pytorch implementation of dftd2 & dftd3

Overview

torch-dftd

pytorch implementation of dftd2 [1] & dftd3 [2, 3]

Install

# Install from pypi
pip install torch-dftd

# Install from source (for developers)
git clone https://github.com/pfnet-research/torch-dftd
pip install -e .

Quick start

from ase.build import molecule
from torch_dftd.torch_dftd3_calculator import TorchDFTD3Calculator

atoms = molecule("CH3CH2OCH3")
# device="cuda:0" for fast GPU computation.
calc = TorchDFTD3Calculator(atoms=atoms, device="cpu", damping="bj")

energy = atoms.get_potential_energy()
forces = atoms.get_forces()

print(f"energy {energy} eV")
print(f"forces {forces}")

Dependency

The library is tested under following environment.

  • python: 3.6
  • CUDA: 10.2
torch==1.5.1
ase==3.21.1
# Below is only for 3-body term
cupy-cuda102==8.6.0
pytorch-pfn-extras==0.3.2

Development tips

Formatting & Linting

pysen is used to format the python code of this repository.
You can simply run below to get your code formatted :)

# Format the code
$ pysen run format
# Check the code format
$ pysen run lint

CUDA Kernel function implementation with cupy

cupy supports users to implement CUDA kernels within python code, and it can be easily linked with pytorch tensor calculations.
Element wise kernel is implemented and used in some pytorch functions to accelerate speed with GPU.

See document for details about user defined kernel.

Citation

Please always cite original paper of DFT-D2 [1] or DFT-D3 [2, 3], if you used this software for your publication.

DFT-D2:
[1] S. Grimme, J. Comput. Chem, 27 (2006), 1787-1799. DOI: 10.1002/jcc.20495

DFT-D3:
[2] S. Grimme, J. Antony, S. Ehrlich and H. Krieg, J. Chem. Phys, 132 (2010), 154104. DOI: 10.1063/1.3382344

If BJ-damping is used in DFT-D3:
[3] S. Grimme, S. Ehrlich and L. Goerigk, J. Comput. Chem, 32 (2011), 1456-1465. DOI: 10.1002/jcc.21759

Comments
  • [WIP] Cell-related gradient modifications

    [WIP] Cell-related gradient modifications

    I found that the current implementation has several performance issues regarding gradient wrt. cell. This PR modifies that. Since the changes are relatively much, I will put some comments.

    Change summary:

    • Use shift for gradient instead of cell.
    • shift is now length scale instead cell unit.
    • Calculate Voigt notation style stress directly

    Also, this PR contains bugfix related to sked cell.

    bug enhancement 
    opened by So-Takamoto 1
  • Raise Error with single atom inputs.

    Raise Error with single atom inputs.

    When the length of atoms is 1, the routine raises error.

    from ase.build import molecule
    from ase.calculators.dftd3 import DFTD3
    from torch_dftd.torch_dftd3_calculator import TorchDFTD3Calculator
    
    if __name__ == "__main__":
        atoms = molecule("H")
        # device="cuda:0" for fast GPU computation.
        calc = TorchDFTD3Calculator(atoms=atoms, device="cpu", damping="bj")
    
        energy = atoms.get_potential_energy()
        forces = atoms.get_forces()
    
        print(f"energy {energy} eV")
        print(f"forces {forces}")
    
    
    Traceback (most recent call last):
      File "quick.py", line 12, in <module>
        energy = atoms.get_potential_energy()
      File "/home/ahayashi/envs/dftd/lib/python3.8/site-packages/ase/atoms.py", line 731, in get_potential_energy
        energy = self._calc.get_potential_energy(self)
      File "/home/ahayashi/envs/dftd/lib/python3.8/site-packages/ase/calculators/calculator.py", line 709, in get_potential_energy
        energy = self.get_property('energy', atoms)
      File "/home/ahayashi/torch-dftd/torch_dftd/torch_dftd3_calculator.py", line 141, in get_property
        dftd3_result = Calculator.get_property(self, name, atoms, allow_calculation)
      File "/home/ahayashi/envs/dftd/lib/python3.8/site-packages/ase/calculators/calculator.py", line 737, in get_property
        self.calculate(atoms, [name], system_changes)
      File "/home/ahayashi/torch-dftd/torch_dftd/torch_dftd3_calculator.py", line 119, in calculate
        results = self.dftd_module.calc_energy(**input_dicts, damping=self.damping)[0]
      File "/home/ahayashi/torch-dftd/torch_dftd/nn/base_dftd_module.py", line 75, in calc_energy
        E_disp = self.calc_energy_batch(
      File "/home/ahayashi/torch-dftd/torch_dftd/nn/dftd3_module.py", line 86, in calc_energy_batch
        E_disp = d3_autoev * edisp(
      File "/home/ahayashi/torch-dftd/torch_dftd/functions/dftd3.py", line 189, in edisp
        c6 = _getc6(Zi, Zj, nci, ncj, c6ab=c6ab, k3=k3)  # c6 coefficients
      File "/home/ahayashi/torch-dftd/torch_dftd/functions/dftd3.py", line 97, in _getc6
        k3_rnc = torch.where(cn0 > 0.0, k3 * r, -1.0e20 * torch.ones_like(r)).view(n_edges, -1)
    RuntimeError: cannot reshape tensor of 0 elements into shape [0, -1] because the unspecified dimension size -1 can be any value and is ambiguous
    
    opened by AkihideHayashi 1
  • use shift for gradient calculation instead of cell

    use shift for gradient calculation instead of cell

    I found that the current implementation has several performance issues regarding gradient wrt. cell. This PR modifies it. Since the changes are relatively much, I will put some comments.

    Change summary:

    • Use shift for gradient instead of cell.
    • shift is now length scale instead cell unit.
    • Calculate Voigt notation style stress directly

    Also, this PR contains bugfix related to sked cell.

    bug enhancement 
    opened by So-Takamoto 0
  • Bugfix: batch calculation with abc=True

    Bugfix: batch calculation with abc=True

    I found that test function test_calc_energy_force_stress_device_batch_abc unintentionally ignores abc argument.

    This PR modified related implementation to work it.

    In addition, corner case correspondence when the total number of atom is zero is also added. (n_graphs cannot be calculated from batch_edge when len(batch_edge) == 0.)

    bug 
    opened by So-Takamoto 0
  • Fixed a bug for inputs with 0 adjacencies.

    Fixed a bug for inputs with 0 adjacencies.

    The _gettc6 routine now works correctly even when the number of adjacencies is 0. Instead of calling calc_neighbor_by_pymatgen when the number of atoms is 0 and the periodic boundary condition, it now return edge_index, S for adjacency 0. In my environment, using the result of torch.sum for the size of torch.zeros caused an error, so I changed it to cast the result of sum to int.

    bug 
    opened by AkihideHayashi 0
  •  Bug in test for stress

    Bug in test for stress

    In test_torch_dftd3_calculator.py/_assert_energy_force_stress_equal, there is a code below.

        if np.all(atoms.pbc == np.array([True, True, True])):
            s1 = atoms.get_stress()
            s2 = atoms.get_stress()
            assert np.allclose(s1, s2, atol=1e-5, rtol=1e-5)
    

    This code cannot compare the results of stresses of calc1 and calc2. Both s1 and s2 are the stress of calc2.

    opened by AkihideHayashi 0
Releases(v0.3.0)
  • v0.3.0(Apr 25, 2022)

    This is the release note of v0.3.0.

    Highlights

    • use shift for gradient calculation instead of cell #13 (Thank you @So-Takamoto )
      • It includes 1. speed up of stress calculation for batch atoms, and 2. bug fix for stress calculation when cell is skewed.
    Source code(tar.gz)
    Source code(zip)
  • v0.2.0(Sep 4, 2021)

    This is the release note of v0.2.0.

    Highlights

    • Add PFP citation in README.md #2
    • Use pymatgen for pbc neighbor search speed up #3

    Bug fixes

    • Fixed a bug for inputs with 0 adjacencies. #6 (Thank you @AkihideHayashi )
    • Remove RuntimeError on no-cupy environment #8 (Thank you @So-Takamoto )
    • Bugfix: batch calculation with abc=True #9 (Thank you @So-Takamoto )

    Others

    • move pysen to develop dependency #10 (Thank you @So-Takamoto )
    Source code(tar.gz)
    Source code(zip)
  • v0.1.0(May 10, 2021)

FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation.

FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation [Project] [Paper] [arXiv] [Home] Official implementation of FastFCN:

Wu Huikai 815 Dec 29, 2022
StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks

StackGAN Pytorch implementation Inception score evaluation StackGAN-v2-pytorch Tensorflow implementation for reproducing main results in the paper Sta

Han Zhang 1.8k Dec 21, 2022
Implementation of "The Power of Scale for Parameter-Efficient Prompt Tuning"

Prompt-Tuning Implementation of "The Power of Scale for Parameter-Efficient Prompt Tuning" Currently, we support the following huggigface models: Bart

Andrew Zeng 36 Dec 19, 2022
Beyond Image to Depth: Improving Depth Prediction using Echoes (CVPR 2021)

Beyond Image to Depth: Improving Depth Prediction using Echoes (CVPR 2021) Kranti Kumar Parida, Siddharth Srivastava, Gaurav Sharma. We address the pr

Kranti Kumar Parida 33 Jun 27, 2022
Multi-Modal Machine Learning toolkit based on PyTorch.

简体中文 | English TorchMM 简介 多模态学习工具包 TorchMM 旨在于提供模态联合学习和跨模态学习算法模型库,为处理图片文本等多模态数据提供高效的解决方案,助力多模态学习应用落地。 近期更新 2022.1.5 发布 TorchMM 初始版本 v1.0 特性 丰富的任务场景:工具

njustkmg 1 Jan 05, 2022
Age Progression/Regression by Conditional Adversarial Autoencoder

Age Progression/Regression by Conditional Adversarial Autoencoder (CAAE) TensorFlow implementation of the algorithm in the paper Age Progression/Regre

Zhifei Zhang 603 Dec 22, 2022
PyTorch code to run synthetic experiments.

Code repository for Invariant Risk Minimization Source code for the paper: @article{InvariantRiskMinimization, title={Invariant Risk Minimization}

Facebook Research 345 Dec 12, 2022
Code repo for "Cross-Scale Internal Graph Neural Network for Image Super-Resolution" (NeurIPS'20)

IGNN Code repo for "Cross-Scale Internal Graph Neural Network for Image Super-Resolution" [paper] [supp] Prepare datasets 1 Download training dataset

Shangchen Zhou 278 Jan 03, 2023
A spatial genome aligner for analyzing multiplexed DNA-FISH imaging data.

jie jie is a spatial genome aligner. This package parses true chromatin imaging signal from noise by aligning signals to a reference DNA polymer model

Bojing Jia 9 Sep 29, 2022
Face recognition system using MTCNN, FACENET, SVM and FAST API to track participants of Big Brother Brasil in real time.

BBB Face Recognizer Face recognition system using MTCNN, FACENET, SVM and FAST API to track participants of Big Brother Brasil in real time. Instalati

Rafael Azevedo 232 Dec 24, 2022
REBEL: Relation Extraction By End-to-end Language generation

REBEL: Relation Extraction By End-to-end Language generation This is the repository for the Findings of EMNLP 2021 paper REBEL: Relation Extraction By

Babelscape 222 Jan 06, 2023
Frequency Domain Image Translation: More Photo-realistic, Better Identity-preserving

Frequency Domain Image Translation: More Photo-realistic, Better Identity-preserving This is the source code for our paper Frequency Domain Image Tran

Mu Cai 52 Dec 23, 2022
Security evaluation module with onnx, pytorch, and SecML.

🚀 🐼 🔥 PandaVision Integrate and automate security evaluations with onnx, pytorch, and SecML! Installation Starting the server without Docker If you

Maura Pintor 11 Apr 12, 2022
Face Recognition & AI Based Smart Attendance Monitoring System.

In today’s generation, authentication is one of the biggest problems in our society. So, one of the most known techniques used for authentication is h

Sagar Saha 1 Jan 14, 2022
TensorFlow port of PyTorch Image Models (timm) - image models with pretrained weights.

TensorFlow-Image-Models Introduction Usage Models Profiling License Introduction TensorfFlow-Image-Models (tfimm) is a collection of image models with

Martins Bruveris 227 Dec 20, 2022
PyTorch implementation of DreamerV2 model-based RL algorithm

PyDreamer Reimplementation of DreamerV2 model-based RL algorithm in PyTorch. The official DreamerV2 implementation can be found here. Features ... Run

118 Dec 15, 2022
CVPR 2021 - Official code repository for the paper: On Self-Contact and Human Pose.

selfcontact This repo is part of our project: On Self-Contact and Human Pose. [Project Page] [Paper] [MPI Project Page] It includes the main function

Lea Müller 68 Dec 06, 2022
Funnels: Exact maximum likelihood with dimensionality reduction.

Funnels This repository contains the code needed to reproduce the experiments from the paper: Funnels: Exact maximum likelihood with dimensionality re

2 Apr 21, 2022
Low-dose Digital Mammography with Deep Learning

Impact of loss functions on the performance of a deep neural network designed to restore low-dose digital mammography ====== This repository contains

WANG-AXIS 6 Dec 13, 2022
System Combination for Grammatical Error Correction Based on Integer Programming

System Combination for Grammatical Error Correction Based on Integer Programming This repository contains the code and scripts that implement the syst

NUS NLP Group 0 Mar 29, 2022