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The Journal of Physical Chemistry A/B/C Virtual Special Issue on Machine Learning in Physical Chemistry

Ferguson, Andrew L. and Hachmann, Johannes and Miller, Thomas F. and Pfaendtner, Jim (2020) The Journal of Physical Chemistry A/B/C Virtual Special Issue on Machine Learning in Physical Chemistry. Journal of Physical Chemistry B, 124 (44). pp. 9767-9772. ISSN 1520-6106.

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Physical chemistry stands today at an exciting transition state where the integration of machine learning and data science tools into all corners of the field stands poised to do nothing short of revolutionizing the discipline. These powerful techniques—when appropriately combined with domain knowledge, tools, and expertise—have led to new physical insights, better understanding, accelerated discovery, rational design, and inverse engineering that transcend traditional approaches to materials, molecular, and chemical science and engineering. The primary driver of this trend has been the impressive advances enabled by machine learning, artificial intelligence, and data science tools, ranging from the discovery of novel electronic and optical materials by high-throughput virtual screening, to the massive acceleration of molecular simulations using learned classical force fields with quantum accuracy, to the powering of “self-driving laboratories” for automated chemical discovery. The 2011 White House Materials Genome Initiative (MGI), the 2017 NSF Data-Driven Discovery Science in Chemistry (D3SC) initiative, and the 2019 NSF Big Idea Harnessing the Data Revolution are some of the US federal programs that have provided incentive, attention, momentum, and support to power these advances and help drive the field forward. Necessity is also the mother of invention, and the prevalence of large data sets routinely generated by high-throughput virtual screening or automated experimentation have spurred the need for scalable data science and machine learning techniques to parse, explore, and harness the full power of these voluminous data streams. It bears remembering that physical chemistry is no stranger to machine learning, most visibly in the cheminformatics and quantitative structure property relation (QSPR) work that emerged in the 1980s. Some of the techniques being implemented today are, to some degree, reinventions of these ideas, but others are fundamentally new concepts that have been adopted and adapted from diverse fields including computer vision, manifold learning, and deep learning. This Virtual Special Issue on Machine Learning in Physical Chemistry covering all sections of The Journal of Physical Chemistry A/B/C pays tribute to this development, and the relevance and popularity of this topic is reflected in the depth and breadth of excellent articles in this exciting collection.

Item Type:Article
Related URLs:
URLURL TypeDescription
Ferguson, Andrew L.0000-0002-8829-9726
Hachmann, Johannes0000-0003-4501-4118
Miller, Thomas F.0000-0002-1882-5380
Pfaendtner, Jim0000-0001-6727-2957
Additional Information:© 2020 American Chemical Society. Published as part of The Journal of Physical Chemistry virtual special issue “Machine Learning in Physical Chemistry”. This Preface is published jointly in The Journal of Physical Chemistry A/B/C.
Subject Keywords:Neural networks, Physical chemistry, Peptides and proteins, Molecular modeling, Machine learning
Issue or Number:44
Record Number:CaltechAUTHORS:20201105-152206460
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Official Citation:The Journal of Physical Chemistry A/B/C Virtual Special Issue on Machine Learning in Physical Chemistry Andrew L. Ferguson, Johannes Hachmann, Thomas F. Miller, and Jim Pfaendtner The Journal of Physical Chemistry B 2020 124 (44), 9767-9772 DOI: 10.1021/acs.jpcb.0c09206
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:106460
Deposited By: George Porter
Deposited On:06 Nov 2020 15:34
Last Modified:24 Nov 2020 17:32

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