Forces, Stress and structural optimization
Transcript of Forces, Stress and structural optimization
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Forces, Stress
and
structural optimization
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Forces
* finite step methods Steepest Descent Damped Dynamics (friction,quickmin)
* Line Minimization methods: Conjugate Gradients Quasi Newton methods BFGS
* Stress, VCS relaxation and MD
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Hellmann-Feynman forcesIn the Born-Oppenheimer approximation the total energy is a function of the ionic coordinates and define a 3N-dimensional hyper-surface, called the Potential Energy Surface (PES)
The forces acting on the ions are given by (minus) the gradient of the PES.Using Hellmann-Feynman theorem
where the electron-nucleus and the ion-ion electrostatic interactions
are the only terms explicitly dependent on the ionic positions
It is just what we would compute classically !
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Hellmann-Feynman forcesWhen using a plane-wave basis set no corrections (Pulay's forces) are needed to the previous formula !
….except taking care of the modified form of the external (pseudo)-potential.
The evaluation of the forces is then a cheap byproduct of the electronic structure calculation. The quality of the forces depends on the quality of the electronic structure calculation.
From the forces:● Structural optimization from the equilibrium condition
● Molecular dynamics● Higher-order derivatives (phonons, ...)
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Structural OptimizationSeveral algorithms for searching an equilibrium configuration, close to the initial ionic configuration ( a local minimum of the PES). For instance:
Steepest Descent Optimization
discretizinig
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VERLET DYNAMICS Rnew = 2 R - Rold + dt*dt*F/M
V = (Rnew – Rold) / 2 dt
DAMPED VERLET DYNAMICSAs above but stop the particle whenever <F|V> < 0Or rather project the velocity in the direction of the force
V_new = F max ( 0,<F|V>) / <F|F>
VELOCITY VERLET DYNAMICS V = Vaux + dt/2 * F / M vel @ time t
Vaux = V + dt/2 * F / M aux vel @ time t+dt/2
Rnew = R + Vaux dt pos @ time t+dt
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Conjugate Gradients
E = ½ x A x - b x + c
F = -dE/dx = b - Ax = g(x)
Xn = Xn-1 + λ hn
hi * A * hj = 0, hi * gj = 0 for i≠j
small memory needs, good for quadratic functions, may need preconditioning
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Quasi-Newton ionic relaxationThe Broyden-Fletcher-Goldfarb-Shanno algorithm
Taylor expansion of the energy around a point (hopefully) close to a stationary point ( )
gradient vector
Hessian matrix
displacement
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Quasi-Newton ionic relaxationThe Broyden-Fletcher-Goldfarb-Shanno algorithm
Equivalently, for the gradient vector we have the condition:
The stationary condition is The Newton-Raphson step is
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Quasi-Newton ionic relaxationThe Broyden-Fletcher-Goldfarb-Shanno algorithm
The inverse Hessian matrix is updated using the BFGS scheme:
trust radiusNewton-Raphson step
where
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Structural Optimization : Convergence
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THE END