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This paper discusses the evolution of surrogate potential energy functions in computational chemistry, highlighting their potential to achieve quantum accuracy at force-field speeds. A significant advancement has been the emergence of foundation machine learning interatomic potentials, which eliminate the need for large, system-specific training datasets previously required. The authors suggest that these advancements could lead to the replacement of Density Functional Theory (DFT) as the primary method in computational chemistry within the next decade.
Foundation machine learning interatomic potentials are poised to disrupt computational chemistry by offering quantum accuracy at force-field speeds, potentially replacing DFT as the go-to method.
Computational chemistry has become an indispensable tool for generating data and insights, pervading all branches of experimental chemistry. Its most central concept is the potential energy hypersurface, key to all chemistry and materials science, as it assigns an energy to a molecular structure, the necessary ingredient for reaction mechanism elucidation and reaction rate calculation. Density functional theory (DFT) has been the most important method in practice for obtaining such energies, which is mirrored in the use of high-performance computing hardware. In the last two decades, a new class of surrogate potential energy functions has been evolving with remarkable properties: quantum accuracy combined with force-field speed. Until very recently, their application was hampered by the fact that they needed to be trained on truly large system-specific data sets, generated before a computational chemistry study could be started (in sharp contrast to DFT, which, as a first-principles method, works out of the box, but at a far higher price of computational cost). Very recently, this roadblock has been overcome by so-called foundation machine learning interatomic potentials, which are poised to completely change the way we do computational chemistry, likely prompting us to abandon DFT as the prime method of choice for this purpose in less than a decade.
