# RSoft **Repository Path**: shandetr/RSoft ## Basic Information - **Project Name**: RSoft - **Description**: No description available - **Primary Language**: Unknown - **License**: Not specified - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2025-04-07 - **Last Updated**: 2025-04-07 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # RSoft 1.0 The following document is an introduction to the RSoft project. This project is meant to be a sharable softness code with slight modifications to structure functions listed in Cubuk et. al. PRL 2015. Original code was written by Robert Ivancic. Please feel free to email ivancic91 [at] gmail [dot] com. Publications with this code are forthcoming. ## Structure function definitions The structure functions currently calculated in this code come in two types: $$G_{R}\left(i; \mu, L, X, Y \right) = \sum_{j} \max \left(e^{-\left(R_{ij}-\mu\right)^2/L^2}-\epsilon_{R}, 0\right)$$ $$G_{A}\left(i; \xi, \lambda, \zeta, X, Y, Z\right)=\sum_{jk} \max \left(e^{-\left(R_{ij}^2+R_{ik}^2+R_{jk}^2\right)/\xi^2}\left(\frac{1+\lambda \cos \left(\theta_{ijk}\right)}{2}\right)^{\zeta}-\epsilon_{A}, 0\right)$$ where most constants are defined in Cubuk et. al. PRL 2015 and $\epsilon_i$ are the error tolerances for the radial (R) and angular (A) structure functions. These values should fall between 0 and 1; typically I have choosen 0.01 or 0.05. This formulation is used for two reasons. First, structure functions are smooth allowing for better interpretation of softness as an energy barrier for rearrangement (Schoenholz et. al. Nat. Phys. 2016). Second, it prevents unreasonable choices of parameters by the user. ## Installation Here, we assume that installed you have (a) python 3 (b) anaconda, and (c) git on your system. **(Step 1)** Go to your home directory and clone this repository. This can be done through the commands: ```bash cd ~ git clone https://github.com/Ivancic91/RSoft ``` **(Step 2)** Create a conda environment. This will ensure that you can use the packages you need without interfering with existing packages. You may do this from the yml environment in the RSoft directory. ```bash cd ~/RSoft conda env create -f RSoft.yml ``` Make sure installation completed correctly by reading the conda output. To activate/deactive the RSoft environment, use the commands: ```bash conda activate RSoft # For MacOS, use `source activate RSoft` conda deactivate ``` **(Step 3)** Link c++ libraries, include the c++ headers, and add ~/RSoft/RSoftPython to your PYTHONPATH. To do this, you can run: ```bash cd ~/RSoft python setup.py source ~/.bashrc ``` Congratulations! You have installed RSoft. ## Tutorial ### Kob-Andersen The following is a tutorial on how to develop a softness field for a 3D Kob-Andersen 80-20 mixture. **(Step 1)** Get data and convert it to an AMBER format. We have already run a LAMMPs simulation of this system with 1000 particles for approximately 1 alpha relaxation time for you. Details about the simulation can be found in the data directory of the Kob-Andersen tutorial. For now, we are simply going to grab the trajectory and use it. ```bash cd ~/RSoft/Tutorial/Kob-Andersen/ cp data/train/run.lammpstrj work/train.lammpstrj ``` Next, we will now copy a script to convert our trajectory to a AMBER (netCDF) file. Note that this script is written only for a Kob-Andersen simulation and for other types of simulations and experiments it may need to be modified. More information about the functions used in this script may be found: https://ivancic91.github.io/LammpsIO/. ```bash cp ~/RSoft/ARCHIVE/PYTHON/ConvDumpToNc_KA.py work conda activate RSoft cd work python ConvDumpToNc_KA.py train.lammpstrj train.nc ``` It is important to note that AMBER style trajectories assume a *constant* number of particles in each frame. This assumption is not limiting however because we can add *NaN* as the x, y, and z coordinate of any particle which is not in the frame. Even though there are a constant number of particles in this example, the script used above performs this operation and can be used as a template for experiments and simulations with differing numbers of particles. To check that the conversion completed correctly you may use the ncdump command which will print the contents of train.nc to the terminal. ```bash ncdump -h train.nc ncdump train.nc conda deactivate ``` **(Step 2)** Evaluate a dynamic quantity for each particle and frame. The traditional dynamic quantities used in softness development are $D^2_{min}$ in mechanically strained systems and $p_{hop}$ in quiescent systems. More information about both of these quantities can be found in [Falk and Langer, Phy. Rev. E (1998)] and [Smessaert and Rottler, Phys. Rev. E (2013)] respectively. Because we are not straining our Kob-Andersen glass we will simply use $p_{hop}$ for this analysis. To do this, we are going to use some pre-written C++ code from the code archive. This code is well-commented and may be modified if necessary. ```bash cd ~/RSoft cp ARCHIVE/STANDARD_ANALYSIS/CalcPhop.cpp RSoft_MAIN.cpp ``` We now will make an executable from RSoft_MAIN.cpp and move it to the work directory. ```bash cd Debug make all mv RSoft.exe ~/RSoft/Tutorial/Kob-Andersen/work/Phop.exe ``` Finally, we will evaluate $p_{hop}$ for every particle at every frame of the training set. This function will cut off a few frames at the beginning and end of the trajectory due to the rearrangement time [tR]. Note that basic usage for this executable can be found by executing the function with no arguments as shown below and detailed usage is shown in the C++ code comments. ```bash cd ~/RSoft/Tutorial/Kob-Andersen/work/ ./Phop.exe # Shows basic usage ./Phop.exe train.nc 10 p p p phop.nc ``` The above code will take around 5 minutes to run. The parameter tR = 10 was taken from Schoenholz et. al. Nat. Phys. 2016. I highly recommend viewing your results using OVITO to visualize your results here: https://www.ovito.org. It may be used to color particles by their $p_{hop}$ values to ensure results are reasonable. **(Step 3)** Generate an RSoftSF file *with* particles evaluated. We now wish to evaluate a set of structure functions for every particle and every frame. In order to do this, we need to tell RSoft which structure functions we want to evaluate. We do this by providing it an RSoftSF file *without* particles evaluated. This file is simply a list of the structure function parameters we wish to use in our softness calculation. You may generate your own RSoftSF file using RSoftPython (https://ivancic91.github.io/RSoft/modules.html#). An example script is available in ~/RSoft/ARCHIVE/PYTHON/MakeRSoftSF.py. In this tutorial, we are simply going to use a pre-made set of structure functions for a bi-dispersed system where the large particle diameter is 1. I highly encourage the use of these "canned" structure function sets because (a) typically unless your system is unusual, these structure functions perform relatively well, (b) use of these sets of structure functions may allow for quantitative comparisons between various simulations and experiments, and (c) it is easy to make mistakes when choosing structure functions that lead to a noisy softness fields or lead to slow calculations (see FAQs below). To get a pre-made set of structure functions, we will copy them from the archive. ```bash cd ~/RSoft/Tutorial/Kob-Andersen/work/ cp ~/RSoft/ARCHIVE/RSoftSF/bidispersed_radial_short.SF.nc KA.SF.nc ``` We can now make an executable to evaluate these structure functions for all particles in phop.nc. There is pre-made, well-documented code in the ARCHIVE to do this. We will simply follow the similar steps we used to make the phop executable. ```bash cd ~/RSoft cp ARCHIVE/RSoft/CalcSF.cpp RSoft_MAIN.cpp cd Debug make all mv RSoft.exe ~/RSoft/Tutorial/Kob-Andersen/work/CalcSF.exe ``` We can now run this executable. Again basic usage for this executable can be found by executing the function with no arguments just as with phop. ```bash cd ~/RSoft/Tutorial/Kob-Andersen/work/ ./CalcSF.exe # Shows basic usage ./CalcSF.exe phop.nc KA.SF.nc p p p phop.SF.nc ``` The above code will take around 1 minute to run. The result, phop.SF.nc is a RSoftSF file in which all of the structure functions listed in KA.SF.nc are evaluated for every particle in phop.nc. Therefore, phop.SF.nc is an RSoftSF file *with* particles evaluated. *NOTE:* even though this is a relatively small file with relatively few structure functions the size of phop.SF.nc is 182M! File sizes for these files can typically run several gigabytes. This is typically not much of a limitation, however, because this file can (and should) be deleted after training has occured. **(Step 4)** Traditional approach to training. We will now attempt to train a softness field using the cannonical method that is well described in Schoenholz et. al. Nat. Phys. 2016. Here, we pick a high cutoff in phop above which we say a particle has "rearranged". We also choose a lower cutoff and a long time period. If a particle is below the lower cutoff for the time period, we say that it has "not rearranged". The idea is to then separate these two sets (which are taken to be sampled to be equal in size) and separate their structure functions with a linear SVM. This is all implemented via the RSoftPython code using sklearn. An example script is provided in the ARCHIVE. One provides this script with an RSoftSF file with particles evaluated as as well as a corresponding AMBER file with some sort of dynamics (phop, D2min, ect) is evaluated for each particle. An example script is provided in the ARCHIVE. ```bash cd ~/RSoft/Tutorial/Kob-Andersen/work/ cp ~/RSoft/ARCHIVE/PYTHON/TrainPlane.py . conda activate RSoft python TrainPlane.py ``` This code will output an RSoftPlane file. This file has everything one needs to create a softness field. You may view the contents of this file using ncdump. ```bash ncdump KA.plane.nc conda deactivate ``` Note that two hyperplanes have been trained; one for type 1 and one for type 2 particles. More information about the training function can be found at https://ivancic91.github.io/RSoft/. **(Step 5)** Testing the softness field. We would now like to test our softness results on a set of independent data. To do this we will calculate a phop and softness field for a set of independent test data. We start by making an executable to calculate softness quickly. ```bash cd ~/RSoft/ cp ARCHIVE/RSoft/CalcSoft.cpp RSoft_MAIN.cpp cd Debug make all cp RSoft.exe ~/RSoft/Tutorial/Kob-Andersen/work/CalcSoft.exe ``` Now let's take some independent "test" data that we generated in LAMMPS, convert it to AMBER format, and evaluate phop on it. ```bash cd ~/RSoft/Tutorial/Kob-Andersen cp data/test/run.lammpstrj work/test.lammpstrj cd work conda activate RSoft python ConvDumpToNc_KA.py test.lammpstrj test.nc conda deactivate ./Phop.exe test.nc 10 p p p phop_test.nc ./CalcSoft.exe phop_test.nc KA.plane.nc p p p soft_test.nc ``` One basic test of a softness field is to plot the probability that a particle is soft given a certain dynamical value, i.e. $P(S>0|p_{hop})$ . This plot (which can be made for both types of particles) typically monotonically increases to some value. We may interpret this value as the maximum predictive strength of softness given a particular set of structure functions and training method. Other simple tests can be found in published results. ```bash cp ~/RSoft/ARCHIVE/PYTHON/PSoftGivenDynamics.py . conda activate RSoft python PSoftGivenDynamics.py conda deactivate ``` This will provide two datasets where the first column is phop and the second is P(S>0|phop). The third column is an error estimate. The data should look roughly like this: ![](Tutorial/Kob-Andersen/data/images/phop_vs_P_soft_given_phop.png) The output of the code gives a couple of quick numbers to quantify the resulting hyperplane as well. An example of the output is below: ```bash TYPE = 1 Percentage of rearranging particles that are soft: P(S>0|R) = 0.724390243902439 Percentage of particles that are soft: P(S>0) = 0.3808097880928355 TYPE = 2 Percentage of rearranging particles that are soft: P(S>0|R) = 0.8434085943190095 Percentage of particles that are soft: P(S>0) = 0.49391019172552975 ``` The idea here is that if the percentage of the rearranging particle that are soft is high, but the total percentage of particles that are soft is low, the quality of the hyperplane is good. Typically, people publish numbers similar to these and you can see how your numbers match up against other people's numbers. In this case, the type 2 particle plane is great, but the type 1 particles have significantly worse accuracy (typically people expect P(S>0|R) ~ 85-95% for simulated data and around 75-90% for experimental data). Poor hyperplane development occurs for one of three reasons. First, the chosen structure functions may be poor descriptors of the particle's local environment. This may mean that you need to add more of the standard structure functions or you may need to add your own structure functions. One common example of this is in polydispersed experimental systems; it's been found that adding the particle's radii as a structure function is usful in these situations. Second, you may not have provided the SVM clear enough examples of rearranging and non-rearranging particles. This could mean that the set of parameters which characterize your dynamics are not very good (for example [tR] in this case), or it could mean that your training parameters are not very good (the rearrangement threshold is too small, the timescale to dictate a non-rearrangement is too short, the non-rearrangement cutoff is tol large). These can be easily tweeked by changing the TrainPlane.py file. Third, you may not have enough rearranging particle examples. Because the hyperplane is a fit to the training data, if you do not have enough training data your hyperplane will be poor. You may test this by building hyperplanes with less data and checking their test-set accuracy. If you have enough training data so that your test-set accuracy has flattened out, you have enough training data. I suspect the problem with this data is that (a) the non-rearrangement timescale is a little short for the type 1 (large) particles (only 1/2 of an alpha-relaxation time) and (b) that the structure functions around these particles are fairly short-ranged. If you want you can try, fixing these problems yourself as an exercise! ## FAQs - **How do I only train/calculate softness in a particular region of my experiment or simulation?** - **How do I determine which structure functions to use?** - **Why is my softness calculation so slow?** ## Documentation - **RsoftTools** : This is a set of python code used extensively to perform the less computationally intensive parts of the softness calculates. Documentation is available here: https://ivancic91.github.io/RSoft/ - **LammpsIO** : This is a set of python code that was re-written in C++ for this project. It allows for quickly inputing and outputing LAMMPS style dump and AMBER files. It is provided during the installation of Rctivate RSoft python TrainPlane.py conda deactivateSoft. Complete documentationis provided here: https://ivancic91.github.io/LammpsIO/ - **AMBER** : A file format based on netCDF that was developed for storing data generated by molecular dynamics simulations. More information about this format is available here: http://ambermd.org/netcdf/nctraj.xhtml - **OVITO** : A molecular dynamics visualization tool. It is an easily downloadable GUI and can be used to visualize AMBER and LAMMPS dump files quickly and painlessly: https://www.ovito.org ## Publications