Quantum chemistry structures and properties of 134 kilo molecules

Ramakrishnan, Raghunathan, Dral, Pavlo O., Rupp, Matthias, von Lilienfeld, O. Anatole·2014·Scientific Data
DOI 10.1038/sdata.2014.22

Cited by (1,001)

Items 101–200 of 1,001

  1. Graph Neural Networks in TensorFlow and Keras with Spektral [Application Notes]
    Daniele Grattarola, Cesare Alippi · 2021 · IEEE Computational Intelligence Magazine
  2. Benchmarking Machine Learning Models for Polymer Informatics: An Example of Glass Transition Temperature
    Lei Tao, Vikas Varshney, Ying Li · 2021 · Journal of Chemical Information and Modeling
  3. Autonomous Discovery in the Chemical Sciences Part II: Outlook
    Connor W. Coley, Natalie S. Eyke, Klavs F. Jensen · 2019 · Angewandte Chemie International Edition
  4. A Survey on Graph Representation Learning Methods
    Shima Khoshraftar, Aijun An · 2023 · ACM Transactions on Intelligent Systems and Technology
  5. A quantitative uncertainty metric controls error in neural network-driven chemical discovery
    Janet, Jon Paul, Duan, Chenru, Yang, Tzuhsiung, Nandy, Aditya, Kulik, Heather J. · 2019 · Chemical Science
  6. A Statistical Learning Framework for Materials Science: Application to Elastic Moduli of k-nary Inorganic Polycrystalline Compounds
    de Jong, Maarten, Chen, Wei, Notestine, Randy, Persson, Kristin, Ceder, Gerbrand, Jain, Anubhav, Asta, Mark, Gamst, Anthony · 2016 · Scientific Reports
  7. Atomistic calculations and materials informatics: A review
    Logan T. Ward, Chris M. Wolverton · 2016 · Current Opinion in Solid State and Materials Science
  8. Quantum Machine Learning in Chemical Compound Space
    O. Anatole von Lilienfeld · 2017 · Angewandte Chemie International Edition
  9. Quantum machine learning using atom-in-molecule-based fragments selected on the fly
    Bing Huang, O. Anatole von Lilienfeld · 2020 · Nature Chemistry
  10. Evidential Deep Learning for Guided Molecular Property Prediction and Discovery
    Ava P. Soleimany, Alexander Amini, Samuel Goldman, Daniela L. Rus, Sangeeta N. Bhatia, Connor W. Coley · 2021 · ACS Central Science
  11. TorchMD: A Deep Learning Framework for Molecular Simulations
    Doerr, Stefan, Majewski, Maciej, Pérez, Adrià, Krämer, Andreas, Clementi, Cecilia, Noe, Frank, Giorgino, Toni, De Fabritiis, Gianni · 2021 · Journal of Chemical Theory and Computation
  12. A universal strategy for the creation of machine learning-based atomistic force fields
    Tran Doan Huan, Rohit Batra, James Chapman, Sridevi Krishnan, Lihua Chen, Rampi Ramprasad · 2017 · npj Computational Materials
  13. A polymer dataset for accelerated property prediction and design
    Tran Doan Huan, Arun Mannodi‐Kanakkithodi, Chiho Kim, Vinit Sharma, Ghanshyam Pilania, Rampi Ramprasad · 2016 · Scientific Data
  14. Cormorant: Covariant Molecular Neural Networks
    Brandon Anderson, Truong-Son Hy, Risi Kondor · 2019 · arXiv (Cornell University)
  15. 14 examples of how LLMs can transform materials science and chemistry: A reflection on a large language model hackathon
    Jablonka, Kevin Maik, Ai, Qianxiang, Al-Feghali, Alexander, Badhwar, Shruti, Bocarsly, Joshua D., Bran, Andres M., Bringuier, Stefan, Brinson, L. Catherine, Choudhary, Kamal, Circi, Defne et al. · 2023 · Digital Discovery
  16. Fourier series of atomic radial distribution functions: A molecular fingerprint for machine learning models of quantum chemical properties
    O. Anatole von Lilienfeld, Raghunathan Ramakrishnan, Matthias Rupp, Aaron M. Knoll · 2015 · International Journal of Quantum Chemistry
  17. Directional Message Passing for Molecular Graphs
    Johannes Klicpera, Janek Groß, Stephan Günnemann · 2020 · arXiv (Cornell University)
  18. Machine Learning for Quantum Mechanical Properties of Atoms in Molecules
    Matthias Rupp, Raghunathan Ramakrishnan, O. Anatole von Lilienfeld · 2015 · The Journal of Physical Chemistry Letters
  19. Machine learning meets volcano plots: computational discovery of cross-coupling catalysts
    Meyer, Benjamin, Sawatlon, Boodsarin, Heinen, Stefan, von Lilienfeld, O. Anatole, Corminboeuf, Clémence · 2018 · Chemical Science
  20. Delocalization error: The greatest outstanding challenge in density‐functional theory
    Kyle R. Bryenton, Adebayo A. Adeleke, Stephen G. Dale, Erin R. Johnson · 2022 · Wiley Interdisciplinary Reviews Computational Molecular Science
  21. SPICE, A Dataset of Drug-like Molecules and Peptides for Training Machine Learning Potentials
    Peter K. Eastman, Pavan Kumar Behara, David Dotson, Raimondas Galvelis, John E. Herr, Joshua T. Horton, Yuezhi Mao, John D. Chodera, Benjamin P. Pritchard, Yuanqing Wang et al. · 2023 · Scientific Data
  22. Virtual Chemical Libraries
    W. Patrick Walters · 2018 · Journal of Medicinal Chemistry
  23. Molecular excited states through a machine learning lens
    Pavlo O. Dral, Mario Barbatti · 2021 · Nature Reviews Chemistry
  24. Evaluation of the MACE force field architecture: From medicinal chemistry to materials science
    Dávid Péter Kovács, Ilyes Batatia, Eszter Sára Arany, Gábor Cśanyi · 2023 · The Journal of Chemical Physics
  25. Ab Initio Machine Learning in Chemical Compound Space
    Bing Huang, O. Anatole von Lilienfeld · 2021 · Chemical Reviews
  26. Accurate Multiobjective Design in a Space of Millions of Transition Metal Complexes with Neural-Network-Driven Efficient Global Optimization
    Janet, Jon Paul, Ramesh, Sahasrajit, Duan, Chenru, Kulik, Heather J. · 2020 · ACS Central Science
  27. Accelerating Chemical Discovery with Machine Learning: Simulated Evolution of Spin Crossover Complexes with an Artificial Neural Network
    Janet, Jon Paul, Chan, Lydia, Kulik, Heather J. · 2018 · The Journal of Physical Chemistry Letters
  28. Generative Deep Learning for Targeted Compound Design
    T. Ferra de Sousa, João Vitor Mariano Correia, Vítor Pereira, Miguel Rocha · 2021 · Journal of Chemical Information and Modeling
  29. Recent Advances in Machine Learning‐Assisted Multiscale Design of Energy Materials
    Bohayra Mortazavi · 2024 · Advanced Energy Materials
  30. Predicting Reaction Yields via Supervised Learning
    Andrzej M. Żurański, Jesus I. Martinez Alvarado, Benjamin J. Shields, Abigail G. Doyle · 2021 · Accounts of Chemical Research
  31. Inverse design of 3d molecular structures with conditional generative neural networks
    Niklas W. A. Gebauer, Michael Gastegger, Stefaan S. P. Hessmann, Klaus‐Robert Müller, Kristof T. Schütt · 2022 · Nature Communications
  32. Data Generation for Machine Learning Interatomic Potentials and Beyond
    Maksim Kulichenko, Benjamin T. Nebgen, Nicholas Lubbers, Justin S. Smith, Kipton M. Barros, Alice E. A. Allen, Adela Habib, Emily Shinkle, Nikita S. Fedik, Ying Wai Li et al. · 2024 · Chemical Reviews
  33. Computational prediction of chemical reactions: current status and outlook
    Ola Engkvist, Per‐Ola Norrby, Nidhal Selmi, Yu‐hong Lam, Zhengwei Peng, Edward C. Sherer, Willi Amberg, Thomas Erhard, Lynette A. Smyth · 2018 · Drug Discovery Today
  34. Molecular Property Prediction: A Multilevel Quantum Interactions Modeling Perspective
    Chengqiang Lu, Qi Liu, Chao Wang, Zhenya Huang, Peize Lin, Lixin He · 2019 · Proceedings of the AAAI Conference on Artificial Intelligence
  35. Materials science in the artificial intelligence age: high-throughput library generation, machine learning, and a pathway from correlations to the underpinning physics
    Rama Krishnan Vasudevan, Kamal Kumar Choudhary, Apurva Mehta, Ryan P. Smith, Aaron Gilad Kusne, Francesca Tavazza, Lukáš Vlček, Maxim A Ziatdinov, Sergei V. Kalinin, Jason R. Hattrick-Simpers · 2019 · MRS Communications
  36. Machine learning for chemical discovery
    Alexandre Tkatchenko · 2020 · Nature Communications
  37. Big data and machine learning for materials science
    Jose Fernando Rodrigues Junior, Larisa Florea, Maria Cristina Ferreira de Oliveira, Dermot Diamond, Osvaldo N. Oliveira · 2021 · Discover Materials
  38. Deep Learning for Deep Chemistry: Optimizing the Prediction of Chemical Patterns
    Tânia F. G. G. Cova, Alberto A. C. C. Pais · 2019 · Frontiers in Chemistry
  39. Molecular generative Graph Neural Networks for Drug Discovery
    Pietro Bongini, Monica Bianchini, Franco Scarselli · 2021 · Neurocomputing
  40. Machine learning of molecular properties: Locality and active learning
    Konstantin Gubaev, Evgeny V. Podryabinkin, Alexander V. Shapeev · 2018 · The Journal of Chemical Physics
  41. Retrospective on a decade of machine learning for chemical discovery
    O. Anatole von Lilienfeld, Kieron Burke · 2020 · Nature Communications
  42. FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks
    Chaoyang He, Keshav Balasubramanian, Emir Ceyani, Yang, Carl, Xie, Han, Sun, Lichao, He, Lifang, Yang, Liangwei, Yu, Philip S., Rong, Yu et al. · 2021 · arXiv (Cornell University)
  43. Message Passing Neural Networks
    Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, George E. Dahl · 2020 · Lecture notes in physics
  44. GraphNVP: An Invertible Flow Model for Generating Molecular Graphs
    Kaushalya Madhawa, Ishiguro, Katushiko, Kosuke Nakago, Abe, Motoki · 2019 · arXiv (Cornell University)
  45. Toward Design of Novel Materials for Organic Electronics
    Pascal Friederich, Artem Fediai, Simon Kaiser, Manuel Konrad, Nicole Jung, Wolfgang Wenzel · 2019 · Advanced Materials
  46. Accelerated materials property predictions and design using motif-based fingerprints
    Tran Doan Huan, Arun Mannodi‐Kanakkithodi, Rampi Ramprasad · 2015 · Physical Review B
  47. Extending machine learning beyond interatomic potentials for predicting molecular properties
    Nikita S. Fedik, R.I. Zubatyuk, Maksim Kulichenko, Nicholas Lubbers, Justin S. Smith, Benjamin T. Nebgen, Richard A. Messerly, Ying Wai Li, Alexander I. Boldyrev, Kipton M. Barros et al. · 2022 · Nature Reviews Chemistry
  48. Machine Learning in Materials Discovery: Confirmed Predictions and Their Underlying Approaches
    James E. Saal, Anton O. Oliynyk, Bryce Meredig · 2020 · Annual Review of Materials Research
  49. Molecular Geometry Prediction using a Deep Generative Graph Neural Network
    Elman Mansimov, Omar Mahmood, Seokho Kang, Kyunghyun Cho · 2019 · Scientific Reports
  50. Strategies and Software for Machine Learning Accelerated Discovery in Transition Metal Chemistry
    Nandy, Aditya, Duan, Chenru, Janet, Jon Paul, Gugler, Stefan, Kulik, Heather J. · 2018 · Industrial & Engineering Chemistry Research
  51. Provably Powerful Graph Networks
    Haggai Maron, Heli Ben-Hamu, Hadar Serviansky, Yaron Lipman · 2019 · arXiv (Cornell University)
  52. Quantum machine learning for electronic structure calculations
    Rongxin Xia, Sabre Kais · 2018 · Nature Communications
  53. Fast and Accurate Uncertainty Estimation in Chemical Machine Learning
    Félix Musil, Michael J. Willatt, Mikhail Langovoy, Michele Ceriotti · 2019 · Journal of Chemical Theory and Computation
  54. Machine learning in solid heterogeneous catalysis: Recent developments, challenges and perspectives
    Yani Guan, Donovan Chaffart, Guihua Liu, Zhaoyang Tan, Dongsheng Zhang, Yanji Wang, Jingde Li, Luis Ricardez‐Sandoval · 2021 · Chemical Engineering Science
  55. Reactants, products, and transition states of elementary chemical reactions based on quantum chemistry
    Colin A. Grambow, Lagnajit Pattanaik, William H. Green · 2020 · Scientific Data
  56. QM7-X, a comprehensive dataset of quantum-mechanical properties spanning the chemical space of small organic molecules
    Johannes Hoja, Leonardo Medrano Sandonas, Brian G. Ernst, Álvaro Vázquez‐Mayagoitia, Robert A. DiStasio, Alexandre Tkatchenko · 2021 · Scientific Data
  57. Deep learning approaches for de novo drug design: An overview
    Mingyang Wang, Zhe Wang, Huiyong Sun, Jike Wang, Chao Shen, Gaoqi Weng, Xin Chai, Honglin Li, Dongsheng Cao, Tingjun Hou · 2021 · Current Opinion in Structural Biology
  58. Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
    Cheol Woo Park, Mordechai Kornbluth, Jonathan Vandermause, Chris M. Wolverton, Boris Kozinsky, Jonathan P. Mailoa · 2021 · npj Computational Materials
  59. Structure-based sampling and self-correcting machine learning for accurate calculations of potential energy surfaces and vibrational levels
    Pavlo O. Dral, Alec Owens, Sergei N. Yurchenko, Walter Thiel · 2017 · The Journal of Chemical Physics
  60. Representations of molecules and materials for interpolation of quantum-mechanical simulations via machine learning
    Marcel F. Langer, Alex Goeßmann, Matthias Rupp · 2022 · npj Computational Materials
  61. QMugs, quantum mechanical properties of drug-like molecules
    Clemens Isert, Kenneth Atz, José Jiménez-Luna, Gisbert Schneider · 2022 · Scientific Data
  62. Message-passing neural networks for high-throughput polymer screening
    Peter C. St. John, Caleb T. Phillips, Travis W. Kemper, A. Nolan Wilson, Yanfei Guan, Michael F. Crowley, Mark R. Nimlos, Ross E. Larsen · 2019 · The Journal of Chemical Physics
  63. ASGN: An Active Semi-supervised Graph Neural Network for Molecular Property Prediction
    Zhongkai Hao, Chengqiang Lu, Zhenya Huang, Hao Wang, Zheyuan Hu, Qi Liu, Enhong Chen, Chee‐Kong Lee · 2020 · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)
  64. Pretraining of attention-based deep learning potential model for molecular simulation
    Duo Zhang, Hangrui Bi, Fu‐Zhi Dai, Wanrun Jiang, Xinzijian Liu, Linfeng Zhang, Han Wang · 2024 · npj Computational Materials
  65. tmQM Dataset─Quantum Geometries and Properties of 86k Transition Metal Complexes
    Balcells, David, Skjelstad, Bastian Bjerkem · 2020 · Journal of Chemical Information and Modeling
  66. Feature optimization for atomistic machine learning yields a data-driven construction of the periodic table of the elements
    Willatt, Michael J., Musil, Félix, Ceriotti, Michele · 2018 · Physical Chemistry Chemical Physics
  67. RadonPy: automated physical property calculation using all-atom classical molecular dynamics simulations for polymer informatics
    Yoshihiro Hayashi, Junichiro Shiomi, Junko Morikawa, Ryo Yoshida · 2022 · npj Computational Materials
  68. Transfer learning with graph neural networks for improved molecular property prediction in the multi-fidelity setting
    David Buterez, Jon Paul Janet, Steven John Kiddle, Dino Oglić, Píetro Lió · 2024 · Nature Communications
  69. Artificial Intelligence in Drug Discovery: A Comprehensive Review of Data-driven and Machine Learning Approaches
    Hyunho Kim, Eunyoung Kim, Ingoo Lee, Bongsung Bae, Minsu Park, Hojung Nam · 2020 · Biotechnology and Bioprocess Engineering
  70. Polymer Informatics at Scale with Multitask Graph Neural Networks
    Rishi Gurnani, Christopher Kuenneth, Aubrey R. Toland, Rampi Ramprasad · 2023 · Chemistry of Materials
  71. Deep Learning for Molecular Generation
    Youjun Xu, Kangjie Lin, Shiwei Wang, Lei Wang, Chenjing Cai, Chen Song, Luhua Lai, Jianfeng Pei · 2019 · Future Medicinal Chemistry
  72. Machine Learning-Assisted High-Donor-Number Electrolyte Additive Screening toward Construction of Dendrite-Free Aqueous Zinc-Ion Batteries
    Haoran Luo, Qianzhi Gou, Yujie Zheng, Kaixin Wang, Ruduan Yuan, Sida Zhang, Wei Fang, Ziga Luogu, Yu Lin Hu, Huaping Mei et al. · 2025 · ACS Nano
  73. Quantum chemical calculations for over 200,000 organic radical species and 40,000 associated closed-shell molecules
    Peter C. St. John, Yanfei Guan, Yeonjoon Kim, Brian D. Etz, Seonah Kim, Robert S. Paton · 2020 · Scientific Data
  74. Large-Scale Atomic Simulation via Machine Learning Potentials Constructed by Global Potential Energy Surface Exploration
    Pei‐Lin Kang, Cheng Shang, Zhi‐Pan Liu · 2020 · Accounts of Chemical Research
  75. Computer Simulations of Deep Eutectic Solvents: Challenges, Solutions, and Perspectives
    Dmitry A. Tolmachev, N. V. Lukasheva, Ruslan R. Ramazanov, Victor M. Nazarychev, Natalia I. Borzdun, Igor V. Volgin, Maria V. Andreeva, Artyom D. Glova, Sofia D. Melnikova, Alexey Yu. Dobrovskiy et al. · 2022 · International Journal of Molecular Sciences
  76. Machine learning-based screening of complex molecules for polymer solar cells
    Peter Bjørn Jørgensen, Murat Mesta, Suranjan Shil, Juan Maria García Lastra, Karsten W. Jacobsen, Kristian Sommer Thygesen, Mikkel Nørgaard Schmidt · 2018 · The Journal of Chemical Physics
  77. A Comprehensive Survey on Geometric Deep Learning
    Wenming Cao, Zhiyue Yan, Zhiquan He, Zhihai He · 2020 · IEEE Access
  78. Advances of machine learning in molecular modeling and simulation
    Mojtaba Haghighatlari, Johannes Hachmann · 2019 · Current Opinion in Chemical Engineering
  79. Dataset’s chemical diversity limits the generalizability of machine learning predictions
    Marta Glavatskikh, Jules Leguy, Gilles Hunault, Thomas Cauchy, Benoit Da Mota · 2019 · Journal of Cheminformatics
  80. The Harvard organic photovoltaic dataset
    Steven A. Lopez, Edward O. Pyzer‐Knapp, Gregor N. C. Simm, Trevor Lutzow, Kewei Li, László R. Seress, Johannes Hachmann, Alán Aspuru‐Guzik · 2016 · Scientific Data
  81. Neural network potentials for chemistry: concepts, applications and prospects
    Silvan Käser, Luis Itza Vazquez-Salazar, Markus Meuwly, Kai Töpfer · 2022 · Digital Discovery
  82. Accurate Learning of Graph Representations with Graph Multiset Pooling
    Jinheon Baek, Minki Kang, Sung Ju Hwang · 2021 · arXiv (Cornell University)
  83. Boosting Quantum Machine Learning Models with a Multilevel Combination Technique: Pople Diagrams Revisited
    Peter Zaspel, Bing Huang, Helmut Harbrecht, O. Anatole von Lilienfeld · 2018 · Journal of Chemical Theory and Computation
  84. Transition1x - a dataset for building generalizable reactive machine learning potentials
    Mathias Schreiner, Arghya Bhowmik, Tejs Vegge, Jonas Busk, Ole Winther · 2022 · Scientific Data
  85. Automated structure discovery in atomic force microscopy
    Benjamin Alldritt, Prokop Hapala, Niko Oinonen, Fedor Urtev, Ondřej Krejčí, Filippo Federici Canova, Juho Kannala, Fabian Schulz, Peter Liljeroth, Adam Stuart Foster · 2020 · Science Advances
  86. General-Purpose Machine Learning Potentials Capturing Nonlocal Charge Transfer
    Tsz Wai Ko, Jonas A. Finkler, Stefan Goedecker, Jörg Behler · 2021 · Accounts of Chemical Research
  87. A Generative Approach to Materials Discovery, Design, and Optimization
    Dhruv Menon, Raghavan Ranganathan · 2022 · ACS Omega
  88. Diffusion Models in De Novo Drug Design
    Amira A. Alakhdar, Barnabás Póczos, Newell R. Washburn · 2024 · Journal of Chemical Information and Modeling
  89. Intrinsic Bond Energies from a Bonds-in-Molecules Neural Network
    Yao, Kun, Herr, John E., Brown, Seth N., Parkhill, John · 2017 · The Journal of Physical Chemistry Letters
  90. Toolkit for the Construction of Reproducing Kernel-Based Representations of Data: Application to Multidimensional Potential Energy Surfaces
    Oliver T. Unke, Markus Meuwly · 2017 · Journal of Chemical Information and Modeling
  91. Teaching a neural network to attach and detach electrons from molecules
    R.I. Zubatyuk, Justin S. Smith, Benjamin T. Nebgen, Sergei Tretiak, Olexandr Isayev · 2021 · Nature Communications
  92. A survey of generative AI for de novo drug design: new frontiers in molecule and protein generation
    Xiangru Tang, Howard Dai, Elizabeth Knight, Fang Wu, Yunyang Li, Tianxiao Li, Mark Gerstein · 2024 · Briefings in Bioinformatics
  93. Machine Learning Prediction of Nine Molecular Properties Based on the SMILES Representation of the QM9 Quantum-Chemistry Dataset
    Gabriel A. Pinheiro, Johnatan Mucelini, Marinalva Dias Soares, Ronaldo C. Prati, Juarez L. F. Da Silva, Marcos G. Quiles · 2020 · The Journal of Physical Chemistry A
  94. Prototypical Graph Contrastive Learning
    Shuai Lin, Chen Liu, Pan Zhou, Zi-Yuan Hu, Shuojia Wang, Ruihui Zhao, Yefeng Zheng, Liang Lin, Eric P. Xing, Xiaodan Liang · 2022 · IEEE Transactions on Neural Networks and Learning Systems
  95. Quantum Generative Models for Small Molecule Drug Discovery
    Junde Li, Rasit Onur Topaloglu, Swaroop Ghosh · 2021 · IEEE Transactions on Quantum Engineering
  96. Mapping Materials and Molecules
    Bingqing Cheng, Ryan‐Rhys Griffiths, Simon Wengert, Christian Künkel, Tamás K. Stenczel, Bonan Zhu, Volker L. Deringer, Noam Bernstein, Johannes T. Margraf, Karsten Reuter et al. · 2020 · Accounts of Chemical Research
  97. Constant size descriptors for accurate machine learning models of molecular properties
    Christopher R. Collins, Geoffrey J. Gordon, O. Anatole von Lilienfeld, David Yaron · 2018 · The Journal of Chemical Physics
  98. Operators in quantum machine learning: Response properties in chemical space
    Anders S. Christensen, Felix A. Faber, O. Anatole von Lilienfeld · 2019 · The Journal of Chemical Physics
  99. Machine learning for condensed matter physics
    Edwin A. Bedolla-Montiel, Luis Carlos Padierna, Ramón Castañeda-Priego · 2020 · Journal of Physics Condensed Matter
  100. Representation of molecular structures with persistent homology for machine learning applications in chemistry
    Jacob Townsend, Cassie Putman Micucci, John H. Hymel, Vasileios Maroulas, Konstantinos D. Vogiatzis · 2020 · Nature Communications