Crystalline solids play a fundamental role in a host of materials and technologies, ranging from pharmaceuticals to renewable energy. The thermodynamic properties of these solids are crucial determinants of their stability and therefore their behavior. The advent of large density functional theory databases with properties of solids has stimulated research on predictive methods for their thermodynamic properties, especially for the enthalpy of formation Δ_fH. Increasingly sophisticated artificial intelligence and machine learning (ML) models have primarily driven development in this field in recent years. However, these models can suffer from lack of generalizability and poor interpretability. In this work, we explore a different route and develop and evaluate a framework for the application of reaction network (RN) theory to the prediction of Δ_fH of crystalline solids. For an experimental dataset of 1550 compounds we are able to obtain a mean absolute error w.r.t Δ_fH of 29.6 meV atom⁻¹ using the RN approach. This performance is better than existing ML-based predictive methods and close to the experimental uncertainty. Moreover, we show that the RN framework allows for straightforward estimation of the uncertainty of the predictions.

结晶固体在许多材料和技术中发挥着重要作用，从制药到可再生能源。这些固体的热力学特性是其稳定性及其行为的关键决定因素。具有固体性质的大密度泛函理论数据库的出现刺激了对其热力学性质的预测方法的研究，特别是对形成焓 Δ_fH。近年来，日益复杂的人工智能和机器学习 （ML） 模型主要推动了该领域的发展。然而，这些模型可能缺乏泛化性和可解释性差。在这项工作中，我们探索了一条不同的路线，并开发和评估了反应网络 （RN） 理论应用于晶体固体 Δ_fH 预测的框架。对于 1550 种化合物的实验数据集，我们能够使用 RN 方法获得 29.6 meV 原子^-1 的平均绝对误差 w.r.t Δ_fH。这种性能优于现有的基于 ML 的预测方法，并且接近实验的不确定性。此外，我们表明 RN 框架允许直接估计预测的不确定性。