Editorial pubs.acs.org/JPCB
Cite This: J. Phys. Chem. B 2018, 122, 1347−1347
Machine Learning structures needed to be computed to arrive at a DFT-based free energy diagram. This list and partitioning are by no means complete or unique. However, we hope this selection provides a representative snapshot of the field at this point. We expect machine learning and related artificial intelligent algorithms to play an increasingly prominent role in physical chemistry research and in the pages of The Journal of Physical Chemistry.
The recent complete rout of top Go players by a deep learning neural network self-learned by AlphaGo has taken the world by storm. The neural network is one example of a larger class of machine learning algorithms and corresponding conceptual models that have have penetrated many fields, including our own of physical chemistry. In this Virtual Issue, we collect together 25 examples of machine learning applications that have appeared in The Journal of Physical Chemistry in 2016 and 2017. The problems addressed and approaches used are diverse, and in an effort to highlight common themes, we have organized these 25 into five separate classes. Though this partitioning is not unique (the optimal partitioning is a machine learning problem in its own right!), we hope it is helpful especially to those new to the area. The first set of eight papers are representative of models that map some readily observable descriptor or descriptors of a material to a physical property of interest, in the spirit of quantitative structure−property relationships (QSPR) that have long been of interest. For example, Yao et al. (http://dx.doi. org/10.1021/acs.jpclett.7b01072) reports a model in which the bonds in a molecule (the human-inferable descriptors) are mapped to a total energy (the property), and Jinnouchi et al. (http://pubs.acs.org/doi/10.1021/acs.jpclett.7b02010) an ambitious approach to relate particle size and composition to catalytic activity. In the second set of two papers, an optimal material composition is identified by relating a feature descriptor, or fingerprint, to a property of interest. For example, Kim et al. (http://dx.doi.org/10.1021/acs.jpcc. 6b05068) relate band gap and phonon frequency descriptors to dielectric breakdown strength and, by searching through these descriptors, identify novel perovskite compositions with high breakdown strength. In a third (and highly popular in this journal!) class, the potential energy surface of a system is represented in terms of fingerprints of the local environment around each atom. Representative of this diverse class is the work of Boes et al. (https://doi.org/10.1021/acs.jpcc. 6b12752), who develop a neural network representation of the AuPd alloy and use it to predict composition and temperature-dependent surface segregation through Monte Carlo models. In the same spirit but very different context, Kolb et al. (http://dx.doi.org/10.1021/acs.jpca.7b01182) describe a Gaussian Process approach for representing the potential energy surface useful for reactive scattering calculations, and Botu et al. make a case for representing forces rather than energies with neural nets. In the fourth class, machine learning techniques are used to tease out information from either experimental or computational data. For example, Timoshenko et al. (http://pubs.acs.org/doi/10.1021/acs. jpclett.7b02364) use neural network analysis to relate observed X-ray absorption near edge spectra (XANES) to nanoparticle structure and composition. Lastly are examples of machine learning tools “in the loop” to accelerate the development of computational models. Representative of this class is the work of Ulissi et al. (https://doi.org/10.1021/acs.jpclett.6b01254), who use a machine learning approach to reduce the number of © 2018 American Chemical Society
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William F. Schneider Hua Guo ASSOCIATED CONTENT
S Supporting Information *
The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.jpcb.8b00035. Table of contents for the Machine Learning Virtual Issue (PDF)
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AUTHOR INFORMATION
ORCID
William F. Schneider: 0000-0003-0664-2138 Hua Guo: 0000-0001-9901-053X Notes
Views expressed in this editorial are those of the authors and not necessarily the views of the ACS. This Editorial is jointly published in The Journal of Physical Chemistry A/B/C and The Journal of Physical Chemistry Letters.
Published: February 1, 2018 1347
DOI: 10.1021/acs.jpcb.8b00035 J. Phys. Chem. B 2018, 122, 1347−1347
