Machine-learned interatomic potential
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Machine-learned interatomic potentials (MLIPs), or simply machine
learning potentials (MLPs), are interatomic potentials constructed using
machine learning. Beginning in the 1990s, researchers have employed such
programs to construct interatomic potentials by mapping atomic
structures to their potential energies. These potentials are referred to
as MLIPs or MLPs.
Such machine learning potentials promised to fill the gap between
density functional theory, a highly accurate but computationally
intensive modelling method, and empirically derived or
intuitively-approximated potentials, which were far lighter
computationally but substantially less accurate. Improvements in
artificial intelligence technology heightened the accuracy of MLPs while
lowering their computational cost, increasing the role of machine
Machine learning potentials began by using neural networks to tackle
low-dimensional systems. While promising, these models could not
systematically account for interatomic energy interactions; they could
be applied to small molecules in a vacuum, or molecules interacting with
frozen surfaces, but not much else – and even in these applications, the
models often relied on force fields or potentials derived empirically or
with simulations.cite-ref-ml-1-1[1] These models thus remained confined to academia.
Modern neural networks construct highly accurate and computationally
light potentials, as theoretical understanding of materials science was
increasingly built into their architectures and preprocessing. Almost
all are local, accounting for all interactions between an atom and its
neighbor up to some cutoff radius. There exist some nonlocal models, but
these have been experimental for almost a decade. For most systems,
Almost all neural networks intake atomic coordinates and output
potential energies. For some, these atomic coordinates are converted
into atom-centered symmetry functions. From this data, a separate atomic
neural network is trained for each element; each atomic network is
evaluated whenever that element occurs in the given structure, and then
the results are pooled together at the end. This process – in
particular, the atom-centered symmetry functions which convey
translational, rotational, and permutational invariances – has greatly
improved machine learning potentials by significantly constraining the
neural network search space. Other models use a similar process but
emphasize bonds over atoms, using pair symmetry functions and training
Other models to learn their own descriptors rather than using
predetermined symmetry-dictating functions. These models, called
message-passing neural networks (MPNNs), are graph neural networks.
Treating molecules as three-dimensional graphs (where atoms are nodes
and bonds are edges), the model takes feature vectors describing the
atoms as input, and iteratively updates these vectors as information
about neighboring atoms is processed through message functions and
convolutions. These feature vectors are then used to predict the final
potentials. The flexibility of this method often results in stronger,
more generalizable models. In 2017, the first-ever MPNN model (a deep
tensor neural network) was used to calculate the properties of small
organic molecules.
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Gaussian Approximation Potential (GAP)
One popular class of machine-learned interatomic potential is the
descriptors of local atomic environmentscite-ref-8[8] with Gaussian process
regressioncite-ref-9[9] to machine learn the potential energy surface of a given
system. To date, the GAP framework has been used to successfully develop
a number of MLIPs for various systems, including for elemental systems
as well as for multicomponent systems such as Ge2Sb2Te5cite-ref-15[15] and
References
cite-note-ml-11. ↑ citerefkocerkobehler2022Kocer, Emir; Ko, Tsz Wai; Behler, Jorg (2022). "Neural Network Potentials: A Concise Overview of Methods". Annual Review of Physical Chemistry. 73: 163–86. arXiv:2107.03727. Bibcode:2022ARPC...73..163K. doi:10.1146/annurev-physchem-082720-034254. PMID 34982580.
cite-note-33. ↑ citerefghasemihofstettersahagoedecker2015Ghasemi, SA; Hofstetter, A; Saha, S; Goedecker, S (2015). "Interatomic potentials for ionic systems with density functional accuracy based on charge densities obtained by a neural network". Physical Review B. 92 (4): 045131. arXiv:1501.07344. Bibcode:2015PhRvB..92d5131G. doi:10.1103/PhysRevB.92.045131.
cite-note-55. ↑ citerefbart-kpaynekondorcs-nyi2010Bartók, Albert P.; Payne, Mike C.; Kondor, Risi; Csányi, Gábor (2010-04-01). "Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons". Physical Review Letters. 104 (13): 136403. arXiv:0910.1019. Bibcode:2010PhRvL.104m6403B. doi:10.1103/PhysRevLett.104.136403. PMID 20481899.
cite-note-66. ↑ citerefbart-kdepoelkingbernstein2017Bartók, Albert P.; De, Sandip; Poelking, Carl; Bernstein, Noam; Kermode, James R.; Csányi, Gábor; Ceriotti, Michele (December 2017). "Machine learning unifies the modeling of materials and molecules". Science Advances. 3 (12): e1701816. arXiv:1706.00179. Bibcode:2017SciA....3E1816B. doi:10.1126/sciadv.1701816. ISSN 2375-2548. PMC 5729016. PMID 29242828.
cite-note-77. ↑ "Gaussian approximation potential – Machine learning atomistic simulation of materials and molecules". Retrieved 2024-04-04.
cite-note-1010. ↑ citerefrowederingergasparottocs-nyi2020Rowe, Patrick; Deringer, Volker L.; Gasparotto, Piero; Csányi, Gábor; Michaelides, Angelos (2020-07-21). "An accurate and transferable machine learning potential for carbon". The Journal of Chemical Physics. 153 (3). arXiv:2006.13655. doi:10.1063/5.0005084. ISSN 0021-9606.
cite-note-1212. ↑ citerefbart-kkermodebernsteincs-nyi2018Bartók, Albert P.; Kermode, James; Bernstein, Noam; Csányi, Gábor (2018-12-14). "Machine Learning a General-Purpose Interatomic Potential for Silicon". Physical Review X. 8 (4): 041048. arXiv:1805.01568. Bibcode:2018PhRvX...8d1048B. doi:10.1103/PhysRevX.8.041048.
cite-note-1313. ↑ citerefderingercarocs-nyi2020Deringer, Volker L.; Caro, Miguel A.; Csányi, Gábor (2020-10-29). "A general-purpose machine-learning force field for bulk and nanostructured phosphorus". Nature Communications. 11 (1): 5461. Bibcode:2020NatCo..11.5461D. doi:10.1038/s41467-020-19168-z. ISSN 2041-1723. PMC 7596484. PMID 33122630.
cite-note-1414. ↑ citerefszlachtabart-kcs-nyi2014Szlachta, Wojciech J.; Bartók, Albert P.; Csányi, Gábor (2014-09-24). "Accuracy and transferability of Gaussian approximation potential models for tungsten". Physical Review B. 90 (10): 104108. Bibcode:2014PhRvB..90j4108S. doi:10.1103/PhysRevB.90.104108.
cite-note-1515. ↑ citerefmocanukonstantinouleebernstein2018Mocanu, Felix C.; Konstantinou, Konstantinos; Lee, Tae Hoon; Bernstein, Noam; Deringer, Volker L.; Csányi, Gábor; Elliott, Stephen R. (2018-09-27). "Modeling the Phase-Change Memory Material, Ge 2 Sb 2 Te 5, with a Machine-Learned Interatomic Potential". The Journal of Physical Chemistry B. 122 (38): 8998–9006. doi:10.1021/acs.jpcb.8b06476. ISSN 1520-6106. PMID 30173522.
cite-note-1616. ↑ citerefshenoywoodgatestauntonbart-k2024Shenoy, Lakshmi; Woodgate, Christopher D.; Staunton, Julie B.; Bartók, Albert P.; Becquart, Charlotte S.; Domain, Christophe; Kermode, James R. (2024-03-22). "Collinear-spin machine learned interatomic potential for ${\mathrm{Fe}}_{7}{\mathrm{Cr}}_{2}\mathrm{Ni}$ alloy". Physical Review Materials. 8 (3): 033804. arXiv:2309.08689. doi:10.1103/PhysRevMaterials.8.033804.