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We introduce the elEmBERT model for chemical classification tasks.
Opls all-atom force field for carbohydrates
Wolfgang Damm, Antonio Frontera, Julian Tirado Rives, and William L Jorgensen · 1955
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Crystallography open database – an open-access collection of crystal structures
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Crystallography open database (cod): an open-access collection of crystal structures and platform for world-wide collaboration
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Efficient estimation of word representations in vector space
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On representing chemical environments
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The materials application programming interface (api): A simple, flexible and efficient api for materials data based on representational state transfer (rest) principles
Shyue Ping Ong, Shreyas Cholia, Anubhav Jain, Miriam Brafman, Dan Gunter, Gerbrand Ceder, and Kristin A. Persson · 2014
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Machine learning predictions of molecular properties: Accurate many-body potentials and nonlocality in chemical space
Katja Hansen, Franziska Biegler, Raghunathan Ramakrishnan, Wiktor Pronobis, O. Anatole Von Lilienfeld, Klaus Robert Müller, and Alexandre Tkatchenko · 2015
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The sider database of drugs and side effects
Michael Kuhn, Ivica Letunic, Lars Juhl Jensen, and Peer Bork · 2016
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The atomic simulation environment—a python library for working with atoms
Ask Hjorth Larsen, Jens Jørgen Mortensen, Jakob Blomqvist, Ivano E Castelli, Rune Christensen, Marcin Dułak, Jesper Friis, Michael N Groves, Bjørk Hammer, Cory Hargus, Eric D Hermes, Paul C Jennings, Peter Bjerre Jensen, James Kermode, John R Kitchin, Esben Leonhard Kolsbjerg, Joseph Kubal, Kristen Kaasbjerg, Steen Lysgaard, Jón Bergmann Maronsson, Tristan Maxson, Thomas Olsen, Lars Pastewka, Andrew Peterson, Carsten Rostgaard, Jakob Schiøtz, Ole Schütt, Mikkel Strange, Kristian S Thygesen, Tejs Vegge, Lasse Vilhelmsen, Michael Walter, Zhenhua Zeng, and Karsten W Jacobsen · 2017
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Review on forecasting of photovoltaic power generation based on machine learning and metaheuristic techniques
Muhammad Naveed Akhter, Saad Mekhilef, Hazlie Mokhlis, and Noraisyah Mohamed Shah · 2018
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Mol2vec: Unsupervised machine learning approach with chemical intuition
Sabrina Jaeger, Simone Fulle, and Samo Turk · 2018
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Smiles2vec: An interpretable general-purpose deep neural network for predicting chemical properties
Garrett B. Goh, Nathan O. Hodas, Charles Siegel, and Abhinav Vishnu · 2018
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Machine learning modeling of superconducting critical temperature
Valentin Stanev, Corey Oses, A. Gilad Kusne, Efrain Rodriguez, Johnpierre Paglione, Stefano Curtarolo, and Ichiro Takeuchi · 2018
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Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties
Tian Xie and Jeffrey C. Grossman · 2018
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Insightful classification of crystal structures using deep learning
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Heterogeneous molecular graph neural networks for predicting molecule properties
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Graph convolutional neural networks with global attention for improved materials property prediction
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Benchmarking graph neural networks for materials chemistry
Victor Fung, Jiaxin Zhang, Eric Juarez, and Bobby G. Sumpter · 2021
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Few-shot graph learning for molecular property prediction
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Four generations of high-dimensional neural network potentials
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Atomsets as a hierarchical transfer learning framework for small and large materials datasets
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Unsupervised word embeddings capture latent knowledge from materials science literature
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Accurate learning of graph representations with graph multiset pooling
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Machine learning techniques for prediction of capacitance and remaining useful life of supercapacitors: A comprehensive review
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Unified representation of molecules and crystals for machine learning
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Glam : An adaptive graph learning method for automated molecular interactions and properties predictions
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Chemformer: A pre-trained transformer for computational chemistry
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Attention is all you need
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