https://hackerpaper.kaipfister.de/p/0910.1019/Collected HN comments and submissions linking this paper.enMon, 02 Nov 2026 11:16:22 GMThttps://news.ycombinator.com/item?id=5770641urn:hackerpapers:mention:0910.1019:5770641Sun, 26 May 2013 09:53:12 GMT<p>People have tried applying machine learning to learning many-body potentials in physics for use in speeding up quantum molecular dynamics while maintaining most of the accuracy. What you&#x27;d do is say that the total energy of the system is a sum of local potentials on each atom R, where the input is the local environment of atom R: E = \sum_R e(env(R)) You use some method to create some features env(R) that makes the translational and rotational invariance of e() easy, with a radial cutoff beyond some distance, and then model e somehow. I think the most promising method is Gaussian…</p><p><a href="https://hackerpaper.kaipfister.de/p/0910.1019/">Paper and discussion history</a>. A mention is not an endorsement.</p>physics.comp-ph