Machine learning unravels quantum atomic vibrations in materials
Caltech scientists have developed an artificial intelligence (AI)–based method that dramatically speeds up calculations of the quantum interactions that take place in materials. In new work, the group focuses on interactions among atomic vibrations, or phonons—interactions that govern a wide range of material properties, including heat transport, thermal expansion, and phase transitions. The new machine learning approach could be extended to compute all quantum interactions, potentially enabling encyclopedic knowledge about how particles and excitations behave in materials. The case of phonon interactions is even more complex. These interactions are encoded in multidimensional objects called tensors, generalizations of vectors and matrices in higher dimensions. The complexity of these tensors grows exponentially with the number of particles involved, limiting scientists' understanding of interactions involving three or more phonons. Now, inspired by recent advances in machine lear...
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