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Register a new userA Machine Learning Inversion Scheme for Determining Interaction from Scattering
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Added by
Jan Michael Carrillo
Publication date
2021
fields
Physics
and research's language is
English
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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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We present a practical $S$-matrix to potential inversion procedure for coupled-channel scattering. The inversion technique developed is applied to non-diagonal $S^J_{ll}$ for spin one projectiles, yielding a tensor interaction $T_{rm R}$, and is also applicable to spin-1/2 plus spin-1/2 scattering. The method is a generalization of the iterative-perturbative, IP, method. It is tested and evaluated and we investigate the degree of uniqueness of the potential, particularly for cases where there is insufficient information to define the potential uniquely. We examine the potentials which result when the $S$-matrix is generated from a $T_{rm P}$ interaction. We also develop the generalisation, using established procedures, of IP $S$-matrix-to-potential inversion to direct observable-to-potential inversion. This `direct inversion procedure is demonstrated to be an efficient method for finding a multi-component potential including a $T_{rm R}$ interaction fitting multi-energy $sigma$, ${rm i}T_{11}$, $T_{20}$, $T_{21}$ and $T_{22}$ data for the scattering of spin-1 nuclei from spin-zero target. It is applicable to other channel spin 1 cases.
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