We present an algorithm for computing melting points by autonomously learning from coexistence simulations in the NPT ensemble. Given the interatomic interaction model, the method makes decisions regarding the number of atoms and temperature at which to conduct simulations, and based on the collected data predicts the melting point along with the uncertainty, which can be systematically improved with more data. We demonstrate how incorporating physical models of the solid-liquid coexistence evolution enhances the algorithm's accuracy and enables optimal decision-making to effectively reduce predictive uncertainty. To validate our approach, we compare the results of 20 melting point calculations from the literature to the results of our calculations, all conducted with same interatomic potentials. Remarkably, we observe significant deviations in about one-third of the cases, underscoring the need for accurate and reliable algorithms for materials property calculations.

翻译：我们提出一种算法，通过从NPT系综中的共存模拟进行自主学习来计算熔点。给定原子间相互作用模型，该方法自主决定模拟所需的原子数量和温度，并根据收集的数据预测熔点及其不确定性，该不确定性可通过增加数据量系统性地降低。我们展示了如何将固液共存演化的物理模型融入算法，从而提升其精度，并实现最优决策以有效减少预测不确定性。为验证方法的有效性，我们将文献中20个熔点计算结果与我们的计算结果进行了对比（所有计算均使用相同的原子间势函数）。值得注意的是，约三分之一的案例中存在显著偏差，这凸显了对材料性质计算采用精确可靠算法的迫切需求。