Timeline for Metropolis algorithm reduces energy in molecular simulation, but does not decrease euclidean distance
Current License: CC BY-SA 4.0
12 events
| when toggle format | what | by | license | comment | |
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| Jan 12, 2022 at 19:42 | comment | added | Martin - マーチン | Please do not cross-post to/from other sites of the network. I will delete the corresponding post on Chemistry. | |
| Mar 11, 2021 at 4:17 | vote | accept | Squishy | ||
| Mar 9, 2021 at 19:10 | answer | added | dwhswenson | timeline score: 9 | |
| Mar 9, 2021 at 15:59 | comment | added | Squishy | I see. Since temperature may be setting the average geometry, I lowered the simulation temperature to 1 kelvin. It still doesn't seem to change the results in any significant manner i.e distance still increases and energy decreases very similarly to above. Godzilla, if it were sampling high conformational entropy states, would it not show in the energy plot above? I'll try running the simulation for longer and come back with an update! | |
| Mar 9, 2021 at 5:40 | history | edited | Cody Aldaz | CC BY-SA 4.0 |
remove some unnecessary text.
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| Mar 8, 2021 at 23:24 | comment | added | jheindel | The distance will probably also converge before too long. There is effectively a temperature in MCMC, so the system will fluctuate around some average geometry. Depending on what states are accessible, this average geometry may be fairly close to the initial geometry, but the energy and distance will never instantaneously return to the initial values. | |
| Mar 8, 2021 at 20:34 | history | edited | Squishy | CC BY-SA 4.0 |
Added a visualization of simulation (GIF) as per a comment's suggestion.
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| Mar 8, 2021 at 0:32 | comment | added | Godzilla | Also, in this setting MCMC is a sampling, rather than optimization algorithm so you can't expect the MCMC structures to match your initial one. On the contrary, I'd expect the conformation to diverge over time and sample states of high conformational entropy instead | |
| Mar 8, 2021 at 0:27 | comment | added | Godzilla | I think the easiest way to see what's happening is to output a trajectory and visualise it in e.g. PyMOL. Sometimes there could be an error in the analysis, rather than the simulation and visualisation makes the analysis a bit easier and less prone to errors. | |
| Mar 8, 2021 at 0:04 | history | edited | Nike Dattani - No Free Time |
edited tags
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| Mar 7, 2021 at 23:20 | review | First posts | |||
| Mar 8, 2021 at 9:14 | |||||
| Mar 7, 2021 at 23:11 | history | asked | Squishy | CC BY-SA 4.0 |