MOFi MCP server

MOFi: An AI Harness for Autonomous Materials Research Materials Optimization through Feedback-driven intelligence

Quick setup

pip install mofi-tools

Introduction

MOFi is a research harness that connects AI agents, materials simulation tools, and feedback loops to study autonomous improvement. It investigates physics-grounded agentic improvement loops as a proof of concept for AI recursive self-improvement (RSI). We start from building an AI harness with the longer-term aim of autonomous materials research capabilities through scientific feedback.

Motivation: Can physics-informed models drive AI recursive self-improvement?

Hackathon Target: Build an AI harness for autonomous materials research and demonstrate agentic workflows driven by scientific feedback in metal–organic framework (MOF) research. Long-Term Target: Systematically compare human-guided and autonomous AI-driven improvement of scientific models and workflows, measuring their improvement capabilities, effectiveness and efficiency under matched information and bounded compute.

Team: Qiankun Mo, Yi Cao, Xingfei Wei, and Junsung Lim.

MOFi: An Outer (Evolution) Loop proposes versioned improvements to models, tools, and workflows. An Inner (Research) Loop applies them in a reproducible scientific environment. An independent evaluator assesses the evidence against fixed criteria and tracks computational costs. Both loops use LLMs through supported API or CLI interfaces.

This repository contains functional components and user documentation. Development records and historical experiment outputs are maintained separately; see the component and input boundaries. Engineering validation and evidence of unseen-task generalization remain distinct.

Research objective

Can an AI system autonomously improve its research procedures so that a successor solves unseen scientific tasks more effectively under a fixed budget? We begin with MOFs because structure construction, diagnostics, property prediction, simulation and scientific feedback can form an evaluable loop. Transfer to other materials is a research target, not an established result.

Here RSI initially means improving the versioned research system around a fixed LLM. It does not require continuous local LLM fine-tuning, and it does not yet establish that the outer improvement process improves itself.

Human-guided versus autonomous improvement

We study three scientific model settings: ordinary GNNs, physics-informed approaches, and machine-learned interatomic potentials (MLIPs). They are experimental settings rather than mutually exclusive categories: MACE is itself a physics-aware GNN.

Shortened. The full README is on GitHub.