Inelastic neutron scattering studies on the eight-spin zigzag-chain compound <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msub><mml:mi>KCu</mml:mi><mml:mn>4</mml:mn></mml:msub><mml:msub><mml:mi mathvariant="normal">P</mml:mi><mml:mn>3</mml:mn></mml:msub><mml:msub><mml:mi mathvariant="normal">O</mml:mi><mml:mn>12</mml:mn></mml:msub></mml:mrow></mml:math>: Confirmation of the validity of a data-driven technique based on machine learning

We performed inelastic neutron scattering (INS) experiments on KCu4P3O12 powder and compared the experimental results with those calculated for the spin model (an eight-spin zigzag chain with S = 1/2 ) using the data-driven technique based on machine learning. We observed magnetic excitations at approximately 3.0, 4.1, 5.9, and 8.8 meV at 5.5 K and at approximately 3.8 and 5.9 meV at 49 K. The excitations corresponding to 3.0, 4.1, and 8.8 meV were magnetic excitations from the ground state to the first, second, and fourth excited states (2.87, 4.23, and 8.53 meV from the calculations), respectively. The excitations corresponding to 3.8 and 5.9 meV were magnetic excitations from the first excited state to the third and fourth excited states (3.78 and 5.67 meV from the calculations), respectively. An excitation was likely to exist between the first and second excited states at approximately 1.35 meV in the experimental results. The excitation energies obtained from the INS experiments were almost consistent with those calculated from the exchange interaction values via the data-driven technique. This consistency indicates that the data-driven technique is a powerful tool for evaluating multiple exchange interactions.