Name: Rapid in-silico Battery Electrolyte Electrochemical Reaction Generation using 3T-VASP Multi-Scale Energy Minimization (Video) | JoVE Journal
Uploaded: 2025-08-22T13:50:00.000+00:00
Duration: 5 min 37 s
Description: Discover rapid in-silico battery electrolyte electrochemical reaction generation using 3T-VASP multi-scale energy minimization. This computational method reduces DFT steps for modeling lithium ion pathways and SEI formation.

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5 minutes

This work is supported by funding from the &#34;Pioneer&#34; and &#34;Leading Goose&#34; R&#38;D Program of Zhejiang, grant number 2025C01222. The computational work was funded by Tencent and performed using the Tencent Elastic First-principles Simulation (TEFS) platform in the Tencent Cloud computing system.

1. Setting up a computing environment for running 3T-VASP
NOTE: It is recommended to use Linux27 for the following steps.
<ol>
	<li>Set up the conda environment28 dedicated to running 3T-VASP.
	<ol>
		<li>Install conda (such as miniconda) on the Linux machine by following the official instructions (refer to the Table of Materials for the link).</li>
		<li>Create a new 3T conda environment, activate it, and install git by running the following commands: 
		conda create --name 3T python=3.11 
		conda activate 3T 
		conda install git -c conda-forge</li>
		<li>Clone the 3T-VASP github repository and enter the directory 
		git clone https://github.com/jpmailoa/External_3T.git 
		cd External_3T</li>
		<li>Install mamba to manage conda dependencies, and install the required libraries for the 3T conda environment: 
		conda install mamba -c conda-forge 
		mamba install --file requirements.txt -c pytorch -c conda-forge -c rdkit</li>
	</ol>
	</li>
	<li>Install the software GROMACS in the 3T environment.
	<ol>
		<li>Install GROMACS in the 3T conda environment (the installation instructions can be found in Table of Materials). 
		NOTE: If the user does not plan to process any new molecule into a .pkl format (see step 2.3) and can work with existing molecules that have been previously processed (in the form of .pkl file), this GROMACS installation (step 1.2) may be skipped.</li>
	</ol>
	</li>
	<li>Install the modified version of InterMol in the 3T environment.
	<ol>
		<li>Follow these instructions to install a bugfix version of InterMol: 
		cd utils/Convert_Gromacs_LAMMPS/InterMol 
		python setup.py build 
		python setup.py install 
		cd ../../.. 
		NOTE: If the user does not plan to process any new molecule into a .pkl format (see step 2.3) and can work with existing molecules which have been previously processed (in the form of .pkl file), this InterMol installation (step 1.3) may be skipped.</li>
	</ol>
	</li>
	<li>Specify VASP software path in 3T-VASP code.
	<ol>
		<li>Install the VASP software on the 3T environment. The instructions and tutorials can be found in Table of Materials.</li>
		<li>Open the file 'utils/calculator_3T_VASP.py' and check the 'run_VASP' function. Look for the following default line in the Python script: 
		os.system('nohup mpirun -n '+n_gpu+' --allow-run-as-root ~/software/vasp.6.2.1/bin/vasp_std')
		<ol>
			<li>The above default line should be modified to the OS system call which points to the user's VASP executable and specifies the computing resources, for example: 
			os.system('mpirun -np 2 /path/to/vasp/bin/vasp_std')</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Ensure that necessary non-Python third-party libraries are available in the 3T conda environment by running these commands in the Linux terminal (or install if necessary): 
	gmx 
	wget 
	unzip 
	packmol 
	NOTE: These commands will only be called during the molecule force field format file pre-processing, so if the user is only planning to work with pre-processed molecule .pkl files, the 'wget' and 'unzip' libraries do not need to be installed.</li>
	<li>Perform a test run to ensure that 3T-VASP has been configured correctly.
	<ol>
		<li>Use Python to run either of the following test scripts, which are provided based on a prior publication24: 
		python randomize_3T_bulk_electrolyte_reduction.py 
		​python randomize_3T_bulk_electrolyte_oxidation.py</li>
		<li>A successful execution will generate logs in the output file 'default.log', which can be monitored in a separate Linux terminal (example shown in Figure 2).</li>
	</ol>
	</li>
</ol>
<img alt="Step-time data analysis, numerical results from computational model; shown in sequential list format." class="xfigimg" src="/files/ftp_upload/68854/68854fig2.jpg" data-altupdatedat="1762509930000" data-alt="Figure 2"> 
Figure 2: Example content of a 3T logfile during a test run after a successful installation. The default logfile simply logs the current step number of a 3T cycle and the computing time that has elapsed since the beginning of the cycle. <a href="https://www.jove.com/files/ftp_upload/68854/68854fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
2. Preparing 3T-VASP input files
<ol>
	<li>Prepare periodic box lattice structure in VASP POSCAR file format.
	<ol>
		<li>Name the 3T-VASP lattice files as '&lt;file_name&gt;.vasp' and save them in a subfolder of the 'input' folder (such as 'input/Electrolyte_Reduction' folder). Write the files in the format of a VASP POSCAR file. An example of such PBC lattice POSCAR file with three lithium cations inside it is shown below in Figure 3.</li>
	</ol>
	</li>
	<li>Prepare periodic box lattice structure property override file.
	<ol>
		<li>Name these 3T-VASP lattice structure property override files as '&lt;file_name&gt;.override', place them in the subfolder of the 'input' folder (such as 'input/Electrolyte_Reduction' folder from step 2.1), and write them in the JSON format as a dictionary with the key entry 'movable_group' and 'atom_charge_proximity' (see example in Figure 4).</li>
	</ol>
	</li>
	<li>Prepare molecule structure files in the XYZ file format.
	<ol>
		<li>Name these 3T-VASP molecule structure files as '&lt;file_name&gt;.xyz' (see example in Figure 5) and place them in the 'input' folder. 3T-VASP will check whether the '&lt;file_name&gt;.xyz' has previously been converted into a .pkl file or not, and if not, the molecule will be processed using a free external third party SwissParam force field parametrization webserver29. If force field parametrization is based on another webserver such as LigParGen30 or manual parametrization is desired, please refer to step 2.6.</li>
	</ol>
	</li>
	<li>Prepare template VASP input files to be used during each 3T-VASP step.
	<ol>
		<li>Use the standard VASP input files such as INCAR, KPOINTS, and POTCAR (POSCAR not needed). In the INCAR file, ensure that the number of steps in molecular dynamics runs NSW is either not set or set to the default value of NSW = 0 (example shown in Figure 6). Place these files in a subfolder of 'templates/VASP' folder (such as 'templates/VASP/Electrolyte_Reduction').</li>
	</ol>
	</li>
	<li>Prepare a 3T config file that configures all the other input files. 
	NOTE: This config file should be provided in the form of a JSON list format (with each member of the list being a dictionary block which indicates the settings of one 3T cycle)24 and placed in the 'configs' folder (such as 'configs/Electrolyte_Reduction.json', example shown in Figure 7 below). Unspecified parameters in each cycle will inherit the parameters from the configuration of the previous cycle.</li>
	<li>(Optional) If needed, use manual parametrization or LigParGen webserver to parametrize the molecule force field.
	<ol>
		<li>Use LigParGen or manual parametrization for molecules that cannot be parametrized by SwissParam, such as charged molecules, resonant structures, or radicals. Refer to the additional examples provided for guidance: 
		python example_LigParGen_FF_assignment.py 
		python example_manual_FF_assignment.py</li>
	</ol>
	</li>
</ol>
<img alt="Lithium crystal structure analysis: coordinates data (A); atomic 3D model diagram (B)." class="xfigimg" src="/files/ftp_upload/68854/68854fig3.jpg" data-altupdatedat="1762509930000" data-alt="Figure 3"> 
Figure 3: Example of PBC lattice POSCAR file filled with a few lithium ions. (A) The box size is 14 × 14 × 14 Å3, and there is sufficient empty space to be subsequently packed by electrolyte molecules using PackMol. (B) The PBC lattice file, which is essentially a VASP POSCAR file that can be directly visualized using third-party software such as Visual Molecular Dynamics (VMD)31. <a href="https://www.jove.com/files/ftp_upload/68854/68854fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Chemical data JSON, movable_group array, atom_charge_proximity values, text representation." class="xfigimg" src="/files/ftp_upload/68854/68854fig4.jpg" data-altupdatedat="1762509930000" data-alt="Figure 4"> 
Figure 4: Example of PBC lattice property override file. This dictionary describes the property modifications which should be applied on atoms inside the PBC lattice. The example nested list entry for the key ‘movable_group’ means that Lithium atom 0,1,2 are all individual groups of atoms which can move independently during the 3T simulation. In addition to that, partial charge value of +0.5 are applied on each Lithium atom using the key ‘atom_charge_proximity’. <a href="https://www.jove.com/files/ftp_upload/68854/68854fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Molecular structure visualization; XYZ coordinates from Avogadro, 3D molecular model diagram." class="xfigimg" src="/files/ftp_upload/68854/68854fig5.jpg" data-altupdatedat="1762509930000" data-alt="Figure 5"> 
Figure 5: Example of a molecule XYZ file. (A) This file is in the format of a standard molecule XYZ file containing just the atom elements and coordinates. (B) This molecule XYZ file can be directly visualized using third-party software such as VMD. <a href="https://www.jove.com/files/ftp_upload/68854/68854fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="VASP input parameters, electronic relaxation, ionic relaxation, convergence, DOS settings." class="xfigimg" src="/files/ftp_upload/68854/68854fig6.jpg" data-altupdatedat="1762509930000" data-alt="Figure 6"> 
Figure 6: Example of an acceptable 3T template VASP INCAR file. Most settings in this file should be tuned based on the user's needs. The only requirement for 3T-VASP relaxation is that NSW value is either not set or set to NSW = 0. <a href="https://www.jove.com/files/ftp_upload/68854/68854fig6large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Electrolyte Reduction Simulation; JSON code structure; VASP, 3T-FF cycles, Li-ion components; diagram." class="xfigimg" src="/files/ftp_upload/68854/68854fig7.jpg" data-altupdatedat="1762509930000" data-alt="Figure 7"> 
Figure 7: Example of 3T config file. Each element in the list (a block) represents the settings of a 3T cycle ('mode' determines whether the cycle is a 3T-FF or a 3T-VASP minimization). When a key is not specified in a block's dictionary, it means the key-value pair from the previous block will be used instead. 'lattice_poscar' specifies the location of VASP-style lattice structure POSCAR file ('file', step 2.1) and its override file ('override', step 2.2, if any is available). 'molecule_xyz' specifies all the additional molecule species we would like to add into the PBC box, with 'file' specifying the molecule XYZ file location and 'count' specifying the number of such molecule species to be added into the PBC box. 'mode' specifies either 'FF' (3T-FF mode) or 'VASP' (3T-VASP mode). 'n_epoch' specifies the number of minimization steps which should be done in that 3T cycle. 'out_tag' specifies the name which should be used for that cycle's associated log files (and hence the user should give the cycles unique names to prevent accidentally overwriting important log or output files). 'print_freq' specifies how frequently the output log file should be written to (1 = output to log file on each step). When 3T-VASP mode starts being used, the user should further specify the folder location of the VASP template files in the 'VASP_template' key of the 'lattice_poscar' dictionary. <a href="https://www.jove.com/files/ftp_upload/68854/68854fig7large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
3. Run 3T-FF and 3T-VASP energy minimizations
<ol>
	<li>Perform single 3T-FF/3T-VASP trajectory generation.
	<ol>
		<li>For an example config file from step 2.5 ('configs/Electrolyte_Reduction.json'), run the commands: 
		python 
		&gt;&gt; from main_run_utils import main 
		&gt;&gt; main('configs/Electrolyte_Reduction.json') 
		NOTE: Depending on the user's machine configuration, it may take several hours to finish this trajectory generation (it contains 250 VASP DFT static calls). The user may track the progress by inspecting the content of the 'default.log' file in a separate Linux terminal (step 1.6).</li>
	</ol>
	</li>
	<li>If large-scale trajectory generation is desired, prepare a Python script to automate config file generation and utilization.
	<ol>
		<li>For the large-scale automation script (Figure 8), write a short function (such as 'config_modify_func') to replace specific phrases in the config template file (such as 'configs/Electrolyte_Reduction_template.json'). This will automatically generate new config files to produce different 3T-VASP trajectories. For example, run the following large-scale automation script: 
		python randomize_3T_bulk_electrolyte_reduction.py 
		NOTE: Once the 3T-VASP code has been set up correctly and the simulation has been started, the 3T multi-scale structure minimization will run in the background. Depending on the user's machine configuration, it may take several days to finish the generation of multiple trajectories. This protocol example contains 2500 VASP DFT static calls (10 3T-VASP trajectories), which will be finished within 30-40 h on a compute node with 4 Nvidia V100 GPUs. As mentioned in step 3.1.1, the overall progress can be tracked by checking the content of the 'default.log' file in a separate Linux terminal. 3T-VASP will also periodically dump the output trajectory, which can be reviewed in real time. In the config file example (Figure 7, step 2.5), the 'out_tag' field in a 3T block describes the name of the output files where the results from that 3T cycle will be written. For example, 'out_tag' value of 'FF_step3' means that the entire cycle's atomic trajectory will be written onto 'FF_step3.xyz' (in the form of multi-frame XYZ file format) while the energy calculated by the built-in 3T force field calculator of the 'FF' mode (or DFT energy calculated by VASP in the 'VASP' mode) will be written onto 'FF_step3_outE.txt' (the energy unit in this file is in Kilocalorie per mole [kcal/mol]).</li>
	</ol>
	</li>
</ol>
<img alt="Electrolyte reduction code using Python for config template modification, showing molecule insertion process." class="xfigimg" src="/files/ftp_upload/68854/68854fig8.jpg" data-altupdatedat="1762509930000" data-alt="Figure 8"> 
Figure 8: Example of 3T-VASP trajectory generation automation script. A template config file needs to be provided as the 'tag' input to the 'multiple_runs' function. In addition to that, the number of trajectories to be generated (10) and the function that modifies the content of the template config file to generate new random config files (the 'config_modify_func' function) also need to be provided. <a href="https://www.jove.com/files/ftp_upload/68854/68854fig8large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

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<li>Berendsen, H. J. C., van der Spoel, D., van Drunen, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((GROMACS%3A+A+message-passing+parallel+molecular+dynamics+implementation%5BTitle%5D)+AND+%22Comput+Phys+Commun%22%5BJournal%5D)">GROMACS: A message-passing parallel molecular dynamics implementation.</a> Comput Phys Commun. 91 (1-3), 43-56 (1995).</li>
<li>Shirts, M. R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Lessons+learned+from+comparing+molecular+dynamics+engines+on+the+SAMPL5+dataset%5BTitle%5D)+AND+%22J+Comput+Aided+Mol+Des%22%5BJournal%5D)">Lessons learned from comparing molecular dynamics engines on the SAMPL5 dataset.</a> J Comput Aided Mol Des. 31 (1), 147-161 (2017).</li>
<li>Linux kernel source tree. , 
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<li>Zoete, V., Cuendet, M. A., Grosdidier, A., Michielin, O. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((SwissParam%3A+A+fast+force+field+generation+tool+for+small+organic+molecules%5BTitle%5D)+AND+%22J+Comput+Chem%22%5BJournal%5D)">SwissParam: A fast force field generation tool for small organic molecules.</a> J Comput Chem. 32 (11), 2359-2368 (2012).</li>
<li>Dodda, L. S., De Vaca, I. C., Tirado-Rives, J., Jorgensen, W. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((LigParGen+web+server%3A+An+automatic+OPLS-AA+parameter+generator+for+organic+ligands%5BTitle%5D)+AND+%22Nucleic+Acids+Res%22%5BJournal%5D)">LigParGen web server: An automatic OPLS-AA parameter generator for organic ligands.</a> Nucleic Acids Res. 45 (W1), W331-W336 (2017).</li>
<li>Humphrey, W., Dalke, A., Schulten, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((VMD%3A+visual+molecular+dynamics%5BTitle%5D)+AND+%22J+Mol+Graph%22%5BJournal%5D)">VMD: visual molecular dynamics.</a> J Mol Graph. 14 (1), 33-38 (1996).</li>
<li>Hermes, E. D., Sargsyan, K., Najm, H. N., Zádor, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Sella%2C+an+open-source+automation-friendly+molecular+saddle+point+optimizer%5BTitle%5D)+AND+%22J+Chem+Theory+Comput%22%5BJournal%5D)">Sella, an open-source automation-friendly molecular saddle point optimizer.</a> J Chem Theory Comput. 18 (11), 6974-6988 (2022).</li>
<li>Martinez, L., Andrade, R., Birgin, E. G., Martinez, J. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((PackMol%3A+A+package+for+building+initial+configurations+for+molecular+dynamics+simulations%5BTitle%5D)+AND+%22J+Comput+Chem%22%5BJournal%5D)">PackMol: A package for building initial configurations for molecular dynamics simulations.</a> J Comput Chem. 30 (13), 2157-2164 (2012).</li>
<li>Thompson, A. P., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((LAMMPS-a+flexible+simulation+tool+for+particle-based+materials+modeling+at+the+atomic%2C+meso%2C+and+continuum+scales%5BTitle%5D)+AND+%22Comput+Phys+Commun%22%5BJournal%5D)">LAMMPS-a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales.</a> Comput Phys Commun. 271, 108171(2022).</li>
<li>Landry, C. A., Bergeron, J. G., Mathieu, O., Gering, K. L., Thomas, J. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Density+modeling+of+battery+electrolyte+mixtures%5BTitle%5D)+AND+%22Batteries%22%5BJournal%5D)">Density modeling of battery electrolyte mixtures.</a> Batteries. 11 (2), 44(2025).</li>
<li>Elstner, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Self-consistent-charge+density-functional+tight-binding+method+for+simulations+of+complex+materials+properties%5BTitle%5D)+AND+%22Phys+Rev+B%22%5BJournal%5D)">Self-consistent-charge density-functional tight-binding method for simulations of complex materials properties.</a> Phys Rev B. 58 (11), 7260-7268 (1998).</li>
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The 3T-VASP code is publicly available (https://www.github.com/jpmailoa/External_3T), and it is offered as a commercial service in Tencent Elastic First-principle Simulation (TEFS) platform on Tencent Cloud. The authors declare no additional conflict of interest.

The main advantage of the 3T algorithm is its ability to enable fast structure energy minimization, which can easily escape trivial local energy minimums by performing the structure minimization in a hierarchical manner through multi-scale tensor gradient backpropagation. Because of this, the 3T optimizer tends to generate physical low-energy structures in complex systems, which are harder to optimize using other structure energy minimization algorithms24. While the structure transformation and gr...

Investigation of electrochemical reaction pathways in new battery electrolyte compositions is important for identifying methods to improve electric vehicle battery performance and lifetime1. However, such studies are often difficult because battery electrolytes are often made by mixing different components, such as solvent, salt, and additive molecules, which interact with themselves and with different surfaces (cathode and anode electrodes) under different battery operating voltages and temperatures2. In experiments, some of the irreversible electrolyte electrochemical decomposition reactions are easier to investigate because they produce small gas molecule byproducts which are released from the electrolyte solution into the air (correspondingly making it easier to probe using experimental techniques such as gas chromatography3,4,5). However, many of these byproducts are larger molecules or complex organic-inorganic solid mixtures, such as complex organolithium polymer compounds6, which will not be released into the environment as gas molecules. This makes it difficult to study these more complex electrochemical reaction pathways experimentally2.
Ab-initio modeling, such as density functional theory (DFT), is a promising method to study potential electrochemical degradation pathways in the electrolyte in silico7. However, simple DFT energy minimization of complex electrolyte structure (liquid mixture with experimentally reasonable electrolyte component concentrations in a periodic boundary condition box) will mostly fail to generate low-energy structures corresponding to physically meaningful electrochemical reaction byproducts. The DFT-minimized structures typically have higher energy and represent non-physical byproducts that correspond to incorrect electrochemical degradation pathways, because the structures are trapped in local energy minimums during the DFT energy minimization procedure. Methods such as ab-initio molecular dynamics (AIMD)8,9, neural network force field10,11,12,13,14,15,16, or DFT-based metadynamics17,18,19 are often used to explore more diverse electrolyte structures, which may include more meaningful electrochemical reaction byproducts. Computational screening to generate electrolyte components that are predicted to be electrochemically more stable is also often attempted20. However, all these methods have their own drawbacks, such as: requiring large and lengthy computational resources7, large amount of ab-initio training data13 or prior knowledge about the eventual electrochemical reaction byproducts to bias the simulation into specific results21.
We have recently introduced a method to perform ab-initio structure energy minimization in a multi-scale manner by connecting the energy gradients obtained from any ab-initio method, such as DFT atomic forces calculated using Vienna Ab-initio Simulation Package (VASP) software22, with the structure transformation function gradients calculated using PyTorch (3T-VASP)23,24. This approach enables the user to simply set up a simulation box filled with multiple types and counts of electrolyte component molecules and run an external atomic energy and force calculator (such as a classical force field or VASP DFT). The structure energy minimization is then automatically performed in a multi-scale manner, which respects the natural structure transformation modes of the relevant molecules (translations and rotations of different segments of the molecule). Because a 3T-VASP simulation is typically 'completed' within 100-150 static DFT calls while generating physically meaningful reaction byproducts (many of these were experimentally observed in prior literature)24, it is possible to run many different ensembles of 3T-VASP trajectories to generate potentially meaningful electrochemical reaction pathways and byproducts in the electrolyte for further computational or experimental investigation. The principles and math behind the multi-scale gradient structure minimization have been discussed in depth in a prior publication24. In this work, we aim to further detail the 3T-VASP software architecture and computational workflow, the setup of the computing environment necessary to run 3T-VASP, the preparation of input files for 3T-VASP simulation of new systems, the monitoring of 3T-VASP simulations, and the example of output trajectories.
First, it is necessary to understand the overall architecture of the 3T-VASP software, as shown in Figure 1. 3T-VASP works by identifying and exploiting the natural structure transformation modes in the molecule (because these are structure transformations that are easier for the molecules to perform while maintaining relatively low ab-initio structure energy)24. While it is possible to manually program these structure transformation modes for each new molecule species (it is cumbersome to do so), 3T-VASP offers to automate this process for the user by exploiting the information available in the classical force field file representing the molecule24. Once these structure transformation modes are determined for the desired electrolyte molecule components, the user needs to specify the periodic boundary condition box and its lattice vector parameters (the box may originally be empty, or partially filled with individual cations or electrode surface) to be further filled with desired types and numbers of other electrolyte molecules (such as solvents, salt ions, additives). The 3T algorithm will then use either classical force field (a built-in classical force field calculator is included with 3T-VASP) or the computing environment's VASP software to calculate atomic energy and forces to perform multi-scale structure transformation, which can minimize the structure energy in a modest number of static DFT calls24. This computational cost reduction makes it possible to repeat the electrochemical reaction trajectory and byproduct generation for large number of initial structures.
<img alt="Molecular optimization process diagram with file conversion and structure packing; uses force field and VASP." class="xfigimg" src="/files/ftp_upload/68854/68854fig1.jpg" data-altupdatedat="1762509930000" data-alt="Figure 1"> 
Figure 1: Computational workflow of 3T-VASP. (A) Preprocessing of 3T-VASP input structures (periodic boundary condition lattice and small molecules). The small molecule force field parametrization can be done either automatically using SwissParam webserver, manually modified after LigParGen webserver parametrization, or loaded from a cache based on previously finished parametrization. The micro and macro group segmentation of 3T algorithm is done automatically by parsing the molecule substructures based on their rotatable bonds. The PBC lattice input file is in the format of VASP POSCAR file and should be designed to have enough empty physical space to pack the desired number of input molecules. The PBC lattice can be an empty box filled with a few ions or a surface structure with some empty space. (B) The desired types and numbers of small molecules are put into the empty space in the PBC lattice using PackMol. (C) Multi-scale gradient 3T structure minimization using built-in force field calculator (3T-FF, mostly to enable liquid dispersion and create a physically reasonable initial structure for 3T-VASP without allowing for chemical reactions). (D) Multi-scale gradient 3T structure minimization using external software such as the VASP calculator (3T-VASP enables electrochemical reactions to proceed). <a href="https://www.jove.com/files/ftp_upload/68854/68854fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Various open-source external software, such as GROningen Machine for Chemical Simulations (GROMACS), a classical force field-based molecular dynamics software, which is useful for running large-scale molecular dynamics simulations25 and InterMol, a Python library codebase developed by Shirts et al. for molecular dynamics file format conversions26, are utilized to enable automated molecule force field parametrization and rotatable bond extraction. VASP is used to enable electrochemical reaction during the ab initio phase (3T-VASP cycles). The steps needed to install these dependencies are presented in this article.

<table><tbody><tr><td>Conda</td><td>Conda</td><td></td><td>The official instructions to install Conda on the Linux machine can be found in: https://docs.conda.io/projects/conda/en/stable/user-guide/install/linux.html&amp;nbsp;</td></tr><tr><td>GROMACS software</td><td>Biophysical Chemistry Department, University of Groningen</td><td>2021.3</td><td>Gromacs executable release from August 18, 2021. Standard executable in the newest version should work as well, although not tested yet. The installation instructions for GROMACS in the 3T conda environment can be found in this link: https://manual.gromacs.org/documentation/2021.3/download.html and https://manual.gromacs.org/2021.3/install-guide/index.html</td></tr><tr><td>InterMol software</td><td>Shirts Group, University of Colorado Boulder</td><td>External_3T version</td><td>The original InterMol code developed by Shirts group has a bug associated with Gromacs -&gt; LAMMPS force field conversion. The InterMol version included in External_3T Github distribution is required to fix this bug.</td></tr><tr><td>JoVE article input files</td><td>Tencent Quantum Laboratory</td><td></td><td>Input file examples for this JoVE manuscript available from the 3T-VASP Github page at https://www.github.com/jpmailoa/External_3T</td></tr><tr><td>JoVE article scripts</td><td>Tencent Quantum Laboratory</td><td></td><td>Python scripts for this JoVE manuscript available from the 3T-VASP Github page at https://www.github.com/jpmailoa/External_3T</td></tr><tr><td>VASP software</td><td>Computational Material Physics, Dept. of Physics, University of Vienna</td><td>vasp.6.2.1</td><td>VASP executable release from May 17, 2021. Standard parallel VASP executable in the newest version should work as well, although not tested yet. Instructions and tutorials for VASP can be found in these links: https://www.vasp.at/&lt;br/&gt; https://www.vasp.at/tutorials/latest/&amp;nbsp;&amp;nbsp;</td></tr><tr><td>VMD software</td><td>Theoretical and Computational Biophysics Group, University of Illinois at Urbana-Champaign</td><td>1.9.3</td><td>Standard VMD executable in the newest version.</td></tr></tbody></table>

rapid in-silico battery electrolyte electrochemical reaction generation using 3t-vasp multi-scale energy minimization

Rapid in-silico Battery Electrolyte Electrochemical Reaction Generation using 3T-VASP Multi-Scale Energy Minimization

Electrolytes are important components in lithium-ion batteries. However, battery degradation due to irreversible electrochemical reactions in the electrolyte can consume electrolyte molecules and severely reduce its effective operation lifetime. It is hence important to study the electrochemical reaction pathways in the battery electrolyte to further improve lithium-ion battery reliability. Unfortunately, it is difficult to experimentally study the exact electrolyte electrochemical reaction pathways in the electrolyte, which usually contains many chemical species (different solvents, salts, additive molecules, and electrode interfaces). These reaction byproducts may form complex solid electrolyte interphase (SEI) on the electrolyte-electrode interface (LiF, Li2CO3, Li2O, LiOH, organolithium compounds, etc), which are more difficult to analyze, instead of being released as the easier-to-analyze small gas molecules. Our recent publication on the tiered tensor transform technique in combination with density functional theory software (tiered tensor transform- Vienna Ab-initio Simulation Package (VASP), or 3T-VASP) enables the practical usage of the ab-initio approach to generate physically meaningful electrolyte electrochemical reaction byproducts within just 100-150 DFT steps. In this work, we describe the inner workings of the 3T-VASP code (publicly available on Github) in greater detail, along with the simulation preparation steps needed to correctly set up the 3T-VASP workflow for new electrolyte systems of interest beyond the examples presented in the original 3T-VASP publication.

If the 3T energy minimization is set up correctly, one should observe a gradual dispersion of electrolyte molecules inside the PBC box during the 3T-FF phase, without any chemical reaction happening (Figure 9A). It is important to note that 3T-FF relies on classical force field energy, so it is necessary that the force field parameters are good enough to ensure that molecules maintain their correct geometries and that the 3T trajectory neither collapses (inter-species attraction too strong) ...

The 3T-VASP framework combines hierarchical structure transformation with ab initio multi-scale gradients to significantly reduce the number of steps needed to escape local energy minima and model electrochemical reactions. This protocol presents a method for generating electrochemical reaction byproducts for various electrolyte component combinations using only 100-150 static DFT calculations.

Electrolytes are important components in lithium-ion batteries. However, battery degradation due to irreversible electrochemical reactions in the ...

This research uses 3T-VASP to start a lithium ion battery electrolyte reaction pathways, analyzing byproducts and SEI to tackle experimental challenges and set up workflows for new systems. Existing experimental and computational methods struggle to study process electrolyte reaction pathways due to the many types and hard to analyze complex SEI. Ab-initio methods, like DFT, are slow for finding real electrochemical byproducts, needing many steps.This paper fills the gap with a fast, multi-scale approach that use far fewer DFT steps. To begin, create a new Conda environment named name 3T with Python version 3.11 by entering the appropriate command in the terminal. Activate the 3T environment using the activate command.Then use conda to install git from the conda-forge channel. Clone the 3T-VASP github repository using the URL. Then enter the cloned repository directory named External_3T.Use Conda to install mamba from the conda-forge channel. Then use mamba to install the required libraries for the 3T conda environment. Next, install the software GROMACS and InterMol within the 3T environment.After installing the modified version of InterMol in the 3T environment, open the Python script file named calculator_3T_VASP. py located in the utils directory using a code editor. Scroll to the run_VASP function and locate the default OS system call used to execute the VASP software.Then modify the command line to reflect the path of the user's VASP executable and specify the computing resources as needed. In the Linux terminal, verify that non-Python third-party utilities required by 3T are available by executing the commands for gmx, wget, unzip, packmol. To test if 3T-VASP has been configured correctly, use Python to run randomize_3T_bulk_electrolyte_reduction.py from the terminal. Open another terminal to monitor the output file named default. log as the simulation runs.Name the 3T-VASP lattice file using the vasp convention and save each file into an appropriate subfolder inside the input directory. Create override files for each periodic box lattice structure with the file extension.override. Place them in the same subfolder under the input directory as the corresponding VASP files.Then save these files as JSON dictionaries with the required keys movable_group and atom_charge_proximity. To generate molecule structure files in the XYZ format, name them using the xyz pattern and place these files directly inside the input folder. Prepare the template VASP input files required for each 3T-VASP step, including INCAR, KPOINTS, and POTCAR files.In the INCAR file, ensure the molecular dynamics step count parameter NSW is either omitted or explicitly set to zero. Next, prepare a 3T configuration file that references and integrates all the created input files. To perform a single 3T-FF or 3T-VASP trajectory generation, launch Python in the terminal.Import the main function from the main run utils module. Execute the trajectory generation using a specific configuration file. Monitor the progress by viewing the default.log file in a separate terminal. To perform large scale trajectory generation, write a short function to replace specific phrases in the config template file, generating new config files to produce different 3T-VASP trajectories. A gradual dispersion of electrolyte molecules was observed inside the periodic boundary condition box during the 3T-FF phase without any chemical reactions occurring, confirming a stable force field setup as seen across the three snapshots.During the 3T-VASP phase, electrochemical reduction of ethylene carbonate into ethylene and carbonate anion was commonly observed. While a secondary reaction producing ethane-1, 2-diolate anion and carbon monoxide occurred less frequently.

rapid-silico-battery-electrolyte-electrochemical-reaction-generation

Research

JoVE Journal

Chemistry

We describe the use of synchrotron X-ray absorption spectroscopy (XAS) and X-ray diffraction (XRD) techniques to probe details of intercalation/deintercalation processes in electrode materials for Li-ion and Na-ion batteries. Both in situ and ex situ experiments are used to understand structural behavior relevant to the operation of...

Characterization of Electrode Materials for Lithium Ion and Sodium Ion Batteries Using Synchrotron Radiation Techniques

Synchrotron-based hard X-ray microtomography is used to image the electrochemical growth of dendrites from a lithium metal electrode through a solid polymer electrolyte membrane.

Failure Analysis of Batteries Using Synchrotron-based Hard X-ray Microtomography

The incorporation of reference electrodes in a Li-ion battery provides valuable information to elucidate degradation mechanisms at high voltages. In this article, we present a cell design that accommodates multiple reference electrodes, along with the assembly steps to assure maximum accuracy of the data obtained in electrochemical measurements.

In Situ Lithiated Reference Electrode: Four Electrode Design for In-operando Impedance Spectroscopy

Here, we present a protocol for typical experiments of soft X-ray absorption spectroscopy (sXAS) and resonant inelastic X-ray scattering (RIXS) with applications in battery material studies.

Elemental-sensitive Detection of the Chemistry in Batteries through Soft X-ray Absorption Spectroscopy and Resonant Inelastic X-ray Scattering

Three-electrode cells are useful in studying the electrochemistry of lithium-ion batteries. Such an electrochemical setup allows the phenomena associated with the cathode and anode to be decoupled and examined independently. Here, we present a guide for construction and use of a three-electrode coin cell with emphasis on lithium plating analytics.

Three-electrode Coin Cell Preparation and Electrodeposition Analytics for Lithium-ion Batteries

This work aims at determining the reaction kinetics of Li-ion battery cathode and anode materials undergoing thermal runaway (TR). Simultaneous Thermal Analysis (STA)/Fourier Transform Infrared (FTIR) spectrometer/Gas Chromatography Mass Spectrometry (GC-MS) were used to reveal thermal events and to detect evolved gases.

Identification and Quantification of Decomposition Mechanisms in Lithium-Ion Batteries; Input to Heat Flow Simulation for Modeling Thermal Runaway

A simulation model specifically supporting the preliminary design of an electro-variable displacement pump (EVDP) is developed and partially verified by experiments. The control performance, life, reliability, etc., can all be evaluated using the proposed model, which covers the main performance requirements under the EVDP preliminary design task.

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump

We summarize a workflow to computationally model a retinal neuron's behaviors in response to electrical stimulation. The computational model is versatile and includes automation steps that are useful in simulating a range of physiological scenarios and anticipating the outcomes of future in vivo/in vitro studies.

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

As an analytical technique, nanoimpact electrochemistry, an increasingly important approach to counting and characterizing nanometer-scale, electro-inactive particles, suffers from poor precision due to the heterogeneous current distributions that arise from its use of ultramicroelectrodes. Outlined here is a generalized approach, termed "electrocatalytic interruption," that enhances precision in such measurements.

Precise Electrochemical Sizing of Individual Electro-Inactive Particles

Here, we present the adaptive simulated annealing method (ASAM) to optimize an approximate quadratic response surface model (QRSM) corresponding to a dusty particulate matter-covered battery heat management system and fulfill the temperature drops back by adjusting the airflow velocities combination of system inlets.

Optimization of An Air-Based Heat Management System for Dusty Particulate Matter-Covered Lithium-Ion Battery Packs

Discover rapid in-silico battery electrolyte electrochemical reaction generation using 3T-VASP multi-scale energy minimization. This computational method reduces DFT steps for modeling lithium ion pathways and SEI formation.

Rapid in-silico Battery Electrolyte Electrochemical Reaction Generation using 3T-VASP Multi-Scale Energy Minimization

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