The list of speakers at the &quot;Machine Learning Methods in Materials Modeling&quot; stream at this recent conference [1] are a few people in the area, though some of those are more predicting properties than making atomic potentials.<p>I chatted to Gabor Csanyi (Cambridge [2]) during the conference, who I think is probably one of the furthest ahead in the area, and they&#x27;ve recently moved away from their gaussian process based methods to kernel methods. With regard to NNs, he seemed of the opinion that CNNs (the more obvious NN model) were too expensive and ultimately unnecessary compared to carefully chosen, physically motivated kernels. I have to admit I didn&#x27;t quite understand everything he presented, and I can&#x27;t seem to find a recent publication, but I&#x27;m sure there&#x27;s one out there.<p>Despite enthusiastic presentations with lovely results, I suspect from the slow progress in this area that transferability is the main problem plaguing these methods. You want a local atomic potential which doesn&#x27;t depend on its environment beyond a certain radius, sort of like a convolutional kernel, but a lot of this sort of materials modelling&#x2F;quantum chemistry is pretty inherently delocalised. Machine learning isn&#x27;t magic and ultimately has to reflect and represent the underlying physics.<p>[1] <a href="http:&#x2F;&#x2F;nano-bio.ehu.es&#x2F;psik2015&#x2F;programme.html" rel="nofollow">http:&#x2F;&#x2F;nano-bio.ehu.es&#x2F;psik2015&#x2F;programme.html</a><p>[2] <a href="https:&#x2F;&#x2F;camtools.cam.ac.uk&#x2F;wiki&#x2F;site&#x2F;5b59f819-0806-4a4d-0046-bcad6b9ac70f&#x2F;publications.html" rel="nofollow">https:&#x2F;&#x2F;camtools.cam.ac.uk&#x2F;wiki&#x2F;site&#x2F;5b59f819-0806-4a4d-0046...</a>