一种由散射确定相互作用的机器学习反演方法
A Machine Learning Inversion Scheme for Determining Interaction from Scattering
摘要：
我们概述了一种机器学习策略，用于确定使用散射的物质凝聚相的有效相互作用。通过一个胶体悬浮液的案例研究，我们表明，有效的潜力可以从散射光谱的概率推断，没有任何限制所施加的模型假设。现有的参数化方法的比较表明，这种方法在准确性，效率和适用性的优越性能。这种方法可以有效地量化相互作用的高度相关的系统，使用散射和衍射实验。
英文摘要：
We outline a machine learning strategy for determining the effective interaction in the condensed phases of matter using scattering. Via a case study of colloidal suspensions, we showed that the effective potential can be probabilistically inferred from the scattering spectra without any restriction imposed by model assumptions. Comparisons to existing parametric approaches demonstrate the superior performance of this method in accuracy, efficiency, and applicability. This method can effectively enable quantification of interaction in highly correlated systems using scattering and diffraction experiments.
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