Endowing Molecular Language with Geometry Perception via Modality Compensation for High-Throughput Quantum Hamiltonian Prediction

2026-01-22

AI Summary

Quantum Hamiltonian prediction typically relies on costly molecular geometry data, limiting high-throughput computation. This work proposes a geometric modality compensation mechanism that endows SMILES-based molecular language models with implicit geometric awareness through multimodal alignment, enabling efficient Hamiltonian prediction without explicit geometric inputs. Combined with weakly supervised fine-tuning, the approach substantially improves data efficiency. Theoretical analysis demonstrates that the method’s generalization error can be effectively bounded. Experimental results show that the proposed method achieves a 100-fold speedup over conventional quantum mechanical approaches while maintaining comparable accuracy, and it has been successfully applied to electrolyte formulation screening.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Learning to Search
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

The quantum Hamiltonian is a fundamental property that governs a molecule's electronic structure and behavior, and its calculation and prediction are paramount in computational chemistry and materials science. Accurate prediction is highly reliant on extensive training data, including precise molecular geometries and the Hamiltonian matrices, which are expensive to acquire via either experimental or computational methods. Towards a fast yet accurate method for Hamiltonian prediction, we first introduce a geometry information-aware molecular language model to bypass the use of expensive molecular geometries by only using the readily available molecular language -- simplified molecular input line entry system (SMILES). Our method employs multimodal alignment to bridge the relationship between SMILES strings and their corresponding molecular geometries. Recognizing that the molecular language inherently lacks explicit geometric information, we propose a geometry modality compensation strategy to imbue molecular language representations with essential geometric features, thereby enabling accurate predictions using SMILES. In addition, given the high cost of acquiring Hamiltonian data, we devise a weakly supervised strategy to fine-tune the molecular language model, thus improving the data efficiency. Theoretically, we prove that the prediction generalization error without explicit molecular geometry can be bounded through our modality compensation scheme. Empirically, our method achieves superior computational efficiency, providing up to 100x speedup over conventional quantum mechanical methods while maintaining comparable prediction accuracy. We further demonstrate the practical case study of our approach in the screening of electrolyte formulations.

Problem

• quantum Hamiltonian prediction
• molecular geometry
• SMILES
• data efficiency
• high-throughput screening

Innovation

• geometry modality compensation
• molecular language model
• quantum Hamiltonian prediction
• weakly supervised learning
• multimodal alignment