1. Technical Capabilities

Containerized Microservices: Delivered as Docker containers, enabling rapid deployment on GPU infrastructure.
RESTful API: Schema-driven HTTP endpoints for seamless integration with laboratory informatics systems (LIMS, ELN, workflow managers).
High Throughput & Batching: Dynamic batching for efficient GPU utilization, supporting large-scale, concurrent workloads.
Async/Agentic Workflows: Supports high-volume, asynchronous request patterns for automated pipelines.
Operational Simplicity: Configurable via environment variables; minimal setup required.

Batched Geometry Relaxation (BGR): High-throughput, batched geometry optimization for molecules and materials.
Batched Molecular Dynamics (BMD): High-throughput, batched MD simulations, supporting NVE, NVT, and NPT ensembles.
Periodic & Non-Periodic Systems: Full support for periodic boundary conditions (PBC) and isolated systems.
Cell Optimization: Variable-cell optimization for periodic systems, with per-request overrides.
Partial Optimization: Per-atom active masks for selective optimization.

Supported Models: MACE, AIMNet2 (multiple variants), TensorNet, and user-supplied custom models.
DFT-D3(BJ) Dispersion Correction: Optional, for improved van der Waals interactions.
Implicit Solvation: AIMNet2-CPCM variant for implicit solvent effects.
External Electric Fields: Can apply during MD simulations.
Custom DFT-D3 Parameters: User-supplied damping parameters.

Input Formats: Standard chemistry/materials formats via ASE (CIF, XYZ, extXYZ, etc.).
Batch Processing: Multiple structures per request (BGR); single system per request (BMD).
Metadata Support: User-provided metadata fields in requests and replies.
Rich Output: Optimized structures, MD trajectories, energies, forces, stress, charges, and metadata.

Server Configuration: Model selection, batch size, DFT-D3 enable/params, cell optimization, tolerances, and more via environment variables.
Per-Request Overrides: Force/pressure tolerances, cell optimization, active masks, etc.
Automatic Batch Size Estimation: Benchmarks optimal batch size based on GPU and memory.

Ensembles: NVE, NVT, NPT with Langevin thermostat and Monte Carlo barostat (including anisotropic).
Restartable Simulations: Long MD runs can be chunked and resumed.
Advanced Controls: External fields, DFT-D3, implicit solvation, custom models.

2. User Capabilities

Python Client Scripts: Provided for both geometry optimization and MD, with async and restart support.
Command-Line Utilities: For server readiness checks, batch submission, and workflow control.
Example Datasets: For molecules and materials, periodic and non-periodic, to facilitate onboarding and benchmarking.

Schema-Driven Requests/Replies: Detailed control over all simulation parameters and metadata.
OpenAPI Schema: For programmatic integration and validation.
Health & Status Endpoints: For monitoring and orchestration in automated pipelines.

User Metadata: Enables traceability and data provenance.
Custom Models & Parameters: Extensible to user-supplied MLIPs and simulation settings.
Batch and Async Processing: Scales to high-throughput screening and automated discovery workflows.

Area	Capabilities
Deployment	Docker containers, GPU-accelerated, ENV VAR config
API	RESTful, JSON schema, OpenAPI, health/status/config endpoints
Models	MACE, AIMNet2 (variants), TensorNet, custom models
Workflows	Batched geometry optimization, batched molecular dynamics
Chemistry Support	Periodic/non-periodic, cell optimization, DFT-D3, implicit solvation, external fields
Batching	Dynamic, auto-tuned batch size, high throughput
User Control	Per-request overrides, active atom masks, metadata, restartable MD
Integration	Python scripts, standard file formats, schema-driven, suitable for informatics pipelines
Output	Optimized structures, MD trajectories, energies, forces, stress, charges, metadata

Designed for Integration: RESTful, schema-driven API and standard file formats enable plug-and-play with existing informatics infrastructure.
High-Throughput Automation: Batch and async processing support large-scale virtual screening and automated discovery.
Traceability & Data Management: Rich metadata and provenance features align with best practices in scientific data management.
Extensible & Customizable: Open to new models, simulation settings, and advanced workflows.