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43 | 43 | "from atomate2.vasp.sets.core import StaticSetGenerator, TightRelaxSetGenerator"
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44 | 44 | ]
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45 | 45 | },
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46 |
| - { |
47 |
| - "cell_type": "markdown", |
48 |
| - "id": "3", |
49 |
| - "metadata": {}, |
50 |
| - "source": [ |
51 |
| - "# Grüneisen Workflow Tutorial with VASP" |
52 |
| - ] |
53 |
| - }, |
54 |
| - { |
55 |
| - "cell_type": "markdown", |
56 |
| - "id": "4", |
57 |
| - "metadata": {}, |
58 |
| - "source": [ |
59 |
| - "## Background\n", |
60 |
| - "The Grüneisen workflow is based on the implementation in Phonopy.\n", |
61 |
| - "\n", |
62 |
| - "If you want to read more about Phonopy, please read Togo’s paper: https://doi.org/10.7566/JPSJ.92.012001" |
63 |
| - ] |
64 |
| - }, |
65 |
| - { |
66 |
| - "cell_type": "markdown", |
67 |
| - "id": "5", |
68 |
| - "metadata": {}, |
69 |
| - "source": [ |
70 |
| - "## Let's run the workflow\n", |
71 |
| - "Now, we load a structure and other important functions and classes for running the Grüneisen workflow." |
72 |
| - ] |
73 |
| - }, |
74 | 46 | {
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75 | 47 | "cell_type": "code",
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76 | 48 | "execution_count": null,
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77 |
| - "id": "6", |
| 49 | + "id": "3", |
78 | 50 | "metadata": {},
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79 | 51 | "outputs": [],
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80 | 52 | "source": [
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91 | 63 | {
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92 | 64 | "cell_type": "code",
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93 | 65 | "execution_count": null,
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94 |
| - "id": "7", |
| 66 | + "id": "4", |
95 | 67 | "metadata": {},
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96 | 68 | "outputs": [],
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97 | 69 | "source": [
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129 | 101 | {
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130 | 102 | "cell_type": "code",
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131 | 103 | "execution_count": null,
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132 |
| - "id": "8", |
| 104 | + "id": "5", |
133 | 105 | "metadata": {},
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134 | 106 | "outputs": [],
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135 | 107 | "source": [
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165 | 137 | },
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166 | 138 | {
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167 | 139 | "cell_type": "markdown",
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168 |
| - "id": "9", |
| 140 | + "id": "6", |
169 | 141 | "metadata": {},
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170 | 142 | "source": [
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171 | 143 | "Then one can use the `GruneisenMaker` to generate a `Flow`."
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174 | 146 | {
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175 | 147 | "cell_type": "code",
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176 | 148 | "execution_count": null,
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177 |
| - "id": "10", |
| 149 | + "id": "7", |
178 | 150 | "metadata": {},
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179 | 151 | "outputs": [],
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180 | 152 | "source": [
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181 | 153 | "flow = GruneisenMaker(\n",
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182 | 154 | " symprec=1e-4,\n",
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183 | 155 | " bulk_relax_maker=phonon_bulk_relax_maker_isif3,\n",
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184 | 156 | " phonon_maker=PhononMaker(\n",
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185 |
| - " generate_frequencies_eigenvectors_kwargs={\"tmin\": 0, \"tmax\": 1000, \"tstep\": 10},\n", |
| 157 | + " generate_frequencies_eigenvectors_kwargs={\n", |
| 158 | + " \"tmin\": 0,\n", |
| 159 | + " \"tmax\": 1000,\n", |
| 160 | + " \"tstep\": 10,\n", |
| 161 | + " },\n", |
186 | 162 | " min_length=10,\n",
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187 | 163 | " bulk_relax_maker=None,\n",
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188 | 164 | " born_maker=None,\n",
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|
194 | 170 | },
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195 | 171 | {
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196 | 172 | "cell_type": "markdown",
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197 |
| - "id": "11", |
| 173 | + "id": "8", |
198 | 174 | "metadata": {},
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199 | 175 | "source": [
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200 | 176 | "The Grüneisen parameter workflow will perform 3 different phonon runs at 3 different volumes at and around the equilibrium to compute the mode Grüneisen values."
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203 | 179 | {
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204 | 180 | "cell_type": "code",
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205 | 181 | "execution_count": null,
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206 |
| - "id": "12", |
| 182 | + "id": "9", |
207 | 183 | "metadata": {},
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208 | 184 | "outputs": [],
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209 | 185 | "source": [
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212 | 188 | },
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213 | 189 | {
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214 | 190 | "cell_type": "markdown",
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215 |
| - "id": "13", |
| 191 | + "id": "10", |
216 | 192 | "metadata": {},
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217 | 193 | "source": [
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218 | 194 | "We now run the flow with `run_locally`. We mock the run here. Normally, you would simply use `run_locally` without the `with mock_vasp`"
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221 | 197 | {
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222 | 198 | "cell_type": "code",
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223 | 199 | "execution_count": null,
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224 |
| - "id": "14", |
| 200 | + "id": "11", |
225 | 201 | "metadata": {},
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226 | 202 | "outputs": [],
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227 | 203 | "source": [
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238 | 214 | {
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239 | 215 | "cell_type": "code",
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240 | 216 | "execution_count": null,
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241 |
| - "id": "15", |
| 217 | + "id": "12", |
242 | 218 | "metadata": {},
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243 | 219 | "outputs": [],
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244 | 220 | "source": [
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258 | 234 | {
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259 | 235 | "cell_type": "code",
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260 | 236 | "execution_count": null,
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261 |
| - "id": "16", |
| 237 | + "id": "13", |
262 | 238 | "metadata": {},
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263 | 239 | "outputs": [],
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264 | 240 | "source": [
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