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Recent years have seen the advent of molecular simulation datasets that are orders of magnitude larger and more diverse.
A Neural Device for Searching Direct Correlations between Structures and Properties of Chemical Compounds
Igor I. Baskin, Vladimir A. Palyulin, and Nikolai S. Zefirov · 1997
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Supervised neural networks for the classification of structures
A. Sperduti and A. Starita · 1997
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Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces
Jörg Behler and Michele Parrinello · 2007
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Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons
Albert P. Bartók, Mike C. Payne, Risi Kondor, and Gábor Csányi · 2010
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On representing chemical environments
Albert P. Bartók, Risi Kondor, and Gábor Csányi · 2013
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Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD)
James E. Saal, Scott Kirklin, Muratahan Aykol, Bryce Meredig, and C. Wolverton · 2013
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Spectral Networks and Deep Locally Connected Networks on Graphs
Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun · 2014
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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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Convolutional Networks on Graphs for Learning Molecular Fingerprints
David K. Duvenaud, Dougal Maclaurin, Jorge Aguilera-Iparraguirre, Rafael Gómez-Bombarelli, Timothy Hirzel, Alán Aspuru-Guzik, and Ryan P. Adams · 2015
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Machine learning unifies the modeling of materials and molecules
Albert P. Bartók, Sandip De, Carl Poelking, Noam Bernstein, James R. Kermode, Gábor Csányi, and Michele Ceriotti · 2017
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Machine learning of accurate energy-conserving molecular force fields
Stefan Chmiela, Alexandre Tkatchenko, Huziel E. Sauceda, Igor Poltavsky, Kristof T. Schütt, and Klaus-Robert Müller · 2017
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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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Deep Learning Scaling is Predictable, Empirically
Joel Hestness, Sharan Narang, Newsha Ardalani, Gregory Diamos, Heewoo Jun, Hassan Kianinejad, Md Mostofa Ali Patwary, Yang Yang, and Yanqi Zhou · 2017
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Semi-Supervised Classification with Graph Convolutional Networks
Thomas N. Kipf and Max Welling · 2017
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SchNet: A continuous-filter convolutional neural network for modeling quantum interactions
Kristof Schütt, Pieter-Jan Kindermans, Huziel Enoc Sauceda Felix, Stefan Chmiela, Alexandre Tkatchenko, and Klaus-Robert Müller · 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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On the Convergence of Adam and Beyond
Sashank J. Reddi, Satyen Kale, and Sanjiv Kumar · 2018
Cited alongside, same era.
Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties
Tian Xie and Jeffrey C. Grossman · 2018
Cited alongside, same era.
Cormorant: Covariant Molecular Neural Networks
Brandon M. Anderson, Truong-Son Hy, and Risi Kondor · 2019
Cited alongside, same era.
Reconciling modern machine-learning practice and the classical bias–variance trade-off
Mikhail Belkin, Daniel Hsu, Siyuan Ma, and Soumik Mandal · 2019
Cited alongside, same era.
Fast Graph Representation Learning with PyTorch Geometric
Matthias Fey and Jan E. Lenssen · 2019
Cited alongside, same era.
Predict then Propagate: Graph Neural Networks Meet Personalized PageRank
Johannes Gasteiger, Aleksandar Bojchevski, and Stephan Günnemann · 2019
Molecular Mechanics-Driven Graph Neural Network with Multiplex Graph for Molecular Structures
Shuo Zhang, Yang Liu, and Lei Xie · 2020
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On the Bottleneck of Graph Neural Networks and its Practical Implications
Uri Alon and Eran Yahav · 2021
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SE(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials
Simon Batzner, Tess E. Smidt, Lixin Sun, Jonathan P. Mailoa, Mordechai Kornbluth, Nicola Molinari, and Boris Kozinsky · 2021
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Open Catalyst 2020 (OC20) Dataset and Community Challenges
Lowik Chanussot, Abhishek Das, Siddharth Goyal, Thibaut Lavril, Muhammed Shuaibi, Morgane Riviere, Kevin Tran, Javier Heras-Domingo, Caleb Ho, Weihua Hu, Aini Palizhati, Anuroop Sriram, Brandon Wood, Junwoong Yoon, Devi Parikh, C. Lawrence Zitnick, and Zachary Ulissi · 2021
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OrbNet Denali: A machine learning potential for biological and organic chemistry with semi-empirical cost and DFT accuracy
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Cited alongside, same era.
Do Better ImageNet Models Transfer Better?
Simon Kornblith, Jonathon Shlens, and Quoc V. Le · 2019
Cited alongside, same era.
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Köpf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
Cited alongside, same era.
PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments, and Partial Charges
Oliver T. Unke and Markus Meuwly · 2019
Cited alongside, same era.
Small Data, Big Decisions: Model Selection in the Small-Data Regime
Jorg Bornschein, Francesco Visin, and Simon Osindero · 2020
Cited alongside, same era.
A Close Look at Deep Learning with Small Data
Lorenzo Brigato and Luca Iocchi · 2020
Cited alongside, same era.
On the role of gradients for machine learning of molecular energies and forces
Anders S. Christensen and O. Anatole von Lilienfeld · 2020
Cited alongside, same era.
Anders S. Christensen, Sai Krishna Sirumalla, Zhuoran Qiao, Michael B. O’Connor, Daniel G. A. Smith, Feizhi Ding, Peter J. Bygrave, Animashree Anandkumar, Matthew Welborn, Frederick R. Manby, and Thomas F. Miller · 2021
Later among the works it cites.
Quantum chemical benchmark databases of gold-standard dimer interaction energies
Alexander G. Donchev, Andrew G. Taube, Elizabeth Decolvenaere, Cory Hargus, Robert T. McGibbon, Ka-Hei Law, Brent A. Gregersen, Je-Luen Li, Kim Palmo, Karthik Siva, Michael Bergdorf, John L. Klepeis, and David E. Shaw · 2021
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GemNet: Universal Directional Graph Neural Networks for Molecules
Johannes Gasteiger, Florian Becker, and Stephan Günnemann · 2021
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ForceNet: A Graph Neural Network for Large-Scale Quantum Calculations
Weihua Hu, Muhammed Shuaibi, Abhishek Das, Siddharth Goyal, Anuroop Sriram, Jure Leskovec, Devi Parikh, and C. Lawrence Zitnick · 2021
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Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
Cheol Woo Park, Mordechai Kornbluth, Jonathan Vandermause, Chris Wolverton, Boris Kozinsky, and Jonathan P. Mailoa · 2021
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Equivariant message passing for the prediction of tensorial properties and molecular spectra
Kristof T. Schütt, Oliver T. Unke, and Michael Gastegger · 2021
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Rotation Invariant Graph Neural Networks using Spin Convolutions
Muhammed Shuaibi, Adeesh Kolluru, Abhishek Das, Aditya Grover, Anuroop Sriram, Zachary Ulissi, and C. Lawrence Zitnick · 2021
Later among the works it cites.
SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effects
Oliver T. Unke, Stefan Chmiela, Michael Gastegger, Kristof T. Schütt, Huziel E. Sauceda, and Klaus-Robert Müller · 2021
Later among the works it cites.
Tensor Programs V: Tuning Large Neural Networks via Zero-Shot Hyperparameter Transfer
Ge Yang, Edward Hu, Igor Babuschkin, Szymon Sidor, Xiaodong Liu, David Farhi, Nick Ryder, Jakub Pachocki, Weizhu Chen, and Jianfeng Gao · 2021
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Do Transformers Really Perform Badly for Graph Representation?
Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng, Guolin Ke, Di He, Yanming Shen, and Tie-Yan Liu · 2021
Later among the works it cites.
Towards Training Billion Parameter Graph Neural Networks for Atomic Simulations
Anuroop Sriram, Abhishek Das, Brandon M. Wood, Siddharth Goyal, and C. Lawrence Zitnick · 2022
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