{"id":66,"date":"2023-12-25T12:49:45","date_gmt":"2023-12-25T12:49:45","guid":{"rendered":"https:\/\/www.tifrh.res.in\/~ramakrishnan\/?page_id=66"},"modified":"2023-12-25T12:49:45","modified_gmt":"2023-12-25T12:49:45","slug":"big-datasets","status":"publish","type":"page","link":"https:\/\/www.tifrh.res.in\/~ramakrishnan\/?page_id=66","title":{"rendered":"Big Datasets"},"content":{"rendered":"

[et_pb_section fb_built=”1″ _builder_version=”4.23″ _module_preset=”default” custom_padding=”5px|||||” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_row _builder_version=”4.23″ _module_preset=”default” border_width_bottom=”1px” border_color_bottom=”#e2e2e2″ global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_column type=”4_4″ _builder_version=”4.23″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_post_title meta=”off” featured_image=”off” _builder_version=”4.23″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”][\/et_pb_post_title][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”4.23″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content” custom_padding=”||4px|||”][et_pb_column type=”4_4″ _builder_version=”4.23″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_text _builder_version=”4.23″ _module_preset=”default” hover_enabled=”0″ global_colors_info=”{}” theme_builder_area=”post_content” sticky_enabled=”0″]<\/p>\n

All the files below are shared without any restrictions. But, I will gradually move them to Git Hub to improve the accessibility.<\/span><\/p>\n

[\/et_pb_text][et_pb_text _builder_version=”4.23″ _module_preset=”default” hover_enabled=”0″ global_colors_info=”{}” theme_builder_area=”post_content” sticky_enabled=”0″]<\/p>\n

1. Data for delta-ML of 134 kilo (GDB1-GDB9) molecules<\/h4>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”4.23″ _module_preset=”default” column_structure=”1_2,1_2″ theme_builder_area=”post_content”][et_pb_column _builder_version=”4.23″ _module_preset=”default” type=”1_2″ theme_builder_area=”post_content”][et_pb_text _builder_version=”4.23″ _module_preset=”default” theme_builder_area=”post_content” hover_enabled=”0″ sticky_enabled=”0″]<\/p>\n

This dataset was used to generate Fig. 8 and Fig. 9 of\u00a0<\/span><\/span>JCTC, 11 (2015) 2087\u20132096<\/a>. The dataset comprises of structures (in Angstroms) of 133885 (134k) molecules relaxed at the PM7 level, along with heats of atomization (in kcal\/mol) at PM7 and B3LYP\/6-31G(2df,p) levels. Note that B3LYP heats were computed for structures relaxed at the B3LYP level, as reported in\u00a0<\/span><\/span>Scientific Data, 1 (2014) 140022<\/a>.<\/span><\/p>\n

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Reference<\/b>
Big data meets quantum chemistry approximations: The delta-machine learning approach<\/a>
Raghunathan Ramakrishnan, Pavlo O. Dral, Matthias Rupp, O. Anatole von Lilienfeld
Journal of Chemical Theory and Computation, 11 (2015) 2087\u20132096.<\/p>\n

Quantum chemistry structures and properties of 134 kilo molecules<\/a>
Raghunathan Ramakrishnan, Pavlo O. Dral, Matthias Rupp, O. Anatole von Lilienfeld
Scientific Data 1, 140022 (2014).<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

[\/et_pb_text][\/et_pb_column][et_pb_column _builder_version=”4.23″ _module_preset=”default” type=”1_2″ theme_builder_area=”post_content”][et_pb_image src=”https:\/\/www.tifrh.res.in\/~ramakrishnan\/wp-content\/uploads\/2023\/12\/134k.png” _builder_version=”4.23″ _module_preset=”default” theme_builder_area=”post_content” title_text=”134k” hover_enabled=”0″ sticky_enabled=”0″][\/et_pb_image][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”4.23″ _module_preset=”default” theme_builder_area=”post_content”][et_pb_column _builder_version=”4.23″ _module_preset=”default” type=”4_4″ theme_builder_area=”post_content”][et_pb_text _builder_version=”4.23″ _module_preset=”default” theme_builder_area=”post_content” hover_enabled=”0″ sticky_enabled=”0″]<\/p>\n

2. Data for delta-ML of atomization energies 6 kilo C7H10O2 constitutional isomers<\/h4>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”4.23″ _module_preset=”default” column_structure=”1_2,1_2″ theme_builder_area=”post_content”][et_pb_column _builder_version=”4.23″ _module_preset=”default” type=”1_2″ theme_builder_area=”post_content”][et_pb_text _builder_version=”4.23″ _module_preset=”default” theme_builder_area=”post_content” hover_enabled=”0″ sticky_enabled=”0″]<\/p>\n

<\/span>The dataset comprises of structures (in Angstroms) of 6095 (6k) constitutional isomers of stoichiometry C7H10O2 relaxed at the B3LYP\/6-31G(2df,p) level (from\u00a0<\/span>Scientific Data, 1 (2014) 140022)<\/a>, along with atomization energies (in kcal\/mol) at HF\/6-31G(d), MP2\/6-31G(d), CCSD\/6-31G(d), and CCSD(T)\/6-31G(d) levels.<\/span><\/p>\n

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Reference<\/b>
Big data meets quantum chemistry approximations: The delta-machine learning approach<\/a>
Raghunathan Ramakrishnan, Pavlo O. Dral, Matthias Rupp, O. Anatole von Lilienfeld
Journal of Chemical Theory and Computation, 11 (2015) 2087\u20132096.<\/p>\n

Quantum chemistry structures and properties of 134 kilo molecules<\/a>
Raghunathan Ramakrishnan, Pavlo O. Dral, Matthias Rupp, O. Anatole von Lilienfeld
Scientific Data 1, 140022 (2014).<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 [\/et_pb_text][\/et_pb_column][et_pb_column _builder_version=”4.23″ _module_preset=”default” type=”1_2″ theme_builder_area=”post_content”][et_pb_image src=”https:\/\/www.tifrh.res.in\/~ramakrishnan\/wp-content\/uploads\/2023\/12\/corr.png” _builder_version=”4.23″ _module_preset=”default” theme_builder_area=”post_content” title_text=”corr” hover_enabled=”0″ sticky_enabled=”0″][\/et_pb_image][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”4.23″ _module_preset=”default” theme_builder_area=”post_content”][et_pb_column _builder_version=”4.23″ _module_preset=”default” type=”4_4″ theme_builder_area=”post_content”][et_pb_text _builder_version=”4.23″ _module_preset=”default” theme_builder_area=”post_content” hover_enabled=”0″ sticky_enabled=”0″]<\/p>\n

3. Data for delta-ML of atomization energies 9 kilo diastereomers of C7H10O2 constitutional isomers<\/h4>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”4.23″ _module_preset=”default” column_structure=”1_2,1_2″ theme_builder_area=”post_content”][et_pb_column _builder_version=”4.23″ _module_preset=”default” type=”1_2″ theme_builder_area=”post_content”][et_pb_text _builder_version=”4.23″ _module_preset=”default” theme_builder_area=”post_content” hover_enabled=”0″ sticky_enabled=”0″]<\/p>\n

<\/span>The dataset comprises of structures (in Angstroms) of 9868 (10k) \u201cadditional\u201d diastereomers isomers of parent 6095 C7H10O2 isomers relaxed at the B3LYP\/6-31G(2df,p) level. Note that the original 6095 isomers are part of the GDB9 dataset published in\u00a0<\/span>Scientific Data, 1 (2014) 140022)<\/a>. The 10k diastereomers were generated separately in order to validate the transferability of the delta-ML model trained on the parent isomers.<\/span><\/p>\n

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Reference<\/b>
Big data meets quantum chemistry approximations: The delta-machine learning approach<\/a>
Raghunathan Ramakrishnan, Pavlo O. Dral, Matthias Rupp, O. Anatole von Lilienfeld
Journal of Chemical Theory and Computation, 11 (2015) 2087\u20132096.<\/p>\n

Quantum chemistry structures and properties of 134 kilo molecules<\/a>
Raghunathan Ramakrishnan, Pavlo O. Dral, Matthias Rupp, O. Anatole von Lilienfeld
Scientific Data 1, 140022 (2014).<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

[\/et_pb_text][\/et_pb_column][et_pb_column _builder_version=”4.23″ _module_preset=”default” type=”1_2″ theme_builder_area=”post_content”][et_pb_image src=”https:\/\/www.tifrh.res.in\/~ramakrishnan\/wp-content\/uploads\/2023\/12\/dia.png” _builder_version=”4.23″ _module_preset=”default” theme_builder_area=”post_content” title_text=”dia” hover_enabled=”0″ sticky_enabled=”0″][\/et_pb_image][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”4.23″ _module_preset=”default” theme_builder_area=”post_content”][et_pb_column _builder_version=”4.23″ _module_preset=”default” type=”4_4″ theme_builder_area=”post_content”][et_pb_text _builder_version=”4.23″ _module_preset=”default” theme_builder_area=”post_content” hover_enabled=”0″ sticky_enabled=”0″]<\/p>\n

4. Data for delta-ML of electronic spectra of 22 kilo (GDB1-GDB8) molecules<\/h4>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”4.23″ _module_preset=”default” column_structure=”1_2,1_2″ theme_builder_area=”post_content”][et_pb_column _builder_version=”4.23″ _module_preset=”default” type=”1_2″ theme_builder_area=”post_content”][et_pb_text _builder_version=”4.23″ _module_preset=”default” theme_builder_area=”post_content” hover_enabled=”0″ sticky_enabled=”0″]<\/p>\n

This dataset was used in\u00a0<\/span>JCP, 143 (2015) 084111<\/a>. The dataset comprises of structures (in Angstroms) of 21786 (22k) molecules relaxed at the B3LYP\/6-31G(2df,p) level (from\u00a0<\/span>Scientific Data, 1 (2014) 140022)<\/a>, along with valence electronic excitation energy (in hartree) and oscillator strengths (in atomic unit, length representation) computed at the levels RICC2\/def2TZVP, LR-TD-PBE0\/def2SVP, LR-TD-PBE0\/def2TZVP, LR-TD-CAMB3LYP\/def2TZVP.<\/p>\n

Here is the link for the dataset on Github:\u00a0<\/span>https:\/\/github.com\/raghurama123\/ExcitedStatesQM8<\/a><\/p>\n

\u00a0<\/span><\/p>\n

[\/et_pb_text][\/et_pb_column][et_pb_column _builder_version=”4.23″ _module_preset=”default” type=”1_2″ theme_builder_area=”post_content”][et_pb_image src=”https:\/\/www.tifrh.res.in\/~ramakrishnan\/wp-content\/uploads\/2023\/12\/density03.png” _builder_version=”4.23″ _module_preset=”default” theme_builder_area=”post_content” title_text=”density03″ hover_enabled=”0″ sticky_enabled=”0″][\/et_pb_image][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”4.23″ _module_preset=”default” theme_builder_area=”post_content” custom_padding=”3px|||||”][et_pb_column _builder_version=”4.23″ _module_preset=”default” type=”4_4″ theme_builder_area=”post_content”][et_pb_text _builder_version=”4.23″ _module_preset=”default” theme_builder_area=”post_content” hover_enabled=”0″ sticky_enabled=”0″]<\/p>\n\n\n\n
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Reference<\/b>
Electronic spectra from TDDFT and machine learning in chemical space<\/a>
Raghunathan Ramakrishnan, Mia Hartmann, Enrico Tapavicza, O. Anatole von Lilienfeld
Journal of Chemical Physics, 143 (2015) 084111 (1-8).<\/p>\n

Quantum chemistry structures and properties of 134 kilo molecules<\/a>
Raghunathan Ramakrishnan, Pavlo O. Dral, Matthias Rupp, O. Anatole von Lilienfeld
Scientific Data 1, 140022 (2014).<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”4.23″ _module_preset=”default” theme_builder_area=”post_content”][et_pb_column _builder_version=”4.23″ _module_preset=”default” type=”4_4″ theme_builder_area=”post_content”][et_pb_text _builder_version=”4.23″ _module_preset=”default” theme_builder_area=”post_content” hover_enabled=”0″ sticky_enabled=”0″]<\/p>\n

5. Data for \u201cmany properties one kernel\u201d of 112 kilo (only GDB9, 9 heavy atoms only) molecules<\/h4>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”4.23″ _module_preset=”default” column_structure=”1_2,1_2″ theme_builder_area=”post_content”][et_pb_column _builder_version=”4.23″ _module_preset=”default” type=”1_2″ theme_builder_area=”post_content”][et_pb_text _builder_version=”4.23″ _module_preset=”default” theme_builder_area=”post_content” hover_enabled=”0″ sticky_enabled=”0″]<\/p>\n

This dataset was used in\u00a0<\/span><\/span>Chimia, 69 (2015) 182-186<\/a>. The dataset comprises of structures (in Angstroms) of 111597 (112k) molecules relaxed at the B3LYP\/6-31G(2df,p) level (from\u00a0<\/span><\/span>Scientific Data, 1 (2014) 140022)<\/a>, along with 13 properties computed at the same level. Please see the top two lines of the file \u201c112k_properties.dat\u201d for definitions and units of various properties.\u00a0<\/span><\/span>See also the supporting information archived in Basel.<\/a><\/p>\n

[\/et_pb_text][et_pb_text _builder_version=”4.23″ _module_preset=”default” theme_builder_area=”post_content” hover_enabled=”0″ sticky_enabled=”0″]<\/p>\n\n\n\n
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Reference<\/b>
Many molecular properties from one kernel in chemical space<\/a>
Raghunathan Ramakrishnan, O. Anatole von Lilienfeld
Chimia, 69 (2015) 182-186.<\/p>\n

Quantum chemistry structures and properties of 134 kilo molecules<\/a>
Raghunathan Ramakrishnan, Pavlo O. Dral, Matthias Rupp, O. Anatole von Lilienfeld
Scientific Data 1, 140022 (2014).<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

[\/et_pb_text][\/et_pb_column][et_pb_column _builder_version=”4.23″ _module_preset=”default” type=”1_2″ theme_builder_area=”post_content”][et_pb_image src=”https:\/\/www.tifrh.res.in\/~ramakrishnan\/wp-content\/uploads\/2023\/12\/chimia.png” _builder_version=”4.23″ _module_preset=”default” theme_builder_area=”post_content” title_text=”chimia” hover_enabled=”0″ sticky_enabled=”0″][\/et_pb_image][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":"

All the files below are shared without any restrictions. But, I will gradually move them to Git Hub to improve the accessibility.1. Data for delta-ML of 134 kilo (GDB1-GDB9) moleculesThis dataset was used to generate Fig. 8 and Fig. 9 of\u00a0JCTC, 11 (2015) 2087\u20132096. The dataset comprises of structures (in Angstroms) of 133885 (134k) molecules […]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":""},"class_list":["post-66","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/www.tifrh.res.in\/~ramakrishnan\/index.php?rest_route=\/wp\/v2\/pages\/66","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.tifrh.res.in\/~ramakrishnan\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.tifrh.res.in\/~ramakrishnan\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.tifrh.res.in\/~ramakrishnan\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.tifrh.res.in\/~ramakrishnan\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=66"}],"version-history":[{"count":2,"href":"https:\/\/www.tifrh.res.in\/~ramakrishnan\/index.php?rest_route=\/wp\/v2\/pages\/66\/revisions"}],"predecessor-version":[{"id":79,"href":"https:\/\/www.tifrh.res.in\/~ramakrishnan\/index.php?rest_route=\/wp\/v2\/pages\/66\/revisions\/79"}],"wp:attachment":[{"href":"https:\/\/www.tifrh.res.in\/~ramakrishnan\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=66"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}