发表机构
Cleer Science; Institute of Automation, Chinese Academy of Sciences; Imperial College London; Tsinghua University（克利尔科学公司; 中国科学院自动化研究所; 伦敦帝国学院; 清华大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究推出整合17类超80万分子样本的Chem World化学性质预测基准，并提出Mixture-PINN物理信息神经网络框架，经实验验证其可提升预测性能，为可信赖AI系统研发奠定基础。
AI 中文摘要
化学性质预测在加速化学、材料科学及药物开发领域的科学发现中发挥关键作用。然而，现有基准常存在任务多样性有限、数据集碎片化、评估协议不一致的问题，导致难以系统评估AI模型的可靠性与泛化性。本研究推出Chem World，这是一个用于化学性质预测的综合基准，整合了17个不同的化学数据集，包含超过800,000个分子样本，覆盖密度、电导率、溶解度及其他分子特性等多种性质，Chem World为跨多个性质预测任务评估AI模型提供了统一平台。此外，我们提出Mixture-PINN，这是一种基于物理信息神经网络的预测框架，将化学先验知识融入数据驱动学习，提升化学性质预测的准确性、鲁棒性与可靠性。在Chem World上开展的大量实验表明，与现有方法相比，我们的方法具有有效性。通过结合大规模标准化评估与物理信息学习，Chem World为开发用于计算化学的可信赖AI系统及推进AI驱动的科学发现奠定了基础。
英文摘要
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.
