Recommendable! Once more Google is pushing the boundaries of our knowledge!
"A team led by scientists at the London-based artificial-intelligence company DeepMind has developed a machine-learning model that suggests a molecule’s characteristics by predicting the distribution of electrons within it. The approach, described in the 10 December issue of Science1, can calculate the properties of some molecules more accurately than existing techniques. ...
The DeepMind team has made probably the most ambitious attempt yet to deploy AI to calculate electron density, the end result of DFT calculations. “It’s sort of the ideal problem for machine learning: you know the answer, but not the formula you want to apply,” ... The team trained an artificial neural network on data from 1,161 accurate solutions derived from the Schrödinger equations. To improve accuracy, they also hard-wired some of the known laws of physics into the network. They then tested the trained system on a set of molecules that are often used as a benchmark for DFT, and the results were impressive ... “This is the best the community has managed to come up with, and they beat it by a margin,” ..."
The DeepMind team has made probably the most ambitious attempt yet to deploy AI to calculate electron density, the end result of DFT calculations. “It’s sort of the ideal problem for machine learning: you know the answer, but not the formula you want to apply,” ... The team trained an artificial neural network on data from 1,161 accurate solutions derived from the Schrödinger equations. To improve accuracy, they also hard-wired some of the known laws of physics into the network. They then tested the trained system on a set of molecules that are often used as a benchmark for DFT, and the results were impressive ... “This is the best the community has managed to come up with, and they beat it by a margin,” ..."
From the abstract:
"... The resulting functional, DM21 (DeepMind 21), correctly describes typical examples of artificial charge delocalization and strong correlation and performs better than traditional functionals on thorough benchmarks for main-group atoms and molecules. DM21 accurately models complex systems such as hydrogen chains, charged DNA base pairs, and diradical transition states. More crucially for the field, because our methodology relies on data and constraints, which are continually improving, it represents a viable pathway toward the exact universal functional."
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