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The demonstrated success of transfer learning has popularized approaches that involve pretraining models from massive data sources and subsequent finetuning towards a specific task.
The flexible, polarizable, Thole-type interaction potential for water (TTM2-F) revisited
George S Fanourgakis and Sotiris S Xantheas · 2006
Earlier work this paper cites.
Development of a “first principles” water potential with flexible monomers: Dimer potential energy surface, VRT spectrum, and second virial coefficient
Volodymyr Babin, Claude Leforestier, and Francesco Paesani · 2013
Earlier work this paper cites.
Development of a “first principles” water potential with flexible monomers. II: Trimer potential energy surface, third virial coefficient, and small clusters
Volodymyr Babin, Gregory R Medders, and Francesco Paesani · 2014
Earlier work this paper cites.
A survey of FPGA-based neural network accelerator
Kaiyuan Guo, Shulin Zeng, Jincheng Yu, Yu Wang, and Huazhong Yang · 2017
Earlier work this paper cites.
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
Earlier work this paper cites.
SMILES-BERT: large scale unsupervised pre-training for molecular property prediction
Sheng Wang, Yuzhi Guo, Yuhong Wang, Hongmao Sun, and Junzhou Huang · 2019
Earlier work this paper cites.
Exploring the GDB-13 chemical space using deep generative models
Josep Arús-Pous, Thomas Blaschke, Silas Ulander, Jean-Louis Reymond, Hongming Chen, and Ola Engkvist · 2019
Earlier work this paper cites.
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
Cited alongside, same era.
Fast graph representation learning with PyTorch Geometric
Matthias Fey and Jan E. Lenssen · 2019
Cited alongside, same era.
ChemBERTa: large-scale self-supervised pretraining for molecular property prediction
Seyone Chithrananda, Gabriel Grand, and Bharath Ramsundar · 2020
Cited alongside, same era.
Cogmol: target-specific and selective drug design for covid-19 using deep generative models
Vijil Chenthamarakshan, Payel Das, Samuel Hoffman, Hendrik Strobelt, Inkit Padhi, Kar Wai Lim, Benjamin Hoover, Matteo Manica, Jannis Born, Teodoro Laino, et al · 2020
Cited alongside, same era.
Sutanay Choudhury, Jenna A. Bilbrey, Logan T. Ward, Sotiris S. Xantheas, Ian T. Foster, Joseph P. Heindel, Ben Blaiszik, and Marcus E. Schwarting · 2020
Strategies for pre-training graph neural networks
Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, and Jure Leskovec · 2020
Later among the works it cites.
Do large scale molecular language representations capture important structural information?
Jerret Ross, Brian Belgodere, Vijil Chenthamarakshan, Inkit Padhi, Youssef Mroueh, and Payel Das · 2021
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Molformer: Motif-based transformer on 3d heterogeneous molecular graphs
Fang Wu, Qiang Zhang, Dragomir Radev, Jiyu Cui, Wen Zhang, Huabin Xing, Ningyu Zhang, and Huajun Chen · 2021
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Scalable geometric deep learning on molecular graphs
Nathan C Frey, Siddharth Samsi, Joseph McDonald, Lin Li, Connor W Coley, and Vijay Gadepally · 2021
Later among the works it cites.
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Cited alongside, same era.
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
Cited alongside, same era.
An updated survey of efficient hardware architectures for accelerating deep convolutional neural networks
Maurizio Capra, Beatrice Bussolino, Alberto Marchisio, Muhammad Shafique, Guido Masera, and Maurizio Martina · 2020
Cited alongside, same era.
SchNetPack: A deep learning toolbox for atomistic systems
KT Schütt, Pan Kessel, Michael Gastegger, KA Nicoli, Alexandre Tkatchenko, and K-R Müller
Cited in the paper.
SchNet–a deep learning architecture for molecules and materials
Kristof T Schütt, Huziel E Sauceda, P-J Kindermans, Alexandre Tkatchenko, and K-R Müller
Cited in the paper.
Walid Ahmad, Elana Simon, Seyone Chithrananda, Gabriel Grand, and Bharath Ramsundar · 2022
Closest in time.
https://sites.uw.edu/wdbase
Database of water clusters, Accessed September 2022 · 2022
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Implementating spatio-temporal graph convolutional networks on Graphcore IPUs
Johannes Moe, Konstantin Pogorelov, Daniel Thilo Schroeder, and Johannes Langguth · 2022
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