The stopping power of a material upon interaction with an energetic ion is the key measure of how far that ion will travel. The implications of accurate particle range calculations are tremendous, affecting every single application in which particle radiation is involved, from nuclear power to medicine. An approach is presented which attempts to overcome current shortcomings in the theoretical understanding of stopping power, as well as the methods used to interpret and exploit measured data. This is a considerable challenge, however the use of a novel machine learning methodology is shown to hold great promise in this endeavour: the ultimate aim being the ability to correctly predict the stopping value for any energy, ion and target combination, having no pre-existing experimental data.

A random forest regression algorithm is trained using over 34,000 experimental measurements, representing stopping power values for 522 ion-target combinations across the energy range ${10}^{-}3$ to ${10}^2$ MeV/amu, and ion and target atomic masses 1 to >240. Evaluation is carried out using several fundamental error metrics, over the whole dataset as well as for individual combinations, to provide the most comprehensive understanding of performance when tested under strict cross-validation criteria. The resulting model is shown to yield predicted stopping power curves corresponding closely to those of the true experimental values, with an ability to generalise across target elements, compounds, mixtures, alloys and polymers, irrespective of phase, and for a wide range of ion masses.

材料与高能离子相互作用时的阻止能力是该离子将移动多远的关键度量。精确的粒子范围计算的意义是巨大的，从核电到医学，都会影响涉及粒子辐射的每个应用。提出了一种方法，该方法试图克服在理论上对停止功率的理解方面的缺点，以及用于解释和利用测量数据的方法。这是一个巨大的挑战，但是在这种努力中，使用新颖的机器学习方法显示出巨大的希望：最终目的是能够正确预测任何能量，离子和目标组合的停止值，而无需预先现有实验数据。

使用超过34,000个实验测量值对随机森林回归算法进行了训练，这些测量值代表了整个能量范围内522个离子目标组合的停止功率值 ${10}^{--}3$ 至 ${10}^2$ MeV / amu，离子和目标原子质量1至> 240。在整个数据集以及单个组合上，使用几种基本误差度量进行评估，以在严格的交叉验证条件下进行测试时提供对性能的最全面的了解。所得模型显示出产生的预测停止功率曲线与真实实验值非常接近，并且能够概括目标元素，化合物，混合物，合金和聚合物，无论相位如何，并且适用于各种离子质量。