Package: tad-mctc
Torch Autodiff UtilityCompatibility:Availability:Status:This library is a collection of utility functions that are used in PyTorch (re-)implementations of projects from theGrimme group.
In particular, thetad-mctclibrary provides:autograd functions (Jacobian, Hessian)batch utility (packing, masks, ...)atomic data (radii, EN, example molecules, ...)io (reading coordinate files)coordination numberssafeops (autograd-safe implementations of common functions)typing (base class for tensor-like behavior of arbitrary classes)unitsThe name is inspired by the Fortran pendant "modular computation tool chain library" (mctc-lib).Installationpiptad-mctccan easily be installed withpip.pipinstalltad-mctcFrom sourceThis project is hosted on GitHub attad-mctc/tad-mctc.
Obtain the source by cloning the repository withgitclonehttps://github.com/tad-mctc/tad-mctccdtad-mctcWe recommend using acondaenvironment to install the package.
You can setup the environment manager using amambaforgeinstaller.
Install the required dependencies from the conda-forge channel.mambaenvcreate-ntorch-fenvironment.yaml
mambaactivatetorchInstall this project withpipin the environmentpipinstall.The following dependencies are requirednumpytorchpytest(tests only)DevelopmentFor development, additionally install the following tools in your environment.mambainstallblackcovdefaultsmypypre-commitpylintpytestpytest-covpytest-xdisttox
pipinstallpytest-random-orderWith pip, add the option-efor installing in development mode, and add[dev]for the development dependenciespipinstall-e.[dev]The pre-commit hooks are initialized by running the following command in the root of the repository.pre-commitinstallFor testing all Python environments, simply runtox.toxNote that this randomizes the order of tests but skips "large" tests. To modify this behavior,toxhas to skip the optionalposargs.tox--testExamplesThe following example shows how to calculate the coordination number used in the EEQ model for a single structure.importtorchimporttad_mctcasmctcnumbers=mctc.convert.symbol_to_number(symbols="C C C C N C S H H H H H".split())# coordinates in Bohrpositions=torch.tensor([[-2.56745685564671,-0.02509985979910,0.00000000000000],[-1.39177582455797,+2.27696188880014,0.00000000000000],[+1.27784995624894,+2.45107479759386,0.00000000000000],[+2.62801937615793,+0.25927727028120,0.00000000000000],[+1.41097033661123,-1.99890996077412,0.00000000000000],[-1.17186102298849,-2.34220576284180,0.00000000000000],[-2.39505990368378,-5.22635838332362,0.00000000000000],[+2.41961980455457,-3.62158019253045,0.00000000000000],[-2.51744374846065,+3.98181713686746,0.00000000000000],[+2.24269048384775,+4.24389473203647,0.00000000000000],[+4.66488984573956,+0.17907568006409,0.00000000000000],[-4.60044244782237,-0.17794734637413,0.00000000000000],])# calculate EEQ coordination numbercn=mctc.ncoord.cn_eeq(numbers,positions)torch.set_printoptions(precision=10)print(cn)# tensor([3.0519218445, 3.0177774429, 3.0132560730, 3.0197706223,#         3.0779352188, 3.0095663071, 1.0991339684, 0.9968624115,#         0.9943327904, 0.9947233200, 0.9945874214, 0.9945726395])The next example shows the calculation of the coordination number used in DFT-D4 for a batch of structures.importtorchimporttad_mctcasmctc# S22 system 4: formamide dimernumbers=mctc.batch.pack((mctc.convert.symbol_to_number("C C N N H H H H H H O O".split()),mctc.convert.symbol_to_number("C O N H H H".split()),))# coordinates in Bohrpositions=mctc.batch.pack((torch.tensor([[-3.81469488143921,+0.09993441402912,0.00000000000000],[+3.81469488143921,-0.09993441402912,0.00000000000000],[-2.66030049324036,-2.15898251533508,0.00000000000000],[+2.66030049324036,+2.15898251533508,0.00000000000000],[-0.73178529739380,-2.28237795829773,0.00000000000000],[-5.89039325714111,-0.02589114569128,0.00000000000000],[-3.71254944801331,-3.73605775833130,0.00000000000000],[+3.71254944801331,+3.73605775833130,0.00000000000000],[+0.73178529739380,+2.28237795829773,0.00000000000000],[+5.89039325714111,+0.02589114569128,0.00000000000000],[-2.74426102638245,+2.16115570068359,0.00000000000000],[+2.74426102638245,-2.16115570068359,0.00000000000000],]),torch.tensor([[-0.55569743203406,+1.09030425468557,0.00000000000000],[+0.51473634678469,+3.15152550263611,0.00000000000000],[+0.59869690244446,-1.16861263789477,0.00000000000000],[-0.45355203669134,-2.74568780438064,0.00000000000000],[+2.52721209544999,-1.29200800956867,0.00000000000000],[-2.63139587595376,+0.96447869452240,0.00000000000000],]),))# calculate coordination numbercn=mctc.ncoord.cn_d4(numbers,positions)torch.set_printoptions(precision=10)print(cn)# tensor([[2.6886456013, 2.6886456013, 2.6314170361, 2.6314167976,#          0.8594539165, 0.9231414795, 0.8605306745, 0.8605306745,#          0.8594539165, 0.9231414795, 0.8568341732, 0.8568341732],#         [2.6886456013, 0.8568335176, 2.6314167976, 0.8605306745,#          0.8594532013, 0.9231414795, 0.0000000000, 0.0000000000,#          0.0000000000, 0.0000000000, 0.0000000000, 0.0000000000]])ContributingThis is a volunteer open source projects and contributions are always welcome.
Please, take a moment to read thecontributing guidelines.LicenseThis project is free software: you can redistribute it and/or modify it under the terms of the Lesser GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.This project is distributed in the hope that it will be useful, but without any warranty; without even the implied warranty of merchantability or fitness for a particular purpose. See the Lesser GNU General Public License for more details.Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in this project by you, as defined in the Lesser GNU General Public license, shall be licensed as above, without any additional terms or conditions.