Foundation Machine Learning Force Field (MLFF) at Scale

Repository for MLOps pipeline for training, deploying, and monitoring MLFFs for chemistry and drug discovery.

The terms Machine Learning Force Field (MLFF) and Machine Learning Interatomic Potential (MLIP) are used interchangeably.

MLFF for (Quantum) Chemistry and Drug Discovery

Foundation models such as UMA (Universal Models for Atoms) and MACE (Message Passing Atomic Cluster Expansion) use massive pre-training datasets to capture complex, multi-body interactions and physical symmetries of molecules. In drug discovery, these pre-trained potentials allow for rapid, high-fidelity geometry optimizations, conformer searches, and molecular dynamics simulations of drug-target complexes without requiring system-specific retraining.

🚀 MLOps Framework & Resources

Hands-on guide for training, deploying, and monitoring MLFF model that scales automatically.

📖 MLOps Comprehensive Guide

The guide covers

1. Lifecycles - Active learning loop and data verification.
2. Scaling - Distributed training architectures for HPC (SLURM) and AWS Cloud (SageMaker, FSx for Lustre).
3. Serving - High-throughput FastAPI and Triton Inference Server wrapping.
4. Monitoring - Real-time out-of-distribution (OOD) geometry & bond-clash detection.

🛠️ MLOps Core Scripts

Our pipeline consists of the following components under the mlops/ folder

• dataset_prep.py: Converts .xyz/.extxyz coordinates into PyTorch Geometric graph datasets based on distance cutoffs.
• train_pipeline.py: Distributed DDP training script in PyTorch that computes energies and derives forces analytically using double autograd. Logs metrics to MLflow.
• inference_service.py: FastAPI microservice exposing /predict (energies and forces) and /optimize (structure relaxation integrating an ASE LBFGS optimizer).
• monitor_drift.py: Detects atomic bond clashes and checks geometric drift using pairwise distance distributions to alert on OOD structures.

🏢 Orchestration & Infrastructure Templates

• submit_hpc.sh: A SLURM submit template for multi-node, multi-GPU training clusters via torchrun.
• run_sagemaker.py: AWS SageMaker SDK launcher targeting large multi-GPU instances (e.g. ml.p4d.24xlarge) utilizing FSx for Lustre.

🤖 Agent skills

Specialized agent skills have been integrated under the .agents/skills/ directory to guide AI (like Claude and Gemini) through computational chemistry and ML interatomic potential development tasks.

📋 Skill Agents Registry

Skill / Component	Agent Name	Action / Purpose
Molecular Graph & Scaffold Splitting	ScaffoldSplitAgent	Prevents chemical space data leakage between train/val/test splits.
Multi-Task Learning with ACS	MultitaskACSAgent	Mitigates negative transfer by checkpointing task-specific states independently.
Differentiable Information Imbalance	DIIFeatureSelector	Selects relevant molecular feature subsets via gradient descent.
Deep Ensembles UQ	EnsembleUQAgent	Partitions total predictive uncertainty into aleatoric and epistemic components.
Activity Cliff Awareness	ActivityCliffAgent	Adjusts representation coordinates around cliff compounds using TSM loss.
Delta-ML & Transfer Learning	DeltaTransferMLAgent	Achieves chemical accuracy with small high-fidelity data via pre-training/fine-tuning.
Conformation Generation & DFT Input	ConformationDFTAgent	Prepares optimized conformer coordinates and inputs for DFT (ORCA/Gaussian) solvers.
MLIP ASE Calculators	MLIPASECalculatorAgent	Bridges PyTorch ML models to the Atomic Simulation Environment interface.
Geometry Optimization & MD	ASEDynamicsAgent	Relaxes configurations and evaluates thermodynamic trajectory snapshots.
MLIP Active Learning Loops	MLIPActiveLearningAgent	Automates database expansion focusing labeling budgets on high-uncertainty regions.

⚙️ How to setup & use skill agents

Customizations are automatically discovered and loaded by agentic platforms (like Google Gemini and Anthropic Claude systems supporting custom workspace contexts) from standard workspace or global roots.

1. Installation

To install the skills in your active workspace, copy or create the skills directory in the workspace root:

# Workspace level installation
mkdir -p .agents/skills/
cp -r path/to/skills/* .agents/skills/

Alternatively, for global installation across all projects, copy the skills into the global config folder:

# Global configuration level
mkdir -p ~/.gemini/config/skills/
cp -r path/to/skills/* ~/.gemini/config/skills/

2. How it works

Every skill folder contains a SKILL.md manifest with frontmatter (e.g. name and description triggers). When you prompt Claude or Gemini in the IDE with a related task (e.g. "Run a Langevin dynamics simulation on this structure"), the agent:

1. Triggers matching rules based on your query description.
2. Automatically loads the instructions inside SKILL.md.
3. Discovers the target helper script and executes it or guides you in running it.

3. Manual command execution

You can also run any of the helper scripts directly from your terminal using the commands listed in the registry table above. Ensure dependencies (torch, ase, rdkit, numpy) are installed in your active Python environment:

pip install torch ase numpy rdkit

👨‍💻 Author

Rangsiman Ketkaew
ML PostDoc Researcher, ETH Zurich, Switzerland