Computational Chemistry Highlights

Important recent papers in computational and theoretical chemistry
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A community-powered search of machine learning strategy space to find NMR property prediction models

Lars A. Bratholm, Will Gerrard, Brandon Anderson, Shaojie Bai, Sunghwan Choi, Lam Dang, Pavel Hanchar, AddisonHoward, Guillaume Huard, Sanghoon Kim, Zico Kolter, Risi Kondor, Mordechai Kornbluth, YouhanLee, Youngsoo Lee, Jonathan P. Mailoa, Thanh Tu Nguyen, Milos Popovic, Goran Rakocevic, Walter Reade, Wonho Song, Luka Stojanovic, Erik H. Thiede, Nebojsa...

OrbNet: Deep Learning for Quantum Chemistry Using Symmetry-Adapted Atomic-Orbital Features

Zhuoran Qiao, Matthew Welborn, Animashree Anandkumar, Frederick R. Manby, Thomas F. Miller III (2020)Highlighted by Jan JensenFigure 4 from the paper. (c) the authors 2020.This method takes information from a GFN1-xTB calculation as input to a graph-convolution (GC) NN to predict the difference between DFT and GFN1-xTB total energies. In conventional...

What Does the Machine Learn? Knowledge Representations of Chemical Reactivity

Joshua A. Kammeraad, Jack Goetz, Eric A. Walker, Ambuj Tewari, and Paul M. Zimmerman (2020)Highlighted by Jan JensenFigure 1 from the paper (c) American Chemical Society 2020While I don't agree with everything said in the paper, I highlight it here because I found it very thought provoking. The paper tests several feature sets and ML modes for the prediction...

Learning Molecular Representations for Medicinal Chemistry

Kangway V. Chuang, Laura M. Gunsalus, and Michael J. Keiser (2020)Highlighted by Jan JensenFigure 3 from the paper. (c) ACS 2020.I found this miniperspective a very enjoyable read. It covers much more than the title suggests (at least to me), such as a mini history of deep learning in MedChem, when to use deep learning and when to use other ML techniques...

Open Graph Benchmark: Datasets for Machine Learning on Graphs

Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, Jure Leskovec. arXiv:2005:00687v1Contributed by Jesper MadsenA diverse collection of datasets for use in ML applications to graphs has been collected by Hu et al. The Benchmark is intuitively structured and includes evaluation protocols and metrics. Furthermore,...

Reactants, products, and transition states of elementary chemical reactions based on quantum chemistry

Colin A. Grambow, Lagnajit Pattanaik, William H. Green (2020)Highlighted by Jan JensenFigure 1 from the paper. Reproduced under the CC BY-NC-ND 4.0 licenceThis paper describes a new data set of DFT barrier heights for 12,000 diverse chemical reactions and should stimulate a lot of new ML studies on chemical reactivity.The molecules are sampled from...

Semiautomated Transition State Localization for Organometallic Complexes with Semiempirical Quantum Chemical Methods

Sebastian Dohm, Markus Bursch, Andreas Hansen, Stefan Grimme (2020)Highlighted by Jan JensenAutomated and efficient TS searches is difficult and there are only a few benchmark studies out there. But this is the first paper I have come across where they attempt this for organometallics. Given the typical size of organometallic compounds, one needs something...

The Synthesizability of Molecules Proposed by Generative Models

Wenhao Gao and Connor W. Coley (2020)Highlighted by Jan JensenFigure 1 from the paper. (c) The authors 2020. The paper tests method c, d, and eDisclaimer: I implemented one of the methods (graph based GA) being tested. It is well known that generative models (including genetic algorithms) can suggest very weird-looking molecules when used to optimise...

On the Completeness of Atomic Structure Representations

Sergey N. Pozdnyakov, Michael J. Willatt, Albert P. Bartók, Christoph Ortner, Gábor Csányi, Michele Ceriotti. arXiv:2001:11696v1Contributed by Jesper MadsenHere, I highlight an interesting recent preprint that tries to formalize and quantify something that I previously have posted here at Computational Chemistry Highlights (see the post on Atomistic...

Discovery of a Difluoroglycine Synthesis Method through Quantum Chemical Calculations

Tsuyoshi Mita, Yu Harabuchi and Satoshi Maeda (2020)Highlighted by Jan JensenTOC graphic. © The Authors 2020. Reproduced under the CC-BY-NC-ND 4.0 license.In this paper the authors use DFT calculations to identify a synthetic route to difluoroglycine. They started by applying the single component artificial force induced reaction (SC-AFIR) method to...

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