3.3. Dumping/Loading a Hamiltonian to/from a file¶
HORTON supports two formats for Hamiltonians: (i) an internal binary format based
on HDF5 (extension .h5) and (ii) Molpro’s FCIDUMP text format (containing FCIDUMP
somewhere in the file name). The internal format is more flexible and can store
Hamiltonians in various ways. The FCIDUMP format is more restricted
but can be used to interface HORTON with different codes, e.g. Molpro. HORTON can
also load integrals from a Gaussian log file but this is absolutely not
recommended for any serious calculation.
For general information on how to load and dump data with HORTON in different data file formats, refer to Data file formats (input and output).
3.3.1. HORTON’s internal format¶
3.3.1.1. Dumping¶
You can store all of the operators in the atomic-orbital (AO) basis. For example, you can store the core energy and all the integrals in the Cholesky decomposed format:
data/examples/hamiltonian/dump_internal_ao.py¶
from horton import * # pylint: disable=wildcard-import,unused-wildcard-import
# Set up molecule, define basis set
# ---------------------------------
# get the XYZ file from HORTON's test data directory
fn_xyz = context.get_fn('test/water.xyz')
mol = IOData.from_file(fn_xyz)
obasis = get_gobasis(mol.coordinates, mol.numbers, 'cc-pvdz')
lf = CholeskyLinalgFactory(obasis.nbasis)
# Construct Hamiltonian
# ---------------------
mol.lf = lf
mol.kin = obasis.compute_kinetic(lf)
mol.na = obasis.compute_nuclear_attraction(mol.coordinates, mol.pseudo_numbers, lf)
mol.er = obasis.compute_electron_repulsion(lf)
mol.core_energy = compute_nucnuc(mol.coordinates, mol.pseudo_numbers)
# Write to a HDF5 file
# --------------------
mol.to_file('hamiltonian_ao.h5')
# CODE BELOW IS FOR horton-regression-test.py ONLY. IT IS NOT PART OF THE EXAMPLE.
import numpy as np # pylint: disable=wrong-import-position
rt_results = {
# Basic data to rule out silly problems.
'coordinates': mol.coordinates,
'numbers': mol.numbers,
'centers': obasis.centers,
'shell_map': obasis.shell_map,
'nprims': obasis.nprims,
'shell_types': obasis.shell_types,
'alphas': obasis.alphas,
'con_coeffs': obasis.con_coeffs,
'scales': obasis.get_scales(),
# Stuff that is actually computed.
'kin': mol.kin._array.ravel()[::10],
'na': mol.na._array.ravel()[::10],
# ERIs as such are not included.
# This is a Cholesky decomposition, which is not unique.
# The actual depend strongly on minor details (last digits) of the ERIs and the
# BLAS implementation.
#'er': mol.er._array.ravel()[::1000],
'er': np.einsum('aij,akl->ijkl', mol.er._array, mol.er._array).ravel()[::100],
'core_energy': mol.core_energy,
}
# We can be extra picky because the integrals are the result of relatively simple
# deterministic algorithms. The numerical noise should be low.
rt_atols = {
'kin': 1e-12,
'na': 1e-12,
'er': 1e-12,
'core_energy': 1e-12,
}
# BEGIN AUTOGENERATED CODE. DO NOT CHANGE MANUALLY.
rt_previous = {
'alphas': np.array([
13.01, 1.962, 0.4446, 0.122, 0.72699999999999998, 11720.0, 1759.0,
400.80000000000001, 113.7, 37.030000000000001, 13.27, 5.0250000000000004,
1.0129999999999999, 11720.0, 1759.0, 400.80000000000001, 113.7,
37.030000000000001, 13.27, 5.0250000000000004, 1.0129999999999999,
0.30230000000000001, 17.699999999999999, 3.8540000000000001, 1.046,
0.27529999999999999, 1.1850000000000001, 13.01, 1.962, 0.4446, 0.122,
0.72699999999999998
]),
'centers': np.array([
1.4812372636344324, -0.93019123325686148, -0.0, -0.0, 0.11720081482579606, -0.0,
-1.4812372636344324, -0.93019123325686148, -0.0
]).reshape(3, 3),
'con_coeffs': np.array([
0.033498726389998235, 0.23480080117413188, 0.81368295788289957,
1.0000000000000002, 0.99999999999999978, 0.00070964594651844724,
0.0054672722921914173, 0.027823118610188751, 0.1047477397114553,
0.28292084635690801, 0.44849523869832547, 0.27081688521276948,
0.015450291607439656, -0.00031444341168873326, -0.0024821376810179379,
-0.01231635538158307, -0.050538917343671642, -0.13938490331632322,
-0.32507749481778159, -0.22984830758784872, 1.0953793467882866, 1.0,
0.062679166281505785, 0.33353656587936059, 0.74123964163972922, 1.0,
1.0000000000000004, 0.033498726389998235, 0.23480080117413188,
0.81368295788289957, 1.0000000000000002, 0.99999999999999978
]),
'coordinates': np.array([
1.4812372636344324, -0.93019123325686148, -0.0, -0.0, 0.11720081482579606, -0.0,
-1.4812372636344324, -0.93019123325686148, -0.0
]).reshape(3, 3),
'core_energy': 9.1571750364299866,
'er': np.array([
0.89725180225471179, 0.7750370389423169, 0.54947200535592522, 0.44160814505865353,
0.49874814897477898, 0.3172227129453763, 0.30001594996347442, 0.0,
-0.010020058744939447, 0.0, 0.0, 0.04135769358995333, 0.15260808260294856, 0.0,
0.0, -0.015754562095667005, 0.0, -5.3394470580291571e-18, 0.15260808260294845,
0.0, 0.049656674637086423, 0.0, 0.0, 0.0, 0.15260808260294853,
0.027007256750328432, 0.0, 0.032487133987260081, -0.012669408027262602,
0.010004464740140951, 0.0064269377729517874, 0.0, 0.0, 0.00073063854874406769,
0.0, 0.078610078418025447, 0.071280648017013684, 0.04629804879716351,
-0.015825575630799692, 0.018194071571171627, 0.0, -0.062347103865683866,
0.17713450973371495, 0.0, 0.0, -0.084510715316741244, -0.051414604949693479,
0.011442179767392081, 0.06898003365869286, 0.0, 0.0, -0.02707568159129807, 0.0,
0.0, 0.041217571335121744, -0.046070578589291235, -0.0085311610610867214,
0.042835814932061823, -0.0060896639688343794, 0.0, 0.013455203309772318,
0.0041467602182828087, 0.0, 0.0, 0.0, 0.08131842843115232, 0.21083968590677038,
0.10284062503515221, -0.034841870492159949, 0.0, 0.0, -0.10881146881077347,
0.11528627973349891, 0.056080906203282349, 0.0051335061813653571, 0.0, 0.0, 0.0,
0.019733189426005531, 0.0043184027238586283, 0.0, 0.02543118152118376, 0.0,
0.011285781402612464, 0.029837511085797162, 0.0, 0.012556235482928536, 0.0,
-0.0049385573912613979, 0.0095707556097843725, 0.020598504971250962,
0.0025745554165149311, 0.00041478521293242482, 0.0, 0.021250266064046293, 0.0,
0.013505883936290973, -0.0088802030637487161, 0.02996072087157494, 0.0, 0.0,
0.005448415045728433, 0.033134999666043663, 0.0, -0.027418867034147896,
0.0013703399090779097, 0.0, 0.0, 0.071242063457804039, 0.0, -0.015381951539409517,
-0.0073038902095968229, -0.010069239418365603, -0.0024248750877733909,
0.0083678830869999173, 0.0031583828064153489, 0.053635474148762803,
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0.049521290910514271, -0.021587058987684992, -0.010403292016104992,
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0.36334320757331645, 0.40732887612612212, 0.28916015357083508,
-6.9419471654026315e-17, 0.0, 0.0052734188043823838, 0.0, 0.0,
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0.0, 0.012607575435098219, 0.0, 0.04644694427548917, 0.0, -0.0049393674355961235,
-0.0080764249965371536, 0.012607575435098219, 0.0, 0.0, -1.1692891436918254e-05,
0.0, 0.0, 0.055779665030958346, 0.016177246267157702, 0.0, 0.0,
0.004560340155107848, 0.0, 0.1620287964885137, 0.11379320334701988,
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0.032629468882567757, -0.0093111233712128635, 0.0093491731499273419, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0099781335292100995, 0.0, 0.0, -0.0033219807866524493,
0.01352729102812948, 0.0, -0.036783004799335065, -0.076069838954618732,
-0.037454006646242134, 0.02707568159129807, 0.0, -0.0012615407681109217, 0.0, 0.0,
0.0, 0.0, 0.011848961533771242, 0.0026547288296351725, -0.0011832927835609454,
0.0, 0.0, 0.0021840994989754101, 0.0, -3.170695592342013e-05, 0.0, 0.0,
-0.0081629223638165927, 0.0, 0.0, 0.029944570672513555, -0.0049596187680795182,
-0.0052962242734458098, -0.0025889408761541497, 0.0031731175581738527,
-0.0041068992539271577, 0.0, 0.0031501260523017077, 0.0, 0.0,
-0.01471440618448764, 0.0, 0.0, 0.0050619064565629928, -0.0026489484945178311,
0.0, 0.0051830750133002104, -0.00073957319524986509, 0.0, 0.0,
-0.026443640407069351, 0.0, -0.055531660146113694, -0.012027783574484375,
-0.016090074780267694, -0.0047855398595116339, 0.030450782808595003,
0.011835134156022525, 0.03000043348529844, -0.024595881627145523, 0.0, 0.0,
0.032726449038747982, 0.0, 0.0, -0.00083734418561985885, 0.0, 0.0,
3.2639904955485671e-18, 0.0073278648838073076, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
-0.018248102126328269, 0.0, 0.0, 0.0033595656765543916, 0.0, 0.0,
-0.0043079408731227318, -0.00049352716682940631, 0.0048219982497500402,
-0.041357693589953337, 0.7750370389423169, 0.68950825483386158,
0.56395977780882989, 0.44378710315982745, 0.52639324615606009,
0.32550511289449746, 6.2803014027449953e-26, 0.0, 0.0041299373452998341, 0.0, 0.0,
2.0049224180148546e-18, 0.0, -0.010320567460251067, -0.0076579950938655177, 0.0,
-0.010467734621014871, 0.0, -0.0097501391237798749, 0.0, -0.041627569402247155,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0043107888739801698, 0.0, 0.0, -0.066309212916184537,
-0.018662395415542477, 0.0, 0.0, -0.027464365209302184, 0.0,
-0.055531660146113694, -0.061198324147510259, -0.038658360115119417,
0.028215041808016761, -0.029790887975944338, 0.0, -0.054658743463986297,
0.11138479240449299, 0.0, 0.0, -0.071472199421718827, -0.045825752921799551, 0.0,
0.0, 0.013047997453327027, 0.0026261574453122789, 0.0, 0.0, 0.0,
0.011423068208342158, -0.0016462231548409285, -0.01012591420879024,
0.0067427491846061508, 0.0, 0.012939986012490487, 0.0, 0.0, -0.040749927892846607,
0.03205930810641789, -0.017224668621078583, 0.0, 0.0, 0.0, 0.0,
0.0081520585775795504, 0.0, 0.029080726718386758, -0.035241006690277396,
-0.01644131406204484, 0.0013394192415364195, 0.0, 0.0, 0.0, -0.013628524918760088,
-0.0051605168281378989, 0.0, 0.0, -0.0024150474111346259, 0.0, 0.0,
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0.023471143920589067, 0.0, -0.0045176831002181417, 0.0, 0.0, -0.02234525984277742,
0.0, 0.0, 0.028293452025718972, 0.00066443879921949988, 0.0, -0.01342098691189365,
0.0010084738630798309, 0.0, 0.0, 0.033647398142560632, 0.0, 0.036139712664656723,
0.021206561334588196, 0.029883705181987644, 0.0063757288368023815,
-0.019969673345657371, -0.0074999995435759614, -0.0036494280625862323,
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0.0, 0.0094993721852464838, 0.02471834308859789, 0.010690485276623436,
0.011766689452784536, 0.0039691828494795509, 0.0, 0.028897736122522073, 0.0, 0.0,
0.000342442703096959, 0.0, 0.0, -0.014232827261674736, 0.0002703976121612939,
0.0030009112554317593, 5.1369590350978296e-18, 0.7750370389423169,
0.68950825483386158, 0.55301549380551407, 0.42897568399061825,
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0.0, 0.097636786468943734, 0.0, 0.0, 0.0, 0.0, 0.0, 0.00077280609369165503, 0.0,
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0.0027991708575656666, 0.0, 0.0, 0.0, -0.010473763050818119,
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0.0, 0.0, 0.0, 0.0, -0.0026791482777050223, 0.0, -0.00029259469975634355, 0.0,
0.0, 0.011442179767392081, 0.0, 0.0, 0.0094324712362641502, 0.011636955296707403,
0.0, 0.0018373332494505696, -8.7825251885071605e-05, 0.0, 0.0,
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0.099352518053861125, 0.022277844499050035, -0.054399058567187103,
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0.0, 0.0, 0.0013702754062059077, 0.00037996780164464253, 0.0,
8.1516748341091877e-05, -0.0055518755989678722, -0.0028261600266777059,
-0.004657760657511896, -0.00011219137120257842, 0.0031583828064153489,
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0.010004464740140941, 0.0, 0.0, -1.1692891436920597e-05, 0.0, 0.0,
0.0082263841520525335, 0.0, -0.0040856720078869214, -0.041764197839024075,
0.0017783106483997833, 0.00010256085230182094, -0.0017744107752643878, 0.0,
0.0052896889677227993, 0.0, 0.0, 0.00064012155690763893, 0.0,
0.00032960390685491065, 0.0, 0.0, 0.0, 0.0, 0.54491261509976607,
0.49382703778497777, 1.4430304338455688, 0.55807172218749623, 0.92610809252415272,
0.4371215579283228, -0.077655357510534095, 0.0, 1.2999529022003201e-26, 0.0, 0.0,
-0.0283112045901469, -0.027819246321276317, 0.0, 0.0, 1.0066419021508165e-18, 0.0,
0.016563196299642893, 0.0094993721852464838, 0.0, -2.8181514264772843e-26, 0.0,
0.0, 0.0, 0.0, 0.0, 3.8560907993804915e-20, 0.0, 0.0, 0.0, 0.0,
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5.2301796850937051e-26, 0.32409997307693239, 0.31263522348546552,
0.55301549380551407, 0.42897568399061825, 0.48055279943695406, 0.4910730839055914,
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0.0, -0.00031854403699438507, 0.011835134156022525, 0.0078612800775987347,
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0.037454006646242134, -0.0021088621594861356, 0.0, -0.0033073344186628963,
0.001194622163704113, 0.0, 0.0, 0.02004367098697428, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0054346943504989314, -0.0081629223638165927, 0.0,
0.015015422940804686, 0.0030652572894219409, 0.0, 0.032487133987260081,
-0.01708649424150515, 0.0, 0.012607575435098219, 0.0, 0.0, 0.0, 0.0, 0.0,
-0.0056513980637949669, 0.0, -0.0064185784026458709, -0.0031000561685754387, 0.0,
0.003390199240210006, 0.0015147454513751068, 0.0, 0.0, 0.0, 0.0,
0.32409997307693239, 0.31566471438821575, 0.49904581007425008,
0.42052264377363641, 0.47690389080879952, 0.4910730839055914
]),
'kin': np.array([
0.96945722711242355, 0.0, 0.036292401476283279, 0.07995516390306795, 0.0,
1.8174999999999994, 0.27452182030061434, -9.8774829880917554e-18,
0.4858203080864158, -0.22493046394464195, 1.8174999999999994, 0.0,
-0.0029726129513667956, 0.0, 0.018376480943548955, 10.361104211507204, 0.0,
-0.24514942860277961, 0.0, 0.17334668011290896, 4.3248263329243191,
7.9993407779445172e-17, 0.0, -4.6184215514858166e-17, 0.0, 4.3248263329243191,
0.0, 0.0, 0.0, 0.27452182030061434, 0.68824999999999992, 0.093096714010858797,
0.0, 0.0, 0.0, 4.1475000000000017, 0.0, 0.0, 0.0, 0.0, 4.1475000000000026,
0.12248253843499435, 0.0, 0.57792718386389474, 7.9993407779445172e-17,
4.1475000000000026, 0.0, -0.17882475033034573, 0.036292401476283279, 0.0, 0.183,
0.34077676967706871, 0.0, -9.8774829880917554e-18, 0.093096714010858797,
1.8174999999999994, 0.0, 0.0
]),
'na': np.array([
-6.0797251408814796, 0.0, -1.8318359795245827, -2.5150668886409582, 0.0,
-6.061928702514308, -1.8185022809671232, 0.28314544048255308, -2.5612286623684115,
0.14889784275391382, -5.1821395397642984, 0.0, -1.6065246363266326, 0.0,
-2.1193055903316758, -20.636604199574929, 0.0, 3.2140498161241444,
0.22793252967254229, -2.2275870688292225, -12.890881502223545,
0.20355767445439643, 0.0, -0.10488028427486198, 0.0, -12.778416588080809, 0.0,
0.0, 0.0, -1.8185022809671232, -5.4140292951278415, -0.93660650303069326, 0.0,
0.0, 0.0, -8.4711446777306918, 0.0, 0.0, 0.0, 0.0, -8.4345065860561981,
-0.88778762785900955, -0.069054309343865417, -2.0036653123921448,
0.20355767445439643, -8.6284201435269861, 0.0, 0.91259495636933452,
-1.8318359795245827, 0.0, -4.3875277704507676, -2.7053738330031201, 0.0,
0.28314544048255308, -0.93660650303069326, -5.6021960154514723, 0.0, 0.0
]),
'nprims': np.array([3, 1, 1, 8, 8, 1, 3, 1, 1, 3, 1, 1]),
'numbers': np.array([1, 8, 1]),
'scales': np.array([
4.8822280848273554, 1.1815016827472116, 0.38804955165732685, 0.14712279442857354,
0.95688137505956572, 0.95688137505956572, 0.95688137505956572, 802.79711109646587,
193.57954815870016, 63.841910773797906, 24.815923446879857, 10.698561222364761,
4.9552239190766754, 2.3920053231708036, 0.7196431174847403, 802.79711109646587,
193.57954815870016, 63.841910773797906, 24.815923446879857, 10.698561222364761,
4.9552239190766754, 2.3920053231708036, 0.7196431174847403, 0.29056192400885111,
51.74956110608008, 51.74956110608008, 51.74956110608008, 7.6971460252584345,
7.6971460252584345, 7.6971460252584345, 1.5078380330019092, 1.5078380330019092,
1.5078380330019092, 0.28424828438655292, 0.28424828438655292, 0.28424828438655292,
2.2152184980417999, 3.8368709884748156, 3.8368709884748156, 2.2152184980417999,
3.8368709884748156, 2.2152184980417999, 4.8822280848273554, 1.1815016827472116,
0.38804955165732685, 0.14712279442857354, 0.95688137505956572,
0.95688137505956572, 0.95688137505956572
]),
'shell_map': np.array([0, 0, 0, 1, 1, 1, 1, 1, 1, 2, 2, 2]),
'shell_types': np.array([0, 0, 1, 0, 0, 0, 1, 1, -2, 0, 0, 1]),
}
The internal format will store all attributes of the
IOData instance. Note that the attributes
coordinates, numbers and title were loaded from the .xyz file
and will also be dumped into the internal format. Deleting an attribute, e.g.
del mol.title, or adding a new attribute, e.g. mol.egg = 'spam', will
result in the exclusion or the inclusion of the appropriate attribute,
respectively.
In the HDF5 file, all data is stored in binary form with full precision, which means that all significant digits are written to file. In the example above, the dumped HDF5 file will have the following layout:
$ h5dump -n hamiltonian_ao.h5
HDF5 "hamiltonian.h5" {
FILE_CONTENTS {
group /
dataset /coordinates
dataset /core_energy
group /er
dataset /er/array
group /kin
dataset /kin/array
group /lf
group /na
dataset /na/array
dataset /numbers
dataset /title
}
}
The attributes of the FCIDUMP format, one_mo, two_mo, core_energy,
nelec and ms2 can also be used in the internal format. We can create an
empty IOData instance and assign each of these
attributes. For example,
data/examples/hamiltonian/dump_internal_ao_fcidump.py¶
import numpy as np
from horton import * # pylint: disable=wildcard-import,unused-wildcard-import
# Set up Neon atom, define basis set
# ----------------------------------
coordinates = np.array([[0.0, 0.0, 0.0]])
numbers = np.array([10])
obasis = get_gobasis(coordinates, numbers, 'cc-pvdz')
lf = DenseLinalgFactory(obasis.nbasis)
# Construct Hamiltonian
# ---------------------
one_mo = lf.create_two_index()
obasis.compute_kinetic(one_mo)
obasis.compute_nuclear_attraction(coordinates, numbers.astype(float), one_mo)
two_mo = obasis.compute_electron_repulsion(lf)
core_energy = compute_nucnuc(coordinates, numbers.astype(float))
# Write to a HDF5 file
# --------------------
data = IOData()
data.lf = lf
data.one_mo = one_mo
data.two_mo = two_mo
data.core_energy = core_energy
data.nelec = 10
data.ms2 = 0
data.to_file('hamiltonian_ao_fcidump.h5')
# CODE BELOW IS FOR horton-regression-test.py ONLY. IT IS NOT PART OF THE EXAMPLE.
rt_results = {
'one_mo': data.one_mo._array.ravel()[::10],
'two_mo': data.two_mo._array.ravel()[::1000],
'core_energy': data.core_energy,
}
# BEGIN AUTOGENERATED CODE. DO NOT CHANGE MANUALLY.
rt_previous = {
'core_energy': 0.0,
'one_mo': np.array([
-96.143299148359503, 0.0, 0.0, -11.134992899248306, 0.0, 0.0, -18.917972852223652,
0.0, 0.0, -6.9898816487336894, 0.0, 0.0, -6.9898816487336894, 0.0, 0.0,
-12.6292465706633, 0.0, 0.0, -12.629246570663298, 0.0
]),
'two_mo': np.array([
5.9688416466791034, 0.0, 0.0, 0.22652795134616616, 0.08668542315127381, 0.0,
0.11914642982846606, 0.0, 0.0, 0.1723657034224888, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.008602665981441826, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0
]),
}
will results in the following HDF5 layout:
$ h5dump -n hamiltonian_ao_fcidump.h5
HDF5 "hamiltonian_ao_fcidump.h5" {
FILE_CONTENTS {
group /
dataset /core_energy
group /lf
dataset /ms2
dataset /nelec
group /one_mo
dataset /one_mo/array
group /two_mo
dataset /two_mo/array
}
}
Note that the integrals in this example are actually stored in the AO basis
(despite the _mo suffix). Read the section Preparing for a Post-Hartree-Fock calculation
if you want to compute (and store) integrals in the molecular orbital (MO) basis.
3.3.1.2. Loading¶
You can load integrals from the HORTON’s internal format, as follows:
data/examples/hamiltonian/load_internal_ao.py¶
from horton import * # pylint: disable=wildcard-import,unused-wildcard-import
# Load the integrals from the file
data = IOData.from_file('hamiltonian_ao.h5')
# Access some attributes. In more realistic cases, some code follows that does a
# useful calculation.
print data.kin.nbasis
# CODE BELOW IS FOR horton-regression-test.py ONLY. IT IS NOT PART OF THE EXAMPLE.
import numpy as np # pylint: disable=wrong-import-position
rt_results = {
'kin': data.kin._array.ravel()[::10],
'na': data.na._array.ravel()[::10],
'er': np.einsum('aij,akl->ijkl', data.er._array, data.er._array).ravel()[::997],
'core_energy': data.core_energy,
}
# BEGIN AUTOGENERATED CODE. DO NOT CHANGE MANUALLY.
rt_previous = {
'core_energy': 9.1571750364299866,
'er': np.array([
0.89725180225471179, 0.0, 0.00033874359041040982, 0.01090112101449916, 0.0, 0.0,
0.0, 0.036147271172379432, 0.0, 0.0040640114916747141, 0.0, 0.0,
0.013444933423984205, 0.0028381227650030889, -0.00042795298347795066, 0.0,
0.05130981975356564, 0.0, -0.0046360708799739131, 0.0098781829019023125, 0.0,
0.0040668395640886985, 0.0015185846895349707, 0.0, 0.0, 0.0,
-0.027040616303246352, 0.0, 0.0, 0.0, -0.0065386476970877043, 0.0,
0.0081157987592952018, -0.069122886500332881, -0.021410792310862282,
0.054399058567187117, -0.020841424187200024, 0.0, 0.0026239820872657076,
-0.016873138877661474, 0.0, 0.0, 0.0, 0.0, 0.0, -0.017058306792934779,
-0.004600697652327409, -0.0038418180601910556, 0.00033874359041042082, 0.0, 0.0,
-0.0014676502653727659, 0.0, 0.0089441951700941213, 0.00094052487129774964,
-8.8262360181099557e-20, 0.0, 0.0012055361497495703, 0.0, -0.0025159601429339891,
0.0, 0.0, 0.0, 0.010570378077338292, -0.00012439346928733684,
0.0011946221637041178, 0.0, 0.0029967650306810122, -0.0037040978520667435, 0.0,
0.002604496280339682, 0.0034025398011936331, 0.00058848568106909568, 0.0,
0.017563363847050692, 0.0, 0.0009465700526952467, 0.0, 0.0, 0.0,
2.5159201485508032e-27, 0.0, 0.0, 0.0, 0.12174714602332004, 0.031257998409878619,
0.0, -0.043448109696737125, 0.0, 0.0, 2.3720575472164658e-18, 0.0, 0.0,
0.011814805717517866, 0.0, 0.0, 0.0, 0.0096528403296462541, 0.0, 0.0, 0.0,
0.031653447273040335, 0.0, 0.0, -0.011670645846521036, 0.0, 0.026989203899261519,
-0.0013733630822690482, -0.0013246028899376451, 0.0, -0.00385932250766926,
0.18667539201263672, 0.0, -0.00090056337181693147, -1.2789320351518064e-18,
0.018289494958134873, 0.013408081173179799, 0.0, 7.1583523243064581e-05,
0.0094619252425462309, 0.0, -2.2579280128800512e-27, 0.0, 0.0,
-0.029434885761734853, -0.0085311610610867127, -0.10522721392488943,
-0.0061436654596083336, 0.0, -0.03918844282630083, 0.0, 0.003089131825926939,
-1.0729144872136499e-27, 0.00031452249466716272, 0.0, 0.0, 0.0,
-0.017058306792934779, -0.0067445266865708035, 0.0, 0.0, -0.014207855945394596,
0.0, 0.0, 0.0, 0.21975794306849333, 0.0, 0.0, -0.048180877918008028, 0.0,
-0.010306106655729636, 0.0, -0.021441233789480978, 0.030072793137145122,
-0.012099393737987489, 0.0039223803077603229, -0.013319403089036707,
-0.0079822657126292513, -6.3608156798879568e-18, 0.035547666381795767, 0.0, 0.0,
0.0, 0.0049029052902886524, -0.25293604550018872, -0.0056538319533783162, 0.0,
-0.036467571394448749, 0.023715887039910951, 0.014652421300649891,
0.022016582578517199, 0.013743484354730714, 0.0, 0.0, -0.004420846485903557,
0.015754562095667009, 0.053918402272623268, 0.029790887975944355, 0.0, 0.0,
0.014652421300649891, 0.0, 0.0, -0.0073444670724073964, -2.3936504573251828e-29,
0.0, 0.0, -1.3181042050251931e-28, -0.030280965269392942, -0.001040641018981648,
0.010780067751729554, 0.0, 0.0, 0.0092903072571129675, -0.031900924150459316, 0.0,
0.0, 0.00032665264401178702, -1.6940685144621967e-19, 0.0, 0.014319708806296978,
0.035091373916862317, 0.0, 0.0038064511228685666, 0.012602950677423182,
-4.300688211078124e-27, 0.0, -0.0046006976523274125, -0.010030455992492086, 0.0,
0.0, 0.0, 0.0, 0.086681020705171516, 0.0020571840763740209, 0.0,
0.00034502752202664113, 0.0, 0.0, 0.0086615877701873706, -0.00010229544844849651,
0.0, 0.0, -0.012669408027262602, 0.0, -0.0013160281742988383, 0.0,
0.0051106755973458814, 0.0, 0.0, 0.0, 0.00065776559484166718, 0.0, 0.0, 0.0, 0.0,
0.0070298766341244934, -0.0099426911159988737, 0.095102492046631737, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.21451187351854378, -0.010323508855164837,
-0.015768424785218515, 0.0, 0.0, 0.01988505289377469, 0.016136154963033594,
-1.863014133554293e-06, 0.0, -0.0017646379813294408, 0.0, -0.00038519424640437477,
0.0, 0.0, 0.16483284008403978, -0.033293363614808613, -0.036184607685237472, 0.0,
0.0, 0.012132216313498513, -0.0036281486656409376, 0.0, 0.0,
-0.0045419489475092036, 0.060677162850807306, 0.0, 0.036763801330999844, 0.0, 0.0,
0.7750370389423169, 0.026239789058393363, 0.0, 0.0, -0.0060221264018631834, 0.0,
-0.0021070052578709923, 0.006533721582747014, -0.029054135424347712, 0.0, 0.0,
0.0, 0.0, 0.00266640244051787, 0.0, 0.0, 0.0, 0.024973062647718546,
0.0030600664914270585, 0.0, -0.039480903355905116, -0.19300759574811735, 0.0, 0.0,
-0.012562890882079118, 0.0, -0.054027365598984273, 0.0026239820872657055,
0.012531626186517635, -0.0011378398426452521, 0.0, 0.0, -0.0030538804243518654,
-0.0097786183121112511, -0.011459506074534484, 0.0, 0.0, 0.05233280757944573,
-0.0001446041679077717, 0.0, 0.0, 0.00069368933470645195, 0.0, 0.0, 0.0, 0.0, 0.0,
-0.00062594363466936676, 0.019564908540566448, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
-0.0032684422270475425, 0.0, -0.010750325711678417
]),
'kin': np.array([
0.96945722711242355, 0.0, 0.036292401476283279, 0.07995516390306795, 0.0,
1.8174999999999994, 0.27452182030061434, -9.8774829880917554e-18,
0.4858203080864158, -0.22493046394464195, 1.8174999999999994, 0.0,
-0.0029726129513667956, 0.0, 0.018376480943548955, 10.361104211507204, 0.0,
-0.24514942860277961, 0.0, 0.17334668011290896, 4.3248263329243191,
7.9993407779445172e-17, 0.0, -4.6184215514858166e-17, 0.0, 4.3248263329243191,
0.0, 0.0, 0.0, 0.27452182030061434, 0.68824999999999992, 0.093096714010858797,
0.0, 0.0, 0.0, 4.1475000000000017, 0.0, 0.0, 0.0, 0.0, 4.1475000000000026,
0.12248253843499435, 0.0, 0.57792718386389474, 7.9993407779445172e-17,
4.1475000000000026, 0.0, -0.17882475033034573, 0.036292401476283279, 0.0, 0.183,
0.34077676967706871, 0.0, -9.8774829880917554e-18, 0.093096714010858797,
1.8174999999999994, 0.0, 0.0
]),
'na': np.array([
-6.0797251408814796, 0.0, -1.8318359795245827, -2.5150668886409582, 0.0,
-6.061928702514308, -1.8185022809671232, 0.28314544048255308, -2.5612286623684115,
0.14889784275391382, -5.1821395397642984, 0.0, -1.6065246363266326, 0.0,
-2.1193055903316758, -20.636604199574929, 0.0, 3.2140498161241444,
0.22793252967254229, -2.2275870688292225, -12.890881502223545,
0.20355767445439643, 0.0, -0.10488028427486198, 0.0, -12.778416588080809, 0.0,
0.0, 0.0, -1.8185022809671232, -5.4140292951278415, -0.93660650303069326, 0.0,
0.0, 0.0, -8.4711446777306918, 0.0, 0.0, 0.0, 0.0, -8.4345065860561981,
-0.88778762785900955, -0.069054309343865417, -2.0036653123921448,
0.20355767445439643, -8.6284201435269861, 0.0, 0.91259495636933452,
-1.8318359795245827, 0.0, -4.3875277704507676, -2.7053738330031201, 0.0,
0.28314544048255308, -0.93660650303069326, -5.6021960154514723, 0.0, 0.0
]),
}
3.3.2. FCIDUMP format¶
3.3.2.1. Dumping¶
The FCIDUMP format is useful when exchanging Hamiltonians with different codes. Unlike the internal format, there are some restrictions:
- One-body operators (usually kinetic energy and nuclear attraction) must all be added into a single two-index object.
- Integrals can only be stored in a restricted (MO) basis set, i.e. using different basis sets for the alpha and beta orbitals is not possible.
- Two-electron integrals must be stored as a
DenseFourIndexobject, so the Cholesky decomposition of the ERI is not supported.
The FCIDUMP format is normally used for storing integrals in the MO basis but in the following example we will use the AO basis. Read the section Preparing for a Post-Hartree-Fock calculation if you want to compute (and store) integrals in the MO basis. The storage of integrals in AO basis in FCIDUMP format is as follows:
data/examples/hamiltonian/dump_fcidump_ao.py¶
import numpy as np
from horton import * # pylint: disable=wildcard-import,unused-wildcard-import
# Set up Neon dimer, define basis set
# -----------------------------------
coordinates = np.array([[0.0, 0.0, 0.0], [0.0, 0.0, 5.0]])
numbers = np.array([10, 10])
obasis = get_gobasis(coordinates, numbers, 'cc-pvdz')
lf = DenseLinalgFactory(obasis.nbasis)
# Construct Hamiltonian
# ---------------------
# The one-body operator is first created and then the kinetic energy and
# nuclear attraction integrals are added in-place.
one_mo = lf.create_two_index()
obasis.compute_kinetic(one_mo)
obasis.compute_nuclear_attraction(coordinates, numbers.astype(float), one_mo)
two_mo = obasis.compute_electron_repulsion(lf)
core_energy = compute_nucnuc(coordinates, numbers.astype(float))
# Write to a HDF5 file
# --------------------
data = IOData(one_mo=one_mo, two_mo=two_mo, core_energy=core_energy, nelec=20, ms2=0)
data.to_file('hamiltonian_ao.FCIDUMP')
# CODE BELOW IS FOR horton-regression-test.py ONLY. IT IS NOT PART OF THE EXAMPLE.
rt_results = {
'one_mo': one_mo._array.ravel()[::100],
'two_mo': two_mo._array.ravel()[::10000],
'core_energy': core_energy,
}
# BEGIN AUTOGENERATED CODE. DO NOT CHANGE MANUALLY.
rt_previous = {
'core_energy': 20.0,
'one_mo': np.array([
-98.143299148359503, 0.0, -6.8232742572718648, -0.0014333406428630481,
-0.0015606243552076162, 0.12476697580764212, 0.0, 0.0
]),
'two_mo': np.array([
5.9688416466791034, -2.9422895177057246e-07, 0.0, 0.0, -0.00059940882213425917,
0.0, -1.8851102379070744e-07, 0.0, 0.0, 0.0, 0.0, 1.2785965319495516e-06, 0.0,
0.0, 1.0046676893268627e-06, 0.0, 0.0, 0.0, 8.6908816309957149e-07, 0.0,
-2.7850857129776117e-12, 0.0, 0.0, 0.0, 4.8282318941349867e-14, 0.0, 0.0,
-6.9135313768308591e-07, -2.9422895177057246e-07, 0.0, 0.0, 0.0, 0.0, 0.0,
-5.6706841716128415e-14, 2.6118860993636095e-24, 1.7350385251530308e-05, 0.0, 0.0,
0.0, 0.0, 0.0, -3.3218351285338633e-05, 0.0, 0.00012749115201133096, 0.0,
0.037156902375664713, 0.0, 0.0, -2.3670600726458522e-07, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0
]),
}
In this example, we set the IOData attributes by
using keyword arguments in the constructor. The file hamiltonian_ao.FCIDUMP
will contain the following:
&FCI NORB=28,NELEC=20,MS2=0,
ORBSYM= 1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,
ISYM=1
&END
5.9786161694613265e+00 1 1 1 1
-1.0433683639500952e+00 2 1 1 1
1.9823368119430076e-01 2 1 2 1
6.1978840317716133e-01 2 2 1 1
.
.
.
-1.4611205008249136e+01 27 27 0 0
-8.5804159203863803e-12 28 14 0 0
-1.4611205008249140e+01 28 28 0 0
2.0000000000000000e+01 0 0 0 0
This file is divided into two blocks. The first block (between &FCI and
&END) contains system-specific information:
NORB: number of orbitals/basis functions NELEC: number of electrons MS2: spin projection ORBSYM: irreducible representation of each orbital ISYM: total symmetry of the wavefunction
The second block (after &END) contains the one- and two-electron integrals
and the core energy in that order:
- All symmetry-unique elements of the two-electron integrals are listed,
where the first column is the value of the integral, followed by the orbital
indices. Note that the orbital indices start from one and that the orbital indices
(
i j k l) in an FCIDUMP file are written in chemists’ notation,
\[(ij\vert kl) = \langle ik \vert jl \rangle = \int \phi_i^*(\mathbf{x}_1) \phi_k^*(\mathbf{x}_2) \frac{1}{r_{12}} \phi_j(\mathbf{x}_1) \phi_l(\mathbf{x}_2) d\mathbf{x}_1 d\mathbf{x}_2\]
- All symmetry-unique elements of the one-electron integrals are listed, where the first column is the value of the integral, followed by the orbital indices. Note that the last two columns contain zeros.
- Core energy (for instance, the nuclear repulsion term, etc.) is written on the last line with all orbital indices equal 0.
If the value of an integral is zero, the corresponding line is not included in the the FCIDUMP file. It’s important to note that HORTON does not (yet) support geometric symmetries, so all the orbitals will be assigned a symmetry label 1, which corresponds to the point group C1 in the FCIDUMP format.
3.3.2.2. Loading¶
You can load integrals, stored in an FCIDUMP file, as follows:
data/examples/hamiltonian/load_fcidump_ao.py¶
from horton import * # pylint: disable=wildcard-import,unused-wildcard-import
# Load the integrals from the file
data = IOData.from_file('hamiltonian_ao.FCIDUMP')
# Access some attributes. In more realistic cases, some code follows that does a
# useful calculation.
print data.core_energy
print data.one_mo.get_element(0, 0)
# CODE BELOW IS FOR horton-regression-test.py ONLY. IT IS NOT PART OF THE EXAMPLE.
rt_results = {
'one_mo': data.one_mo._array.ravel()[::100],
'two_mo': data.two_mo._array.ravel()[::10000],
'core_energy': data.core_energy,
}
# BEGIN AUTOGENERATED CODE. DO NOT CHANGE MANUALLY.
import numpy as np # pylint: disable=wrong-import-position
rt_previous = {
'core_energy': 20.0,
'one_mo': np.array([
-98.143299148359503, 0.0, -6.8232742572718648, -0.0014333406428630481,
-0.0015606243552076162, 0.12476697580764212, 0.0, 0.0
]),
'two_mo': np.array([
5.9688416466791034, -2.9422895177057246e-07, 0.0, 0.0, -0.00059940882213425917,
0.0, -1.8851102379070744e-07, 0.0, 0.0, 0.0, 0.0, 1.2785965319495516e-06, 0.0,
0.0, 1.0046676893268627e-06, 0.0, 0.0, 0.0, 8.6908816309957149e-07, 0.0,
-2.7850857129776117e-12, 0.0, 0.0, 0.0, 4.8282318941349867e-14, 0.0, 0.0,
-6.9135313768308591e-07, -2.9422895177057246e-07, 0.0, 0.0, 0.0, 0.0, 0.0,
-5.6706841716128415e-14, 2.6118860993636095e-24, 1.7350385251530308e-05, 0.0, 0.0,
0.0, 0.0, 0.0, -3.3218351285338633e-05, 0.0, 0.00012749115201133096, 0.0,
0.037156902375664713, 0.0, 0.0, -2.3670600726458522e-07, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0
]),
}