Commit 1783109b authored by Your Name's avatar Your Name
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parent 537c1485
......@@ -42,7 +42,7 @@
"<label style=\"text-align: left; color: #20335d; font-weight: 900; font-size: 18pt; padding-top: 2em;\">",
" Predicting energy differences between crystal structures</label><br/><label style=\"color: #20335d;font-weight: 900; font-size: 15pt;\"> (Meta-)stability of octet-binary compounds</label>",
" </p>",
" <p style=\"font-size: 15px;\">Angelo Ziletti, Emre Ahmetcik, Runhai Ouyang, Ankit Kariryaa, Fawzi Mohamed, Luca Ghiringhelli, Matthias Scheffler [version 2017-01-25]<span style=\"font-size: smaller;\">[version 2017-02-02]</span></p>",
" <p style=\"font-size: 15px;\">Angelo Ziletti, Emre Ahmetcik, Runhai Ouyang, Ankit Kariryaa, Fawzi Mohamed, Luca Ghiringhelli, Matthias Scheffler [version 2017-02-02]<span style=\"font-size: smaller;\"></span></p>",
" ",
"<div style=\"padding-top: 1em;\">",
"This tutorial shows how to find descriptive parameters (short formulas) that predict the crystal structure (here, rocksalt (RS), zincblende (ZB) or CsCl), using the example of octet binary compounds. It is based on the algorithm Sure Independent Screening followed by l0 minimization (SIS+l0), that enables to search for optimal descriptor by scanning huge feature spaces.",
......@@ -66,7 +66,7 @@
"result": {
"type": "BeakerDisplay",
"innertype": "Html",
"object": "<script>\nvar beaker = bkHelper.getBeakerObject().beakerObj;\n</script>\n<label style=\"text-align: left; color: #20335d; font-weight: 900; font-size: 18pt; padding-top: 2em;\">\n Predicting energy differences between crystal structures</label><br><label style=\"color: #20335d;font-weight: 900; font-size: 15pt;\"> (Meta-)stability of octet-binary compounds</label>\n <p></p>\n <p style=\"font-size: 15px;\">Angelo Ziletti, Emre Ahmetcik, Runhai Ouyang, Ankit Kariryaa, Fawzi Mohamed, Luca Ghiringhelli, Matthias Scheffler [version 2017-01-25]<span style=\"font-size: smaller;\">[version 2017-02-02]</span></p>\n \n<div style=\"padding-top: 1em;\">\nThis tutorial shows how to find descriptive parameters (short formulas) that predict the crystal structure (here, rocksalt (RS), zincblende (ZB) or CsCl), using the example of octet binary compounds. It is based on the algorithm Sure Independent Screening followed by l0 minimization (SIS+l0), that enables to search for optimal descriptor by scanning huge feature spaces.\n <div style=\"padding: 1ex; margin-top: 1ex; margin-bottom: 1ex; border-style: dotted; border-width: 1pt; border-color: blue; border-radius: 3px;\">\nR. Ouyang, E. Ahmetcik, L. M. Ghiringhelli, and M. Scheffler: <span style=\"font-style: italic;\">Descriptor identification for material properties via compressed sensing</span>, in preparation.\n</div>\nWith the default settings, the method reproduces the same results from:\n<div style=\"padding: 1ex; margin-top: 1ex; margin-bottom: 1ex; border-style: dotted; border-width: 1pt; border-color: blue; border-radius: 3px;\">\nL. M. Ghiringhelli, J. Vybiral, S. V. Levchenko, C. Draxl, M. Scheffler: <span style=\"font-style: italic;\">Big Data of Materials Science: Critical Role of the Descriptor</span>, Phys. Rev. Lett. 114, 105503 (2015) <a href=\"http://journals.aps.org/prl/abstract/10.1103/PhysRevLett.114.105503\">[PDF]</a>,\n</div>\nthat were obtained obtained by applying the <a href=\"http://analytics-toolkit.nomad-coe.eu/tutorial-LASSO_L0\">[LASSO+l0 algorithm]</a>. Click on <b>Run</b> below to reproduce results from this publication; click <b>Background</b> for an explanation of the approach; or, modify <b>Settings</b> to produce your own results.\n</div>\n<div style=\"padding-top: 2ex;\">\n<span style=\"font-weight: bold;\">Idea: </span> Starting from simple physical quantities (\"building blocks\", here properties of the constituent free atoms such as orbital radii), millions (or billions) of candidate formulas are generated by applying arithmetic operations combining building blocks, for example forming sums and products of them. These candidate formulas constitute the so-called \"feature space\". Then a feature selection method (SIS+l0) is used to select only a few of these formulas that explain the data.\n</div>"
"object": "<script>\nvar beaker = bkHelper.getBeakerObject().beakerObj;\n</script>\n<label style=\"text-align: left; color: #20335d; font-weight: 900; font-size: 18pt; padding-top: 2em;\">\n Predicting energy differences between crystal structures</label><br><label style=\"color: #20335d;font-weight: 900; font-size: 15pt;\"> (Meta-)stability of octet-binary compounds</label>\n <p></p>\n <p style=\"font-size: 15px;\">Angelo Ziletti, Emre Ahmetcik, Runhai Ouyang, Ankit Kariryaa, Fawzi Mohamed, Luca Ghiringhelli, Matthias Scheffler [version 2017-02-02]<span style=\"font-size: smaller;\"></span></p>\n \n<div style=\"padding-top: 1em;\">\nThis tutorial shows how to find descriptive parameters (short formulas) that predict the crystal structure (here, rocksalt (RS), zincblende (ZB) or CsCl), using the example of octet binary compounds. It is based on the algorithm Sure Independent Screening followed by l0 minimization (SIS+l0), that enables to search for optimal descriptor by scanning huge feature spaces.\n <div style=\"padding: 1ex; margin-top: 1ex; margin-bottom: 1ex; border-style: dotted; border-width: 1pt; border-color: blue; border-radius: 3px;\">\nR. Ouyang, E. Ahmetcik, L. M. Ghiringhelli, and M. Scheffler: <span style=\"font-style: italic;\">Descriptor identification for material properties via compressed sensing</span>, in preparation.\n</div>\nWith the default settings, the method reproduces the same results from:\n<div style=\"padding: 1ex; margin-top: 1ex; margin-bottom: 1ex; border-style: dotted; border-width: 1pt; border-color: blue; border-radius: 3px;\">\nL. M. Ghiringhelli, J. Vybiral, S. V. Levchenko, C. Draxl, M. Scheffler: <span style=\"font-style: italic;\">Big Data of Materials Science: Critical Role of the Descriptor</span>, Phys. Rev. Lett. 114, 105503 (2015) <a href=\"http://journals.aps.org/prl/abstract/10.1103/PhysRevLett.114.105503\">[PDF]</a>,\n</div>\nthat were obtained obtained by applying the <a href=\"http://analytics-toolkit.nomad-coe.eu/tutorial-LASSO_L0\">[LASSO+l0 algorithm]</a>. Click on <b>Run</b> below to reproduce results from this publication; click <b>Background</b> for an explanation of the approach; or, modify <b>Settings</b> to produce your own results.\n</div>\n<div style=\"padding-top: 2ex;\">\n<span style=\"font-weight: bold;\">Idea: </span> Starting from simple physical quantities (\"building blocks\", here properties of the constituent free atoms such as orbital radii), millions (or billions) of candidate formulas are generated by applying arithmetic operations combining building blocks, for example forming sums and products of them. These candidate formulas constitute the so-called \"feature space\". Then a feature selection method (SIS+l0) is used to select only a few of these formulas that explain the data.\n</div>"
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"selectedType": "BeakerDisplay",
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......@@ -825,12 +825,12 @@
"",
"random_name = ''.join(random.SystemRandom().choice(string.ascii_uppercase + string.digits) for _ in range(20))",
"try:",
" IP = os.environ['IP']",
" IP = os.environ['OPENMPIHOST']",
" ",
"except:",
" IP = '172.17.0.42'",
"try:",
" PORT = int(os.environ['PORT'])",
" PORT = int(os.environ['OPENMPIPORT'])",
"except:",
" PORT = 22",
" ",
......
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