MIT & Samsung's AI Tools Push Scientific Discovery with 'Physics-Aware' Algorithms
Two innovative AI systems, SCIGEN and PaRS, are pushing the boundaries of scientific discovery by integrating physics into their algorithms. Developed by researchers at MIT and Samsung respectively, these 'physics-aware AI' tools are showing promising results in the fields of quantum computing, energy, and electronics.
The vast space of possible materials makes traditional trial-and-error discovery slow and expensive. To tackle this challenge, MIT's SCIGEN project suggests compounds with exotic properties like topological phases or superconductivity, keeping material suggestions realistic by integrating physics and chemistry priors. Meanwhile, Samsung's PaRS improves accuracy and reduces 'physics violations' in tests on device recipes, filtering AI-generated designs to reject those that break physics.
Both SCIGEN and PaRS reflect a trend towards 'physics-aware AI' for scientific discovery. These early research projects have shown promising initial results, with the potential payoff being significant in various scientific fields. By guiding generative AI to propose physically feasible materials, these tools aim to explore promising regions of the material space more efficiently than manual screening.
SCIGEN and PaRS, developed by MIT and Samsung respectively, are pioneering 'physics-aware AI' for scientific discovery. By integrating physics into their algorithms, these tools are making significant strides in exploring the vast space of possible materials, with potential applications in quantum computing, energy, and electronics. As early research projects, they have shown promising initial results and could pave the way for more efficient and accurate material discovery.
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