1.2 Stochastic methods
1.2.4 Diffusion Monte Carlo and Lattice Regularized Diffusion Monte Carlo 29
1.2.4.1 Diffusion Monte Carlo
Contrary to VMC, DMC is a projector technique in which the trial QMC WF is projected on the ground state WF of the system[90]. Indeed, one can obtain the exact ground state energy
of the E0 via the following mixed estimator of the quantum HamiltonianH:ˆ E0= hΨ0|H|Ψˆ Ti
hΨ0|ΨTi . (1.56)
However, the main issue arising from Eq. (1.56) is the need to know the exact ground state WFΨ0. Let us recall the time-dependent Schrödinger equation written in the imaginary time t→it
−∂Ψ(R, t)
∂t = ( ˆH−Λ)Ψ(R, t), (1.57)
where Λ is an energy offset that is for the moment left arbitrary. It is straightforward to obtain the formal solution of Eq. (1.57) given by
ΨG(R, t) =e−( ˆH−Λ)tΨT(R, t). (1.58) Using the spectral decomposition of theguiding WF ΨT, it is easy to show that the long-time behavior of the projected WF ΨG is dominated by the Hamiltonian lowest-energy eigenstate Ψ0 having a non zero overlap with the initial ΨT. Consequently, in the infinite time limit, one can compute the DMC energy as the following mixed estimator
EDMC = hΨG|H|Ψˆ Ti hΨG|ΨTi =
Z
dR Ψ∗G(R, t)ΨT(R) R dR0Ψ∗G(R0, t)ΨT(R0)
HΨˆ T(R)
ΨT(R) (1.59)
= Z
dR¯π(R, t)eL(R) =heLi.
The above equation is the DMC version of Eq. (1.50) where we have operated the substi-tution |ΨT|2 → Ψ∗GΨT and the importance sampling is made on the mixed density π¯ =
Ψ∗G(R,t)ΨT(R)
RdR0Ψ∗G(R0,t)ΨT(R0). One can apply Eqs.(1.53) and (1.54) to evaluate the DMC observables and their variance, since Eq. (1.59) also contains the zero variance property.
The only significant difference between DMC and VMC comes from the sampling of the mixed densityπ(R, t)¯ since it cannot be directly interpreted as a probability density because of the sign ofΨGΨT (we consider real WF for simplicity without loss of generality). Indeed, if one applies a naive sampling of π(R, t), the projected¯ ΨG would be bosonic in the long time limit. This would clearly violate Pauli’s antisymmetrization principle that imposes the ground state WF of a quantum system to be fermionic. To solve that issue, we apply the very efficient Fixed-Node approximation (FN) which consists in restricting the importance sampling in a region of the space of electronic configurations where the ΨT and ΨG have the same sign[29]. Such a region is called anodal pocket and the tiling theorem ensures that they all are strictly equivalent and contain all the necessary information about π(r, t)¯ [91]. Within the FN approximation, it is clear that the projected WF ΨG(R, t) will have the same nodal structure that the guiding WFΨT(R)which is by principle not exact becauseΨT is not equal to the ground state WFΨ0. The systematic error induced by this hypothesis is the so-called FN error and can be fortunately neglected in our thesis. Indeed, Caffarelet al. demonstrated the ability of DMC to reproduce almost exactly the experimentally estimated ground state
energy of the water moleculeEH2O=−76.4389Ha[92] since they obtained the striking value ofEH2O=−76.43894(12)Ha[93].
Let us denotef(R, t) = ΨG(R, t)ΨT(R)≥0 ∀ {R, t}the unnormalized distribution we want to sample within FN approximation. Its time evolution is dictated by the time-dependent Schrödinger equation which reads as
where we denotedrift velocityvD(R) =∇ln ΨT. The above equation can thus be interpreted as a master equation driving the diffusion of QMC walkers in the configurational space of electronic positions. The first term of the r.h.s of Eq. (1.60) is a purely diffusive contribution, while the second term takes into account the drift impacting the random walk. Finally, the last term governs the walkers’ population by deciding which walker will die or survive during the Markovian dynamics via itsbranching ratio.
Afterwards, we express the analytical solution off(R, t)in its integral form as follows f(R, t) =
Z
dR0G(R;R0, t)f(R0,0), (1.61) where the propagatorG(R;R0, t) is the Green function of Eq. (1.60) which fulfills the initial conditionG(R;R0,0) =δ(R,R0). An explicit expression of this propagator can be derived by simple application of the Trotter-Suzuki formula[94,95] for the time evolution of the exponen-tial operator e−τHˆ for small values of τ. This approximation leads to a time-step error that will be discussed more extensively in a different context in Chapter3.
From a practical point of view, the DMC simulation starts with a VMC thermalization of the walkers where we setf(R,0) = Ψ2VMC. Then, we simulate the diffusion process described in Eq. (1.60) and we regularly update the walkers’ dynamics by killing or generating new walkers via the branching algorithm. The energy shiftΛis modulated on-the-fly to push the random walk in the regions where the mixed probability density f(R, t) is large. We invite the interested reader to the complete reviews[91,96]for further details.
The statistical estimation of physical quantities in DMC is a bit more complex than in VMC since the used estimators are no longer pure, but mixed due to the sampling of the mixed probability distributionπ(R, t). Hence, one can rewrite the Eq. (1.53) for the mixed estimator¯ of a given observableO
where{Ri} are sampled configurations from the mixed distribution f(R). It can be shown that this mixed estimatorOmix coincides with the "pure" averageO0= hΨhΨ0|O|Ψˆ 0i
0|Ψ0i if and only
if [ ˆO,H] = 0. Otherwise, it is possible to extrapolate the pure, hence the physical, averageˆ via the following relation:
O0 '2Omix−OVMC+O(δΨ2). (1.63) In the above equation,δΨsimply represents the difference between the true ground state WF Ψ0 and the QMC trial WF ΨT. This implies that the applicability of the formula given in Eq. (1.63) is limited by the quality of the trial WF ΨT which means that we have to do a very careful VMC minimization of the WF to obtain satisfactory results.
From a practical point of view, DMC provides excellent results, always in agreement with experimental data and advanced electronic structure calculations. Indeed, already in 1994, DMC calculations carried on rigid water dimer gave reasonable value for both geometries and energetics[97]. Rigid body DMC has then been used to study tunnelling splitting (i.e. energy difference between two states of different symmetry) of the water dimer and trimer[98]. The DMC dissociation energy of the water dimer is found to be between 5.03(7) and 5.47(9) kcal/mol, which is in excellent agreement with the experimental value De = 5.44 ±0.7 kcal/mol[87]. DMC calculations also predict good binding energies of excess electrons in the (H2O)−6 cluster[99]. More recently, Gillanet al. studied in detail the six first water clusters (H2O)n for n= 1, . . . ,6. They verified that DMC results are clearly superior to DFT results (obtained with hybrid functionals), especially for the description of the various isomers of water hexamer, always in good agreement with CCSD(T) results[100]. They conclude their work by claiming that DMC can now be used as a benchmark for larger clusters and liquid water.
1.2.4.2 Lattice Regularized Diffusion Monte Carlo
In spite of its extreme accuracy, the use of DMC is still quite limited in practice due to its important computational cost. Indeed, its scale has been proven to be of the order of O(Z5.5−6.5) with the atomic number Z for small and medium size systems[101,102]. To over-come this bottleneck, the core electrons (of oxygen atoms in our case) need to be replaced by cheap but accurate pseudopotentials. As already mentioned, we use pseudopotentials specifi-cally designed for QMC simulations developed by Burzatki, Filippi and Dolg[80]. The use of a pseudopotential for the core electrons has a direct impact on the many-body Hamiltonian of the system Hˆ that now contains an effective contribution for the potential energy. The latter is a semi-local (it contains both local and non-local parts) and angular momentum dependent operator whose role is to reproduce the core-valence Coulombic repulsion and the orthogonality between core and valence states.
Such an inclusion of non-local pseudopotentials into the quantum Hamiltonian Hˆ has a dra-matic impact within the FN approximation since it seems impossible to constrain the non-local contributions of the pseudopotential within a single nodal pocket. Consequently, one solution consists in neglecting the non-local terms in the imaginary time equation (1.60) using the locality approximation[103]. Nevertheless, the price to pay is the breaking of the variational
principle since the local Hamiltonian eigenstate can have a lower energy than the true ground state one. To solve that issue, Casula and his colleagues developed the Lattice Regularized Diffusion Monte Carlo (LRDMC) that restaures the variational principle of standard DMC algorithms[104,105].
In this approach, the Green function driving the propagation of the mixed distributionf(R, t) in the imaginary time t→ itis no longer solved by direct application of Trotter-Suzuki for-mula at short times but is discretized on a grid with lattice parametera. Hence, it is necessary to discretize the non-local part of the Hamiltonian, which reads as
HˆNLa =−1 2
N
X
i=1
∆ra
i(θi, φi) + ˆVNL+O(a2), (1.64) where ∆rai(θi, φi) is the discretized Laplacian and VˆNL is the non-local contribution to the potential coming from the use of a pseudopotential. At each update of the electronic position during the diffusion process of the QMC walkers, the direction of the lattice is chosen with random angles θ and ψ generated from a normal distribution. In this way, by repeating this randomization procedure at each step, one explores continuously the space thus ensuring ergodicity with the nearest neighbors.
So as to gain accuracy, we can also discretize the local part of the pseudopotential, the electron-ion electrostatic interaction, by imposing the continuous and the discretized versions of the local energy to be the sameeaL(R) =eL(R). One obtains the following expression for the local potential:
VˆLa(R) = ˆVL(r)−(∆r−∆ar)ΨT(R)
2ΨT(R) , (1.65)
whereVˆLis the local contribution to the pseudopotential. Finally, the full discretized quantum Hamiltonian reads as
Hˆa= ˆHNLa + ˆVLa(R) + ˆVee+O(a2), (1.66) where only the electron-electron Coulomb interaction is not discretized. The above equation involves a lattice Hamiltonian defined on a continuous space, which is evaluated by a finite number of matrix elements connecting two electron positions. The DMC time step error is here replaced by the lattice space errors that increases quadratically with the lattice parameter a(see Eq. (1.66)). The smaller the lattice parameter is, the more computationally expensive but accurate the LRDMC simulation is. In practice, the exact result can be extrapolated by performing several LRDMC calculations at various values ofaand by taking the limita→0.
Finally, the mixed average of the energy is evaluated using the discretized Hamiltonian ELRDMC= hΨG|Hˆa|ΨTi
hΨG|ΨTi . (1.67)
As its DMC analogue, the reliability of the LRDMC approach has been assessed by Sterpone et al., who demonstrated that the LRDMC extrapolated (a→0) value of the water monomer dipoleµLRDMC = 1.870(10)D is in better agreement with the experimental valueµexp = 1.855 D than within VMCµ= 1.890(8)D. The LRDMC water dimer binding energyDe,exp = 4.9±
0.1 kcal/mol is also very close to reference quantum chemistry calculations: De,MP2 = 4.99 kcal/mol and De,CCSD(T) = 5.02 kcal/mol[88]. These results will be verified on both the bonding and non-bonding water dimer in the Chapter 5 of this thesis. In the meantime, PT static barriers estimated at various inter-oxygen distances in the Zundel complex are improved in LRMDC with respect to VMC, albeit still slightly overestimated (about ∼ 0.3 kcal/mol at dOO = 2.6−2.7Å) compared to CCSD(T)[72]. However, since we aim at performing MD simulations of small water clusters, which require an important amount of QMC estimations of the electronic energy to evaluate the ionic forces driving the system, the LRDMC approach cannot be employed, due to its excessive computational cost. Consquently, during this thesis, the electronic PES and forces will be evaluated by means of VMC, as it will be discussed in the following Subsection.
1.2.5 Forces evaluation in Quantum Monte Carlo
We derive here the method to evaluate forces by means of QMC approach. Unlike in deter-ministic DFT, obtaining forces within a stochastic framework is by far more difficult. Still working within the BO approximation in which the ions move on a PES defined by the elec-tronic energy of the system, one can define the3Nat-dimensional force acting on allNat atoms of the system
f =−∇qEVMC[Ψ]. (1.68)
In the above equation, ∇q is the gradient relative to cartesian coordinates {q} of the nuclei andEVMC[Ψ]is the variational energy already defined in Eq. (1.49). Being a functional ofΨ, EVMC[Ψ] has both animplicit and explicit dependences arising from:
• the Hamiltonian H, defined in Eq. (1.3) that also explicitly depends on the nuclearˆ coordinates {q}as we will see in Chapter 2;
• the QMC WFΨ that explicitly depends on a set of electronic parameters{λ} varying implicitly with the ionic positions {q} through the use of alocalized gaussian basis set (Eq. (1.41)).
Consequently, the local energy eL(R;q) defined in Eq. (1.51) also depends on {q} through both the HamiltonianHˆ and the WFΨ. We can expand Eq. (1.68) into three contributions:
f =fHell-Fey+fPulay+fλ, (1.69)
with
fHell-Fey = −hΨ|∇qH|Ψiˆ hΨ|Ψi ,
fPulay = −2hΨ|OqH|Ψi − hΨ|Oˆ q|ΨiEVMC
hΨ|Ψi ; Oq|Ψi=∇q|Ψi,
fλ = ∇λEVMC.∇qλ. (1.70)
In principle, the termfλis the most complicated because the derivatives∇qλare very difficult to evaluate. Fortunately, this contribution cancels out when the set of electronic parameters
{λ}is fully optimized, as the energy of the system becomes minimal. The Hellmann-Feynman (Hell-Fey) term, which is the only component of the QMC generalized force f in the deter-ministic case, fHell-Fey is the only contribution which would survive if the Hell-Fey theorem were applicable. This is not the case, because the WF is not an eigenstate ofH. Nevertheless,ˆ when the optimized WF approaches an eigenstate ofH, the Pulay term gets smaller and theˆ HF term becomes dominant.
Since there is a systematic and intrinsic noise affecting the QMC calculations due to their stochastic nature, it is clear that the Hell-Fey and the Pulay terms of the generalized QMC force have a finite variance. Consequently, the simple computation of such terms by finite differences of the electronic energies between two different ionic configurations is completely inefficient. Indeed, one has to impose small ionic displacementsδq to solve tiny energy dif-ferences but unfortunately, the energy derivatives diverge as δq1 as δq → 0. This is due to the propagation of errors that make the obtained error on the energy difference has the same amplitude that the ionic force we want to compute.
Computing the Hell-Fey and Pulay terms with finite variance in a fast way is of paramount importance to make QMC-based MD simulations feasible. To overcome the aforementioned issue, some technical improvements, such as the correlated sampling (CS) and the space warp coordinate transformation (SWCT)[106], have been done. A further step in the evaluation of efficient and accurate forces is the algorithmic differentiation (AD), recently introduced by Sorella and Capriotti[107]. Thanks to the AD, computing all components of the ionic forces is only four times more expensive than the cost of an energy calculation.
Thanks to all the techniques described above, we are able in this thesis to evaluate the QMC forces f in an affordable computational cost with increasing system size. These forces are however affected by an intrinsic noise that could bias the dynamics of the water clusters under study. In the Chapter2, we will explain the existing methods to incorporate and control the finite variance of forces into a MD framework.
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Ion dynamics
Contents
2.1 Molecular Dynamics simulations . . . . 38 2.1.1 Time and ensemble averages . . . . 38 2.1.2 Equations of motion and Liouvillian formalism . . . . 40 2.1.3 The velocity-Verlet algorithm . . . . 41 2.1.4 Analytical force fields for water . . . . 42 2.2 Finite temperature simulations . . . . 43 2.2.1 The canonical ensemble . . . . 44 2.2.2 The Langevin thermostat . . . . 44 2.2.3 Classical Langevin Dynamics integration schemes . . . . 46 2.2.3.1 The Bussi algorithm . . . . 47 2.2.3.2 The Attaccalite-Sorella algorithm . . . . 48 2.2.4 Validation of the Langevin thermostat . . . . 51 2.3 Dealing with quantum nuclei . . . . 52 2.3.1 Zero Point Energy and Nuclear Quantum Effects . . . . 53 2.3.2 The Path Integral approach . . . . 54 2.3.3 Path integral Langevin Dynamics . . . . 58
U
nderstanding the mechanisms by which hydronium H3O+ and hydroxyde HO− ions are transported through water has represented both an experimental and a theoreti-cal challenge for more than two centuries[108]. The theoreticians have the advantage to use computer simulations to directly investigate, at the microscopic length scale and on very short timescales, the proton motion inside clusters in the gas phase or the bulk liquid. Such kinds of calculations, named Molecular Dynamics (MD) simulations, are performed by keeping constant some selected thermodynamic parameters of the system, such as the temperature.Thanks to the knowledge of the interactions driving the ion dynamics of the studied system, we are able to generate representative configurations of protonated water clusters. Later, we can extract physical observables that are the thermodynamic averages of the configurations visited by the system.
In the perspective of describing quantitatively and accurately proton transfer in water, we have gathered the first essential ingredient in the Chapter1: the interactions between protons and water molecules are very accurately described by means ofab initio methods. Since the proton diffusion is very sensitive to the fluctuations of the H-bond network[109], controling the temperature of the simulations is of paramount importance. Besides, in addition to thermal fluctuations, one has to take into account the Nuclear Quantum Effects (NQE) due to the light mass of the hydrogen atom. Such quantum effects for the nuclei are crucial, even at
room temperature[110].
In this Chapter, we will provide the necessary framework to describeboththermal and nuclear quantum effects in aqueous systems. First, we recall in Section2.1the general methodology to perform MD simulations, with any force field. Then, we will focus on the Langevin Dynamics (LD), which enables to perform MD simulations at constant temperature (Section 2.2), par-ticularly with stochastic forces. Afterwards, we discuss the importance of NQE in water and aqueous systems in Section 2.3. Finally, we demonstrate the possibility to combine together the Feynman Path Integral (PI) approach (2.3.2) with the Langevin Dynamics to perform Path Integral Langevin Dynamics (PILD) simulations in Subsection 2.3.3.
2.1 Molecular Dynamics simulations
Similarly to the classical Monte Carlo (MC) methods, the MD technique is a way to calculate the equilibrium properties of a given system. Still working under the Born-Oppenheimer (BO) approximation, let us write the classical Hamiltonian operator for the nuclei, which differs to the electronic Hamiltonian given in Eq. (1.3):
H(p,q) =
Nat
X
i=1
p2i
2mi +V(q). (2.1)
In the above expression, V(q) is the potential energy describing the interactions between particles or ions. It can be described empirically using analytical functions of the interatomic coordinatesq, with adjustable parameters parametrized to reproduce at best the experimental data for a given system. Such an approach is computationally cheap, but very system-specific.
This limits its application to a given class of problems. To be more general and transferable to any physical or chemical system, it is by far better to evaluate the potential energy term V(q)starting from the first principles of quantum mechanics. In theab initio approaches, one solves the many-body Schrödinger equation at fixed ionic positions q by means of the tech-niques seen in Chapter 1, to obtain the electronic energy of the system Eelec(q). The latter defines the PES in which the nuclei move during the dynamics, within the BO approximation.
In Eq. (2.1), we denotep ≡(p1, . . . ,pNat) the particles momenta (pi =mivi with mi being the mass of the atomi) andq≡(q1, . . . ,qNat)their coordinates. The 6Nat-dimensional space in which these coordinates (p,q) evolve is referred to as thephase space.