Machine-learned interatomic potential

Beginning in the 1990s, researchers have employed machine learning programs to construct interatomic potentials, mapping atomic structures to their potential energies. These potentials are generally referred to as 'machine-learned interatomic potentials' (MLIPs) or simply 'machine learning potentials' (MLPs). Such machine learning potentials promised to fill the gap between density functional theory, a highly-accurate but computationally-intensive simulation program, and empirically derived or intuitively-approximated potentials, which were far computationally lighter but substantially less accurate. Improvements in artificial intelligence technology have served to heighten the accuracy of MLPs while lowering their computational cost, increasing machine learning's role in fitting potentials.

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