Researchers have developed a machine learning framework to simulate the behavior of irregularly shaped particles more efficiently and accurately. This approach, called a “surrogate model,” significantly reduces the computational cost and time needed to predict how these particles interact in different environments. The model has potential applications in industries such as pharmaceuticals and energy, where understanding particle behavior is crucial for optimizing processes like drug delivery and battery performance.
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