Fully Differentiable Extended Tight-Binding

- Combining semi-empirical quantum chemistry with machine learning in PyTorch -

The xTB methods (GFNn-xTB) are a series of semi-empirical quantum chemical methods that provide a good balance between accuracy and computational cost.

With dxtb, we provide a re-implementation of the xTB methods in PyTorch, which allows for automatic differentiation and seamless integration into machine learning frameworks.

NOTE: If you encounter any bugs or have questions on how to use dxtb, feel free to open an issue.

Installation

pip

dxtb can easily be installed with pip.

pip install dxtb

conda

dxtb will also be available on conda soon.

conda install dxtb

Other

For more options, see the installation guide in the documentation.

Example

The following example demonstrates how to compute the energy and forces using GFN1-xTB.

import torch
import dxtb

dd = {"dtype": torch.double, "device": torch.device("cpu")}

# LiH
numbers = torch.tensor([3, 1], device=dd["device"])
positions = torch.tensor([[0.0, 0.0, 0.0], [0.0, 0.0, 1.5]], **dd)

# instantiate a calculator
calc = dxtb.calculators.GFN1Calculator(numbers, **dd)

# compute the energy
pos = positions.clone().requires_grad_(True)
energy = calc.get_energy(pos)

# obtain gradient (dE/dR) via autograd
(g,) = torch.autograd.grad(energy, pos)

# Alternatively, forces can directly be requested from the calculator.
# (Don't forget to manually reset the calculator when the inputs are identical.)
calc.reset()
pos = positions.clone().requires_grad_(True)
forces = calc.get_forces(pos)

assert torch.equal(forces, -g)

For more examples and details, check out the documentation.

Citation

If you use dxtb in your research, please cite the following paper:

• M. Friede, C. Hölzer, S. Ehlert, S. Grimme, dxtb -- An Efficient and Fully Differentiable Framework for Extended Tight-Binding, J. Chem. Phys., 2024, 161, 062501. (DOI)

The Supporting Information can be found here.

For details on the xTB methods, see

• C. Bannwarth, E. Caldeweyher, S. Ehlert, A. Hansen, P. Pracht, J. Seibert, S. Spicher, S. Grimme, WIREs Comput. Mol. Sci., 2020, 11, e01493. (DOI)
• C. Bannwarth, S. Ehlert, S. Grimme, J. Chem. Theory Comput., 2019, 15, 1652-1671. (DOI)
• S. Grimme, C. Bannwarth, P. Shushkov, J. Chem. Theory Comput., 2017, 13, 1989-2009. (DOI)

Contributing

This is a volunteer open source projects and contributions are always welcome. Please, take a moment to read the contributing guidelines.

License

This project is licensed under the Apache License, Version 2.0 (the "License"); you may not use this project's files except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.