Ctrl+K

Torch Autodiff Utility

Contents

Torch Autodiff Utility#

Python Versions Release PyPI Conda Version Apache-2.0 Test Status Ubuntu Test Status Windows Test Status macOS Documentation Status Coverage pre-commit.ci status

This library is a collection of utility functions that are used in PyTorch (re-)implementations of projects from the Grimme group. In particular, the tad-mctc library provides:

  • autograd functions (Jacobian, Hessian)

  • atomic data (radii, EN, example molecules, …)

  • batch utility (packing, masks, …)

  • conversion functions (numpy, atomic symbols/numbers, …)

  • coordination numbers (DFT-D3, DFT-D4, EEQ)

  • io (reading/writing coordinate files)

  • molecular properties (bond lengths/orders/angles, moment of inertia, …)

  • safeops (autograd-safe implementations of common functions)

  • typing (base class for tensor-like behavior of arbitrary classes)

  • units

The name is inspired by the Fortran pendant “modular computation tool chain library” (mctc-lib).

Examples#

The following example shows how to calculate the coordination number used in the EEQ model for a single structure.

import torch
import tad_mctc as mctc

numbers = mctc.convert.symbol_to_number(symbols="C C C C N C S H H H H H".split())

# coordinates in Bohr
positions = 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 number
cn = 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.

import torch
import tad_mctc as mctc

# S22 system 4: formamide dimer
numbers = 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 Bohr
positions = 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 number
cn = 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]])
Contents