Fun lil paper. One of the most frustrating things about being a theoretical chemist is when someone asks you to calculate something which is not physically well-defined (such as a bond energy). Neural networks can help us solve these problems, by learning the same thing the person is asking for, a number to match a heuristic intuitive concept. ArXiV for details:

https://arxiv.org/pdf/1703.08640.pdf

The K Supercomputer has been operating since 2011, drawing 12.6 MW of power for ~88 thousand cores. It sits at the end of a rail-line with it’s own stop, in a shrine-like building next to a zoo. May not be the newest thing, but it still kinda makes a scientist wanna go ‘derp’.

Just returned from New Orleans where our work was presented at the APS, next week headed out to Kobe, for the RIKEN AICS workshop talking about machine learning and then it’s back to the bay for the ACS meeting.