Density Functional Theory
Advancing the frontier of quantum chemistry by combining deep learning with Density Functional Theory (DFT) to unlock unprecedented accuracy and scalability in electronic structure simulations.
What is DFT?
Molecules and materials are made of atoms, which are held together by their electrons. These electrons act as a glue, determining the stability and properties of the chemical structure. Accurately computing the strength and properties of the electron glue is essential for predicting whether a chemical reaction will proceed, whether a candidate drug molecule will bind to its target protein, whether a material is suitable for carbon capture, or if a flow battery can be optimized for renewable energy storage.
Unfortunately, a brute-force approach amounts to solving the many-electron Schrödinger equation, which requires computation that scales exponentially with the number of electrons. Considering that an atom has dozens of electrons, and that molecules and materials have large numbers of atoms, we could easily end up waiting the age of the universe to complete our computation unless we restrict our attention to small systems with only a few atoms.
DFT, introduced by Walter Kohn and collaborators in 1964-1965, was a true scientific breakthrough, earning Kohn the Nobel Prize in Chemistry in 1998. DFT provides an extraordinary reduction in the computational cost of calculating the electron glue in an exact manner, from exponential to cubic, making it possible to perform calculations of practical value within seconds to hours.
What is the grand challenge in DFT?
But there is a catch: the exact reformulation has a small but crucial term—the exchange-correlation (XC) functional—which Kohn proved is universal (i.e., the same for all molecules and materials), but for which no explicit expression is known. For 60 years, people have designed practical approximations for the XC functional. The magazine Science dubbed the gold rush to design better XC models the “pursuit of the Divine Functional (opens in new tab)”. With time, these approximations have grown into a zoo of hundreds of different XC functionals from which users must choose, often using experimental data as a guide. Owing to the uniquely favorable computational cost of DFT, existing functionals have enabled scientists to gain extremely useful insight into a huge variety of chemical problems. However, the limited accuracy and scope of current XC functionals mean that DFT is still mostly used to interpret experimental results rather than predict them.
Why is it important to increase the accuracy of DFT?
We can contrast the present state of computational chemistry with the state of aircraft engineering and design. Thanks to predictive simulations, aeronautical engineers no longer need to build and test thousands of prototypes to identify one viable design. However, this is exactly what we currently must do in molecular and materials sciences. We send thousands of potential candidates to the lab, because the accuracy of the computational methods is not sufficient to predict the experiments. To make a significant shift in the balance from laboratory to in silico experiments, we need to remove the fundamental bottleneck of the insufficient accuracy of present XC functionals. This amounts to bringing the error of DFT calculations with respect to experiments within chemical accuracy, which is around 1 kcal/mol for most chemical processes. Present approximations typically have errors that are 3 to 30 times larger.
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