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Materials discovery and development are critical for addressing global challenges.
Materials Science and Technology
Sabar D. Hutagalung · 2012
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Visualizing the loss landscape of neural nets
Hao Li, Zheng Xu, Gavin Taylor, Christoph Studer, and Tom Goldstein · 2018
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Sheshera Mysore, Zach Jensen, Edward Kim, Kevin Huang, Haw-Shiuan Chang, Emma Strubell, Jeffrey Flanigan, Andrew McCallum, and Elsa Olivetti · 2019
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Robocrystallographer: automated crystal structure text descriptions and analysis
Alex M. Ganose and Anubhav Jain · 2019
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Smact: Semiconducting materials by analogy and chemical theory
Daniel W Davies, Keith T Butler, Adam J Jackson, Jonathan M Skelton, Kazuki Morita, and Aron Walsh · 2019
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Named entity recognition and normalization applied to large-scale information extraction from the materials science literature
Leigh Weston, Vahe Tshitoyan, John Dagdelen, Olga Kononova, Amalie Trewartha, Kristin A Persson, Gerbrand Ceder, and Anubhav Jain · 2019
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The materials project: Accelerating materials design through theory-driven data and tools
Anubhav Jain, Joseph Montoya, Shyam Dwaraknath, Nils ER Zimmermann, John Dagdelen, Matthew Horton, Patrick Huck, Donny Winston, Shreyas Cholia, Shyue Ping Ong, et al · 2020
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The sofc-exp corpus and neural approaches to information extraction in the materials science domain
Annemarie Friedrich, Heike Adel, Federico Tomazic, Johannes Hingerl, Renou Benteau, Anika Maruscyk, and Lukas Lange · 2020
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Sc-comics: A superconductivity corpus for materials informatics
Kyosuke Yamaguchi, Ryoji Asahi, and Yutaka Sasaki · 2020
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Measuring mathematical problem solving with the math dataset
Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, and Jacob Steinhardt · 2021
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Looking through glass: Knowledge discovery from materials science literature using natural language processing
Vineeth Venugopal, Sourav Sahoo, Mohd Zaki, Manish Agarwal, Nitya Nand Gosvami, and NM Anoop Krishnan · 2021
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Matscibert: A materials domain language model for text mining and information extraction
Tanishq Gupta, Mohd Zaki, NM Anoop Krishnan, and Mausam · 2022
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Crystal diffusion variational autoencoder for periodic material generation
Tian Xie, Xiang Fu, Octavian-Eugen Ganea, Regina Barzilay, and Tommi S. Jaakkola · 2022
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A universal graph deep learning interatomic potential for the periodic table
Chi Chen and Shyue Ping Ong · 2022
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A universal graph deep learning interatomic potential for the periodic table
Chi Chen and Shyue Ping Ong · 2022
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Ulsa: unified language of synthesis actions for the representation of inorganic synthesis protocols
Zheren Wang, Kevin Cruse, Yuxing Fei, Ann Chia, Yan Zeng, Haoyan Huo, Tanjin He, Bowen Deng, Olga Kononova, and Gerbrand Ceder · 2022
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Sparks of artificial general intelligence: Early experiments with gpt-4
Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, et al · 2023
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Autonomous chemical research with large language models
Daniil A Boiko, Robert MacKnight, Ben Kline, and Gabe Gomes · 2023
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Assessment of chemistry knowledge in large language models that generate code
Andrew D White, Glen M Hocky, Heta A Gandhi, Mehrad Ansari, Sam Cox, Geemi P Wellawatte, Subarna Sasmal, Ziyue Yang, Kangxin Liu, Yuvraj Singh, et al · 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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Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al · 2023
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Orca: Progressive learning from complex explanation traces of gpt-4
Subhabrata Mukherjee, Arindam Mitra, Ganesh Jawahar, Sahaj Agarwal, Hamid Palangi, and Ahmed Awadallah · 2023
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MatSci-NLP: Evaluating scientific language models on materials science language tasks using text-to-schema modeling
Yu Song, Santiago Miret, and Bang Liu · 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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DiSCoMaT: Distantly supervised composition extraction from tables in materials science articles
Tanishq Gupta, Mohd Zaki, Devanshi Khatsuriya, Kausik Hira, N M Anoop Krishnan, and Mausam · 2023
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Matsciml: A broad, multi-task benchmark for solid-state materials modeling
Kin Long Kelvin Lee, Carmelo Gonzales, Marcel Nassar, Matthew Spellings, Mikhail Galkin, and Santiago Miret · 2023
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A hitchhiker’s guide to geometric gnns for 3d atomic systems
Alexandre Duval, Simon V Mathis, Chaitanya K Joshi, Victor Schmidt, Santiago Miret, Fragkiskos D Malliaros, Taco Cohen, Pietro Lio, Yoshua Bengio, and Michael Bronstein · 2023
Nlp meets materials science: Quantifying the presentation of materials data in literature
Hasan M Sayeed, Wade Smallwood, Sterling G Baird, and Taylor D Sparks · 2024
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Mattext: Do language models need more than text & scale for materials modeling?
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Are large language models superhuman chemists?
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Honeycomb: A flexible llm-based agent system for materials science
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The open matsci ML toolkit: A flexible framework for machine learning in materials science
Santiago Miret, Kin Long Kelvin Lee, Carmelo Gonzales, Marcel Nassar, and Matthew Spellings · 2023
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Scaling deep learning for materials discovery
Amil Merchant, Simon Batzner, Samuel S Schoenholz, Muratahan Aykol, Gowoon Cheon, and Ekin Dogus Cubuk · 2023
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Yu Song, Santiago Miret, and Bang Liu · 2023
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Meditron-70b: Scaling medical pretraining for large language models
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Meditron-70b: Scaling medical pretraining for large language models, 2023
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Cerebras-gpt: Open compute-optimal language models trained on the cerebras wafer-scale cluster
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Machine Learning for Materials Discovery: Numerical Recipes and Practical Applications
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Reconstructing the materials tetrahedron: challenges in materials information extraction
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Probing the limitations of multimodal language models for chemistry and materials research
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Structured information extraction from scientific text with large language models
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Annotating Materials Science Text: A Semi-automated Approach for Crafting Outputs with Gemini Pro
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How well do large language models understand tables in materials science?
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matbench-genmetrics: A python library for benchmarking crystal structure generative models using time-based splits of materials project structures
Sterling G Baird, Hasan M Sayeed, Joseph Montoya, and Taylor D Sparks · 2024
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Adaptation odyssey in LLMs: Why does additional pretraining sometimes fail to improve?
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Extracting accurate materials data from research papers with conversational language models and prompt engineering
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Crystal structure prediction by joint equivariant diffusion
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FlowMM: Generating materials with riemannian flow matching
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Robust and efficient fine-tuning of llms with bayesian reparameterization of low-rank adaptation
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togethercomputer/RedPajama-Data-1T · Datasets at Hugging Face, July 2024
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