The motivation for this report
In 2018, while at Supercomputing in Dallas I had a couple key encounters that profoundly influenced my work path ever since:
FIRST. At Nvidia, I saw a presentation about ANI1, to approximate molecules energy using deep learning. This is a precondition to accurately calculate dynamic and chemical parameters. The result is a dramatic reduction of time to solution, while achieving the same or better accuracy than using the exact numerical method. Five (yes, 5) orders of magnitude faster time-to-solution are demonstrated on a 54 atoms molecule than using Density Functional Theory. The neural network approximates DFT output data. But molecules vary in size while the input to the network must be of constant size, so the authors extended Behler-Parrinello functions and created special vectors that describe the input to the network.
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Topics: Emerging Technologies