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Metal-organic frameworks (MOFs) are a class of crystalline materials with promising applications in many areas such as carbon capture and drug delivery.
Self-consistent equations including exchange and correlation effects
Walter Kohn and Lu Jeu Sham · 1965
Earlier work this paper cites.
Numerically stable algorithms for the computation of reduced unit cells
Ralf W Grosse-Kunstleve, Nicholas K Sauter, and Paul D Adams · 2004
Earlier work this paper cites.
Metal–organic framework materials as catalysts
JeongYong Lee, Omar K Farha, John Roberts, Karl A Scheidt, SonBinh T Nguyen, and Joseph T Hupp · 2009
Earlier work this paper cites.
Zeolitic polyoxometalate-based metal- organic frameworks (z-pomofs): Computational evaluation of hypothetical polymorphs and the successful targeted synthesis of the redox-active z-pomof1
L Marleny Rodriguez-Albelo, A Rabdel Ruiz-Salvador, Alvaro Sampieri, Dewi W Lewis, Ariel Gómez, Brigitte Nohra, Pierre Mialane, Jérôme Marrot, Francis Sécheresse, Caroline Mellot-Draznieks, et al · 2009
Earlier work this paper cites.
Crystal structure prediction via particle-swarm optimization
Yanchao Wang, Jian Lv, Li Zhu, and Yanming Ma · 2010
Earlier work this paper cites.
Adsorptive removal of methyl orange and methylene blue from aqueous solution with a metal-organic framework material, iron terephthalate (mof-235)
Enamul Haque, Jong Won Jun, and Sung Hwa Jhung · 2011
Earlier work this paper cites.
Ab initio random structure searching
Chris J Pickard and RJ Needs · 2011
Earlier work this paper cites.
Metal–organic frameworks in biomedicine
Patricia Horcajada, Ruxandra Gref, Tarek Baati, Phoebe K Allan, Guillaume Maurin, Patrick Couvreur, Gérard Férey, Russell E Morris, and Christian Serre · 2012
Earlier work this paper cites.
Metal–organic framework materials as chemical sensors
Lauren E Kreno, Kirsty Leong, Omar K Farha, Mark Allendorf, Richard P Van Duyne, and Joseph T Hupp · 2012
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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 Chevrier, Kristin A. Persson, and Gerbrand Ceder · 2012
Earlier work this paper cites.
Algorithms and tools for high-throughput geometry-based analysis of crystalline porous materials
Thomas F Willems, Chris H Rycroft, Michaeel Kazi, Juan C Meza, and Maciej Haranczyk · 2012
Earlier work this paper cites.
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, and Kristin a. Persson · 2013
Earlier work this paper cites.
Metal–organic frameworks as a tunable platform for designing functional molecular materials
Cheng Wang, Demin Liu, and Wenbin Lin · 2013
Earlier work this paper cites.
Construction and characterization of structure models of crystalline porous polymers
Richard Luis Martin and Maciej Haranczyk · 2014
Earlier work this paper cites.
Decoupled weight decay regularization
I Loshchilov · 2017
Earlier work this paper cites.
Attention is all you need
A Vaswani · 2017
Earlier work this paper cites.
Data-driven design of metal-organic frameworks for wet flue gas co2 capture
Peter G Boyd, Arunraj Chidambaram, Enrique García-Díez, Christopher P Ireland, Thomas D Daff, Richard Bounds, Andrzej Gładysiak, Pascal Schouwink, Seyed Mohamad Moosavi, M Mercedes Maroto-Valer, et al · 2018
Earlier work this paper cites.
Neural ordinary differential equations
Ricky TQ Chen, Yulia Rubanova, Jesse Bettencourt, and David K Duvenaud · 2018
Earlier work this paper cites.
On-the-fly machine learning of atomic potential in density functional theory structure optimization
TL Jacobsen, MS Jørgensen, and B Hammer · 2018
Earlier work this paper cites.
Recent advances in gas storage and separation using metal–organic frameworks
Hao Li, Kecheng Wang, Yujia Sun, Christina T Lollar, Jialuo Li, and Hong-Cai Zhou · 2018
Earlier work this paper cites.
Crystal structure prediction accelerated by bayesian optimization
Tomoki Yamashita, Nobuya Sato, Hiori Kino, Takashi Miyake, Koji Tsuda, and Tamio Oguchi · 2018
Earlier work this paper cites.
Identification schemes for metal–organic frameworks to enable rapid search and cheminformatics analysis
Benjamin J Bucior, Andrew S Rosen, Maciej Haranczyk, Zhenpeng Yao, Michael E Ziebel, Omar K Farha, Joseph T Hupp, J Ilja Siepmann, Alán Aspuru-Guzik, and Randall Q Snurr · 2019
Earlier work this paper cites.
Data-driven approach to encoding and decoding 3-d crystal structures
Jordan Hoffmann, Louis Maestrati, Yoshihide Sawada, Jian Tang, Jean Michel Sellier, and Yoshua Bengio · 2019
Earlier work this paper cites.
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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3-d inorganic crystal structure generation and property prediction via representation learning
Callum J Court, Batuhan Yildirim, Apoorv Jain, and Jacqueline M Cole · 2020
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Ab initio prediction of metal-organic framework structures
James P Darby, Mihails Arhangelskis, Athanassios D Katsenis, Joseph M Marrett, Tomislav Friscic, and Andrew J Morris · 2020
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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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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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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Geodiff: A geometric diffusion model for molecular conformation generation
Minkai Xu, Lantao Yu, Yang Song, Chence Shi, Stefano Ermon, and Jian Tang · 2022
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Hybrid algorithm of bayesian optimization and evolutionary algorithm in crystal structure prediction
Tomoki Yamashita, Hiori Kino, Koji Tsuda, Takashi Miyake, and Tamio Oguchi · 2022
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Building normalizing flows with stochastic interpolants
Michael Samuel Albergo and Eric Vanden-Eijnden · 2023
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Distance matrix-based crystal structure prediction using evolutionary algorithms
Jianjun Hu, Wenhui Yang, and Edirisuriya M Dilanga Siriwardane · 2020
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Generative adversarial networks for crystal structure prediction
Sungwon Kim, Juhwan Noh, Geun Ho Gu, Alan Aspuru-Guzik, and Yousung Jung · 2020
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Topological descriptors help predict guest adsorption in nanoporous materials
Aditi S Krishnapriyan, Maciej Haranczyk, and Dmitriy Morozov · 2020
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Mof-based membranes for gas separations
Qihui Qian, Patrick A Asinger, Moon Joo Lee, Gang Han, Katherine Mizrahi Rodriguez, Sharon Lin, Francesco M Benedetti, Albert X Wu, Won Seok Chi, and Zachary P Smith · 2020
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On layer normalization in the transformer architecture
Ruibin Xiong, Yunchang Yang, Di He, Kai Zheng, Shuxin Zheng, Chen Xing, Huishuai Zhang, Yanyan Lan, Liwei Wang, and Tieyan Liu · 2020
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Contact map based crystal structure prediction using global optimization
Jianjun Hu, Wenhui Yang, Rongzhi Dong, Yuxin Li, Xiang Li, Shaobo Li, and Edirisuriya MD Siriwardane · 2021
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Highly accurate protein structure prediction with alphafold
John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Žídek, Anna Potapenko, et al · 2021
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Diffdock: Diffusion steps, twists, and turns for molecular docking
Gabriele Corso, Hannes Stärk, Bowen Jing, Regina Barzilay, and Tommi S Jaakkola · 2023
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Chgnet as a pretrained universal neural network potential for charge-informed atomistic modelling
Bowen Deng, Peichen Zhong, KyuJung Jun, Janosh Riebesell, Kevin Han, Christopher J. Bartel, and Gerbrand Ceder · 2023
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Mofdiff: Coarse-grained diffusion for metal-organic framework design
Xiang Fu, Tian Xie, Andrew S Rosen, Tommi Jaakkola, and Jake Smith · 2023
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Evolutionary-scale prediction of atomic-level protein structure with a language model
Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, et al · 2023
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Flow matching for generative modeling
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Flow straight and fast: Learning to generate and transfer data with rectified flow
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Multisample flow matching: Straightening flows with minibatch couplings
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Se (3)-stochastic flow matching for protein backbone generation
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Flow matching on general geometries
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Alphafold meets flow matching for generating protein ensembles
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Equivariant diffusion for crystal structure prediction
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Towards symmetry-aware generation of periodic materials
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Flowmm: Generating materials with riemannian flow matching
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