by ForceField Silicon
A native macOS molecular-dynamics workbench: a Metal trajectory viewer, a command-line tool, and an MCP server that lets AI agents inspect trajectories and run LAMMPS simulations as detached background jobs.
Status: pre-release. Not yet distributed; the app is ad-hoc signed (builds run on the building machine only until notarization lands).
| Product | What it is |
|---|---|
| MDEngine.app | Metal viewer — orbit / pan / zoom camera, trajectory timeline with 20%/5% tick marks, CPK element colours, XYZ / extended-XYZ, live settings |
| mdengine-cli | info / export / decimate / run on trajectories and decks |
| mdengine-mcp | MCP stdio server — trajectory tools plus a detached LAMMPS job runner (submit_lammps / job_status / job_log / list_jobs / cancel_job) |
Requires macOS 14+ and Xcode command-line tools. LAMMPS (brew install lammps)
is needed only for run / submit_lammps.
swift build -c release # all three products
./scripts/make_app.sh # assemble + install /Applications/MDEngine.app| Example | What it shows |
|---|---|
lj_melt.xyz / lj_melt.in |
Lennard-Jones argon melt — the minimal smoke test |
fe_oxidation.xyz / fe_oxidation.in |
ReaxFF iron oxidation: a bcc Fe slab meeting O₂ gas, using ffield.reax.Fe_O_C_H from the LAMMPS distribution |
Trajectory readers are safe on in-flight dumps — a file a running simulation is still writing parses to its complete frames, so you can inspect a run mid-flight. Rows with non-finite (NaN/inf) coordinates are dropped.
Both decks are ready inputs for mdengine run and submit_lammps. Bare
force-field names resolve automatically: if $LAMMPS_POTENTIALS is unset,
MDEngine derives it from the LAMMPS install.
mdengine-mcp is a dependency-free MCP stdio server, listed in the
MCP Registry as
com.forcefieldsilicon/mdengine (server.json in this repo). Each release ships a
signed macOS MCP Bundle
(mdengine-mcp-<version>-macos-arm64.mcpb): double-click it to install in Claude
Desktop, or unpack it (mcpb unpack) for any other client. Built from source, register
with Claude Code:
claude mcp add mdengine /path/to/.build/release/mdengine-mcpor in Claude Desktop's claude_desktop_config.json:
{ "mcpServers": { "mdengine": { "command": "/path/to/.build/release/mdengine-mcp" } } }| Tool | Does |
|---|---|
trajectory_info |
Frames, atom counts, per-atom fields, elements, bbox, charge range |
z_profile |
Deposition/oxidation depth analysis: substrate surface plane, probe penetration depths (min/mean/max), at-surface & in-flight counts, bound-probe charge, z histogram |
render_video |
Trajectory → MP4 (H.264) or animated GIF via the Metal renderer: camera angles, stride, cinematic orbit, baked scale-bar/frame annotations, per-element colors/sizes, style: "contrast" auto-visibility |
render_image |
One frame → PNG with the same camera/style options — lets an agent see a simulation state |
export_frame |
One frame → XYZ; charges: true → extended-XYZ with the q column |
decimate |
Keep every Nth frame (final frame always kept) |
submit_lammps |
Detached LAMMPS job: survives the server exiting and machine display-sleep (caffeinate), exit code recorded unattended |
job_status / job_log |
State + live thermo tail / raw log tail |
job_files |
Locate a finished job's outputs (run dir + bookkeeping dir) |
list_jobs / cancel_job |
Registry under ~/.mdengine/jobs/ / SIGTERM a run (state becomes cancelled) |
list_hosts |
Execution hosts from ~/.mdengine/hosts.json and which is the default |
fetch_job |
Pull a remote job's run directory (dumps, data) + logs into the local job dir under results/ |
run_lammps |
Synchronous run for short tests only |
The same jobs are reachable from any MCP client that speaks HTTP — Claude Code, Claude.ai custom connectors, Cursor, Goose — via the hosted endpoint's Streamable HTTP server:
claude mcp add --transport http mdengine-cloud https://api.forcefieldsilicon.com/mcpWindows and Linux. The hosted tier is the supported path on both, and it is the full paid product: every tool below works from Claude Code on Windows exactly as on a Mac. In PowerShell, register at user scope so the server follows you into every folder, then sign in once:
claude mcp add --scope user --transport http mdengine-cloud https://api.forcefieldsilicon.com/mcpStart claude, type /mcp, pick mdengine-cloud, choose Authenticate, and paste the key on the
browser consent page (never into the chat). Decks written on Windows (CRLF line endings) are
accepted as-is. The local viewer, renderer and CPU job runner are macOS-only today; a Windows/Linux
local build is planned and demand decides its order, so say so if you need it.
Sign in when the client asks (OAuth 2.1: a consent page where you paste your API key once; the
client keeps a token, the key stays with you). Scripted clients may instead send the key directly
as --header "Authorization: Bearer mde_YOUR_KEY". The key comes with a prepaid credit pack
(forcefieldsilicon.com/mdengine). initialize and
tools/list work without signing in; tool calls without a credential return 401 with the OAuth
discovery pointer, which is what makes clients offer the sign-in. Tools: account, submit_job (deck inline, ≤ 8 MB),
create_job + start_job (big decks via presigned PUT), job_status, job_log,
job_results, list_jobs, delete_results, cancel_job. Discovery card:
https://api.forcefieldsilicon.com/.well-known/mcp/server-card.json. Server code:
hosted/endpoint/mde_mcp.py.
Troubleshooting the hosted connector:
| Symptom | Cause / fix |
|---|---|
| Client says authentication required / 401 | Use the client's sign-in (OAuth) and paste your mde_… key on the consent page, or add the header Authorization: Bearer mde_…. Keys are issued at purchase and shown once. |
| Consent page says the key was not recognised | Keys are mde_ + 32 hex characters; a revoked key no longer works. mdengine account prints the balance for a saved key. |
| "insufficient balance" | Top up at forcefieldsilicon.com/mdengine; submissions need credit for at least 15 min at the GPU rate. |
| "gpu_runners_open_soon" (503) | Runners are temporarily closed; credits are safe. |
| HTTP 429 | Too many keyless requests from one IP; add the key or slow down. |
job_results says results unavailable |
Files were deleted by delete_results, the run produced none, or the 30-day purge ran. |
Job failed with runner_error / lammps_error |
The deck itself failed; job_results (if present) or job_log holds the LAMMPS error text. |
Job failed with pod_lost / no_capacity |
Infrastructure; not billed. Resubmit. |
Jobs run in the deck's own directory (relative read_data paths work) and
launch with -sf omp -pk omp N so the OPENMP package is actually engaged;
$LAMMPS_POTENTIALS is derived from the LAMMPS install when unset.
The job runner can execute on another machine with the same tool contract:
declare hosts in ~/.mdengine/hosts.json and pass host to submit_lammps
(or set a default). The deck's directory is rsynced up (trajectories,
checkpoints and logs excluded), LAMMPS starts under nohup with its exit code
recorded remotely, and job_status / job_log / job_files / cancel_job
work unchanged; fetch_job brings results back for trajectory_info,
z_profile and the renderers.
{
"default": "gpu1",
"hosts": {
"gpu1": {
"ssh": "me@gpu1.example.net",
"workdir": "~/mdengine-jobs",
"lmp": "/usr/local/bin/lmp",
"threads": 8,
"launch": "{lmp} -in {input} -k on g 1 -sf kk -pk kokkos newton on neigh half -log {log}"
}
}
}ssh is anything ssh accepts (key-based, non-interactive); lmp must be an
absolute path (login PATH is not available over ssh); launch is optional —
the default is the OpenMP form, and a GPU host is simply one whose template
carries the KOKKOS flags. A remote deck must be self-contained within its own
directory. The same trust model as local runs applies, on the remote machine.
macOS 14+ (Apple Silicon or Intel). Trajectories are loaded whole into memory — files over 2 GB are refused with guidance to decimate or split first.
Running a LAMMPS input executes whatever the deck says. LAMMPS decks are
programs, not data — they can invoke arbitrary shell commands (LAMMPS has a
literal shell command). Treat a deck from someone else exactly like a shell
script: read it before running it.
This applies doubly to the MCP server: an AI agent connected to
mdengine-mcp can submit decks, and a submitted deck runs with your user's
full privileges on this machine. That is the same trust model as any local
dev tool (an agent that can run make can run anything), but be deliberate
about which decks — and which agents — you hand to the job runner. Sandboxed
execution (containers, no network, resource caps) is how a future hosted tier
makes running untrusted decks safe; the local server does not sandbox.
The local tools collect nothing; see PRIVACY.md, which also covers the hosted GPU tier.
MIT © Gitinama Inc. (d/b/a ForceField Silicon)