4075885785

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Committed by Shyue Ping Ong

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Merge branch 'master' of github.com:materialsproject/pymatgen

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/pymatgen/analysis/tests/test_eos.py

1	# Copyright (c) Pymatgen Development Team.
2	# Distributed under the terms of the MIT License.
3
4
5	from __future__ import annotations	1✔
6
7	import unittest	1✔
8
9	import numpy as np	1✔
10	from pytest import approx	1✔
11
12	from pymatgen.analysis.eos import EOS, NumericalEOS	1✔
13	from pymatgen.util.testing import PymatgenTest	1✔
14
15
16	class EOSTest(PymatgenTest):	1✔
17	def setUp(self):	1✔
18	# Si data from Cormac
19	self.volumes = [	1✔
20	25.987454833,
21	26.9045702104,
22	27.8430241908,
23	28.8029649591,
24	29.7848370694,
25	30.7887887064,
26	31.814968055,
27	32.8638196693,
28	33.9353435494,
29	35.0299842495,
30	36.1477417695,
31	37.2892088485,
32	38.4543854865,
33	39.6437162376,
34	40.857201102,
35	42.095136449,
36	43.3579668329,
37	44.6456922537,
38	45.9587572656,
39	47.2973100535,
40	48.6614988019,
41	50.0517680652,
42	51.4682660281,
43	52.9112890601,
44	54.3808371612,
45	55.8775030703,
46	57.4014349722,
47	58.9526328669,
48	]
49	self.energies = [	1✔
50	-7.63622156576,
51	-8.16831294894,
52	-8.63871612686,
53	-9.05181213218,
54	-9.41170988374,
55	-9.72238224345,
56	-9.98744832526,
57	-10.210309552,
58	-10.3943401353,
59	-10.5427238068,
60	-10.6584266073,
61	-10.7442240979,
62	-10.8027285713,
63	-10.8363890521,
64	-10.8474912964,
65	-10.838157792,
66	-10.8103477586,
67	-10.7659387815,
68	-10.7066179666,
69	-10.6339907853,
70	-10.5495538639,
71	-10.4546677714,
72	-10.3506386542,
73	-10.2386366017,
74	-10.1197772808,
75	-9.99504030111,
76	-9.86535084973,
77	-9.73155247952,
78	]
79	num_eos = EOS(eos_name="numerical_eos")	1✔
80	self.num_eos_fit = num_eos.fit(self.volumes, self.energies)	1✔
81
82	def test_run_all_models(self):	1✔
83	# these have been checked for plausibility,
84	# but are not benchmarked against independently known values
85	test_output = {	1✔
86	"birch": {
87	"b0": 0.5369258244952931,
88	"b1": 4.178644231838501,
89	"e0": -10.8428039082307,
90	"v0": 40.98926572870838,
91	},
92	"birch_murnaghan": {
93	"b0": 0.5369258245417454,
94	"b1": 4.178644235500821,
95	"e0": -10.842803908240892,
96	"v0": 40.98926572528106,
97	},
98	"deltafactor": {
99	"b0": 0.5369258245611414,
100	"b1": 4.178644231924639,
101	"e0": -10.842803908299294,
102	"v0": 40.989265727927936,
103	},
104	"murnaghan": {
105	"b0": 0.5144967693786603,
106	"b1": 3.9123862262572264,
107	"e0": -10.836794514626673,
108	"v0": 41.13757930387086,
109	},
110	"numerical_eos": {
111	"b0": 0.5557257614101998,
112	"b1": 4.344039148405489,
113	"e0": -10.847490826530702,
114	"v0": 40.857200064982536,
115	},
116	"pourier_tarantola": {
117	"b0": 0.5667729960804602,
118	"b1": 4.331688936974368,
119	"e0": -10.851486685041658,
120	"v0": 40.86770643373908,
121	},
122	"vinet": {
123	"b0": 0.5493839425156859,
124	"b1": 4.3051929654936885,
125	"e0": -10.846160810560756,
126	"v0": 40.916875663779784,
127	},
128	}
129
130	for eos_name in EOS.MODELS:	1✔
131	eos = EOS(eos_name=eos_name)	1✔
132	_ = eos.fit(self.volumes, self.energies)	1✔
133	for param in ("b0", "b1", "e0", "b0"):	1✔
134	# TODO: solutions only stable to 2 decimal places
135	# between different machines, this seems far too low?
136	self.assertArrayAlmostEqual(_.results[param], test_output[eos_name][param], decimal=1)	1✔
137
138	def test_fitting(self):	1✔
139	# courtesy of @katherinelatimer2013
140	# known correct values for Vinet
141
142	# Mg
143
144	mp153_volumes = [	1✔
145	16.69182365,
146	17.25441763,
147	17.82951915,
148	30.47573817,
149	18.41725977,
150	29.65211363,
151	28.84346369,
152	19.01777055,
153	28.04965916,
154	19.63120886,
155	27.27053682,
156	26.5059864,
157	20.25769112,
158	25.75586879,
159	20.89736201,
160	25.02003097,
161	21.55035204,
162	24.29834347,
163	22.21681221,
164	23.59066888,
165	22.89687316,
166	]
167
168	mp153_energies = [	1✔
169	-1.269884575,
170	-1.339411225,
171	-1.39879471,
172	-1.424480995,
173	-1.44884184,
174	-1.45297499,
175	-1.4796246,
176	-1.49033594,
177	-1.504198485,
178	-1.52397006,
179	-1.5264432,
180	-1.54609291,
181	-1.550269435,
182	-1.56284009,
183	-1.569937375,
184	-1.576420935,
185	-1.583470925,
186	-1.58647189,
187	-1.591436505,
188	-1.592563495,
189	-1.594347355,
190	]
191
192	mp153_known_energies_vinet = [	1✔
193	-1.270038831,
194	-1.339366487,
195	-1.398683238,
196	-1.424556061,
197	-1.448746649,
198	-1.453000456,
199	-1.479614511,
200	-1.490266797,
201	-1.504163502,
202	-1.523910268,
203	-1.526395734,
204	-1.546038792,
205	-1.550298657,
206	-1.562800797,
207	-1.570015274,
208	-1.576368392,
209	-1.583605186,
210	-1.586404575,
211	-1.591578378,
212	-1.592547954,
213	-1.594410995,
214	]
215
216	# C: 4.590843262
217	# B: 2.031381599
218	mp153_known_e0_vinet = -1.594429229	1✔
219	mp153_known_v0_vinet = 22.95764159	1✔
220
221	eos = EOS(eos_name="vinet")	1✔
222
223	fit = eos.fit(mp153_volumes, mp153_energies)	1✔
224
225	np.testing.assert_array_almost_equal(fit.func(mp153_volumes), mp153_known_energies_vinet, decimal=5)	1✔
226
227	assert mp153_known_e0_vinet == approx(fit.e0, abs=1e-4)	1✔
228	assert mp153_known_v0_vinet == approx(fit.v0, abs=1e-4)	1✔
229
230	# expt. value 35.5, known fit 36.16
231	assert fit.b0_GPa == approx(36.16258687442761, abs=1e-4)	1✔
232
233	# Si
234
235	mp149_volumes = [	1✔
236	15.40611854,
237	14.90378698,
238	16.44439516,
239	21.0636307,
240	17.52829835,
241	16.98058208,
242	18.08767363,
243	18.65882487,
244	19.83693435,
245	15.91961152,
246	22.33987173,
247	21.69548924,
248	22.99688883,
249	23.66666322,
250	20.44414922,
251	25.75374305,
252	19.24187473,
253	24.34931029,
254	25.04496106,
255	27.21116571,
256	26.4757653,
257	]
258
259	mp149_energies = [	1✔
260	-4.866909695,
261	-4.7120965,
262	-5.10921253,
263	-5.42036228,
264	-5.27448405,
265	-5.200810795,
266	-5.331915665,
267	-5.3744186,
268	-5.420058145,
269	-4.99862686,
270	-5.3836163,
271	-5.40610838,
272	-5.353700425,
273	-5.31714654,
274	-5.425263555,
275	-5.174988295,
276	-5.403353105,
277	-5.27481447,
278	-5.227210275,
279	-5.058992615,
280	-5.118805775,
281	]
282
283	mp149_known_energies_vinet = [	1✔
284	-4.866834585,
285	-4.711786499,
286	-5.109642598,
287	-5.420093739,
288	-5.274605844,
289	-5.201025714,
290	-5.331899365,
291	-5.374315789,
292	-5.419671568,
293	-4.998827503,
294	-5.383703409,
295	-5.406038887,
296	-5.353926272,
297	-5.317484252,
298	-5.424963418,
299	-5.175090887,
300	-5.403166824,
301	-5.275096644,
302	-5.227427635,
303	-5.058639193,
304	-5.118654229,
305	]
306
307	# C: 4.986513158
308	# B: 4.964976215
309	mp149_known_e0_vinet = -5.424963506	1✔
310	mp149_known_v0_vinet = 20.44670279	1✔
311
312	eos = EOS(eos_name="vinet")	1✔
313
314	fit = eos.fit(mp149_volumes, mp149_energies)	1✔
315
316	np.testing.assert_array_almost_equal(fit.func(mp149_volumes), mp149_known_energies_vinet, decimal=5)	1✔
317
318	assert mp149_known_e0_vinet == approx(fit.e0, abs=1e-4)	1✔
319	assert mp149_known_v0_vinet == approx(fit.v0, abs=1e-4)	1✔
320
321	# expt. value 97.9, known fit 88.39
322	assert fit.b0_GPa == approx(88.38629337404822, abs=1e-4)	1✔
323
324	# Ti
325
326	mp72_volumes = [	1✔
327	12.49233296,
328	12.91339188,
329	13.34380224,
330	22.80836212,
331	22.19195533,
332	13.78367177,
333	21.58675559,
334	14.23310328,
335	20.99266009,
336	20.4095592,
337	14.69220297,
338	19.83736385,
339	15.16106697,
340	19.2759643,
341	15.63980711,
342	18.72525771,
343	16.12851491,
344	18.18514127,
345	16.62729878,
346	17.65550599,
347	17.13626153,
348	]
349
350	mp72_energies = [	1✔
351	-7.189983803,
352	-7.33985647,
353	-7.468745423,
354	-7.47892835,
355	-7.54945107,
356	-7.578012237,
357	-7.61513166,
358	-7.66891898,
359	-7.67549721,
360	-7.73000681,
361	-7.74290386,
362	-7.77803379,
363	-7.801246383,
364	-7.818964483,
365	-7.84488189,
366	-7.85211192,
367	-7.87486651,
368	-7.876767777,
369	-7.892161533,
370	-7.892199957,
371	-7.897605303,
372	]
373
374	mp72_known_energies_vinet = [	1✔
375	-7.189911138,
376	-7.339810181,
377	-7.468716095,
378	-7.478678021,
379	-7.549402394,
380	-7.578034391,
381	-7.615240977,
382	-7.669091347,
383	-7.675683891,
384	-7.730188653,
385	-7.74314028,
386	-7.778175824,
387	-7.801363213,
388	-7.819030923,
389	-7.844878053,
390	-7.852099741,
391	-7.874737806,
392	-7.876686864,
393	-7.891937429,
394	-7.892053535,
395	-7.897414664,
396	]
397
398	# C: 3.958192998
399	# B: 6.326790098
400	mp72_known_e0_vinet = -7.897414997	1✔
401	mp72_known_v0_vinet = 17.13223229	1✔
402
403	eos = EOS(eos_name="vinet")	1✔
404
405	fit = eos.fit(mp72_volumes, mp72_energies)	1✔
406
407	np.testing.assert_array_almost_equal(fit.func(mp72_volumes), mp72_known_energies_vinet, decimal=5)	1✔
408
409	assert mp72_known_e0_vinet == approx(fit.e0, abs=1e-4)	1✔
410	assert mp72_known_v0_vinet == approx(fit.v0, abs=1e-4)	1✔
411
412	# expt. value 107.3, known fit 112.63
413	assert fit.b0_GPa == approx(112.62927187296167, abs=1e-4)	1✔
414
415	def test_numerical_eoswrapper(self):	1✔
416	# using numerical eos directly vs via EOS wrapper
417	numerical_eos = NumericalEOS(self.volumes, self.energies)	1✔
418	numerical_eos.fit()	1✔
419	assert len(numerical_eos.eos_params) > 3	1✔
420	assert float(numerical_eos.e0) == approx(self.num_eos_fit.e0, abs=1e-3)	1✔
421	assert float(numerical_eos.v0) == approx(self.num_eos_fit.v0, abs=1e-3)	1✔
422	assert float(numerical_eos.b0) == approx(self.num_eos_fit.b0, abs=1e-3)	1✔
423	assert float(numerical_eos.b1) == approx(self.num_eos_fit.b1, abs=1e-3)	1✔
424	self.assertArrayAlmostEqual(numerical_eos.eos_params, self.num_eos_fit.eos_params)	1✔
425
426	def test_numerical_eos_values(self):	1✔
427	np.testing.assert_almost_equal(self.num_eos_fit.e0, -10.84749, decimal=3)	1✔
428	np.testing.assert_almost_equal(self.num_eos_fit.v0, 40.857201, decimal=1)	1✔
429	np.testing.assert_almost_equal(self.num_eos_fit.b0, 0.55, decimal=2)	1✔
430	# TODO: why were these tests commented out?
431	# np.testing.assert_almost_equal(self.num_eos_fit.b0_GPa, 89.0370727, decimal=1)
432	# np.testing.assert_almost_equal(self.num_eos_fit.b1, 4.344039, decimal=2)
433
434	def test_eos_func(self):	1✔
435	# list vs np.array arguments
436	np.testing.assert_almost_equal(	1✔
437	self.num_eos_fit.func([0, 1, 2]),
438	self.num_eos_fit.func(np.array([0, 1, 2])),
439	decimal=10,
440	)
441	# func vs _func
442	np.testing.assert_almost_equal(	1✔
443	self.num_eos_fit.func(0.0),
444	self.num_eos_fit._func(0.0, self.num_eos_fit.eos_params),
445	decimal=10,
446	)
447	# test the eos function: energy = f(volume)
448	# numerical eos evaluated at volume=0 == a0 of the fit polynomial
449	np.testing.assert_almost_equal(self.num_eos_fit.func(0.0), self.num_eos_fit.eos_params[-1], decimal=6)	1✔
450	birch_eos = EOS(eos_name="birch")	1✔
451	birch_eos_fit = birch_eos.fit(self.volumes, self.energies)	1✔
452	# birch eos evaluated at v0 == e0
453	np.testing.assert_almost_equal(birch_eos_fit.func(birch_eos_fit.v0), birch_eos_fit.e0, decimal=6)	1✔
454
455	# TODO: Reactivate
456	# fig = birch_eos_fit.plot_ax(ax=None, show=False, fontsize=8, title="birch eos")
457	# self.assertTrue(hasattr(fig, "savefig"))
458
459	def test_eos_func_call(self):	1✔
460	# eos_fit_obj.func(volume) == eos_fit_obj(volume)
461	np.testing.assert_almost_equal(self.num_eos_fit.func(0.0), self.num_eos_fit(0.0), decimal=10)	1✔
462
463	def test_summary_dict(self):	1✔
464	d = {	1✔
465	"e0": self.num_eos_fit.e0,
466	"b0": self.num_eos_fit.b0,
467	"b1": self.num_eos_fit.b1,
468	"v0": self.num_eos_fit.v0,
469	}
470	assert self.num_eos_fit.results == d	1✔
471
472
473	if __name__ == "__main__":	1✔
474	unittest.main()	×

materialsproject / pymatgen / 4075885785

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