible avenues into the future of AI4Materials.
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