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Machine Learning Speeds Analysis and Design in All-Atom Molecular Dynamics Simulations - Spectroscopy Online

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Photo credit : Machine Learning Speeds Analysis and Design in All-Atom Molecular Dynamics Simulations | Spectroscopy Online

Siddhartha Das explains how machine learning helps his group sift through roughly a trillion simulation data points. It reveals water and electroosmotic flow behavior and could speed the design of sensor-like channels. In this interview conducted at SciX 2026, which took place in Sparks, Nevada from October 4–9, Siddhartha Das, a Professor of Mechanical Engineering at the University of Maryland, describes how machine learning has become a major tool in his group’s work with all-atom molecular dynamics (MD) simulations. He also explains how pairing the two approaches can accelerate the design of engineered channels. The starting point, Das explains, is the sheer volume of data these simulations produce. A system of one million particles tracked over one million time steps yields roughly one trillion data points. Das describes this as fertile ground for analysis, but notes that working through it manually is impractical. Machine learning, he says, can reveal insights that even the most sophisticated atomistic simulations might miss, and can surface findings far faster than conventional analysis. Das offers two examples.

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