无需机器学习经验，一键预测物质热物性数据。
MLPROP -- an open interactive web interface for thermophysical property prediction with machine learning
- 提供网页交互界面，直接调用先进机器学习模型
- 支持纯组分蒸气压、二元混合物相平衡等多类预测
- 开源代码可集成到现有工作流，适合化工研究者使用
机器学习（ML）能够以空前的范围和精度预测热物性，但复杂的实现流程限制了其实际应用。MLPROP 提供一个交互式网页界面，使用户无需具备机器学习专业知识即可直接应用前沿模型预测热物性，显著提升新方法的可及性。当前包含用于预测纯组分蒸气压的 GRAPPA 模型，以及用于预测二元混合物活度系数与气液平衡的 UNIFAC 2.0、mod. UNIFAC 2.0 和 HANNA 模型，并提供拟合 NRTL 参数的流程。MLPROP 将持续更新，免费开放访问（https://ml-prop.mv.rptu.de/）。所有模型源码开源，便于集成到现有工作流中。
原文摘要 · Abstract (English)
Machine learning (ML) enables the development of powerful methods for predicting thermophysical properties with unprecedented scope and accuracy. However, technical barriers like cumbersome implementation in established workflows hinder their application in practice. With MLPROP, we provide an interactive web interface for directly applying advanced ML methods to predict thermophysical properties without requiring ML expertise, thereby substantially increasing the accessibility of novel models. MLPROP currently includes models for predicting the vapor pressure of pure components (GRAPPA), activity coefficients and vapor-liquid equilibria in binary mixtures (UNIFAC 2.0, mod. UNIFAC 2.0, and HANNA), and a routine to fit NRTL parameters to the model predictions. MLPROP will be continuously updated and extended and is accessible free of charge via https://ml-prop.mv.rptu.de/. MLPROP removes the barrier to learning and experimenting with new ML-based methods for predicting thermophysical properties. The source code of all models is available as open source, which allows integration into existing workflows.
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