Machine learning massively speeds up scouring of periodic table for stable structures
Algorithm investigated 31 million crystal structures with calculations taking seconds instead of hours
An algorithm has been created that can rapidly scan hypothetical crystal structures containing any natural element and find those likely to be stable. The program, which was trained by machine learning on a dataset of 140,000 materials, makes predictions almost as accurately as quantum chemistry simulations and far more efficiently. The researchers searched 31 million potential structures and found 1.8 million stable ones. Such searches could potentially find superior materials for multiple applications.
The Schrödinger equation is unsolvable for chemical systems more complex than the hydrogen atom. Chemists therefore often rely on ab initio simulations of the electron density such as DFT to calculate the properties of multi-electron systems. These can be remarkably accurate, but, in large polyatomic systems, such as crystals, they are extremely computationally costly. ‘As a rule of thumb, DFT can be done for any material with less than 1000 atoms in the unit cell,’ says Shyue Ping Ong of University of California, San Diego, ‘but it scales badly and you cannot do DFT for millions of materials easily, even if they are simple materials.’