Chem World: A Large-Scale Benchmark and Physics-Informed Framework for Trustworthy Chemical Property Prediction

2026-07-30

AI Summary

Existing benchmarks for chemical property prediction suffer from limited task diversity, fragmented datasets, and inconsistent evaluation protocols, hindering comprehensive assessment of model generalization. To address these limitations, this work introduces Chem World, a large-scale, unified benchmark comprising 17 diverse tasks and over 800,000 molecules. Furthermore, the study proposes Mixture-PINN, a physics-informed neural network that integrates chemical prior knowledge through domain-specific inductive biases and multitask learning. Evaluated under a standardized protocol, Mixture-PINN demonstrates significantly improved accuracy, robustness, and reliability across tasks, thereby advancing the development of trustworthy AI for computational chemistry.

Abstract

Chemical property prediction plays a critical role in accelerating scientific discovery in chemistry, materials science, and drug development. However, existing benchmarks often suffer from limited task diversity, fragmented datasets, and inconsistent evaluation protocols, making it challenging to systematically assess the reliability and generalization of AI models. In this work, we introduce Chem World, a comprehensive benchmark for chemical property prediction that integrates 17 diverse chemical datasets with over 800,000 molecular samples, covering various properties including density, electrical conductivity, solubility, and other molecular characteristics. Chem World provides a unified platform for evaluating AI models across multiple property prediction tasks. Furthermore, we propose Mixture-PINN, a physics-informed neural network based prediction framework that incorporates chemical prior knowledge into data-driven learning, improving the accuracy, robustness, and reliability of chemical property prediction. Extensive experiments on Chem World demonstrate the effectiveness of our approach compared with existing methods. By combining large-scale standardized evaluation with physics-informed learning, Chem World establishes a foundation for developing trustworthy AI systems for computational chemistry and advancing AI-driven scientific discovery.

Problem

• chemical property prediction
• benchmark
• generalization
• evaluation protocols
• trustworthy AI

Innovation

• Chem World
• physics-informed neural network
• chemical property prediction
• Mixture-PINN
• trustworthy AI