Hi.<br /><br />My suggestion is to try to improve your dataset if this happens at the very beginning of your MD. For instance, introduce more configurational space. If, say, this unphysical configuration happens after several ps or steps, then you can take a snapshot of the configuration prior to the crash (10 or 15 steps) and perform a DFT calculation on this structure, then use the result to update your dataset. Well, I can't tell much since I don't know much about your forces and energy RMSE as well as the correlation of the test set. What kind of descriptor are you using, etc.? You see, the input attached is input for the RuNNer, and what I can tell is that this is mode 2, which is the training process. You might want to check what kind of descriptor or NN architecture you are using on the data set production (i.e., mode 1). I hope this will help, best of luck <br /><br />Best<br />MuS<br /><br /><div class="gmail_quote"><div dir="auto" class="gmail_attr">On Monday, January 29, 2024 at 2:37:32 AM UTC+1 Marco Gallo wrote:<br/></div><blockquote class="gmail_quote" style="margin: 0 0 0 0.8ex; border-left: 1px solid rgb(204, 204, 204); padding-left: 1ex;">Hi I want to ask Mosfata Abedi, <div><br></div><div>Questions about the paper that he published titled "J. Chem. Theory Comput. 2023, 19, 7825−7832", </div><div>But somehow his e-mail given in the JCTC is not working.</div><div><div><br></div><div>Email: <a href data-email-masked rel="nofollow">mostaf...@brown.edu</a><br></div></div><div><br></div><div>I am trying to create neural network potential for an aqueous -peptide system, and I trained the model using 1320 structures obtained from cp2+metadynamics, </div><div><br></div><div>But the trajectory that I obtain using the nnp potential is very unstable.</div><div>Any help is appreciated.</div></blockquote></div>
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