Package: tad-multicharge
PyTorch implementation of the electronegativity equilibration (EEQ) model for atomic partial charges.
This module allows to process a single structure or a batch of structures for the calculation of atom-resolved dispersion energies.For details on the EEQ model, seeS. A. Ghasemi, A. Hofstetter, S. Saha, and S. Goedecker,Phys. Rev. B,2015,92, 045131. DOI:10.1103/PhysRevB.92.045131E. Caldeweyher, S. Ehlert, A. Hansen, H. Neugebauer, S. Spicher, C. Bannwarth and S. Grimme,J. Chem. Phys.,2019,150, 154122. DOI:10.1063/1.5090222For alternative implementations, also check outmulticharge:Implementation of the EEQ model in Fortran.Installationpiptad-multichargecan easily be installed withpip.pip install tad-multichargeFrom sourceThis project is hosted on GitHub attad-mctc/tad-multicharge.
Obtain the source by cloning the repository withgit clone https://github.com/tad-mctc/tad-multicharge
cd tad-multichargeWe recommend using acondaenvironment to install the package.
You can setup the environment manager using amambaforgeinstaller.
Install the required dependencies from the conda-forge channel.mamba env create -n torch -f environment.yaml
mamba activate torchInstall this project withpipin the environmentpip install .The following dependencies are requirednumpytad-mctctorchpytest(tests only)DevelopmentFor development, additionally install the following tools in your environment.mamba install black covdefaults mypy pre-commit pylint pytest pytest-cov pytest-xdist tox
pip install pytest-random-orderWith pip, add the option-efor installing in development mode, and add[dev]for the development dependenciespip install -e .[dev]The pre-commit hooks are initialized by running the following command in the root of the repository.pre-commit installFor 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 EEQ partial charges and the corresponding electrostatic energy for a single structure.importtorchfromtad_multichargeimporteeqnumbers=torch.tensor([7,7,1,1,1,1,1,1])# coordinates in Bohrpositions=torch.tensor([[-2.98334550857544,-0.08808205276728,+0.00000000000000],[+2.98334550857544,+0.08808205276728,+0.00000000000000],[-4.07920360565186,+0.25775116682053,+1.52985656261444],[-1.60526800155640,+1.24380481243134,+0.00000000000000],[-4.07920360565186,+0.25775116682053,-1.52985656261444],[+4.07920360565186,-0.25775116682053,-1.52985656261444],[+1.60526800155640,-1.24380481243134,+0.00000000000000],[+4.07920360565186,-0.25775116682053,+1.52985656261444],])total_charge=torch.tensor(0.0)cn=torch.tensor([3.0,3.0,1.0,1.0,1.0,1.0,1.0,1.0])eeq_model=eeq.EEQModel.param2019()energy,qat=eeq_model.solve(numbers,positions,total_charge,cn)print(torch.sum(energy,-1))# tensor(-0.1750)print(qat)# tensor([-0.8347, -0.8347,  0.2731,  0.2886,  0.2731,  0.2731,  0.2886,  0.2731])The next example shows the calculation of the electrostatic energy with a simpler API for a batch of structures.importtorchfromtad_multichargeimporteeqfromtad_mctc.batchimportpackfromtad_mctc.convertimportsymbol_to_number# S22 system 4: formamide dimernumbers=pack((symbol_to_number("C C N N H H H H H H O O".split()),symbol_to_number("C O N H H H".split()),))# coordinates in Bohrpositions=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],]),))# total charge of both systemcharge=torch.tensor([0.0,0.0])# calculate electrostatic energy in Hartreeenergy=torch.sum(eeq.get_energy(numbers,positions,charge),-1)torch.set_printoptions(precision=10)print(energy)# tensor([-0.2086755037, -0.0972094536])print(energy[0]-2*energy[1])# tensor(-0.0142565966)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.