Quantum chemistry structures and properties of 134 kilo molecules

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Items 401–500 of 1,001

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   Gabriel A. Pinheiro, Juarez L. F. Da Silva, Marcos G. Quiles · 2022 · Journal of Chemical Information and Modeling
2. Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddings
   Christopher Morris, Gaurav Rattan, Petra Mutzel · 2019 · arXiv (Cornell University)
3. Physical pooling functions in graph neural networks for molecular property prediction
   Artur M. Schweidtmann, Jan G. Rittig, Jana M. Weber, Martin Grohe, Manuel Dahmen, Kai O. Leonhard, Alexander Mitsos · 2023 · Computers & Chemical Engineering
4. Towards understanding structure–property relations in materials with interpretable deep learning
   Tien-Sinh Vu, Minh-Quyet Ha, Duong‐Nguyen Nguyen, Viet-Cuong Nguyen, Yukihiro Abe, Truyen Tran, Tran Doan Huan, Hiori Kino, Takashi Miyake, Koji Tsuda et al. · 2023 · npj Computational Materials
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   Ganesh Sivaraman, Nicholas E. Jackson, Benjamín Sánchez-Lengeling, Álvaro Vázquez‐Mayagoitia, Alán Aspuru‐Guzik, Venkatram Vishwanath, Juan Pablo · 2020 · Machine Learning Science and Technology
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   Zachary del Rosario, Matthias Rupp, Yoolhee Kim, Erin Antono, Julia Ling · 2020 · The Journal of Chemical Physics
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9. Artificial Intelligence for Autonomous Molecular Design: A Perspective
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    Saian Chen, Aziguli Wulamu, Qiping Zou, Han Zheng, Li Wen, Xi Guo, Han Chen, Taohong Zhang, Ying Zhang · 2023 · Journal of Molecular Graphics and Modelling
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    Amir Kotobi, Kanishka Singh, Daniel Höche, Sadia Bari, Robert H. Meißner, Annika Bande · 2023 · Journal of the American Chemical Society
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    Florbela Pereira, João Aires‐de‐Sousa · 2018 · Journal of Cheminformatics
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    Logan T. Ward, Ben J. Blaiszik, Ian T Foster, Rajeev S. Assary, Badri Narayanan, Larry A. Curtiss · 2019 · MRS Communications
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    David van der Spoel · 2020 · Current Opinion in Structural Biology
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    Hatice Gökcan, Olexandr Isayev · 2021 · Wiley Interdisciplinary Reviews Computational Molecular Science
16. AI Approaches to Homogeneous Catalysis with Transition Metal Complexes
    Lucía Morán‐González, Arron L. Burnage, Ainara Nova, David Balcells · 2025 · ACS Catalysis
17. Mechanism Deduction from Noisy Chemical Reaction Networks
    Jonny Proppe, Markus Reiher · 2018 · Journal of Chemical Theory and Computation
18. Permutationally Invariant, Reproducing Kernel-Based Potential Energy Surfaces for Polyatomic Molecules: From Formaldehyde to Acetone
    Debasish Koner, Markus Meuwly · 2020 · Journal of Chemical Theory and Computation
19. Molecular Mechanics-Driven Graph Neural Network with Multiplex Graph for Molecular Structures
    Shuo Zhang, Liu, Yang, Lei Xie · 2020 · arXiv (Cornell University)
20. E(n) Equivariant Normalizing Flows
    Víctor García Satorras, Emiel Hoogeboom, Fabian B. Fuchs, Ingmar Posner, Max Welling · 2021 · arXiv (Cornell University)
21. Efficient implementation of atom-density representations
    Félix Musil, Max Veit, Alexander Goscinski, Guillaume Fraux, Michael J. Willatt, Markus Stricker, Till Junge, Michele Ceriotti · 2021 · The Journal of Chemical Physics
22. Ranking the Drop-Weight Impact Sensitivity of Common Explosives Using Arrhenius Chemical Rates Computed from Quantum Molecular Dynamics Simulations
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    Jan Weinreich, Nicholas J. Browning, O. Anatole von Lilienfeld · 2021 · The Journal of Chemical Physics
24. A Deep Generative Model for Fragment-Based Molecule Generation
    Marco Podda, Davide Bacciu, Alessio Micheli · 2020 · arXiv (Cornell University)
25. Inverse‐QSPR for de novo Design: A Review
    Philippe Gantzer, Benoît Creton, Carlos Nieto‐Draghi · 2019 · Molecular Informatics
26. Deep molecular dreaming: inverse machine learning for de-novo molecular design and interpretability with surjective representations
    Cynthia Shen, Mario Krenn, Sagi Eppel, Alán Aspuru‐Guzik · 2021 · Machine Learning Science and Technology
27. Transfer learning with graph neural networks for optoelectronic properties of conjugated oligomers
    Chee‐Kong Lee, Chengqiang Lu, Yue Yu, Qiming Sun, Chang‐Yu Hsieh, Shengyu Zhang, Qi Liu, Liang Shi · 2021 · The Journal of Chemical Physics
28. Machine learning accelerates quantum mechanics predictions of molecular crystals
    Yanqiang Han, Imran Ali, Zhilong Wang, Junfei Cai, Sicheng Wu, Jiequn Tang, Lin Zhang, Jiahao Ren, Rui Xiao, Qianqian Lu et al. · 2021 · Physics Reports
29. Generating valid Euclidean distance matrices
    Hoffmann, Moritz, Frank Noé · 2019 · arXiv (Cornell University)
30. Artificial Intelligence‐Based Material Discovery for Clean Energy Future
    Reza Maleki, Mohsen Asadnia, Amir Razmjou · 2022 · Advanced Intelligent Systems
31. An end-to-end attention-based approach for learning on graphs
    David Buterez, Jon Paul Janet, Dino Oglić, Píetro Lió · 2025 · Nature Communications
32. Nutmeg and SPICE: Models and Data for Biomolecular Machine Learning
    Peter K. Eastman, Benjamin P. Pritchard, John D. Chodera, Thomas E. Markland · 2024 · Journal of Chemical Theory and Computation
33. Generative Deep Learning for de Novo Drug Design─A Chemical Space Odyssey
    Rıza Özçelik, Helena Brinkmann, Emanuele Criscuolo, Francesca Grisoni · 2025 · Journal of Chemical Information and Modeling
34. Machine Learning for Accelerating Energy Materials Discovery: Bridging Quantum Accuracy with Computational Efficiency
    Kwang S. Kim · 2025 · Advanced Energy Materials
35. Mapping mesoscopic phase evolution during E-beam induced transformations via deep learning of atomically resolved images
    Rama Krishnan Vasudevan, Nouamane Laanait, Erik M. Ferragut, Kai Wang, David B. Geohegan, Kai Xiao, Maxim A Ziatdinov, Stephen Jesse, Ondrej Dyck, Sergei V. Kalinin · 2018 · npj Computational Materials
36. Predicting Kováts Retention Indices Using Graph Neural Networks
    Chen Qu, Barry I. Schneider, Anthony J. Kearsley, Walid Keyrouz, Thomas C. Allison · 2021 · Journal of Chromatography A
37. Transfer Learning from Simulation to Experimental Data: NMR Chemical Shift Predictions
    Herim Han, Sunghwan Choi · 2021 · The Journal of Physical Chemistry Letters
38. Deep Neural Network Computes Electron Densities and Energies of a Large Set of Organic Molecules Faster than Density Functional Theory (DFT)
    Anton V. Sinitskiy, Vijay S. Pande · 2018 · arXiv (Cornell University)
39. Exploring chemical and conformational spaces by batch mode deep active learning
    Viktor Zaverkin, David Holzmüller, Ingo Steinwart, Johannes Kästner · 2022 · Digital Discovery
40. Electronic, redox, and optical property prediction of organic π-conjugated molecules through a hierarchy of machine learning approaches
    Vinayak Bhat, Parker Sornberger, Balaji Sesha Sarath Pokuri, Rebekah Duke, Baskar Ganapathysubramanian, Chad M. Risko · 2022 · Chemical Science
41. Machine Learning for Fast, Quantum Mechanics-Based Approximation of Drug Lipophilicity
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42. De novo drug design through artificial intelligence: an introduction
    Davide Crucitti, Carlos Pérez Míguez, José Ángel Díaz Arias, Diego Beltrán Fernandez Prada, Adrián Mosquera Orgueira · 2024 · Frontiers in Hematology
43. Overcoming the barrier of orbital-free density functional theory for molecular systems using deep learning
    He Zhang, Siyuan Liu, Jiacheng You, Chang Liu, Shuxin Zheng, Ziheng Lu, Tong Wang, Nanning Zheng, Bin Shao · 2024 · Nature Computational Science
44. Making machine learning a useful tool in the accelerated discovery of transition metal complexes
    Heather J. Kulik · 2019 · Wiley Interdisciplinary Reviews Computational Molecular Science
45. Data-Driven Approaches Can Overcome the Cost–Accuracy Trade-Off in Multireference Diagnostics
    Chenru Duan, Fang Liu, Aditya Nandy, Heather J. Kulik · 2020 · Journal of Chemical Theory and Computation
46. Chemiscope: interactive structure-property explorer for materials and molecules
    Guillaume Fraux, Rose K. Cersonsky, Michele Ceriotti · 2020 · The Journal of Open Source Software
47. Molecular machine learning with conformer ensembles
    Simon Axelrod, Rafael Gómez‐Bombarelli · 2023 · Machine Learning Science and Technology
48. Reinforcement Learning for Molecular Design Guided by Quantum Mechanics
    Gregor N. C. Simm, Robert Pinsler, José Miguel Hernández-Lobato · 2020 · arXiv (Cornell University)
49. LieTransformer: Equivariant self-attention for Lie Groups
    Michael Hutchinson, Charline Le Lan, Sheheryar Zaidi, Emilien Dupont, Yee Whye Teh, Hyunjik Kim · 2020 · arXiv (Cornell University)
50. Nested Graph Neural Networks
    Muhan Zhang, Pan Li · 2021 · arXiv (Cornell University)
51. QDπ: A Quantum Deep Potential Interaction Model for Drug Discovery
    Jinzhe Zeng, Yujun Tao, Timothy J. Giese, Darrin M. York · 2023 · Journal of Chemical Theory and Computation
52. Deep generative models for 3D molecular structure
    Benoît Baillif, Jason C. Cole, Patrick McCabe, Andreas Bender · 2023 · Current Opinion in Structural Biology
53. High-throughput quantum theory of atoms in molecules (QTAIM) for geometric deep learning of molecular and reaction properties
    Santiago Vargas, Winston Gee, Anastassia N. Alexandrova · 2024 · Digital Discovery
54. GRAPHAF: A FLOW-BASED AUTOREGRESSIVE MODEL FOR MOLECULAR GRAPH GENERATION
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55. Relative molecule self-attention transformer
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56. Recent progress in the data-driven discovery of novel photovoltaic materials
    Lu Tian, Minjie Li, Wencong Lu, Tong‐Yi Zhang · 2022 · Journal of Materials Informatics
57. Science‐Driven Atomistic Machine Learning
    Johannes T. Margraf · 2023 · Angewandte Chemie International Edition
58. Systematic Enumeration of Elementary Reaction Steps in Surface Catalysis
    Johannes T. Margraf, Karsten Reuter · 2019 · ACS Omega
59. Molecular generation targeting desired electronic properties via deep generative models
    Qi Yuan, Alejandro Santana‐Bonilla, Martijn A. Zwijnenburg, Kim E. Jelfs · 2020 · Nanoscale
60. GF-VAE
    Changsheng Ma, Xiangliang Zhang · 2021 · ACM International Conference on Information and Knowledge Management (CIKM)
61. DenseGNN: universal and scalable deeper graph neural networks for high-performance property prediction in crystals and molecules
    Hongwei Du, Jiamin Wang, Jian Hui, Lanting T. Zhang, Hong Wang · 2024 · npj Computational Materials
62. Good Practices in Database Generation for Benchmarking Density Functional Theory
    Amir Karton, Marcelo Tavares de Oliveira · 2025 · Wiley Interdisciplinary Reviews Computational Molecular Science
63. Reactive molecular dynamics: From small molecules to proteins
    Markus Meuwly · 2018 · Wiley Interdisciplinary Reviews Computational Molecular Science
64. Data-centric science for materials innovation
    Isao Tanaka, Krishna Rajan, Christopher Wolverton · 2018 · MRS Bulletin
65. MolGrow: A Graph Normalizing Flow for Hierarchical Molecular Generation
    Maksim Kuznetsov, Daniil A. Polykovskiy · 2021 · AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI))
66. Enumeration of <i>de novo</i> inorganic complexes for chemical discovery and machine learning
    Gugler, Stefan, Janet, Jon Paul, Kulik, Heather J. · 2020 · Molecular Systems Design &amp; Engineering
67. Conditional Diffusion Based on Discrete Graph Structures for Molecular Graph Generation
    Han Huang, Leilei Sun, Bowen Du, Weifeng Lv · 2023 · Proceedings of the AAAI Conference on Artificial Intelligence
68. Higher-order equivariant neural networks for charge density prediction in materials
    Thomas Edward Koker, Keegan Quigley, Eric Taw, Kevin Tibbetts, Lin Li · 2024 · npj Computational Materials
69. Optimal transport for generating transition states in chemical reactions
    Chenru Duan, Guan-Horng Liu, Yuanqi Du, Tianrong Chen, Qiyuan Zhao, Haojun Jia, Carla Pedro Gomes, Evangelos A. Theodorou, Heather J. Kulik · 2025 · Nature Machine Intelligence
70. Geometrical features can predict electronic properties of graphene nanoflakes
    Michael Fernández, Hongqing Shi, Amanda Susan Barnard · 2016 · Carbon
71. How predictive could alchemical derivatives be?
    Macarena Muñoz, Carlos Cárdenas · 2017 · Physical Chemistry Chemical Physics
72. Machine learning the computational cost of quantum chemistry
    Stefan Heinen, Max Schwilk, Guido Falk von Rudorff, O. Anatole von Lilienfeld · 2020 · Machine Learning Science and Technology
73. Structure-property maps with Kernel principal covariates regression
    Benjamin A. Helfrecht, Rose K. Cersonsky, Guillaume Fraux, Michele Ceriotti · 2020 · Machine Learning Science and Technology
74. Graph-based generative models for de Novo drug design
    Xia Xiao-lin, Jianxing Hu, Yanxing Wang, Liangren Zhang, Zhenming Liu · 2019 · Drug Discovery Today Technologies
75. The effect of noise on the predictive limit of QSAR models
    Scott Kolmar, Chris Grulke · 2021 · Journal of Cheminformatics
76. Unified theory of atom-centered representations and message-passing machine-learning schemes
    Jigyasa Nigam, Sergey N. Pozdnyakov, Guillaume Fraux, Michele Ceriotti · 2022 · The Journal of Chemical Physics
77. Mapping and classifying molecules from a high-throughput structural database
    De, Sandip, Musil, Felix, Ingram, Teresa, Baldauf, Carsten, Ceriotti, Michele · 2017 · Journal of Cheminformatics
78. From Perception to Prediction and Interpretation: Enlightening the Gray Zone of Molecular Bricks of Life With the Help of Machine Learning and Quantum Chemistry
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79. Effective Molecular Descriptors for Chemical Accuracy at DFT Cost: Fragmentation, Error-Cancellation, and Machine Learning
    Eric M. Collins, Krishnan Raghavachari · 2020 · Journal of Chemical Theory and Computation
80. Deep Learning Total Energies and Orbital Energies of Large Organic Molecules Using Hybridization of Molecular Fingerprints
    Obaidur Rahaman, Alessio Gagliardi · 2020 · Journal of Chemical Information and Modeling
81. Kernel charge equilibration: efficient and accurate prediction of molecular dipole moments with a machine-learning enhanced electron density model
    Carsten G. Staacke, Simon Wengert, Christian Künkel, Gábor Cśanyi, Karsten Reuter, Johannes T. Margraf · 2022 · Machine Learning Science and Technology
82. Robust and scalable uncertainty estimation with conformal prediction for machine-learned interatomic potentials
    Yuge Hu, Joseph Musielewicz, Zachary W. Ulissi, Andrew James Medford · 2022 · Machine Learning Science and Technology
83. Current Trends and Challenges in Drug-Likeness Prediction: Are They Generalizable and Interpretable?
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84. Quantum-Chemical Insights from Interpretable Atomistic Neural Networks
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    Angiras Menon, Nenad B. Krdzavac, Markus Kraft · 2019 · Current Opinion in Chemical Engineering
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88. ForceNet: A Graph Neural Network for Large-Scale Quantum Calculations
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    Mathias Schreiner, Arghya Bhowmik, Tejs Vegge, Peter Bjørn Jørgensen, Ole Winther · 2022 · Machine Learning Science and Technology
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    Matthias Kellner, Michele Ceriotti · 2024 · Machine Learning Science and Technology
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    Bidisha Samanta, Abir De, Gourhari Jana, Pratim Kumar Chattaraj, Niloy Ganguly, Manuel Gomez-Rodriguez · 2019 · Proceedings of the AAAI Conference on Artificial Intelligence
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    Yuanqing Wang, Kenichiro Takaba, Michael S. Chen, Marcus Wieder, Yuzhi Xu, Tong Zhu, John Z. H. Zhang, Arnav Nagle, Kuang Yu, Xinyan Wang et al. · 2025 · Applied Physics Reviews
94. An Extended Group Additivity Method for Polycyclic Thermochemistry Estimation
    Kehang Han, Adeel Jamal, Colin A. Grambow, Zachary J. Buras, William H. Green · 2018 · International Journal of Chemical Kinetics
95. Evolutionary Approach to Constructing a Deep Feedforward Neural Network for Prediction of Electronic Coupling Elements in Molecular Materials
    Onur Çaylak, Anil Yaman, Björn Baumeier · 2019 · Journal of Chemical Theory and Computation
96. Predicting Molecular Energy Using Force-Field Optimized Geometries and Atomic Vector Representations Learned from an Improved Deep Tensor Neural Network
    Jianing Lu, Cheng Wang, Yingkai Zhang · 2019 · Journal of Chemical Theory and Computation
97. Dataset for quantum-mechanical exploration of conformers and solvent effects in large drug-like molecules
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98. Fast Graph Generation via Spectral Diffusion
    Tianze Luo, Zhanfeng Mo, Sinno Jialin Pan · 2023 · IEEE Transactions on Pattern Analysis and Machine Intelligence
99. Towards physics-informed explainable machine learning and causal models for materials research
    Ayana Ghosh · 2023 · Computational Materials Science
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     Xuefeng Bai, Ya-Bo Xie, Xin Zhang, Honggui Han, Jian‐Rong Li · 2024 · Journal of Chemical Information and Modeling