Hi Cecília, unfortunately I did not have time to run it myself. But I had a quick look at the files and I think I see what is missing to activate post-update scaling. It should have "scale" in post_update: ``` <post_update>scale</post_update> <post_update_options> <scale>0.25</scale> </post_update_options> ``` Maybe you can try that out, I keep my fingers crossed :)
On Saturday, 6 May 2023 at 12:33:43 UTC+2 Cecília Álvares wrote: > (more pictures lying of distributions in farther steps. > *PS:* In the figure that I showed you before the last message you sent, > step 30 is a weird distribution whilst in the image below it isnt. This is > because I ran the simulation to generate the results shown above with a > smaller amount of atoms in order to get it done faster. That's why they > dont match. In any case, the problem seems to be there regardless of how > big the box is and I do gather enough microstates to do the statistics. > > These curves here and the one from my message above with steps 5,6 and 7 > are from a same simulation containing the same amount of superatoms as in > the file I sent you (1500), and with optimization occuring only in the > angle, so that maybe it is more comparable with your case. > [image: marvin_picture2.png] > > Em sábado, 6 de maio de 2023 às 12:09:52 UTC+2, Cecília Álvares escreveu: > >> Also, here comes potential curves and probability distribution curves >> that are of consecutive steps as you asked. You can see that indeed it is >> going back and forth (at least in these three steps that I prepared). And >> despite understanding how the "pre-factor" idea can help with the cause, I >> dont think that it will really save the day without implementing your codes >> to take care of the onset interpolation. This is because in my case I have >> a lot of onset regions with very tiny values which will result in huge >> values when boltzmann inverted (specially in the g(r), if I go for >> optimizing non-bonded later on, since my material is xrystalline). >> >> You can see that I have these weird peaks that are artificially created >> without the interpolation. I think this is what causing everything to go to >> hell. But I havent managed to use the codes you mentioned in that VOTCA >> branch to give you a feedback. >> >> Anyways, thanks a lot for helping me with all of this ! :) >> [image: pictures_marvin.png] >> >> Em sábado, 6 de maio de 2023 às 11:54:57 UTC+2, Cecília Álvares escreveu: >> >>> Hey Marvin, >>> >>> In fact, maybe I am not setting the scaling factor correctly. I had seen >>> page 56 of VOTCA's manual and understood the factor to be an option <scale> >>> [value you want] </scale> " that should be input inside the >>> <post_update_options> (which is inside <inverse>). I mean, it does say in >>> page 56 of the manual, "*post_update_options.scale *: scale factor for >>> the update (default 1.0)". But now that I took a look comparing the results >>> with and without the factor, instead of simply looking at them two >>> separately, I realize that the curves are exactly the same. So the factor >>> that I am setting is not doing anything.... >>> >>> Sure, I have no problem sharing the files here. If you want to run, I >>> suggest that you try optimizing the bonded potentials (and also only the >>> angle, leaving the bond potential constant throughout the IBI) because it >>> is simpler than doing it for the g(r). I will put it all here in a zip >>> file. Thanks a lot for the offer. >>> PS: In fact, after I re-read your previous email, I realized I had >>> misread the first sentence of your phrase: in my case, I was much more >>> surprised that the optimization of the bonded didnt work. The g(r)s have >>> very complicated shapes, so in fact for me it is more shocking not to be >>> able to reproduce the angle distribution than the g(r) - at least not >>> without the interpolation in the onset regions you mentioned in the paper. >>> >>> Based on your previous suggestion: yesterday I tried narrowing the min >>> and the max to do no accomodate the onset regions and splitting the angles >>> into two types, but upon looking at the results quickly, the iterations are >>> not getting better either, but then I need to analyse this more carefully >>> still. >>> Em sábado, 6 de maio de 2023 às 10:23:49 UTC+2, Marvin Bernhardt >>> escreveu: >>> >>>> Regarding your last picture: I observe that all the even iterations >>>> (10, 30, 100) have spikes at different positions compared to the odd >>>> iterations (15, 19). I really would like to see a plot with consecutive >>>> iterations, i.e. 30-36 to see if it goes forth and back. However, this >>>> should be solved by a scaling factor. Did you try a small scaling factor >>>> like 0.1 or smaller? >>>> >>>> I can offer you to run IBI on my computer and have a closer look. If >>>> you don't want to share the files here, you can also send me a direct >>>> E-Mail. >>>> >>>> Cheers >>>> >>>> On Friday, 5 May 2023 at 11:29:33 UTC+2 Cecília Álvares wrote: >>>> >>>>> PS: sorry, the y axis says g(r) but it is the angle probability >>>>> distribution >>>>> >>>>> Em sexta-feira, 5 de maio de 2023 às 11:24:45 UTC+2, Cecília Álvares >>>>> escreveu: >>>>> >>>>>> (1) indeed I spotted that in some cases they oscilate back and forth >>>>>> around the target distribution (I am attaching a pic as an example). >>>>>> However, this is not something that putting a factor < 1 was able to >>>>>> solve. >>>>>> (2) no, I am working in the NVT ensemble. >>>>>> (3) my thermostat is working: the temperature is quite well >>>>>> equilibrated (no weird spikes). The timestep us also small (I am using >>>>>> 5fs >>>>>> atm). >>>>>> (4) me too :"D >>>>>> >>>>>> R: Regarding the implementation in Votca: I saw that link in the >>>>>> paper. So indeed the interpolation scheme at the onset region that is >>>>>> mentioned in the paper is not implemented in the basic VOTCA >>>>>> installation >>>>>> and we need to use those codes in the branch you mentioned, right? >>>>>> >>>>>> R: Regarding the bonded potentials: Good idea. That is actually >>>>>> something I did not try. I test it. >>>>>> >>>>>> Photo below: evolution of the angle distribution in a scenario in >>>>>> which I am optimizing only one potential (i.e., the angle potential) + >>>>>> using a factor of 0.25 >>>>>> [image: marvin2.png] >>>>>> >>>>>> Em sexta-feira, 5 de maio de 2023 às 09:08:04 UTC+2, Marvin Bernhardt >>>>>> escreveu: >>>>>> >>>>>>> Regarding optimizing non-bonded potentials in crystals, just a list >>>>>>> of things I would check: >>>>>>> Are the distributions at the iterations oscillating around the >>>>>>> target distribution? Or is it rather a slow approach that never gets >>>>>>> there? >>>>>>> Or is it chaotic? >>>>>>> Are you working at constant pressure? If so, I would try at constant >>>>>>> volume. >>>>>>> Is your thermostat working and your time step small enough such that >>>>>>> the temperature is always as expected in each iteration? >>>>>>> Well possible, that it just does not work for your system, however, >>>>>>> I am really surprised, that separating out a single potential in the >>>>>>> whole >>>>>>> system did not work. >>>>>>> >>>>>>> Regarding the implementation in Votca: >>>>>>> It is still in the branch csg/mulit-iie2 at GitHub, you can build it >>>>>>> from there. It has all the methods from the paper. >>>>>>> >>>>>>> Regarding the bonded potentials: >>>>>>> For this situation it helps to restrict the range such that the >>>>>>> problematic regions are not included. Votca should then extrapolate >>>>>>> bonded >>>>>>> potentials linearly. >>>>>>> >>>>>>> >>>>>>> On Thursday, 4 May 2023 at 14:46:49 UTC+2 Cecília Álvares wrote: >>>>>>> >>>>>>>> Let me just ask one more question if I may: >>>>>>>> >>>>>>>> In the section 2.9 of your paper, you talk about how the algorithm >>>>>>>> is set to create an "alternative RDF" which cherishes an interpolation >>>>>>>> in >>>>>>>> the onset region, where the values of the original RDF tend to be very >>>>>>>> small and the region tend to be poorly sampled (which is a quite good >>>>>>>> idea >>>>>>>> btw :) ). In the paper it specifically says range of values that you >>>>>>>> guys >>>>>>>> have had good experience with applying this interpolation procedure. >>>>>>>> In the >>>>>>>> abstract of the paper it says that the methods are implemented in >>>>>>>> VOTCA. Do >>>>>>>> you mean only the specific numerical methods you are using to do the >>>>>>>> iterative process or do you include also other specific things such as >>>>>>>> the >>>>>>>> interpolation protocol you described in section 2.9? >>>>>>>> >>>>>>>> I am asking because in my case, sometimes, the distribution coming >>>>>>>> from the CG simulation ends up having small values that sometimes >>>>>>>> oscillates a bit back and forward in the onset region but the g(r) has >>>>>>>> values a bit larger than the value you mentioned in the paper for >>>>>>>> which the >>>>>>>> itnerpolation is done (1E-4). This causes weird potentials to happen >>>>>>>> which >>>>>>>> could be the reason why everything is going to hell. I am attaching a >>>>>>>> figure to illustrate the point. Is there a way in which I can change >>>>>>>> myself >>>>>>>> the value of the threshold for which I want to apply the >>>>>>>> interpolation? >>>>>>>> Maybe in my case I would need to use values higher than 1E-4. It could >>>>>>>> totally save the day and also make sense: since I am simulating a >>>>>>>> xtalline >>>>>>>> material whose superatoms are allowed less movement compared to a >>>>>>>> liquid, >>>>>>>> the setup of my interpolation needs to be more strict for the IBI to >>>>>>>> work. >>>>>>>> >>>>>>>> [image: marvin.png] >>>>>>>> >>>>>>>> Em quinta-feira, 4 de maio de 2023 às 12:25:09 UTC+2, Cecília >>>>>>>> Álvares escreveu: >>>>>>>> >>>>>>>>> I think at this point I may be ready to just say that indeed IBI >>>>>>>>> cannot be used to converge to a potential that is able to reproduce >>>>>>>>> the >>>>>>>>> structure of xtalline materials (or at least the material I am >>>>>>>>> studying). >>>>>>>>> >>>>>>>>> I've tried >>>>>>>>> (1) diminishing the factor used to update the potential (as you >>>>>>>>> mentioned) and it did not work. >>>>>>>>> (2) updating literally only one potential at a time in the IBI and >>>>>>>>> keeping the others literally constant either in the BI potential or >>>>>>>>> in >>>>>>>>> analytical forms that are able to reproduce perfectly the probability >>>>>>>>> distributions. This would discard the possibility of dependence on >>>>>>>>> the >>>>>>>>> degrees of freedom in that sense that the update of one potential is >>>>>>>>> affecting the distributions related to other potentials. >>>>>>>>> (3) Although the result is not meant to be bin-size-dependent, I >>>>>>>>> tried playing with the bin size of both, the references I am feeding >>>>>>>>> to >>>>>>>>> VOTCA, and of the distributions it is meant to built as the iterative >>>>>>>>> process runs for the different potentials. I thought maybe I was not >>>>>>>>> setting up "proper" bin sizes for the algorithm. >>>>>>>>> (4) I tried dividing the angles lying within each of the two peaks >>>>>>>>> in the initial figure I showed into two different angle types and it >>>>>>>>> also >>>>>>>>> did not work. >>>>>>>>> (5) I read your paper and tried to be more careful with issues >>>>>>>>> that you raised in section 2.9 related to the smoothness of the >>>>>>>>> distributions in the onset region (although VOTCA is supposed to take >>>>>>>>> care >>>>>>>>> of this internally apparently via the extrapolation methodology). >>>>>>>>> Although >>>>>>>>> section 2.10 bring up issues related to IMC, I also tried some more >>>>>>>>> ideas >>>>>>>>> that came to mind from reading that section and it didnt work. >>>>>>>>> (6) I've tried keeping analytical forms for the bonded potentials >>>>>>>>> (I happen to have analytical forms that perfectly reproduce the >>>>>>>>> distributions) and optimize the non-bonded and it also doesnt work. >>>>>>>>> >>>>>>>>> Naturaly, in all cases, together with weird distributions, my >>>>>>>>> potentials are also going to hell as the iterative procedure goes on >>>>>>>>> (which >>>>>>>>> explains why the corresponding distributions are weird). >>>>>>>>> >>>>>>>>> For sure the problem doesnt have to do with the "sharpness" of the >>>>>>>>> probability distribution curves (due to the xtalline material being >>>>>>>>> highly >>>>>>>>> ordered) cause I tried to feed "artificial" target distributions that >>>>>>>>> are >>>>>>>>> wide and thus less step and I dont converge to anything reasonable >>>>>>>>> either. >>>>>>>>> >>>>>>>>> Maybe the shape of the distributions for xtalline materials is not >>>>>>>>> friendly to be used within IBI to converge to a potential, idk... >>>>>>>>> Well.. >>>>>>>>> >>>>>>>>> Em quarta-feira, 26 de abril de 2023 às 15:19:19 UTC+2, Cecília >>>>>>>>> Álvares escreveu: >>>>>>>>> >>>>>>>>>> (In any case let me try your factor idea, some other stuff that >>>>>>>>>> came to mind + finish reading your paper so that maybe I have more >>>>>>>>>> useful >>>>>>>>>> info on the problem) >>>>>>>>>> Em quarta-feira, 26 de abril de 2023 às 14:05:06 UTC+2, Cecília >>>>>>>>>> Álvares escreveu: >>>>>>>>>> >>>>>>>>>>> Indeed, this could be the reason why I have this weird >>>>>>>>>>> non-smoothness in the plots I sent in my 2nd message (the ones >>>>>>>>>>> concerning a >>>>>>>>>>> less coarsened mapping), because indeed in this case I was >>>>>>>>>>> optimizing all >>>>>>>>>>> the three bonded potentials at once. I will try not doing them at >>>>>>>>>>> the same >>>>>>>>>>> time and see if the smoothness-issue improves. >>>>>>>>>>> >>>>>>>>>>> But then this would not explain the issues I had in the original >>>>>>>>>>> post I made, which concerned another mapping (a highly coarsened >>>>>>>>>>> one). If >>>>>>>>>>> the problem was a matter of optimizing more than one bonded >>>>>>>>>>> potential at >>>>>>>>>>> once, I should have had good results when I tried to do IBI only >>>>>>>>>>> for one >>>>>>>>>>> angle type and kept the potential for bonds constant (at a BI >>>>>>>>>>> guess) >>>>>>>>>>> throughout the procedure. But unfortunately my angle distribution >>>>>>>>>>> still >>>>>>>>>>> converges to something ultra weird with 3 peaks. >>>>>>>>>>> >>>>>>>>>>> PS: maybe my last message was too big and maybe it was >>>>>>>>>>> confusing, but the figures I sent in my 1st message and in my 2nd >>>>>>>>>>> message >>>>>>>>>>> are for different mappings. In the first one (let's call it mapping >>>>>>>>>>> A), I >>>>>>>>>>> have only 1 bond type and 1 angle type. For this one I did try >>>>>>>>>>> optimizing >>>>>>>>>>> separately to see if it would fix the problem and yet I reached >>>>>>>>>>> weird >>>>>>>>>>> results. The second message had figures of a less coarsened mapping >>>>>>>>>>> (let's >>>>>>>>>>> call it mapping B) in which I somewhat successfully converge to >>>>>>>>>>> potentials >>>>>>>>>>> that yield more or less rightful distributions (apart from the >>>>>>>>>>> smoothness >>>>>>>>>>> issue). I only brought up the results of the second mapping to show >>>>>>>>>>> that >>>>>>>>>>> the same strategy "worked" for deriving bonded potentials via IBI >>>>>>>>>>> for >>>>>>>>>>> another mapping. Sorry if I made it more confusing! >>>>>>>>>>> >>>>>>>>>>> Em quarta-feira, 26 de abril de 2023 às 08:29:58 UTC+2, Marvin >>>>>>>>>>> Bernhardt escreveu: >>>>>>>>>>> >>>>>>>>>>>> Hey Cecília, >>>>>>>>>>>> >>>>>>>>>>>> Oh ok, then it is probably not the interaction with the >>>>>>>>>>>> non-bonded terms, that causes issues. But I believe something >>>>>>>>>>>> similar is >>>>>>>>>>>> going on, that indeed has something to do with your system being a >>>>>>>>>>>> solid/crystal: >>>>>>>>>>>> IBI is a very good potential update scheme, when the degrees of >>>>>>>>>>>> freedom are well separated. For molecules in liquids, angles and >>>>>>>>>>>> bonds are >>>>>>>>>>>> usually well separated, i.e. changing the potential of one, does >>>>>>>>>>>> not affect >>>>>>>>>>>> the dist of the other much. But multiple occurrences of equivalent >>>>>>>>>>>> DoFs >>>>>>>>>>>> also need to be well separated for IBI to work well. In your case, >>>>>>>>>>>> consider >>>>>>>>>>>> a single angle potential between three beads in the crystal is >>>>>>>>>>>> changed, but >>>>>>>>>>>> all the others are kept constant. It will change the distribution >>>>>>>>>>>> of that >>>>>>>>>>>> angle, but also have effect on different angles. In that case IBI >>>>>>>>>>>> is not >>>>>>>>>>>> providing a good potential update at each iteration. >>>>>>>>>>>> What is happening in detail, I believe, is that the angle >>>>>>>>>>>> potential of all angles is updated by IBI, but this leads to an >>>>>>>>>>>> “overshoot”. The next iteration, IBI tries to compensate, but >>>>>>>>>>>> overshoots >>>>>>>>>>>> again in the other direction. You can easily test if this is what >>>>>>>>>>>> is >>>>>>>>>>>> happening, plotting even and uneven iterations separately, i.e. >>>>>>>>>>>> compare a >>>>>>>>>>>> plot at iterations 10, 12, 14 with 11, 13, 15. >>>>>>>>>>>> This has happened to me before with ring molecules, where the >>>>>>>>>>>> situation is similar. A possible solution is to scale the update, >>>>>>>>>>>> by some >>>>>>>>>>>> factor between 0 and 1 (I'd try 0.25). >>>>>>>>>>>> >>>>>>>>>>>> Also test this for the bond potential, I guess this is >>>>>>>>>>>> happening there too, otherwise it should converge within ~20 >>>>>>>>>>>> iterations. >>>>>>>>>>>> >>>>>>>>>>>> Greetings, >>>>>>>>>>>> Marvin >>>>>>>>>>>> On Tuesday, 25 April 2023 at 10:25:59 UTC+2 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] >>>>>>>>>>>>>>> >>>>>>>>>>>>>> -- Join us on Slack: https://join.slack.com/t/votca/signup --- You received this message because you are subscribed to the Google Groups "votca" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. To view this discussion on the web visit https://groups.google.com/d/msgid/votca/d4e80c3b-1948-40b8-8b59-968075205ec9n%40googlegroups.com.
