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Extending machine learning beyond interatomic potentials for predicting molecular properties

Nature Reviews Chemistry volume 6, pages 653–672 (2022)Cite this article

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A Publisher Correction to this article was published on 16 November 2022

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Abstract

Machine learning (ML) is becoming a method of choice for modelling complex chemical processes and materials. ML provides a surrogate model trained on a reference dataset that can be used to establish a relationship between a molecular structure and its chemical properties. This Review highlights developments in the use of ML to evaluate chemical properties such as partial atomic charges, dipole moments, spin and electron densities, and chemical bonding, as well as to obtain a reduced quantum-mechanical description. We overview several modern neural network architectures, their predictive capabilities, generality and transferability, and illustrate their applicability to various chemical properties. We emphasize that learned molecular representations resemble quantum-mechanical analogues, demonstrating the ability of the models to capture the underlying physics. We also discuss how ML models can describe non-local quantum effects. Finally, we conclude by compiling a list of available ML toolboxes, summarizing the unresolved challenges and presenting an outlook for future development. The observed trends demonstrate that this field is evolving towards physics-based models augmented by ML, which is accompanied by the development of new methods and the rapid growth of user-friendly ML frameworks for chemistry.

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Fig. 1: Coarse atomistic scale of matter from the perspective of a chemist.
Fig. 2: Relationships between chemical structure and properties from local and global perspectives.
Fig. 3: Modern architectures of neural networks for learning local and global properties.
Fig. 4: Machine learning prediction of atomic charges, vibrational spectra, dipoles and quadrupoles.
Fig. 5: Machine learning prediction of spin-polarized charges and total electron density.
Fig. 6: Machine learning prediction of spin-density, bond orders and effective Hamiltonian models.
Fig. 7: Large-scale molecular simulations enabled by machine learning.

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Acknowledgements

The work at Los Alamos National Laboratory (LANL) was supported by the LANL Directed Research and Development Funds (LDRD) and performed in part at the Center for Nonlinear Studies (CNLS) and the Center for Integrated Nanotechnologies (CINT), a US Department of Energy (DOE) Office of Science user facility at LANL. N.F. and M.K. acknowledge financial support from the Director’s Postdoctoral Fellowship at LANL funded by LDRD. K.B. and S.T. acknowledge support from the US DOE, Office of Science, Basic Energy Sciences, Chemical Sciences, Geosciences, and Biosciences Division under Triad National Security (Triad) contract grant number 89233218CNA000001 (FWP: LANLE3F2). This research used resources provided by the LANL Institutional Computing Program. LANL is managed by Triad National Security for the US DOE’s NNSA, under contract number 89233218CNA000001. A.I.B. acknowledges the R. Gaurth Hansen Professorship. O.I. acknowledges support from the National Science Foundation (NSF) grants CHE-1802789 and CHE-2041108. The work performed by O.I. and R.Z. in part was made possible by the Office of Naval Research (ONR) through support provided by the Energetic Materials Program (MURI grant number N00014-21-1-2476).

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Authors and Affiliations

  1. Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA

    Nikita Fedik, Maksim Kulichenko, Justin S. Smith, Benjamin Nebgen, Richard Messerly, Kipton Barros & Sergei Tretiak

  2. Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, NM, USA

    Nikita Fedik, Kipton Barros & Sergei Tretiak

  3. Department of Chemistry and Biochemistry, Utah State University, Logan, UT, USA

    Nikita Fedik, Maksim Kulichenko & Alexander I. Boldyrev

  4. Department of Chemistry, Carnegie Mellon University, Pittsburgh, PA, USA

    Roman Zubatyuk & Olexandr Isayev

  5. Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, NM, USA

    Nicholas Lubbers & Ying Wai Li

  6. NVIDIA, Santa Clara, CA, USA

    Justin S. Smith

  7. Center for Integrated Nanotechnologies, Los Alamos National Laboratory, Los Alamos, NM, USA

    Sergei Tretiak

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Contributions

N.F., R.Z., M.K., J.S.S., B.N., R.M., Y.W.L., A.I.B., K.B., O.I. and S.T. researched data for the article. N.F., R.Z., M.K., J.S.S., B.N., K.B., O.I. and S.T. contributed substantially to discussion of the content. N.F., R.Z., M.K., N.L., O.I. and S.T. wrote the article. All authors reviewed and/or edited the manuscript before submission.

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Correspondence to Sergei Tretiak.

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Glossary

Extensive properties

Properties that intrinsically grow with system size. Examples include enthalpies of formation, which scale with the number of chemical bonds in a system, and entropies of solvation.

Extensibility

The applicability of a ML model to systems substantially larger than those in the training set. An example of extensibility is a ML model trained to small organic molecules that can accurately simulate a protein.

Interaction layers

Groups of NN operations that transfer information between atoms. Typically expressed as a local graph convolution, this operation creates new features for each atom based on the features and distances of local neighbouring atoms. Also called message-passing.

Charge equilibration scheme

An approach for predicting charge distributions in molecules or solids using tabulated or ML-predicted atomic properties such as atomic electronegativity and hardness, or bond polarizability.

Transferability

The applicability of a ML model to systems not originally included in the training set. Transferable models exhibit high accuracy for chemical systems that are structurally different from the ones in the training set.

One-hot scheme

A binary vector representation for unranked categorical values. For example, three atom types C, H and O are represented by the vectors (1, 0, 0), (0, 1, 0) and (0, 0, 1), respectively.

Atomic embedding

A fixed-length vector representation of an atom with no information about its environment. Embedding is a learnable map between the atomic number and a high-dimensional feature space. Can incorporate additional atomic physicochemical properties, such as electronegativity.

Multitask learning

When one model is concurrently trained on multiple labels (for example, total molecular energies and atomic forces). Leverages useful information contained in multiple related tasks to improve the general performance of all tasks.

Non-extensive properties

Properties that do not scale directly with system size. Examples include characteristics of localized electronic states, electronic excitation energies, vibrational frequencies, electron affinities and ionization potentials.

Wiberg bond index

(WBI). A quantitative bonding model that expresses the bond order between pairs of atoms. WBI measures the electron population overlap between atoms and frequently numerically aligns with chemical intuition, for example, the WBI of the C=C bond in ethane C2H4 is expected to be close to two.

Δ-ML

A composite approach in which baseline values, obtained from cheap QM methods (usually DFT), are corrected towards a target line, calculated by a more sophisticated level of theory, for example, the coupled-cluster methods of perturbation theory.

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Fedik, N., Zubatyuk, R., Kulichenko, M. et al. Extending machine learning beyond interatomic potentials for predicting molecular properties. Nat Rev Chem 6, 653–672 (2022). https://doi.org/10.1038/s41570-022-00416-3

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