ForceNet: A Graph Neural Network for Large-Scale Quantum Calculations

2021/03/02 by Weihua Hu, Hu, Weihua, Muhammed Shuaibi +13 · 7 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · Materials Science · #Computational Drug Discovery Methods #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning in Materials Science #Protein Structure and Dynamics

paper · pdf · doi:10.48550/arxiv.2103.01436

openalex publication_date 2021/03/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

With massive amounts of atomic simulation data available, there is a huge opportunity to develop fast and accurate machine learning models to approximate expensive physics-based calculations. The key quantity to estimate is atomic forces, where the state-of-the-art Graph Neural Networks (GNNs) explicitly enforce basic physical constraints such as rotation-covariance. However, to strictly satisfy the physical constraints, existing models have to make tradeoffs between computational efficiency and model expressiveness. Here we explore an alternative approach. By not imposing explicit physical constraints, we can flexibly design expressive models while maintaining their computational efficiency. Physical constraints are implicitly imposed by training the models using physics-based data augmentation. To evaluate the approach, we carefully design a scalable and expressive GNN model, ForceNet, and apply it to OC20 (Chanussot et al., 2020), an unprecedentedly-large dataset of quantum physics calculations. Our proposed ForceNet is able to predict atomic forces more accurately than state-of-the-art physics-based GNNs while being faster both in training and inference. Overall, our promising and counter-intuitive results open up an exciting avenue for future research.

Citations

• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
• Efficient iterative schemes for<i>ab initio</i>total-energy calculations using a plane-wave basis set
• Quantum-chemical insights from deep tensor neural networks
• Neural Message Passing for Quantum Chemistry
• Fast Graph Representation Learning with PyTorch Geometric
• Alchemy: A Quantum Chemistry Dataset for Benchmarking AI Models
• Detecting Functionality-Specific Vulnerabilities via Retrieving Individual Functionality-Equivalent APIs in Open-Source Repositories
• PyTorch: An Imperative Style, High-Performance Deep Learning Library

Cited by

• Faster Molecular Dynamics with Neural Network Potentials via Distilled Multiple Time-Stepping and Nonconservative Forces
• The Convergence Frontier: Integrating Machine Learning and High Performance Quantum Computing for Next-Generation Drug Discovery
• Learning Biomolecular Motion: The Physics-Informed Machine Learning Paradigm
• Context-aware Learned Mesh-based Simulation via Trajectory-Level Meta-Learning
• Frame Averaging for Invariant and Equivariant Network Design
• OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs
• Topological Feature Compression for Molecular Graph Neural Networks

Related

• Accurate and scalable multi-element graph neural network force field and molecular dynamics with direct force architecture
• Synergistic integration of physical embedding and machine learning enabling precise and reliable force field
• Simple and efficient algorithms for training machine learning potentials to force data
• Data for Towards Improved Quantum Machine Learning for Molecular Force Fields
• Machine learning force fields: Construction, validation, and outlook