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Predicting properties from coordinate-category data -- sets of vectors paired with categorical information -- is fundamental to computational science.
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“The nature of the chemical bond”, The George Fisher Baker Non-Resident Lectureship in Chemistry at Cornell University
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“ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction”
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“Organic Chemistry as a Language and the Implications of Chemical Linguistics for Structural and Retrosynthetic Analyses”
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“Charting the complete elastic properties of inorganic crystalline compounds”
Maarten De et al · 2015
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Tianqi Chen and Carlos Guestrin · 2016
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“Xgboost: A scalable tree boosting system”
Tianqi Chen and Carlos Guestrin · 2016
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“SchNet: A continuous-filter convolutional neural network for modeling quantum interactions”
Kristof Schütt et al · 2017
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“SchNet: A continuous-filter convolutional neural network for modeling quantum interactions”
Kristof Schütt et al · 2017
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Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova · 2018
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“Matminer: An open source toolkit for materials data mining”
Logan Ward et al · 2018
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“Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds”
Nathaniel Thomas et al · 2018
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“ElemNet: Deep Learning the Chemistry of Materials From Only Elemental Composition”
Dipendra Jha et al · 2018
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“spglib: a software library for crystal symmetry search”
Atsushi Togo and Isao Tanaka · 2018
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“Bert: Pre-training of deep bidirectional transformers for language understanding”
Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova · 2018
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“Matminer: An open source toolkit for materials data mining”
Logan Ward et al · 2018
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“Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds”
Nathaniel Thomas et al · 2018
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“ElemNet: Deep Learning the Chemistry of Materials From Only Elemental Composition”
Dipendra Jha et al · 2018
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“spglib: a software library for crystal symmetry search”
Atsushi Togo and Isao Tanaka · 2018
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“Molecular Transformer: A Model for Uncertainty-Calibrated Chemical Reaction Prediction”
Philippe Schwaller et al · 2019
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“SMILES-BERT: Large Scale Unsupervised Pre-Training for Molecular Property Prediction”
Sheng Wang et al · 2019
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“Robocrystallographer: automated crystal structure text descriptions and analysis”
Alex Ganose and Anubhav Jain · 2019
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“Identification Schemes for Metal–Organic Frameworks To Enable Rapid Search and Cheminformatics Analysis”
Benjamin. Bucior et al · 2019
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“Molecular Transformer: A Model for Uncertainty-Calibrated Chemical Reaction Prediction”
Philippe Schwaller et al · 2019
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“SMILES-BERT: Large Scale Unsupervised Pre-Training for Molecular Property Prediction”
Sheng Wang et al · 2019
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“Robocrystallographer: automated crystal structure text descriptions and analysis”
Alex Ganose and Anubhav Jain · 2019
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“Identification Schemes for Metal–Organic Frameworks To Enable Rapid Search and Cheminformatics Analysis”
Benjamin. Bucior et al · 2019
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“Scaling Laws for Neural Language Models”
Jared Kaplan et al · 2020
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“Benchmarking materials property prediction methods: the Matbench test set and Automatminer reference algorithm”
Alexander Dunn et al · 2020
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“Self-referencing embedded strings (SELFIES): A 100% robust molecular string representation”
Mario Krenn et al · 2020
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“Transfer learning enables the molecular transformer to predict regio- and stereoselective reactions on carbohydrates”
Giorgio Pesciullesi, Philippe Schwaller, Teodoro Laino and Jean-Louis Reymond · 2020
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“ChemEnv: a fast and robust coordination environment identification tool”
David Waroquiers et al · 2020
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“Scaling Laws for Neural Language Models”
Jared Kaplan et al · 2020
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“Benchmarking materials property prediction methods: the Matbench test set and Automatminer reference algorithm”
Alexander Dunn et al · 2020
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“Self-referencing embedded strings (SELFIES): A 100% robust molecular string representation”
Mario Krenn et al · 2020
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“Transfer learning enables the molecular transformer to predict regio- and stereoselective reactions on carbohydrates”
Giorgio Pesciullesi, Philippe Schwaller, Teodoro Laino and Jean-Louis Reymond · 2020
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“ChemEnv: a fast and robust coordination environment identification tool”
David Waroquiers et al · 2020
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“On the Opportunities and Risks of Foundation Models”
Rishi Bommasani et al · 2021
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“Compositionally restricted attention-based network for materials property predictions”
Anthony-Tung Wang, Steven Kauwe, Ryan Murdock and Taylor Sparks · 2021
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“Robust model benchmarking and bias-imbalance in data-driven materials science: a case study on MODNet”
Pierre-Paul De, Matthew Evans and Gian-Marco Rignanese · 2021
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“Lora: Low-rank adaptation of large language models”
Edward Hu et al · 2021
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“Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences”
Alexander Rives et al · 2021
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“ProtTrans: Toward Understanding the Language of Life Through Self-Supervised Learning”
Ahmed Elnaggar et al · 2021
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“Physics-Inspired Structural Representations for Molecules and Materials”
Felix Musil et al · 2021
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“E(n) equivariant graph neural networks”
Vıctor Satorras, Emiel Hoogeboom and Max Welling · 2021
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“Compositionally restricted attention-based network for materials property predictions”
“Neural scaling of deep chemical models”
Nathan Frey et al · 2023
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“Inverse Scaling: When Bigger Isn’t Better”
Ian. McKenzie et al · 2023
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“Llama 2: Open foundation and fine-tuned chat models”
Hugo Touvron et al · 2023
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“LLM-Prop: Predicting Physical And Electronic Properties Of Crystalline Solids From Their Text Descriptions”
Andre Rubungo, Craig Arnold, Barry. Rand and Adji Dieng · 2023
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“HoneyBee: Progressive Instruction Finetuning of Large Language Models for Materials Science”
Yu Song, Santiago Miret, Huan Zhang and Bang Liu · 2023
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“Evolutionary-scale prediction of atomic-level protein structure with a language model”
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Anthony-Tung Wang, Steven. Kauwe, Ryan. Murdock and Taylor. Sparks · 2021
Cited alongside, same era.
“On the Opportunities and Risks of Foundation Models”
Rishi Bommasani et al · 2021
Cited alongside, same era.
“Compositionally restricted attention-based network for materials property predictions”
Anthony-Tung Wang, Steven Kauwe, Ryan Murdock and Taylor Sparks · 2021
Cited alongside, same era.
“Robust model benchmarking and bias-imbalance in data-driven materials science: a case study on MODNet”
Pierre-Paul De, Matthew Evans and Gian-Marco Rignanese · 2021
Cited alongside, same era.
“Lora: Low-rank adaptation of large language models”
Edward Hu et al · 2021
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“Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences”
Alexander Rives et al · 2021
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“ProtTrans: Toward Understanding the Language of Life Through Self-Supervised Learning”
Ahmed Elnaggar et al · 2021
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Zeming Lin et al · 2023
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“Protst: Multi-modality learning of protein sequences and biomedical texts”
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“Group selfies: a robust fragment-based molecular string representation”
Austin Cheng et al · 2023
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“Transformers and Large Language Models for Chemistry and Drug Discovery”
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“Gotta be SAFE: A New Framework for Molecular Design”
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“GPT-MolBERTa: GPT Molecular Features Language Model for molecular property prediction”
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“Regression Transformer enables concurrent sequence regression and generation for molecular language modelling”
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“Chemical language models for de novo drug design: Challenges and opportunities”
Francesca Grisoni · 2023
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“Searching for High-Value Molecules Using Reinforcement Learning and Transformers”
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“Structure feature vectors derived from Robocrystallographer text descriptions of crystal structures using word embeddings”
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“MOFormer: Self-Supervised Transformer Model for Metal–Organic Framework Property Prediction”
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“The power of motifs as inductive bias for learning molecular distributions”
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“Group SELFIES: a robust fragment-based molecular string representation”
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“A hitchhiker’s guide to geometric gnns for 3d atomic systems”
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“An invertible, invariant crystal representation for inverse design of solid-state materials using generative deep learning”
Hang Xiao et al · 2023
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Daniel Flam-Shepherd and Alán Aspuru-Guzik · 2023
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“Crystal structure generation with autoregressive large language modeling”
Luis Antunes, Keith Butler and Ricardo Grau-Crespo · 2023
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“CHGNet as a pretrained universal neural network potential for charge-informed atomistic modelling”
Bowen Deng et al · 2023
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