(git:374b731)
Loading...
Searching...
No Matches
pao_types.F
Go to the documentation of this file.
1!--------------------------------------------------------------------------------------------------!
2! CP2K: A general program to perform molecular dynamics simulations !
3! Copyright 2000-2024 CP2K developers group <https://cp2k.org> !
4! !
5! SPDX-License-Identifier: GPL-2.0-or-later !
6!--------------------------------------------------------------------------------------------------!
7
8! **************************************************************************************************
9!> \brief Types used by the PAO machinery
10!> \author Ole Schuett
11! **************************************************************************************************
12MODULE pao_types
13 USE dbcsr_api, ONLY: dbcsr_distribution_release,&
14 dbcsr_distribution_type,&
15 dbcsr_release,&
16 dbcsr_type
17 USE kinds, ONLY: default_path_length,&
18 default_string_length,&
19 dp
20 USE linesearch, ONLY: linesearch_type
21#include "./base/base_uses.f90"
22
23 IMPLICIT NONE
24
25 PRIVATE
26
27 CHARACTER(len=*), PARAMETER, PRIVATE :: moduleN = 'pao_types'
28
29 PUBLIC :: pao_env_type, training_matrix_type, pao_finalize
30
31 TYPE filename_type
32 CHARACTER(LEN=default_path_length) :: fn = ""
33 END TYPE filename_type
34
35! **************************************************************************************************
36!> \brief PAO machine learning data for one atomic kind
37!> \var kindname name of atomic kind
38!> \var inputs training points
39!> \var outputs training points
40!> \var prior constant prior which is added to prediction
41!> \var NN trained neural network
42!> \var GP trained gaussian process
43! **************************************************************************************************
44 TYPE training_matrix_type
45 CHARACTER(LEN=default_string_length) :: kindname = ""
46 REAL(dp), DIMENSION(:, :), ALLOCATABLE :: inputs
47 REAL(dp), DIMENSION(:, :), ALLOCATABLE :: outputs
48 REAL(dp), DIMENSION(:), ALLOCATABLE :: prior
49 REAL(dp), DIMENSION(:, :, :), ALLOCATABLE :: nn ! Neural Network
50 REAL(dp), DIMENSION(:, :), ALLOCATABLE :: gp ! Gaussian Process
51 END TYPE training_matrix_type
52
53! **************************************************************************************************
54!> \brief The PAO environment type
55!> \var eps_pao parsed input parameter
56!> \var cg_reset_limit parsed input parameter
57!> \var mixing parsed input parameter
58!> \var regularization parsed input parameter
59!> \var penalty_dist parsed input parameter
60!> \var penalty_strength parsed input parameter
61!> \var check_unitary_tol parsed input parameter
62!> \var check_grad_tol parsed input parameter
63!> \var num_grad_eps parsed input parameter
64!> \var eps_pgf parsed input parameter
65!> \var linpot_precon_delta parsed input parameter
66!> \var linpot_init_delta parsed input parameter
67!> \var linpot_regu_delta parsed input parameter
68!> \var linpot_regu_strength parsed input parameter
69!> \var num_grad_order parsed input parameter
70!> \var max_pao parsed input parameter
71!> \var max_cycles parsed input parameter
72!> \var write_cycles parsed input parameter
73!> \var parameterization parsed input parameter
74!> \var optimizer parsed input parameter
75!> \var cg_init_steps parsed input parameter
76!> \var preopt_dm_file parsed input parameter
77!> \var restart_file parsed input parameter
78!> \var ml_training_set parsed input parameter
79!> \var ml_method parsed input parameter
80!> \var ml_prior parsed input parameter
81!> \var ml_descriptor parsed input parameter
82!> \var ml_tolerance parsed input parameter
83!> \var gp_noise_var parsed input parameter
84!> \var gp_scale parsed input parameter
85!> \var precondition parsed input parameter
86!> \var iw output unit for pao in general
87!> \var iw_atoms output unit for one line summary for each atom
88!> \var iw_gap output unit for gap of the fock matrix
89!> \var iw_fockev output unit for eigenvalues of the fock matrix
90!> \var iw_opt output unit for pao optimizer
91!> \var iw_mlvar output unit for variances of machine learning predictions
92!> \var iw_mldata output unit for dumping training data used for machine learning
93!> \var istep counts pao iterations, ie. number of pao energy evaluations
94!> \var energy_prev energy of previous pao step
95!> \var step_start_time timestamp of when current pao step started
96!> \var norm_G frobenius-norm of matrix_G or matrix_G_preconed
97!> \var linesearch holds linesearch state
98!> \var matrix_X_ready set when matrix_X is initialized
99!> \var matrix_P_ready set when density matrix is initialized
100!> \var constants_ready set when stuff, which does not depend of atomic positions is ready
101!> \var need_initial_scf set when the initial density matrix is not self-consistend
102!> \var matrix_X parameters of pao basis, which eventually determine matrix_U. Uses diag_distribution.
103!> \var matrix_U0 constant pre-rotation which serves as initial guess for exp-parametrization. Uses diag_distribution.
104!> \var matrix_H0 Diagonal blocks of core hamiltonian, uses diag_distribution
105!> \var matrix_Y selector matrix which translates between primary and pao basis.
106!> basically a block diagonal "rectangular identity matrix". Uses s_matrix-distribution.
107!> \var matrix_N diagonal matrix filled with 1/sqrt(S) from primary overlap matrix. Uses s_matrix-distribution.
108!> \var matrix_N_diag copy of matrix_N using diag_distribution
109!> \var matrix_N_inv diagonal matrix filled with sqrt(S) from primary overlap matrix. Uses s_matrix-distribution.
110!> \var matrix_N_inv_diag copy of matrix_N_inv using diag_distribution
111!> \var matrix_X_orig copy made of matrix_X at beginning of optimization cylce, used for mixing. Uses diag_distribution.
112!> \var matrix_G derivative of pao-energy wrt to matrix_X, ie. the pao-gradient. Uses diag_distribution.
113!> \var matrix_G_prev copy of gradient from previous step, used for conjugate gradient method. Uses diag_distribution.
114!> \var matrix_D Current line-search direction, used for conjugate gradient method. Uses diag_distribution.
115!> \var matrix_D_preconed Current line-search direction with preconditioner applied.
116!> This copy is keept, because application of inverse preconditioner
117!> introduces too much numeric noise. Uses diag_distribution.
118!> \var matrix_V_terms Potential terms, used by linpot and gth parametrization, Uses diag_distribution.
119!> \var matrix_BFGS Approximate inverse hessian, used by BFGS optimizer, Uses diag_distribution.
120!> \var matrix_precon preconditioner, uses diag_distribution.
121!> \var matrix_precon_inv inverse of matrix_precon, uses diag_distribution.
122!> \var matrix_R Rgularization, uses diag_distribution
123!> \var ml_training_matrices holds training data and trained machine learning model
124!> \var diag_distribution DBCSR distribution to spreads diagonal blocks evenly across ranks
125! **************************************************************************************************
126 TYPE pao_env_type
127 ! input values
128 REAL(kind=dp) :: eps_pao = 0.0_dp
129 REAL(kind=dp) :: cg_reset_limit = 0.1_dp
130 REAL(kind=dp) :: mixing = 0.0_dp
131 REAL(kind=dp) :: regularization = 0.0_dp
132 REAL(kind=dp) :: penalty_dist = 0.0_dp
133 REAL(kind=dp) :: penalty_strength = 0.0_dp
134 REAL(kind=dp) :: check_unitary_tol = 0.0_dp
135 REAL(kind=dp) :: check_grad_tol = 0.0_dp
136 REAL(kind=dp) :: num_grad_eps = 0.0_dp
137 REAL(kind=dp) :: eps_pgf = 0.0_dp
138 REAL(kind=dp) :: linpot_precon_delta = 0.0_dp
139 REAL(kind=dp) :: linpot_init_delta = 0.0_dp
140 REAL(kind=dp) :: linpot_regu_delta = 0.0_dp
141 REAL(kind=dp) :: linpot_regu_strength = 0.0_dp
142 INTEGER :: num_grad_order = -1
143 INTEGER :: max_pao = -1
144 INTEGER :: max_cycles = -1
145 INTEGER :: write_cycles = -1
146 INTEGER :: parameterization = -1
147 INTEGER :: optimizer = -1
148 INTEGER :: cg_init_steps = -1
149 LOGICAL :: precondition = .false.
150 CHARACTER(LEN=default_path_length) :: preopt_dm_file = ""
151 CHARACTER(LEN=default_path_length) :: restart_file = ""
152 TYPE(filename_type), DIMENSION(:), ALLOCATABLE :: ml_training_set
153
154 INTEGER :: ml_method = -1
155 INTEGER :: ml_prior = -1
156 INTEGER :: ml_descriptor = -1
157 REAL(kind=dp) :: ml_tolerance = 0.0_dp
158 REAL(kind=dp) :: gp_noise_var = 0.0_dp
159 REAL(kind=dp) :: gp_scale = 0.0_dp
160
161 ! output units
162 INTEGER :: iw = -1
163 INTEGER :: iw_atoms = -1
164 INTEGER :: iw_gap = -1
165 INTEGER :: iw_fockev = -1
166 INTEGER :: iw_opt = -1
167 INTEGER :: iw_mlvar = -1
168 INTEGER :: iw_mldata = -1
169
170 ! state variable
171 INTEGER :: istep = -1
172 REAL(kind=dp) :: energy_prev = 0.0_dp
173 REAL(kind=dp) :: step_start_time = 0.0_dp
174 REAL(kind=dp) :: norm_g = 0.0_dp
175 TYPE(linesearch_type) :: linesearch = linesearch_type()
176 LOGICAL :: matrix_x_ready = .false.
177 LOGICAL :: matrix_p_ready = .false.
178 LOGICAL :: constants_ready = .false.
179 LOGICAL :: need_initial_scf = .false.
180
181 ! matrices
182 TYPE(dbcsr_type) :: matrix_x
183 TYPE(dbcsr_type) :: matrix_u0
184 TYPE(dbcsr_type) :: matrix_h0
185 TYPE(dbcsr_type) :: matrix_y
186 TYPE(dbcsr_type) :: matrix_n
187 TYPE(dbcsr_type) :: matrix_n_diag
188 TYPE(dbcsr_type) :: matrix_n_inv
189 TYPE(dbcsr_type) :: matrix_n_inv_diag
190 TYPE(dbcsr_type) :: matrix_x_orig
191 TYPE(dbcsr_type) :: matrix_g
192 TYPE(dbcsr_type) :: matrix_g_prev
193 TYPE(dbcsr_type) :: matrix_d
194 TYPE(dbcsr_type) :: matrix_d_preconed
195 TYPE(dbcsr_type) :: matrix_v_terms
196 TYPE(dbcsr_type) :: matrix_bfgs
197 TYPE(dbcsr_type) :: matrix_precon
198 TYPE(dbcsr_type) :: matrix_precon_inv
199 TYPE(dbcsr_type) :: matrix_r
200
201 TYPE(training_matrix_type), ALLOCATABLE, &
202 DIMENSION(:) :: ml_training_matrices
203
204 TYPE(dbcsr_distribution_type) :: diag_distribution
205 END TYPE
206
207CONTAINS
208
209! **************************************************************************************************
210!> \brief Finalize the PAO environment
211!> \param pao ...
212! **************************************************************************************************
213 SUBROUTINE pao_finalize(pao)
214 TYPE(pao_env_type) :: pao
215
216 CHARACTER(len=*), PARAMETER :: routinen = 'pao_finalize'
217
218 INTEGER :: handle
219
220 CALL timeset(routinen, handle)
221
222 CALL dbcsr_release(pao%matrix_X)
223 CALL dbcsr_release(pao%matrix_Y)
224 CALL dbcsr_release(pao%matrix_N)
225 CALL dbcsr_release(pao%matrix_N_diag)
226 CALL dbcsr_release(pao%matrix_N_inv)
227 CALL dbcsr_release(pao%matrix_N_inv_diag)
228 CALL dbcsr_release(pao%matrix_H0)
229
230 DEALLOCATE (pao%ml_training_set)
231 IF (ALLOCATED(pao%ml_training_matrices)) &
232 DEALLOCATE (pao%ml_training_matrices)
233
234 CALL dbcsr_distribution_release(pao%diag_distribution)
235
236 !TODO: should finish printkey
237
238 CALL timestop(handle)
239
240 END SUBROUTINE pao_finalize
241
242END MODULE pao_types
kinds
Defines the basic variable types.
Definition kinds.F:23
kinds::dp
integer, parameter, public dp
Definition kinds.F:34
kinds::default_string_length
integer, parameter, public default_string_length
Definition kinds.F:57
kinds::default_path_length
integer, parameter, public default_path_length
Definition kinds.F:58
linesearch
A generic framework to calculate step lengths for 1D line search.
Definition linesearch.F:12
pao_types
Types used by the PAO machinery.
Definition pao_types.F:12
pao_types::pao_finalize
subroutine, public pao_finalize(pao)
Finalize the PAO environment.
Definition pao_types.F:214
linesearch::linesearch_type
Definition linesearch.F:94
pao_types::pao_env_type
Definition pao_types.F:126
pao_types::training_matrix_type
Definition pao_types.F:44