Radial distribution function: Difference between revisions

Revision as of 17:34, 8 February 2023

Revision as of 17:35, 8 February 2023

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Given a [[potential energy]] function, the radial distribution function can be computed either via computer simulation methods like the [[Monte Carlo method]], or via the [[Ornstein-Zernike equation]], using approximative closure relations like the [[Percus-Yevick approximation]] or the [[Hypernetted-chain equation|Hypernetted Chain Theory]]. It can also be determined experimentally, by radiation scattering techniques or by direct visualization for large enough (micrometer-sized) particles via traditional or confocal microscopy.

The radial distribution function is of fundamental importance since it can be used, using the [[Kirkwood–Buff solution theory]], to link the microscopic details to macroscopic properties. Moreover, by the reversion of the Kirkwood-Buff theory, it is possible to attain the microscopic details of the radial distribution function from the macroscopic properties. The radial distribution function may also be inverted to predict the potential energy function using techniques such as the the [[Ornstein-Zernike equation]] or structure-optimized potential refinement <ref>{{cite journal |last1=Shanks |first1=B. | last2 = Potoff | first2 = J. | last3 = Hoepfner | first3 = M. |title=Transferable Force Fields from Experimental Scattering Data with Machine Learning Assisted Structure Refinement |journal=J. Phys. Chem. Lett. |date=December 5, 2022 |volume=13 |issue=49 |pages=11512–11520 |doi= 10.1021/acs.jpclett.2c03163}} </ref>.

==Definition==