القائمة
Submit and manage computational jobs on Bohrium (bohrium.com) cloud platform using bohr CLI. Recommended for submitting to Bohrium.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
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| name | bohrium |
| description | Submit and manage computational jobs on Bohrium (bohrium.com) cloud platform using bohr CLI. Recommended for submitting to Bohrium. |
| metadata | {"tools":["run_bash"],"tags":["bohrium","hpc","job-submission","cloud-computing"]} |
Submit and manage computational jobs on Bohrium cloud platform via bohr CLI.
Verify: bohr version
If not installed, run the following command to install it:
curl -fsSL https://bohrium.com/download/bohr | shecho $ACCESS_KEY
If not set, notify the user to provide the access key as an environment variable, or set an environment variable through MatCreator's WebUI.
Tell the users that access key can be found in the Bohrium main dashboard under User Profile -> Access Key.
First, try
echo $BOHRIUM_PROJECT_ID
If not set, then run:
bohr project list --json
# Note the project ID you want to use
And select the first available project ID in the list. If multiple projects are available, notify the users as
the first available project ID may not be the one they want to use, and they had better set the environment variable
BOHRIUM_PROJECT_ID to the desired project ID.
To the users that the project ID can also be found in the Bohrium Cloud dashboard under Projects.
For available machine types and suggestions for choosing machines (Machine SKU reference), see references/bohrium-machines-ref.md.
For submitting, managing and downloading a single job and a batch of jobs,
see example and bohr CLI usage details in references/bohrium-cli-ref.md.
--backward_files to specify outputs — comma-separate values, not making an arraybash script.sh.
This is more reliable and maintainable than long inline --command strings.mpirun -np N where N matches your machine's CPU cores.bohr commands can be slow — because API endpoint can be slow. Wrap commands with timeout 60-120.
When submitting very large directories and files, it may require even longer timeouts. Expect upload speed averaging
~ 1~5 MB/s.bohr <whatever> list commands interactively — without --json keyword, bohr <whatever> list opens
interactive TUI that hangs in non-terminal. Never do that!STDOUTERR (shell stdout/stderr) that comes with logs,
if not redirected to a result file by something like > output.txt 2>&1.--help to explore more options — e.g., bohr job submit --help for all job submission parameters| Issue | Solution |
|---|---|
| Login fails | bohr login again, check credentials |
| Machine not available | Try a different SKU or wait |
| Job stuck in pending | Check quota, try other machines |
| No output files | Check logs for errors, verify command ran |
| Out of memory | Use machine with more RAM |
Generate VASP inputs (INCAR/POSCAR/POTCAR/KPOINTS) via pymatgen.io.vasp.sets for DFT calculations: MPRelaxSet (geometry relaxation), MPStaticSet (SCF), MPNonSCFSet (band structure), MatPESStaticSet (MLFF energy/force labeling). INCAR is driven by pymatgen defaults; the agent only supplies user_incar_settings overrides. Use when the user asks to prepare VASP calculations, run DFT, or generate MLFF training data from structures. Do NOT use for VASP result post-processing / analysis, non-VASP DFT codes, or molecular dynamics — this skill only generates input files and submits jobs.
Deep potential models finetuning and testing using the DeePMD-kit. Use this skill whenever finetuning a Deep Potential (DPA-1 / DPA-2 / DPA-3 / DPA-4) model or running model tests on a dataset. The oldest DP descriptors such as se_e2_a, se_e2_r, and se_e3 are no longer supported. Training from scratch is NEVER advised unless distilling a student model from a teacher model. Multitask fine-tuning is NOT supported.
Skill for running ASE calculations, including energy/force/stress evaluation, molecular dynamics (MD) and structure optimization. Now only use machine-learned force fields (MLFFs) as calculators.
Concept skill for Machine Learning Force Fields (MLFFs). Describes what MLFFs are, the distinction between fine-tuning and distillation, and which tool skills to use. Load this before selecting a specific MLFF framework (DeePMD, MatterSim, etc.).
Concept skill for Molecular Dynamics (MD) simulation. Describes MD ensembles, key parameters, and which tool skills to invoke for MD runs. Use this to understand MD concepts before selecting a specific simulation tool.
Equation-of-State (EOS) benchmark skill — compare DFT, pretrained, and finetuned model E(V) curves to evaluate force-field quality for bulk crystals and simple systems.