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Property prediction accuracy has long been a key parameter of machine learning in materials informatics.
A value for n-person games
Lloyd S Shapley · 1953
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Values of games with a priori unions
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Regression shrinkage and selection via the lasso
Robert Tibshirani · 1996
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Random forests
Leo Breiman · 2001
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Chemical accuracy for the van der waals density functional
Jiří Klimeš, David R Bowler, and Angelos Michaelides · 2009
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Accurate band gaps of semiconductors and insulators with a semilocal exchange-correlation potential
Fabien Tran and Peter Blaha · 2009
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Scikit-learn: Machine learning in python
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al · 2011
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Intelligible models for classification and regression
Yin Lou, Rich Caruana, and Johannes Gehrke · 2012
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Predictive structure–reactivity models for rapid screening of pt-based multimetallic electrocatalysts for the oxygen reduction reaction
Hongliang Xin, Adam Holewinski, and Suljo Linic · 2012
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Aflow: An automatic framework for high-throughput materials discovery
Stefano Curtarolo, Wahyu Setyawan, Gus LW Hart, Michal Jahnatek, Roman V Chepulskii, Richard H Taylor, Shidong Wang, Junkai Xue, Kesong Yang, Ohad Levy, et al · 2012
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Accurate intelligible models with pairwise interactions
Yin Lou, Rich Caruana, Johannes Gehrke, and Giles Hooker · 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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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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Deep inside convolutional networks: Visualising image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2013
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Combinatorial screening for new materials in unconstrained composition space with machine learning
Bryce Meredig, Ankit Agrawal, Scott Kirklin, James E Saal, Jeff W Doak, Alan Thompson, Kunpeng Zhang, Alok Choudhary, and Christopher Wolverton · 2014
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Nanograined half-heusler semiconductors as advanced thermoelectrics: An ab initio high-throughput statistical study
Jesús Carrete, Natalio Mingo, Shidong Wang, and Stefano Curtarolo · 2014
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Big data of materials science: critical role of the descriptor
Luca M Ghiringhelli, Jan Vybiral, Sergey V Levchenko, Claudia Draxl, and Matthias Scheffler · 2015
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Advances in natural language processing
Julia Hirschberg and Christopher D Manning · 2015
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Machine learning energies of 2 million elpasolite (a b c 2 d 6) crystals
Felix A Faber, Alexander Lindmaa, O Anatole Von Lilienfeld, and Rickard Armiento · 2016
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Geometrical properties can predict co2 and n2 adsorption performance of metal–organic frameworks (mofs) at low pressure
Michael Fernandez and Amanda S Barnard · 2016
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"why should i trust you?" explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
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Understanding neural networks through representation erasure
Jiwei Li, Will Monroe, and Dan Jurafsky · 2016
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Google’s neural machine translation system: Bridging the gap between human and machine translation
Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, et al · 2016
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Representation of compounds for machine-learning prediction of physical properties
Atsuto Seko, Hiroyuki Hayashi, Keita Nakayama, Akira Takahashi, and Isao Tanaka · 2017
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Towards a rigorous science of interpretable machine learning
Finale Doshi-Velez and Been Kim · 2017
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Classification and regression trees
Leo Breiman · 2017
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A unified approach to interpreting model predictions
Scott M Lundberg and Su-In Lee · 2017
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Crystal structure representation for neural networks using topological approach
Aleksandr V Fedorov and Ivan V Shamanaev · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 2017
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
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Physical descriptor for the gibbs energy of inorganic crystalline solids and temperature-dependent materials chemistry
Christopher J Bartel, Samantha L Millican, Ann M Deml, John R Rumptz, William Tumas, Alan W Weimer, Stephan Lany, Vladan Stevanović, Charles B Musgrave, and Aaron M Holder · 2018
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Crystal structure prediction via deep learning
Kevin Ryan, Jeff Lengyel, and Michael Shatruk · 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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Peeking inside the black-box: a survey on explainable artificial intelligence (xai)
Amina Adadi and Mohammed Berrada · 2018
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Sisso: A compressed-sensing method for identifying the best low-dimensional descriptor in an immensity of offered candidates
Runhai Ouyang, Stefano Curtarolo, Emre Ahmetcik, Matthias Scheffler, and Luca M Ghiringhelli · 2018
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The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery
Zachary C Lipton · 2018
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Relational inductive biases, deep learning, and graph networks
Peter W Battaglia, Jessica B Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinicius Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, et al · 2018
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Methods for interpreting and understanding deep neural networks
Grégoire Montavon, Wojciech Samek, and Klaus-Robert Müller · 2018
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Explaining explanations: An overview of interpretability of machine learning
Leilani H Gilpin, David Bau, Ben Z Yuan, Ayesha Bajwa, Michael Specter, and Lalana Kagal · 2018
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A survey of methods for explaining black box models
Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Franco Turini, Fosca Giannotti, and Dino Pedreschi · 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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Machine learning with force-field-inspired descriptors for materials: Fast screening and mapping energy landscape
Kamal Choudhary, Brian DeCost, and Francesca Tavazza · 2018
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A strategy to apply machine learning to small datasets in materials science
Ying Zhang and Chen Ling · 2018
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Ultra-strong machine learning: comprehensibility of programs learned with ilp
Stephen H Muggleton, Ute Schmid, Christina Zeller, Alireza Tamaddoni-Nezhad, and Tarek Besold · 2018
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Computational screening of high-performance optoelectronic materials using optb88vdw and tb-mbj formalisms
Kamal Choudhary, Qin Zhang, Andrew CE Reid, Sugata Chowdhury, Nhan Van Nguyen, Zachary Trautt, Marcus W Newrock, Faical Yannick Congo, and Francesca Tavazza · 2018
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Inverse design of solid-state materials via a continuous representation
Juhwan Noh, Jaehoon Kim, Helge S Stein, Benjamin Sanchez-Lengeling, John M Gregoire, Alan Aspuru-Guzik, and Yousung Jung · 2019
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Accelerating crystal structure prediction by machine-learning interatomic potentials with active learning
Evgeny V Podryabinkin, Evgeny V Tikhonov, Alexander V Shapeev, and Artem R Oganov · 2019
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The advantages of the matthews correlation coefficient (mcc) over f1 score and accuracy in binary classification evaluation
Davide Chicco and Giuseppe Jurman · 2020
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A critical examination of compound stability predictions from machine-learned formation energies
Christopher J Bartel, Amalie Trewartha, Qi Wang, Alexander Dunn, Anubhav Jain, and Gerbrand Ceder · 2020
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Dscribe: Library of descriptors for machine learning in materials science
Lauri Himanen, Marc OJ Jäger, Eiaki V Morooka, Filippo Federici Canova, Yashasvi S Ranawat, David Z Gao, Patrick Rinke, and Adam S Foster · 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, et al · 2020
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Captum: A unified and generic model interpretability library for pytorch
Narine Kokhlikyan, Vivek Miglani, Miguel Martin, Edward Wang, Bilal Alsallakh, Jonathan Reynolds, Alexander Melnikov, Natalia Kliushkina, Carlos Araya, Siqi Yan, et al · 2020
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Graph networks as a universal machine learning framework for molecules and crystals
Chi Chen, Weike Ye, Yunxing Zuo, Chen Zheng, and Shyue Ping Ong · 2019
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In defense of the black box
Elizabeth A Holm · 2019
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Five high-impact research areas in machine learning for materials science
Bryce Meredig · 2019
Cited alongside, same era.
Xai—explainable artificial intelligence
David Gunning, Mark Stefik, Jaesik Choi, Timothy Miller, Simone Stumpf, and Guang-Zhong Yang · 2019
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Symbolic regression in materials science
Yiqun Wang, Nicholas Wagner, and James M Rondinelli · 2019
Cited alongside, same era.
Beyond scaling relations for the description of catalytic materials
Mie Andersen, Sergey V Levchenko, Matthias Scheffler, and Karsten Reuter · 2019
Cited alongside, same era.
New tolerance factor to predict the stability of perovskite oxides and halides
Christopher J Bartel, Christopher Sutton, Bryan R Goldsmith, Runhai Ouyang, Charles B Musgrave, Luca M Ghiringhelli, and Matthias Scheffler · 2019
Cited alongside, same era.
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Inverse design of nanoporous crystalline reticular materials with deep generative models
Zhenpeng Yao, Benjamín Sánchez-Lengeling, N Scott Bobbitt, Benjamin J Bucior, Sai Govind Hari Kumar, Sean P Collins, Thomas Burns, Tom K Woo, Omar K Farha, Randall Q Snurr, et al · 2021
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Atomistic line graph neural network for improved materials property predictions
Kamal Choudhary and Brian DeCost · 2021
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Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity
William Fedus, Barret Zoph, and Noam Shazeer · 2021
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Machine learning guided synthesis of multinary chevrel phase chalcogenides
Nicholas R Singstock, Jessica C Ortiz-Rodríguez, Joseph T Perryman, Christopher Sutton, Jesús M Velázquez, and Charles B Musgrave · 2021
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A data-driven perspective on the colours of metal–organic frameworks
Kevin Maik Jablonka, Seyed Mohamad Moosavi, Mehrdad Asgari, Christopher Ireland, Luc Patiny, and Berend Smit · 2021
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Database, features, and machine learning model to identify thermally driven metal–insulator transition compounds
Alexandru B Georgescu, Peiwen Ren, Aubrey R Toland, Shengtong Zhang, Kyle D Miller, Daniel W Apley, Elsa A Olivetti, Nicholas Wagner, and James M Rondinelli · 2021
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Predicting the formability of hybrid organic–inorganic perovskites via an interpretable machine learning strategy
Shilin Zhang, Tian Lu, Pengcheng Xu, Qiuling Tao, Minjie Li, and Wencong Lu · 2021
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Relationships between distortions of inorganic framework and band gap of layered hybrid halide perovskites
Ekaterina I Marchenko, Vadim V Korolev, Sergey A Fateev, Artem Mitrofanov, Nikolay N Eremin, Eugene A Goodilin, and Alexey B Tarasov · 2021
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Parametrization of nonbonded force field terms for metal–organic frameworks using machine learning approach
Vadim V Korolev, Yuriy M Nevolin, Thomas A Manz, and Pavel V Protsenko · 2021
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Physics-inspired structural representations for molecules and materials
Felix Musil, Andrea Grisafi, Albert P Bartók, Christoph Ortner, Gábor Csányi, and Michele Ceriotti · 2021
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Geometric deep learning: Grids, groups, graphs, geodesics, and gauges
Michael M Bronstein, Joan Bruna, Taco Cohen, and Petar Veličković · 2021
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A geometric-information-enhanced crystal graph network for predicting properties of materials
Jiucheng Cheng, Chunkai Zhang, and Lifeng Dong · 2021
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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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Explanation-guided training for cross-domain few-shot classification
Jiamei Sun, Sebastian Lapuschkin, Wojciech Samek, Yunqing Zhao, Ngai-Man Cheung, and Alexander Binder · 2021
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An invertible crystallographic representation for general inverse design of inorganic crystals with targeted properties
Zekun Ren, Siyu Isaac Parker Tian, Juhwan Noh, Felipe Oviedo, Guangzong Xing, Jiali Li, Qiaohao Liang, Ruiming Zhu, Armin G Aberle, Shijing Sun, et al · 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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Bigdml—towards accurate quantum machine learning force fields for materials
Huziel E Sauceda, Luis E Gálvez-González, Stefan Chmiela, Lauro Oliver Paz-Borbón, Klaus-Robert Müller, and Alexandre Tkatchenko · 2022
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Graph neural networks for materials science and chemistry
Patrick Reiser, Marlen Neubert, André Eberhard, Luca Torresi, Chen Zhou, Chen Shao, Houssam Metni, Clint van Hoesel, Henrik Schopmans, Timo Sommer, et al · 2022
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Interpretable machine learning for knowledge generation in heterogeneous catalysis
Jacques A Esterhuizen, Bryan R Goldsmith, and Suljo Linic · 2022
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Explainable machine learning in materials science
Xiaoting Zhong, Brian Gallagher, Shusen Liu, Bhavya Kailkhura, Anna Hiszpanski, and T Yong-Jin Han · 2022
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Interpretable and explainable machine learning for materials science and chemistry
Felipe Oviedo, Juan Lavista Ferres, Tonio Buonassisi, and Keith T Butler · 2022
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Machine learning of material properties: Predictive and interpretable multilinear models
Alice EA Allen and Alexandre Tkatchenko · 2022
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Extracting structural motifs from pair distribution function data of nanostructures using explainable machine learning
Andy S Anker, Emil TS Kjær, Mikkel Juelsholt, Troels Lindahl Christiansen, Susanne Linn Skjærvø, Mads Ry Vogel Jørgensen, Innokenty Kantor, Daniel Risskov Sørensen, Simon JL Billinge, Raghavendra Selvan, et al · 2022
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On-the-fly interpretable machine learning for rapid discovery of two-dimensional ferromagnets with high curie temperature
Shuaihua Lu, Qionghua Zhou, Yilv Guo, and Jinlan Wang · 2022
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Scalable deeper graph neural networks for high-performance materials property prediction
Sadman Sadeed Omee, Steph-Yves Louis, Nihang Fu, Lai Wei, Sourin Dey, Rongzhi Dong, Qinyang Li, and Jianjun Hu · 2022
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Graphlime: Local interpretable model explanations for graph neural networks
Qiang Huang, Makoto Yamada, Yuan Tian, Dinesh Singh, and Yi Chang · 2022
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Efficient and interpretable graph network representation for angle-dependent properties applied to optical spectroscopy
Tim Hsu, Tuan Anh Pham, Nathan Keilbart, Stephen Weitzner, James Chapman, Penghao Xiao, S Roger Qiu, Xiao Chen, and Brandon C Wood · 2022
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Interpretable graph transformer network for predicting adsorption isotherms of metal–organic frameworks
Pin Chen, Rui Jiao, Jinyu Liu, Yang Liu, and Yutong Lu · 2022
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Should artificial intelligence be interpretable to humans?
Matthew D Schwartz · 2022
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Matscibert: A materials domain language model for text mining and information extraction
Tanishq Gupta, Mohd Zaki, and NM Anoop Krishnan · 2022
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Quantifying the advantage of domain-specific pre-training on named entity recognition tasks in materials science
Amalie Trewartha, Nicholas Walker, Haoyan Huo, Sanghoon Lee, Kevin Cruse, John Dagdelen, Alexander Dunn, Kristin A Persson, Gerbrand Ceder, and Anubhav Jain · 2022
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Batterybert: A pretrained language model for battery database enhancement
Shu Huang and Jacqueline M Cole · 2022
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ferret: a framework for benchmarking explainers on transformers
Giuseppe Attanasio, Eliana Pastor, Chiara Di Bonaventura, and Debora Nozza · 2022
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Representations of molecules and materials for interpolation of quantum-mechanical simulations via machine learning
Marcel F Langer, Alex Goeßmann, and Matthias Rupp · 2022
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On scientific understanding with artificial intelligence
Mario Krenn, Robert Pollice, Si Yue Guo, Matteo Aldeghi, Alba Cervera-Lierta, Pascal Friederich, Gabriel dos Passos Gomes, Florian Häse, Adrian Jinich, AkshatKumar Nigam, et al · 2022
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Unified graph neural network force-field for the periodic table for solids
Kamal Choudhary, Brian DeCost, Lily Major, Keith Butler, Jeyan Thiyagalingam, and Francesca Tavazza · 2023
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