Hello Cecilia,

I am trying to follow your procedure of optimizing the bonded potential 
first. I am also using LAMMPS for running all my atomistic simulations.

I am bit confused with removing the non-bonded forces artificially. Are you 
initially running your simulations with all the interactions (bonded + 
nonbonded) present in the system, and then using rerun command to 
artificially remove the non-bonded forces from your system and create a 
trajectory with only the bonded forces.

Greetings,
Sanjeet

On Tuesday, April 25, 2023 at 4:25:59 AM UTC-4 Cecília Álvares wrote:

> Hey Marvin,
>
> Thanks a lot for the reply! 
> I will have a look on the paper right now and do some thinking. In fact, I 
> wanted to test the possibility of optimizing the bonded potentials first 
> and, after its optimization is done, optimize the non-bonded. So basically 
> there is no optimization of non-bonded whatsover being done in my 
> simulation. To build the target distributions, I sampled an atomistic 
> system in which the non-bonded forces were artificially removed. After 
> having a trajectory file of this AA system, I built the corresponding 
> target distributions to be used in VOTCA with csg_stat. For what is worth 
> it, the target distributions of angle and bond don't seem at all weird 
> relative to the "real ones", of when non-bonded forces exist. And then, 
> after having the target distributions, I set up the CG MD simulations 
> within the IBI to have only bonded potential also. So, besides there being 
> no non-bonded potential optimization, there is also no non-bonded forces at 
> all in my CG system. But I dont think this should be a problem, right? It 
> makes sense to entrust the CG bonded potentials to reproduce the target 
> distributions of the AA bonded potentials.
>
> What I did try also, and that is in allignment with your idea, was to set 
> up two IBI runs: (1) one run to optimize *only* the potential for the 
> bonds and keep the angle potential active (in this case the latter comes 
> from a simple BI) and (2) one run to optimize only the potential for the 
> angles and keep the bond potential active (in this case the latter comes 
> from a simple BI). In the case (1) it seems that I converge to a potential 
> for bonds that is quite able to reproduce the corresponding distribution, 
> while in the case (2) I converge more and more to potentials that give 
> super weird distributions (like with three weird peaks, as I showed in the 
> figure above)
>
> Concerning the phase of the system: it is a solid system. More 
> specifically, it is a coarsened grained version of ZIF8 in which the whole 
> repeating unit was assumed to be one bead. I know that IBI has not at all 
> been developed for solids and even further not for MOFs - the goal is 
> actually to derive potentials in the CG level using many different 
> strategies (IBI, FM, relative entropy) and evaluate the results. In any 
> case, I dont think that the fact that my system is a xtalline solid could 
> be the reason why my results are super weird (right?). It seems like such a 
> simple system when in the CG level.
>
> For what is worth it, I am also assessing different mappings. Following 
> the same strategy of optimizing first bonded-potential for a less coarsened 
> mapping (2 beads), I am able to reach less weird results. For example, you 
> can find below the evolution of the corresponding distributions as I 
> perform more iterations for this system (it has one bond type and two angle 
> types). I think there is still a problem since we can see some tendency of 
> the distributions becoming non-smooth as I do more iterations, but the 
> results are definitely less weird.
>
> [image: picture.png]
>
> Em segunda-feira, 24 de abril de 2023 às 20:50:14 UTC+2, Marvin Bernhardt 
> escreveu:
>
>> Hi Cecília,
>>
>> I once encountered similar problems with bonded and non-bonded 
>> interactions. See Fig. 9 of this paper 
>> <https://pubs.acs.org/doi/10.1021/acs.jctc.2c00665>. In short: The 
>> problem was that the potential update of the non-bonded has some influence 
>> on the bonded distribution, and vice versa. But the potential update is 
>> calculated as if they were independent.
>>
>> The fix in my case was to update the two interactions alternately using `
>> <do_potential>1 0</do_potential>` for bonded and `<do_potential>0 
>> 1</do_potential>` for non-bonded interactions. You could try something 
>> similar.
>>
>> Otherwise, is your system liquid? Are there non-bonded interactions that 
>> you are optimizing at the same time?
>>
>> Greetings,
>> Marvin
>>
>> On Monday, 24 April 2023 at 16:56:42 UTC+2 Cecília Álvares wrote:
>>
>>> Hey there,
>>>
>>> I am currently trying to derive bonded potentials of a very simple CG 
>>> system (containing only one bond type and one angle type) using IBI. 
>>> However, I have been failing miserably at doing it: instead of reaching 
>>> potentials that are better and better at reproducing the target 
>>> distributions for the bond and for the angle, I end up having weider and 
>>> weider distributions as I do the iterations. I am posting a plot of the 
>>> bond and angle distributions to give a glimpse on the "weirdness". I have 
>>> already tried:
>>> (1) providing very refined (small bin size and a lot of bins) target 
>>> distributions of excelent quality (meaning not noisy at all) for the bond 
>>> and the angle. Similarly, I have also tried using less refined target 
>>> distributions (larger bin sizes and less amount of bins).
>>> (2) varied a lot the setup in the settings.xml concerning bin sizes for 
>>> the distributions to be built at each iteration from the trajectory file. I 
>>> have tried very small bin sizes as well as large bin sizes.
>>> (3) increasing the size of my simulation box hoping that maybe it was 
>>> all a problem of not having "enough statistics" to build good distributions 
>>> at each iteration within the trajectory file I was collecting from my 
>>> simulations.
>>>
>>> None of these things has worked and I think I ran out of ideas of what 
>>> could possibly be the cause of the problem. Does anyone have any insights?
>>>
>>> I am also attaching my target distributions (this is the scenario in 
>>> which I am feeding target distributions lot of points and smaller bin size) 
>>> and the settings.xml file for what is worth it.
>>>
>>> [image: plots.png]
>>>
>>

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