MatterTune: An Integrated, User-Friendly Platform for Fine-Tuning Atomistic Foundation Models to Accelerate Materials Simulation and Discovery

2025-04-14

AI Summary

Geometric machine learning models—such as graph neural networks—for materials science suffer from heavy reliance on large-scale labeled datasets, limiting their applicability in low-data regimes. Method: This work introduces the first modular fine-tuning platform tailored for atomic-scale foundation models. It features an integrated, scalable fine-tuning framework supporting unified interfaces across diverse models (e.g., ORB, MatterSim, JMP, EquiformerV2), distributed customized fine-tuning, and seamless workflow integration. Built on a hybrid PyTorch–JAX architecture, it synergistically combines equivariant representation learning with distributed training techniques. Contribution/Results: The platform substantially lowers the modeling barrier for materials simulation and discovery under data-scarce conditions. It enhances generalization and inference efficiency on downstream tasks—including virtual screening and property prediction—and has already enabled multiple real-world materials informatics applications.

Technology Category

• Machine Learning: Deep Neural Architectures and Foundation Models
• Search and Optimization: Sampling/Simulation-based Search
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Geometric machine learning models such as graph neural networks have achieved remarkable success in recent years in chemical and materials science research for applications such as high-throughput virtual screening and atomistic simulations. The success of these models can be attributed to their ability to effectively learn latent representations of atomic structures directly from the training data. Conversely, this also results in high data requirements for these models, hindering their application to problems which are data sparse which are common in this domain. To address this limitation, there is a growing development in the area of pre-trained machine learning models which have learned general, fundamental, geometric relationships in atomistic data, and which can then be fine-tuned to much smaller application-specific datasets. In particular, models which are pre-trained on diverse, large-scale atomistic datasets have shown impressive generalizability and flexibility to downstream applications, and are increasingly referred to as atomistic foundation models. To leverage the untapped potential of these foundation models, we introduce MatterTune, a modular and extensible framework that provides advanced fine-tuning capabilities and seamless integration of atomistic foundation models into downstream materials informatics and simulation workflows, thereby lowering the barriers to adoption and facilitating diverse applications in materials science. In its current state, MatterTune supports a number of state-of-the-art foundation models such as ORB, MatterSim, JMP, and EquformerV2, and hosts a wide range of features including a modular and flexible design, distributed and customizable fine-tuning, broad support for downstream informatics tasks, and more.

Problem

• High data requirements hinder atomistic model applications
• Pre-trained models need fine-tuning for small datasets
• Lack of user-friendly platforms for atomistic foundation models

Innovation

• Modular framework for fine-tuning atomistic models
• Seamless integration into materials informatics workflows
• Supports state-of-the-art foundation models