| Abstract Scope |
Microgravity environments profoundly alter physical and biological systems by minimizing buoyancy-driven convection and sedimentation, leading to novel material morphologies and cellular behaviors. This talk reviews the physical-chemistry principles of microgravity and its impact on material and life science experiments, showcasing comparative images of structure formation under gravity versus microgravity conditions. We present machine-learning informed models that quantify the “Delta-to-Gravity™”—the deviation in key physical properties due to microgravity relative to terrestrial benchmarks—achieving accuracies above 80% across alloys, glasses, semiconductors, polymers, and crystals. We then review how these ML-driven insights are key to linking robust data collection with statistical rigor and enable optimization of experimental conditions and predictive modeling of process outcomes. This shift from descriptive to predictive analytics lays the groundwork for scalable, cost-effective in-space manufacturing and accelerates the commercialization of space-enabled products.
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