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pao_types.F
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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 ! **************************************************************************************************
12 MODULE 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 
207 CONTAINS
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 
242 END 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