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Multi-configuration time-dependent Hartree

Multi-configuration time-dependent Hartree (MCTDH) is an approach to quantum molecular dynamics, an algorithm to solve the time-dependent Schrödinger equation for multidimensional dynamical systems consisting of distinguishable particles. The nuclei of molecules is one example of such particles and their vibrational motion is a form of time-dependence. The method uses an overall wavefunction composed of products of single-particle wavefunctions as first proposed by Douglas Hartree in 1927. The "multiconfiguration" part of the method refers to combining multiple such products.

Methods
Basic algorithm Wavefunction expansion \Psi(q_i,...,q_f,t) = \sum_{j_1}^{n_1} ... \sum_{j_f}^{n_f} A_{j_1 ... j_f}(t)\prod_{\kappa=1}^{f}\varphi^{(\kappa)}_{j_{\kappa}}(q_{\kappa},t) Where the number of configurations is given by the product n_1 ... n_f. The single particle functions (SPFs), \varphi^{(\kappa)}_{j_{\kappa}}(q_{\kappa},t), are expressed in a time-independent basis set: \varphi^{(\kappa)}_{j_{\kappa}}(q_{\kappa},t) = \sum_{i_1 = 1}^{N_\kappa}c_{i_\kappa}^{(\kappa, j_\kappa)}(t) \; \chi_{i_\kappa}^{(\kappa)}(q_\kappa) Where \chi_{i_\kappa}^{(\kappa)}(q_\kappa) is a primitive basis function, in general a Discrete Variable Representation (DVR) that is dependent on coordinate q_\kappa. If n_1 ... n_f = 1, one returns to the Time Dependent Hartree (TDH) approach. In MCTDH, both the coefficients and the basis function are time-dependent and optimized using the variational principle. Equations of motion Lagrangian Variational Principle L = \langle\Psi | i \frac{\partial}{\partial t} - H | \Psi \rangle Where: \delta \int_{t_1}^{t_2} L \text{d}t = 0 Which is subject to the boundary conditions \delta L (t_1) = \delta L (t_2) = 0 . After integration, one obtains: \text{Re} \langle \delta \Psi | i \frac{\partial}{\partial t} - H | \Psi \rangle = 0 McLachlan Variational Principle \delta || i \frac{\partial}{\partial t} - H \Psi ||^2 = 0 Where only the time derivative is to be varied. We can rewrite this norm squared term as a scalar product, and vary the bra and ket side of the product: \begin{align} 0 &= \delta\langle i \frac{\partial}{\partial t} \Psi - H \Psi |i \frac{\partial}{\partial t}\Psi - H | \Psi \rangle \\ &= \langle i \delta \frac{\partial}{\partial t} \Psi | i \frac{\partial}{\partial t} - H | \Psi \rangle + \langle (i\frac{\partial}{\partial t} - H)\Psi | i\delta \frac{\partial}{\partial t}\Psi \rangle\\ &= -i \langle \delta \Psi | i \frac{\partial}{\partial t} - H | \Psi \rangle + i\langle (i\frac{\partial}{\partial t} - H)\Psi | \delta \Psi \rangle \\ &= 2 \text{Im} \langle \delta \Psi | i \frac{\partial}{\partial t} - H | \Psi \rangle \end{align} Dirac-Frenkel Variational Principle If each variation of \delta \Psi, i\delta\Psi is an allowed variation, then both the Lagrangian and the McLanchlan Variational Principle turn into the Dirac-Frenkel Variational Principle: \langle \delta \Psi | i \frac{\partial}{\partial t} - H | \Psi \rangle = 0 Which simplest and thus preferred method of deriving the equations of motion. which was then generalized by Vendrell and Meyer. Wave function expansion The generalized ML expansion of Meyer can be written as follows: \begin{align} \varphi^{z-1,\kappa_{l-1}}_{m}(q^{z-1}_{\kappa_{l-1}}) &= \sum^{n_1^z}_{j_1 = 1} ... \sum^{n_{p^z}^z}_{j_{p^z} = 1} A^z_{m;j_1, ..., j_{p^z}} \prod^{p^z}_{\kappa_l = 1}\varphi^{z,\kappa_l}_{j_{\kappa_l}} (q^z_{\kappa_l}) \\ &= \sum_{J}A^z_{m;J}\cdot \Phi^z_J (q^{z-1}_{\kappa_{l-1}}) \end{align} Where the coordinates are combined as q^{z-1}_{\kappa_{l-1}}= (q^z_1, ..., q^z_{p^z}) Equations of motion Where the equations of motion are now represented as follows: \begin{align} i\frac{\partial A^1_J}{\partial t} &= \sum_K \langle \Phi^1_J | \hat{H} - \sum^{p^1}_{\kappa_1 = 1} \hat{g}^{1,\kappa_1}| \Phi^1_K \rangle A^1_K\\ &= \sum_K \langle \Phi^1_J | \hat{H} | \Phi^1_K \rangle A^1_K - \sum^{p^1}_{\kappa_1 = 1}\sum_{i=1}^{n^1_{\kappa_1}}\hat{g}^{1,\kappa_1}_{j_{\kappa_1}i}A_{j_1 ... i ... j_{p^1}} \end{align} The SPF EOMs are formally defined the same for all layers: i\frac{\partial \varphi^{z,\kappa_l}_n}{\partial t} = (1 - P^{z,\kappa_l})\sum^{n^z_{\kappa_l}}_{j,m = 1} (p^{z,\kappa_l})^{-1}_{nj}\cdot \langle\hat{H}\rangle^{z, \kappa_l}_{jm}\varphi^{z, \kappa_l}_m + \sum^{n^z_{\kappa_l}}_{j=1}{g^{z, \kappa_l}_{jn} \varphi^{z,\kappa_l}_j} Where \hat{g} is a Hermitian gauge operator defined as follows: \langle \varphi^{z,\kappa_l}_{j}| i\frac{\partial}{\partial t}\varphi^{z, \kappa_l}_k \rangle = \langle \varphi^{z,\kappa_l}_{j} | \hat{g}^{z, \kappa_l} | \varphi^{z,\kappa_l}_{k} \rangle = g^{z, \kappa_l}_{jk} ==Examples of uses in literature==
Examples of uses in literature
NOCl The first verification of the MCTDH method was with the NOCl molecule. Its size and asymmetry makes it a perfect test bed for MCTDH: it is small and simple enough for its numerics to be manually verified, yet complicated enough for it to already squeeze advantages against conventional product-basis methods. Water clusters The solvation of the hydronium ion is a topic of continued research. Researchers have been able to successfully use MCTDH to model the Zundel and Eigen ions in close agreement with experiment. ==Limitations==
Limitations
For a typical input in ML-MCTDH to be run, a node tree, potential energy surface, and equations of motion must be generated by the user. These prerequisites—along with total compute time—soft-cap the size of systems able to be studied with ML-MCTDH; however, advances in neural networks have been shown to address the difficulty of the generation of potential energy surfaces. These issues can also by circumvented by using the spin-boson or other similar bath models that do not pose the same assignment challenges. ==Software packages implementing the MCTDH method==
Software packages implementing the MCTDH method
Example Usage of the Heidelberg Package for NOCl Input and Operator File Output absorption spectrum ==References==
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