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We propose fine-tuning large language models for generation of stable materials.
A new algorithm for data compression
Philip Gage · 1994
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Ab-initio simulations of materials using vasp: Density-functional theory and beyond
Jürgen Hafner · 2008
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Commentary: The materials project: A materials genome approach to accelerating materials innovation
Anubhav Jain, Shyue Ping Ong, Geoffroy Hautier, Wei Chen, William Davidson Richards, Stephen Dacek, Shreyas Cholia, Dan Gunter, David Skinner, Gerbrand Ceder, et al · 2013
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Python materials genomics (pymatgen): A robust, open-source python library for materials analysis
Shyue Ping Ong, William Davidson Richards, Anubhav Jain, Geoffroy Hautier, Michael Kocher, Shreyas Cholia, Dan Gunter, Vincent L Chevrier, Kristin A Persson, and Gerbrand Ceder · 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 Christopher Wolverton · 2013
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The open quantum materials database (oqmd): assessing the accuracy of dft formation energies
Scott Kirklin, James E Saal, Bryce Meredig, Alex Thompson, Jeff W Doak, Muratahan Aykol, Stephan Rühl, and Chris Wolverton · 2015
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The thermodynamic scale of inorganic crystalline metastability
Wenhao Sun, Stephen T Dacek, Shyue Ping Ong, Geoffroy Hautier, Anubhav Jain, William D Richards, Anthony C Gamst, Kristin A Persson, and Gerbrand Ceder · 2016
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Matminer: An open source toolkit for materials data mining
Logan Ward, Alexander Dunn, Alireza Faghaninia, Nils ER Zimmermann, Saurabh Bajaj, Qi Wang, Joseph Montoya, Jiming Chen, Kyle Bystrom, Maxwell Dylla, et al · 2018
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Plug and play language models: A simple approach to controlled text generation
Sumanth Dathathri, Andrea Madotto, Janice Lan, Jane Hung, Eric Frank, Piero Molino, Jason Yosinski, and Rosanne Liu · 2019
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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, Zach DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
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NVIDIA A100 GPU Benchmarks for Deep Learning
Stephen Balaban · 2020
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Multilingual denoising pre-training for neural machine translation
Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, and Luke Zettlemoyer · 2020
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Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander M. Rush · 2020
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Lora: Low-rank adaptation of large language models
J. Edward Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, and Weizhu Chen · 2021
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Crystal diffusion variational autoencoder for periodic material generation
Tian Xie, Xiang Fu, Octavian-Eugen Ganea, Regina Barzilay, and Tommi Jaakkola · 2021
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A universal graph deep learning interatomic potential for the periodic table
Atom-by-atom protein generation and beyond with language models
Daniel Flam-Shepherd, Kevin Zhu, and Alán Aspuru-Guzik · 2023
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Micah Goldblum, Marc Finzi, Keefer Rowan, and Andrew Gordon Wilson · 2023
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Crystal structure prediction by joint equivariant diffusion on lattices and fractional coordinates
Rui Jiao, Wenbing Huang, Peijia Lin, Jiaqi Han, Pin Chen, Yutong Lu, and Yang Liu · 2023
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Goat: Fine-tuned llama outperforms gpt-4 on arithmetic tasks
Tiedong Liu and Bryan Kian Hsiang Low · 2023
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Chi Chen and Shyue Ping Ong · 2022
Cited alongside, same era.
8-bit optimizers via block-wise quantization
Tim Dettmers, Mike Lewis, Sam Shleifer, and Luke Zettlemoyer · 2022
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The lie derivative for measuring learned equivariance
Nate Gruver, Marc Finzi, Micah Goldblum, and Andrew Gordon Wilson · 2022
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Adsorbml: Accelerating adsorption energy calculations with machine learning
Janice Lan, Aini Palizhati, Muhammed Shuaibi, Brandon M Wood, Brook Wander, Abhishek Das, Matt Uyttendaele, C Lawrence Zitnick, and Zachary W Ulissi · 2022
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Accelerating bayesian optimization for biological sequence design with denoising autoencoders
Samuel Stanton, Wesley Maddox, Nate Gruver, Phillip Maffettone, Emily Delaney, Peyton Greenside, and Andrew Gordon Wilson · 2022
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Crystal structure generation with autoregressive large language modeling
Luis M Antunes, Keith T Butler, and Ricardo Grau-Crespo · 2023
Cited alongside, same era.
Chemcrow: Augmenting large-language models with chemistry tools
Andres M Bran, Sam Cox, Andrew D White, and Philippe Schwaller · 2023
Cited alongside, same era.
Language modeling is compression
Grégoire Delétang, Anian Ruoss, Paul-Ambroise Duquenne, Elliot Catt, Tim Genewein, Christopher Mattern, Jordi Grau-Moya, Li Kevin Wenliang, Matthew Aitchison, Laurent Orseau, et al · 2023
Cited alongside, same era.
Suvir Mirchandani, Fei Xia, Pete Florence, Brian Ichter, Danny Driess, Montserrat Gonzalez Arenas, Kanishka Rao, Dorsa Sadigh, and Andy Zeng · 2023
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Anymal: An efficient and scalable any-modality augmented language model
Seungwhan Moon, Andrea Madotto, Zhaojiang Lin, Tushar Nagarajan, Matt Smith, Shashank Jain, Chun-Fu Yeh, Prakash Murugesan, Peyman Heidari, Yue Liu, et al · 2023
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Stay on topic with classifier-free guidance
Guillaume Sanchez, Honglu Fan, Alexander Spangher, Elad Levi, Pawan Sasanka Ammanamanchi, and Stella Biderman · 2023
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LLaMA 2 on Amazon Sagemaker, a Benchmark
Philipp Schmid · 2023
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An observation on generalization
Ilya Sutskever · 2023
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The future of chemistry is language
Andrew D White · 2023
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Scalable diffusion for materials generation
Mengjiao Yang, KwangHwan Cho, Amil Merchant, Pieter Abbeel, Dale Schuurmans, Igor Mordatch, and Ekin Dogus Cubuk · 2023
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Mattergen: a generative model for inorganic materials design
Claudio Zeni, Robert Pinsler, Daniel Zügner, Andrew Fowler, Matthew Horton, Xiang Fu, Sasha Shysheya, Jonathan Crabbé, Lixin Sun, Jake Smith, et al · 2023
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Large language models can learn rules
Zhaocheng Zhu, Yuan Xue, Xinyun Chen, Denny Zhou, Jian Tang, Dale Schuurmans, and Hanjun Dai · 2023
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