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Intermolecular and long-range interactions are central to phenomena as diverse as gene regulation, topological states of quantum materials, electrolyte transport in batteries, and the universal solvation properties of water.
A theory of water and ionic solution, with particular reference to hydrogen and hydroxyl ions
J. D. Bernal and R. H. Fowler · 1933
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Cooperativity and hydrogen bonding network in water clusters
Sotiris S Xantheas · 2000
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Water clusters: Untangling the mysteries of the liquid, one molecule at a time
Frank N Keutsch and Richard J Saykally · 2001
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Development of transferable interaction models for water. IV. A flexible, all-atom polarizable potential (TTM2-F) based on geometry dependent charges derived from an ab initio monomer dipole moment surface
Christian J Burnham and Sotiris S Xantheas · 2002
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The flexible, polarizable, Thole-type interaction potential for water (TTM2-F) revisited
George S Fanourgakis and Sotiris S Xantheas · 2006
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Chapter 14 - the role of functional groups in drug–receptor interactions
Laurent Schaeffer · 2008
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Comprehensive mapping of long-range interactions reveals folding principles of the human genome
Erez Lieberman-Aiden, Nynke L Van Berkum, Louise Williams, Maxim Imakaev, Tobias Ragoczy, Agnes Telling, Ido Amit, Bryan R Lajoie, Peter J Sabo, Michael O Dorschner, et al · 2009
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Long range interactions in nanoscale science
Roger H French, V Adrian Parsegian, Rudolf Podgornik, Rick F Rajter, Anand Jagota, Jian Luo, Dilip Asthagiri, Manoj K Chaudhury, Yet-ming Chiang, Steve Granick, et al · 2010
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The role of long-range intermolecular interactions in discovery of new drugs
Nevena Veljkovic, Sanja Glisic, Vladimir Perovic, and Veljko Veljkovic · 2011
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Quantum chemistry structures and properties of 134 kilo molecules
Raghunathan Ramakrishnan, Pavlo O Dral, Matthias Rupp, and O Anatole von Lilienfeld · 2014
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ZINC 15–ligand discovery for everyone
Teague Sterling and John J Irwin · 2015
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Machine learning force fields: Construction, validation, and outlook
V. Botu, R. Batra, J. Chapman, and R. Ramprasad · 2016
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Neural message passing for quantum chemistry
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl · 2017
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Ani-1: an extensible neural network potential with dft accuracy at force field computational cost
J. S. Smith, O. Isayev, and A. E. Roitberg · 2017
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ANI-1, a data set of 20 million calculated off-equilibrium conformations for organic molecules
Justin S. Smith, Olexandr Isayev, and Adrian E. Roitberg · 2017
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Graphnvp: An invertible flow model for generating molecular graphs
Kaushalya Madhawa, Katushiko Ishiguro, Kosuke Nakago, and Motoki Abe · 2019
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Atlas of putative minima and low-lying energy networks of water clusters n = 3–25
Avijit Rakshit, Pradipta Bandyopadhyay, Joseph P. Heindel, and Sotiris S. Xantheas · 2019
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Schnetpack: A deep learning toolbox for atomistic systems
K. T. Schütt, P. Kessel, M. Gastegger, K. A. Nicoli, A. Tkatchenko, and K.-R. Müller · 2019
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Parallel multistream training of high-dimensional neural network potentials
Andreas Singraber, Tobias Morawietz, Jörg Behler, and Christoph Dellago · 2019
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Message-passing neural networks for high-throughput polymer screening
Peter C St. John, Caleb Phillips, Travis W Kemper, A Nolan Wilson, Yanfei Guan, Michael F Crowley, Mark R Nimlos, and Ross E Larsen · 2019
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Nicola De Cao and Thomas Kipf · 2018
Cited alongside, same era.
Schnet – a deep learning architecture for molecules and materials
K. T. Schütt, H. E. Sauceda, P.-J. Kindermans, A. Tkatchenko, and K.-R. Müller · 2018
Cited alongside, same era.
MoleculeNet: A benchmark for molecular machine learning
Zhenqin Wu, Bharath Ramsundar, Evan N Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S Pappu, Karl Leswing, and Vijay Pande · 2018
Cited alongside, same era.
Graph convolutional policy network for goal-directed molecular graph generation
Jiaxuan You, Bowen Liu, Zhitao Ying, Vijay Pande, and Jure Leskovec · 2018
Cited alongside, same era.
Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules
Niklas Gebauer, Michael Gastegger, and Kristof Schütt · 2019
Cited alongside, same era.
https://github.com/exalearn/molecular-graph-descriptors
Code repository for computing structural motifs
Cited in the paper.
https://sites.uw.edu/wdbase
Database of water clusters
Cited in the paper.
Optimization of molecules via deep reinforcement learning
Zhenpeng Zhou, Steven Kearnes, Li Li, Richard N. Zare, and Patrick Riley · 2019
Later among the works it cites.
A look inside the black box: Using graph-theoretical descriptors to interpret a Continuous-Filter Convolutional Neural Network (CF-CNN) trained on the global and local minimum energy structures of neutral water clusters
Jenna A. Bilbrey, Joseph P. Heindel, Malachi Schram, Pradipta Bandyopadhyay, Sotiris S. Xantheas, and Sutanay Choudhury · 2020
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Composing molecules with multiple property constraints
Wengong Jin, Regina Barzilay, and Tommi Jaakkola · 2020
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Deepgraphmol, a multi-objective, computational strategy for generating molecules with desirable properties: a graph convolution and reinforcement learning approach
Yash Khemchandani, Steve O’Hagan, Soumitra Samanta, Neil Swainston, Timothy J Roberts, Danushka Bollegala, and Douglas B Kell · 2020
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Graphaf: a flow-based autoregressive model for molecular graph generation
Chence Shi, Minkai Xu, Zhaocheng Zhu, Weinan Zhang, Ming Zhang, and Jian Tang · 2020
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