I have tried to use VASP's machine learning force field calculation during running molecular dynamics simulation with a supercell including some elements of Ti, O, Cu, etc. It does increase the speed dramatically, but I am wondering if I could use the generated force field in other calculations.
For example, I want to simulate a new supercell including elements of Fe, Ti, O, etc. According to the vasp wiki website, It is permissible to change the number of atoms or even to add new elements, when the training is continued.
My question is:
If I train the force field every time I run a new molecular simulation, no matter what elements I am using. Is it possible to build a force field that suits all elements? Or it is gonna fall into some trap of overfitting?
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3$\begingroup$ ML force fields often transfer poorly. Not only between different elements, which often doesn't work at all, but even between different configurations of the same element. If you do simulations which sample vastly different configurations than ones you've already seen, then the ML will likely do very poorly. I assume VASP will fall back to DFT or whatever model you are using as a reference in that case. I don't see any reason why you shouldn't just always keep training the model when you run simulations, but don't expect that your model will transfer well between very different systems. $\endgroup$– jheindelMay 25, 2022 at 16:24
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2$\begingroup$ Another thing to be aware of is that some ML models have been known to "forget" things over time. That is, if you continue training the same set of weights on vastly different configurations for a very long time, certain types of neural networks will lose accuracy on predictions of configurations it used to predict well. This is dependent on the type of network and lots of other details, so this may or may not be a problem for you, but in principle it could matter. Typically this only happens if the network doesn't have enough parameters to "remember" all the information it's seen. $\endgroup$– jheindelMay 25, 2022 at 16:31
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$\begingroup$ @jheindel. Is there any open-source project that focuses on the transferability of machine-learned force fields? $\endgroup$– JackMay 26, 2022 at 3:41
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1$\begingroup$ The colabfit project (colabfit.github.io/colabfit-tools/html/index.html) aims to provide such a framework. $\endgroup$– Raghunathan RamakrishnanMay 26, 2022 at 7:39
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1$\begingroup$ In addition to the previous remarks, you will have scaling issues: you have to represent all the information in your network, and you need a large enough parameter space to do so. ML models scales poorly when you start introducing new chemical environments/elements, so an “all element forcefield” with the present methods is very impractical. $\endgroup$– GregMay 27, 2022 at 15:20
1 Answer
The comments, especially those by jheindel, seem to be enough of an answer. Tyberius said:
"@jheindel I agree with Nike, I think those comments could reasonably be converted to an answer."
jheindel:
"ML force fields often transfer poorly. Not only between different elements, which often doesn't work at all, but even between different configurations of the same element. If you do simulations which sample vastly different configurations than ones you've already seen, then the ML will likely do very poorly. I assume VASP will fall back to DFT or whatever model you are using as a reference in that case. I don't see any reason why you shouldn't just always keep training the model when you run simulations, but don't expect that your model will transfer well between very different systems."
"Another thing to be aware of is that some ML models have been known to "forget" things over time. That is, if you continue training the same set of weights on vastly different configurations for a very long time, certain types of neural networks will lose accuracy on predictions of configurations it used to predict well. This is dependent on the type of network and lots of other details, so this may or may not be a problem for you, but in principle it could matter. Typically this only happens if the network doesn't have enough parameters to "remember" all the information it's seen."
Raghunathan Ramakrishnan:
"The colabfit project (colabfit.github.io/colabfit-tools/html/index.html) aims to provide such a framework."
Greg:
"In addition to the previous remarks, you will have scaling issues: you have to represent all the information in your network, and you need a large enough parameter space to do so. ML models scales poorly when you start introducing new chemical environments/elements, so an “all element forcefield” with the present methods is very impractical."