(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/f4f49984-b62d-4cb4-be13-4af648e93539n%40googlegroups.com.
