Thank you for the reply!
I found that the bug what I asked (i.e., termination ML_FF calculation after printing out DOF) seemingly not occur on global cases, but specified to my own case. I also tested the IBRION =5 + ML_FF combination for the other case, the calculation has been successfully done.
I think it is quit difficult why this termination happened, but I share my input files.
POSCAR
Code: Select all
unknown system
1.00000000000000
10.0431600000000003 0.0000000000000000 0.0000000000000000
0.0000000000000000 4.3488100000000003 0.0000000000000000
0.0000000000000000 0.0000000000000000 34.7949919547301363
P B N
48 32 32
Direct
0.0000000479704525 0.5024172302218768 0.9829901259105888
0.1666404148590806 0.1586785216372398 0.9829109776706766
0.3332410049743103 0.5013226545054458 0.9829722312545602
0.4999999602109294 0.1579264134683900 0.9828484952336063
0.6667589122970512 0.5013231068842177 0.9829722647063603
0.8333596357649804 0.1586779962933235 0.9829110106337643
0.0000000966521290 0.6568851672941729 0.0451421782603636
0.1666651999519846 -0.0000942854833427 0.0448988467162209
0.3334297085187903 0.6570324504359637 0.0450844977737194
0.4999998940148024 0.0002380067200743 0.0448628540203238
0.6665701658663598 0.6570317258325401 0.0450845381947704
0.8333348566847183 -0.0000934642069198 0.0448988932618396
0.0000000115067490 0.0572101524804199 0.1364283041400120
0.1666741117582839 0.4001816892898144 0.1358354760333592
0.3333305137178604 0.0571632030230954 0.1364207103380511
0.4999999819922730 0.4001554259151572 0.1358258886713993
0.6666695338789785 0.0571632726503430 0.1364207246029787
0.8333258182959278 0.4001816270104787 0.1358354873828342
-0.0000000214872880 0.8988842603198575 0.1986054882246096
0.1666738446566494 0.5558491366926843 0.1980800265209932
0.3333349234582820 0.8989434105266239 0.1986047107411991
0.5000000293512319 0.5558843807026168 0.1980727955179495
0.6666650380076778 0.8989433547818926 0.1986046994806499
0.8333261745295195 0.5558491431470232 0.1980800118401050
0.0000000130954311 0.4311817691051696 0.2887621499029351
0.1666646244181119 0.0879927075752353 0.2888002174853139
0.3333267494645972 0.4312277092198860 0.2887569451395237
0.4999999988372724 0.0880377056376928 0.2888002015700850
0.6666732498389993 0.4312277037911273 0.2887569628067588
0.8333353621551315 0.0879927128681067 0.2888002256388837
-0.0000000079975959 0.5870461023461482 0.3510105274213041
0.1666691763745840 0.9302106484828939 0.3509919825897128
0.3333229161831341 0.5870028403244807 0.3510067931900736
0.5000000207717736 0.9301733862926959 0.3509896430581534
0.6666770956512588 0.5870028993081915 0.3510067694389507
0.8333308311520016 0.9302105732960629 0.3509919680779670
0.0000001207498109 0.0014827564404178 0.4421685573508389
0.1665857545527171 0.3445377776603269 0.4416546607076480
0.3333592875880079 0.0018119098522435 0.4421428640790219
0.4999998424331478 0.3446782553242987 0.4416117194694667
0.6666405632054166 0.0018108290076843 0.4421427817092846
0.8334144661577452 0.3445387042323458 0.4416545728313952
0.0000000086811350 0.8441159391044999 0.5042539644660159
0.1667599594598428 0.5007517304170989 0.5038199905877139
0.3333547652223657 0.8433531180878536 0.5041958497747018
0.5000000698024034 0.4996749772315339 0.5038140628514327
0.6666451102389939 0.8433537014641527 0.5041958002355972
0.8332401154233873 0.5007510600983550 0.5038199365134397
-0.0000017221627683 0.3344237676810776 0.6017447651913934
0.1249779519471058 0.8353457355616614 0.6017333100155745
0.2499175308007087 0.3349158789808500 0.6016448879529076
0.3749816688881143 0.8357102681608836 0.6015920708488587
0.5000018931590097 0.3354432736343464 0.6015270878854218
0.6250237602954128 0.8357044544181554 0.6015920309499286
0.7500827751030371 0.3349371054544923 0.6016448202759475
0.8750171408708652 0.8353402590262956 0.6017332646776798
0.0000034931018406 0.6788011941775469 0.6961481706227399
0.1250064256764779 0.1790616719762557 0.6961361780659491
0.2500380353019690 0.6789408600271669 0.6961039924837559
0.3750090713058075 0.1787894344843516 0.6960732159211250
0.4999972149075260 0.6790420747936593 0.6960588648292801
0.6249893370827092 0.1788020056553781 0.6960732298653208
0.7499619772730861 0.6789321959966670 0.6961039126114782
0.8749960530840140 0.1790760999740355 0.6961361929210441
-0.0000020089850368 0.3233590794022165 0.7906779819963394
0.1249546347984885 0.8231401875292894 0.7906640407626008
0.2499489748711586 0.3232002729675522 0.7906284684705709
0.3749492421978882 0.8232173616575705 0.7905955979907729
0.5000019212258763 0.3230046363452347 0.7905776060499575
0.6250525621500173 0.8232227486211046 0.7905956231760878
0.7500509958445754 0.3231945214693389 0.7906284626118930
0.8750432812052481 0.8231460589455777 0.7906640544414961
0.0000008674400243 0.6705873192733220 0.8853534171533231
0.1249915132835089 0.1704443668398578 0.8852771789563695
0.2500459948274679 0.6712134395027555 0.8852394403438482
0.3749993740503551 0.1707683519386726 0.8851561883912028
0.4999984996671081 0.6717570934593251 0.8851421390313935
0.6249994220049151 0.1707697177329970 0.8851561725309132
0.7499537451199971 0.6712095048233380 0.8852395272941075
0.8750091802840395 0.1704447423541346 0.8852771863916959
0.0000023098916645 0.6677921998300472 0.6016557790997666
0.1250310141842520 0.1680415296527433 0.6018644141518965
0.2500462164881654 0.6683757321294845 0.6016099269260904
0.3750148650400890 0.1687460735965728 0.6017650279950917
0.4999978718465458 0.6689740390421491 0.6015316333634627
0.6249809785953323 0.1687424031018545 0.6017651798001630
0.7499536881846941 0.6683735397302809 0.6016101356383534
0.8749738405561361 0.1680376313961198 0.6018645957831142
-0.0000042703272570 0.3456486451065082 0.6961628485564336
0.1249496079623166 0.8457642688906337 0.6961361722260275
0.2498662831092858 0.3457089794478185 0.6961121078992479
0.3749411844141756 0.8456516819174877 0.6960743168612646
0.5000032366333320 0.3457785561122383 0.6960656497035292
0.6250616789006387 0.8456521490176619 0.6960743929536352
0.7501339990206778 0.3457044423951191 0.6961121872939889
0.8750466387458309 0.8457642207255252 0.6961362446659051
0.0000017192843778 0.6565047156256175 0.7906758111336150
0.1250619834940994 0.1564051641501307 0.7906681541191491
0.2500458781934027 0.6564341486937699 0.7906236842129285
0.3750727003290278 0.1564709372316634 0.7905997961885904
0.4999980075773310 0.6563806482972437 0.7905725304111455
0.6249260693007518 0.1564737233326111 0.7905997025301166
0.7499537076412253 0.6564323493827225 0.7906235455460060
0.8749385232227525 0.1564079491145711 0.7906680578156953
0.0000002149105192 0.3370534437563116 0.8853355144178576
0.1249864939748610 0.8374689902739335 0.8851236162645627
0.2498854826489557 0.3377327340113212 0.8852790726165661
0.3749520074459789 0.8379928871932684 0.8850270489998693
0.5000003252244177 0.3384215658278193 0.8852524582602606
0.6250480537860201 0.8379954356285886 0.8850270232597163
0.7501144647786125 0.3377338598459103 0.8852790528191520
0.8750137474297580 0.8374714153656402 0.8851235937486719
INCAR
Code: Select all
ISTART = 0
ICHARG = 2
ENCUT = 500
PREC = Normal
ISYM = -1
ISMEAR = 0
SIGMA = 0.05
IBRION = 5
LWAVE = F
LCHARG = F
GGA = BO
PARAM1 = 0.1833333333
PARAM2 = 0.22
AGGAC = 0.0
LUSE_VDW = .TRUE.
LASPH = .TRUE.
### Machine Learning part
### Major tags for machine learning
ML_LMLFF = .TRUE.
ML_MODE = run
ML_WTIFOR = 100
ML_MB = 3000
LWAVE = F
LCHARG = F
ML_FF header
Code: Select all
ML_FF 0.2.1 binary { "date" : "2023-05-10T16:46:52.305", "ML_LFAST" : True, "ML_DESC_TYPE" : 0, "types" : [ "B", "N", "P" ], "training_structures" : 598, "local_reference_cfgs" : [ 347, 352, 805 ], "descriptors" : [ 1053, 1053, 1053 ], "ML_IALGO_LINREG" : 4, "ML_RCUT1" : 8.0000E+00, "ML_RCUT2" : 5.0000E+00, "ML_W1" : 1.0000E-01, "ML_SION1" : 5.0000E-01, "ML_SION2" : 5.0000E-01, "ML_LMAX2" : 3, "ML_MRB1" : 12, "ML_MRB2" : 8, "ML_IWEIGHT" : 3, "ML_WTOTEN" : 1.0000E+00, "ML_WTIFOR" : 1.0000E+02, "ML_WTSIF" : 1.0000E+00 }
Standard output
Code: Select all
running 128 mpi-ranks, on 4 nodes
distrk: each k-point on 128 cores, 1 groups
distr: one band on 1 cores, 128 groups
vasp.6.4.1 05Apr23 (build May 03 2023 07:59:44) complex
POSCAR found type information on POSCAR P B N
POSCAR found : 3 types and 112 ions
scaLAPACK will be used
-----------------------------------------------------------------------------
| |
| W W AA RRRRR N N II N N GGGG !!! |
| W W A A R R NN N II NN N G G !!! |
| W W A A R R N N N II N N N G !!! |
| W WW W AAAAAA RRRRR N N N II N N N G GGG ! |
| WW WW A A R R N NN II N NN G G |
| W W A A R R N N II N N GGGG !!! |
| |
| For optimal performance we recommend to set |
| NCORE = 2 up to number-of-cores-per-socket |
| NCORE specifies how many cores store one orbital (NPAR=cpu/NCORE). |
| This setting can greatly improve the performance of VASP for DFT. |
| The default, NCORE=1 might be grossly inefficient on modern |
| multi-core architectures or massively parallel machines. Do your |
| own testing! More info at https://www.vasp.at/wiki/index.php/NCORE |
| Unfortunately you need to use the default for GW and RPA |
| calculations (for HF NCORE is supported but not extensively tested |
| yet). |
| |
-----------------------------------------------------------------------------
-----------------------------------------------------------------------------
| |
| ----> ADVICE to this user running VASP <---- |
| |
| You have a (more or less) 'large supercell' and for larger cells it |
| might be more efficient to use real-space projection operators. |
| Therefore, try LREAL= Auto in the INCAR file. |
| Mind: For very accurate calculation, you might also keep the |
| reciprocal projection scheme (i.e. LREAL=.FALSE.). |
| |
-----------------------------------------------------------------------------
LDA part: xc-table for Pade appr. of Perdew
Machine learning selected
Setting communicators for machine learning
Initializing machine learning
The following ML algorithm is executed for production run: FAST.
-----------------------------------------------------------------------------
| |
| W W AA RRRRR N N II N N GGGG !!! |
| W W A A R R NN N II NN N G G !!! |
| W W A A R R N N N II N N N G !!! |
| W WW W AAAAAA RRRRR N N N II N N N G GGG ! |
| WW WW A A R R N NN II N NN G G |
| W W A A R R N N II N N GGGG !!! |
| |
| Your FFT grids (NGX,NGY,NGZ) are not sufficient for an accurate |
| calculation. Thus, the results might be wrong. Good settings for |
| NGX, NGY and NGZ are 2, 2 and 10, respectively. |
| Mind: This setting results in a small but reasonable wrap-around |
| error. It is also necessary to adjust these values to the FFT |
| routines you use. |
| |
-----------------------------------------------------------------------------
POSCAR, INCAR and KPOINTS ok, starting setup
entering main loop
1 F= -.68821541E+03 E0= -.68821541E+03 d E =0.000000E+00
-----------------------------------------------------------------------------
| |
| W W AA RRRRR N N II N N GGGG !!! |
| W W A A R R NN N II NN N G G !!! |
| W W A A R R N N N II N N N G !!! |
| W WW W AAAAAA RRRRR N N N II N N N G GGG ! |
| WW WW A A R R N NN II N NN G G |
| W W A A R R N N II N N GGGG !!! |
| |
| Your timestep is larger than 0.1 Angst. |
| For finite differences, this really does not make sense. I will |
| reset POTIM to 0.015. I recommend to use 0.01 to 0.02 for finite |
| differences. |
| |
-----------------------------------------------------------------------------
Finite differences POTIM= 0.01500 DOF= 336
forrtl: severe (174): SIGSEGV, segmentation fault occurred
Image PC Routine Line Source
libpthread-2.17.s 00002AE1927545D0 Unknown Unknown Unknown
vasp-6.4.1.x 0000000001F0C087 Unknown Unknown Unknown
vasp-6.4.1.x 0000000000655722 Unknown Unknown Unknown
vasp-6.4.1.x 000000000064E144 Unknown Unknown Unknown
vasp-6.4.1.x 00000000006CDCD7 Unknown Unknown Unknown
vasp-6.4.1.x 00000000006C37E1 Unknown Unknown Unknown
vasp-6.4.1.x 000000000074B507 Unknown Unknown Unknown
vasp-6.4.1.x 000000000113B8A3 Unknown Unknown Unknown
vasp-6.4.1.x 0000000001E06E82 Unknown Unknown Unknown
vasp-6.4.1.x 000000000040E85D Unknown Unknown Unknown
libc-2.17.so 00002AE192C853D5 __libc_start_main Unknown Unknown
vasp-6.4.1.x 000000000040E776 Unknown Unknown Unknown
forrtl: severe (174): SIGSEGV, segmentation fault occurred
Image PC Routine Line Source
libpthread-2.17.s 00002AF7DB4975D0 Unknown Unknown Unknown
Stack trace terminated abnormally.
To note, I got the same termination even after I turned off the vdW related tags (apparently not related to this issue)...
SB
