Quantum-chemical insights from deep tensor neural networks

Cited by (1,001)

Items 901–1,000 of 1,001

1. Incidence Networks for Geometric Deep Learning
   Marjan Albooyeh, Bertolini, Daniele, Siamak Ravanbakhsh · 2019 · arXiv (Cornell University)
2. Molecule Property Prediction and Classification with Graph Hypernetworks
   Eliya Nachmani, Lior Wolf · 2020 · arXiv (Cornell University)
3. Learning Potentials of Quantum Systems using Deep Neural Networks
   Arijit Sehanobish, Héctor H. Corzo, Onur Kara, David van Dijk · 2020 · arXiv (Cornell University)
4. Particle Swarm Based Hyper-Parameter Optimization for Machine Learned Interatomic Potentials
   Suresh Kondati Natarajan, Caro, Miguel A. · 2020 · arXiv (Cornell University)
5. Artificial intelligence in the development of structural and functional materials: A transformative frontier and complementary enabler of the rational alloy design framework
   Desmond Edem Primus Klenam · 2025 · Next research.
6. Molecular Conditional Generation and Property Analysis of Non-Fullerene Acceptors with Deep Learning
   Shi-Ping Peng, Xinyu Yang, Yi Zhao · 2021 · International Journal of Molecular Sciences
7. A Hessian-based assessment of atomic forces for training machine learning interatomic potentials
   Marius Herbold, Jörg Behler · 2022 · The Journal of Chemical Physics
8. Machine Learning Applications in Chemical Kinetics and Thermochemistry
   Lung-Yi Chen, Yi‐Pei Li · 2023 · Challenges and advances in computational chemistry and physics
9. Development of machine learning force field for thermal conductivity analysis in MoAlB: Insights into anisotropic heat transfer mechanisms
   Hanchao Zhang, Guoliang Ren, Peng Jia, Xiaofeng Zhao, Na Ni · 2024 · Ceramics International
10. Stability and transferability of machine learning force fields for molecular dynamics applications
    Salatan Duangdangchote, Dwight S. Seferos, Oleksandr Voznyy · 2024 · Digital Discovery
11. Exhaustive search for novel multicomponent alloys with brute force and machine learning
    Viktoriia Zinkovich, Vadim Sotskov, Alexander V. Shapeev, Evgeny V. Podryabinkin · 2024 · npj Computational Materials
12. MLTB: Enhancing Transferability and Extensibility of Density Functional Tight-Binding Theory with Many-body Interaction Corrections
    Daniel J. Burrill, Chang Liu, Michael G. Taylor, Marc J. Cawkwell, Danny Pérez, Enrique R. Batista, Nicholas Lubbers, Ping Xiong Yang · 2025 · Journal of Chemical Theory and Computation
13. Beyond Numerical Hessians: Higher-Order Derivatives for Machine Learning Interatomic Potentials via Automatic Differentiation
    Nils Gönnheimer, Karsten Reuter, Johannes T. Margraf · 2025 · Journal of Chemical Theory and Computation
14. Molecule Graph Networks with Many-Body Equivariant Interactions
    Zetian Mao, Chuan-Shen Hu, Jiawen Li, Chen Liang, Diptesh Das, Masato Sumita, Kelin Xia, Koji Tsuda · 2025 · Journal of Chemical Theory and Computation
15. Machine Learning-Enhanced Molecular Dynamics: Current State, Challenges and Perspectives
    Christos Stavrogiannis, Filippos D. Sofos, Theodoros E. Karakasidis · 2026 · Archives of Computational Methods in Engineering
16. Neural Networks Accelerate the Ab Initio Prediction of Solid–Solid Phase Transitions at High Pressures
    Yanqiang Han, Zhilong Wang, Jinjin Li · 2020 · The Journal of Physical Chemistry Letters
17. Predicting scalar coupling constants by graph angle-attention neural network
    Jia Fang Fang, Linyuan Hu, Jianfeng Dong, Haowei Li, Hui Wang, Huafen Zhao, Yao Zhang, Min Liu · 2021 · Scientific Reports
18. Application of Machine Learning in Developing Quantitative Structure–Property Relationship for Electronic Properties of Polyaromatic Compounds
    Tuan Hoang Nguyen, Lam H. Nguyen, Thanh N. Truong · 2022 · ACS Omega
19. Machine Learning Identification of Active Sites in Graphite-Conjugated Catalysts
    Kunal M. Lodaya, Nathan D. Ricke, Kelly Chen, Troy Van Voorhis · 2023 · The Journal of Physical Chemistry C
20. Analysis of Multi-Class Weather Classification using deep learning models and machine learning classifiers
    Silky Goel, Snigdha Markanday, Shlok Mohanty · 2022 · OITS International Conference on Information Technology (OCIT)
21. Machine learning methods in photochemistry and photophysics
    Jingbai Li, Morgane Vacher, Pavlo O. Dral, Steven A. Lopez · 2023 · Elsevier eBooks
22. Shadow Molecular Dynamics and Atomic Cluster Expansions for Flexible Charge Models
    James Goff, Yu Zhang, Christian F. A. Negre, Andrew Rohskopf, Anders M. N. Niklasson · 2023 · Journal of Chemical Theory and Computation
23. Automatic identification of chemical moieties
    Jonas Lederer, Michael Gastegger, Kristof T. Schütt, Michael Kampffmeyer, Klaus‐Robert Müller, Oliver T. Unke · 2023 · Physical Chemistry Chemical Physics
24. Enhanced Scalable Graph Neural Network via Knowledge Distillation
    Chengyuan Mai, Yaomin Chang, Chuan Chen, Zibin Zheng · 2023 · IEEE Transactions on Neural Networks and Learning Systems
25. Capturing Weak Interactions in Surface Adsorbate Systems at Coupled Cluster Accuracy: A Graph-Theoretic Molecular Fragmentation Approach Improved through Machine Learning
    Timothy C. Ricard, Xiao Zhu, Srinivasan S. Iyengar · 2023 · Journal of Chemical Theory and Computation
26. High-speed and low-power molecular dynamics processing unit (MDPU) with ab initio accuracy
    Pinghui Mo, Yujia Zhang, Zhuoying Zhao, Hanhan Sun, Junhua Li, Dawei Guan, Xi Ding, Xin Zhang, Bo Chen, Mengchao Shi et al. · 2024 · npj Computational Materials
27. Machine learning approach to predict L -edge x-ray absorption spectra of light transition metal ion compounds
    Johann Lüder · 2025 · Physical review. B./Physical review. B
28. Energetic potential of hexogen constructed by machine learning
    Pengju Wang, Junyu Fan, Yan Su, Jijun Zhao · 2020 · Acta Physica Sinica
29. Selecting Correct Methods to Extract Fuzzy Rules from Artificial Neural Network
    Xiao Tan, Yuan Hua Zhou, Zuohua Ding, Yang Liu · 2021 · Mathematics
30. Software and Application Patterns for Explanation Methods
    Maximilian Alber · 2019 · Lecture notes in computer science
31. Graphenic substrates as modifiers of the emission and vibrational responses of interacting molecules: The case of BODIPY dyes
    Emmanuel de la O-Cuevas, Violeta Álvarez-Venicio, Isidro Badillo‐Ramírez, Selene R. Islas, María del Pilar Carreón, José Manuel Saniger · 2020 · Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy
32. On application of deep learning to simplified quantum-classical dynamics in electronically excited states
    Evgeny Posenitskiy, Fernand Spiegelman, Didier Lemoine · 2021 · Machine Learning Science and Technology
33. A Perspective on Deep Learning for Molecular Modeling and Simulations
    Jun Zhang, Yao-Kun Lei, Zhen Zhang, Junhan Chang, Maodong Li, Xu Han, Lijiang Yang, Yi Yang, Yi Qin Gao · 2020 · The Journal of Physical Chemistry B
34. Deeply learning molecular structure-property relationships using graph attention neural network.
    Seongok Ryu, Jaechang Lim, Woo Youn Kim · 2018 · arXiv (Cornell University)
35. Analyzing ImageNet with Spectral Relevance Analysis: Towards ImageNet un-Hans'ed.
    Christopher J. Anders, Talmaj Marinč, David Neumann, Wojciech Samek, Klaus‐Robert Müller, Sebastian Lapuschkin · 2019 · arXiv (Cornell University)
36. Improving materials property predictions for graph neural networks with minimal feature engineering *
    Guojing Cong, Victor Fung · 2023 · Machine Learning Science and Technology
37. Graph-EAM: An Interpretable and Efficient Graph Neural Network Potential Framework
    Jun Yang, Zhitao Chen, Hong Jun Sun, Amit Samanta · 2023 · Journal of Chemical Theory and Computation
38. Elucidating the local structure and properties of molten Na2CO3-K2CO3 salts using Machine Learning-Driven molecular dynamics
    Taixi Feng, Bo Yang, Jia Yue Zhao, Guimin Lu · 2024 · Chemical Engineering Science
39. Transforming MOF Modeling with Machine-Learned Potentials: Progress and Perspectives
    Ömer Tayfuroğlu, Seda Keskın · 2026 · Journal of Chemical Information and Modeling
40. Electronic Configurations of 3d Transition-Metal Compounds Using Local Structure and Neural Networks
    Wenhao Zhang, David Berthebaud, Jean‐François Halet, Takao Mori · 2022 · The Journal of Physical Chemistry A
41. The performance of ANI‐ML potentials for ligand‐n( H2O ) interaction energies and estimation of hydration free energies from end‐point MD simulations
    Mütesir Temel, Ömer Tayfuroğlu, Abdulkadır Kocak · 2022 · Journal of Computational Chemistry
42. Machine Learning Interatomic Potentials: Keys to First-Principles Multiscale Modeling
    Bohayra Mortazavi · 2023 · Computational methods in engineering & the sciences
43. Accuracy Assessment of Atomistic Neural Network Potentials: The Impact of Cutoff Radius and Message Passing
    Junfan Xia, Yaolong Zhang, Bin Jiang · 2023 · The Journal of Physical Chemistry A
44. Force Field Analysis Software and Tools (FFAST): Assessing Machine Learning Force Fields under the Microscope
    Grégory Fonseca, Igor Poltavsky, Alexandre Tkatchenko · 2023 · Journal of Chemical Theory and Computation
45. Intermolecular Non-Bonded Interactions from Machine Learning Datasets
    Junxi Chen, Sheng Der Chao · 2023 · Molecules
46. Transfer learning graph representations of molecules for pKa, 13 C-NMR, and solubility
    Amer Marwan El-Samman, S. De Castro, Brooke Morton, Stijn De Baerdemacker · 2024 · Canadian Journal of Chemistry
47. Chemical environment adaptive learning for optical band gap prediction of doped graphitic carbon nitride nanosheets
    Chen Chen, Enze Xu, Defu Yang, Chenggang Yan, Tao Wei, Hanning Chen, Yong-Kai Wei, Minghan Chen · 2024 · Neural Computing and Applications
48. Methods and Metrics for Explaining Artificial Intelligence Models: A Review
    Puja Banerjee, Rajesh P. Barnwal · 2022 · Intelligent systems reference library
49. A machine learning approach for predicting the fluorination strength of electrophilic fluorinating reagents
    Vaneet Saini · 2022 · Physical Chemistry Chemical Physics
50. Identifying Protein–Ligand Interactions via a Novel Distance Self-Feedback Biomolecular Interaction Network
    Yurong Zou, Ruihan Wang, Meng Du, Xin Wang, Dingguo Xu · 2023 · The Journal of Physical Chemistry B
51. Machine-learning accelerated annealing with fitting-search style for multicomponent alloy structure predictions
    Chuan-Nan Li, Han-Pu Liang, Yifeng Duan, Zijing Lin · 2023 · Physical Review Materials
52. Developing and Assessing a Human-Understandable Metric for Evaluating Local Interpretable Model-Agnostic Explanations
    2023 · International journal of intelligent engineering and systems
53. Efficient generation of stable linear machine-learning force fields with uncertainty-aware active learning
    Valerio Briganti, Alessandro Lunghi · 2023 · Machine Learning Science and Technology
54. Discovery of structure–property relations for molecules via hypothesis-driven active learning over the chemical space
    Ayana Ghosh, Sergei V. Kalinin, Maxim A Ziatdinov · 2023 · APL Machine Learning
55. Trajectory-based machine learning method and its application to molecular dynamics
    Ruocheng Han, Sandra Luber · 2020 · Molecular Physics
56. Leveraging Transfer Learning and Chemical Principles toward Interpretable Materials Properties
    Liyuan Huang, Chen Ling · 2021 · Journal of Chemical Information and Modeling
57. 3D-Transformer: Molecular Representation with Transformer in 3D Space
    Fang Wu, Qiang Zhang, Dragomir Radev, Jiyu Cui, Wen Zhang, Huabin Xing, Ningyu Zhang, Huajun Chen · 2021 · arXiv (Cornell University)
58. Pair-EGRET: enhancing the prediction of protein–protein interaction sites through graph attention networks and protein language models
    Ramisa Alam, Sazan Mahbub, Md. Shamsuzzoha Bayzid · 2024 · Bioinformatics
59. Data‐Efficient Active Learning for Thermodynamic Integration: Acidity Constants of BiVO4 in Water
    Philipp Schienbein, Jochen Blumberger · 2024 · ChemPhysChem
60. A sparse Bayesian Committee Machine potential for oxygen-containing organic compounds
    Soohaeng Yoo Willow, Seungwon Kim, David ChangMo Yang, Miran Ha, Amir Hajibabaei, Jung Woon Yang, Kwang S. Kim, Chang Woo Myung · 2025 · Chemical Physics Reviews
61. Mechanical properties of 3D-Printed aluminum, nickel, and titanium using a hybrid machine learning and computational mechanics approach
    Sara Samine, Mohamed Karouchi, Maria Zemzami, Nabil Hmina, Soufiane Belhouideg · 2025 · Frontiers in Materials
62. Development of Coarse-Grained Lipid Force Fields Based on a Graph Neural Network
    Zhenyu Liao, Ting Si, Tairan Wang, Ji‐Jung Kai, Christophe J. Chipot, Jun Fan · 2025 · Journal of Chemical Theory and Computation
63. Zero-Shot Autonomous Microscopy for Scalable and Intelligent Characterization of 2D Materials
    H. J. Yang, Ruoyan Avery Yin, Chi Jiang, Yuepeng Hu, Xiaokai Zhu, Xingjian Hu, S. Ananda Kumar, Samantha K. Holmes, Xiao Wang, Xiaohua Zhai et al. · 2025 · ACS Nano
64. High-order pair-reduced neural network architecture for global potential energy surface exploration across the periodic table
    Zheng-Xin Yang, Xin-Tian Xie, Zhen-Xiong Wang, Dong-Xiao Chen, Zi-Xing Guo, Jia-Jie Du, Qi-Ming Liang, Qian-Yu Liu, Cheng Shang, Zhi-Pan Liu · 2025 · Science China Chemistry
65. Machine learning-enhanced exploration of 2D MXene van der Waals heterostructures for energy storage
    A. S. Shruthi, Abhilash Ravikumar · 2026 · Discover Electrochemistry.
66. Machine Learning Driven Advances in Molecular Dynamics of Bulk and Interfacial Aqueous Systems
    Ruiyu Wang, Vanessa J. Meraz, Pratyush Tiwary · 2026 · Chemical Reviews
67. Artificial Intelligence for Materials Science: Transforming Research Paradigms
    Yue Li, Shurui Wang, Jianping Wang, Liu Q, Jin Zhang · 2026 · Chemical Reviews
68. Molecular dipole moment learning via rotationally equivariant derivative kernels in molecular-orbital-based machine learning
    Jiace Sun, Lixue Cheng, Thomas F. Miller · 2022 · The Journal of Chemical Physics
69. Neural network potentials
    Jinzhe Zeng, Liqun Cao, Tong Zhu · 2022 · Elsevier eBooks
70. Hyper‐Mol: Molecular Representation Learning via Fingerprint‐Based Hypergraph
    Shicheng Cui, Qianmu Li, Deqiang Li, Zhichao Lian, Jun Hou · 2023 · Computational Intelligence and Neuroscience
71. Neural network to predict 23Na NMR spectra of Nan clusters
    Masanori Kaneko, Ayane Suzaki, Azusa Muraoka, Kazuma Gotoh B, Koichi Yamashita · 2023 · Journal of Materials Informatics
72. Training machine learning potentials for reactive systems: A Colab tutorial on basic models
    Xiaoliang Pan, Ryan Snyder, Jianing Wang, Chance Lander, Carly Wickizer, Richard S. Van, Andrew D. Chesney, Yuanfei Xue, Yuezhi Mao, Ye Mei et al. · 2023 · Journal of Computational Chemistry
73. Molecular relaxation by reverse diffusion with time step prediction
    Khaled Kahouli, Stefaan S. P. Hessmann, Klaus‐Robert Müller, Shinichi Nakajima, Stefan Gugler, Niklas W. A. Gebauer · 2024 · Machine Learning Science and Technology
74. Enhancing Molecular Energy Predictions with Physically Constrained Modifications to the Neural Network Potential
    Weiqiang Fu, Yujie Mo, Xiao Yi, Chang Liu, Feng Zhou, Yan Wang, Jielong Zhou, Yingsheng Zhang · 2024 · Journal of Chemical Theory and Computation
75. Performance Comparisons of NequIP and DPMD Machine Learning Interatomic Potentials for Tobermorites
    Keming Zhu · 2024 · Computational Materials Science
76. Neural Network-Based Interatomic Potential for the Study of Thermal and Mechanical Properties of Siliceous Zeolites
    Luca Brugnoli, Maxime Ducamp, François‐Xavier Coudert · 2024 · The Journal of Physical Chemistry C
77. Role of Water in the Reaction Mechanism and endo/exo Selectivity of 1,3‐Dipolar Cycloadditions Elucidated by Quantum Chemistry and Machine Learning
    Xin Yang, Jun Zou, Yifei Wang, Ying Wen Xue, Shengyong Yang · 2019 · Chemistry - A European Journal
78. A Generic Edge-Empowered Graph Convolutional Network via Node-Edge Mutual Enhancement
    Pengyang Wang, Jiaping Gui, Zhengzhang Chen, Junghwan Rhee, Haifeng Chen, Yanjie Fu · 2020 · International World Wide Web Conference (WWW)
79. A perspective on musical representations of folded protein nanostructures
    Sebastian L. Franjou, Mario Milazzo, Chi‐Hua Yu, Markus J. Buehler · 2020 · Nano Futures
80. Machine-learning-based many-body energy analysis of argon clusters: Fit for size?
    Mozhdeh Shiranirad, Christian J. Burnham, Niall J. English · 2021 · Chemical Physics
81. Deep Learning Coordinate-Free Quantum Chemistry
    Matthew K. Matlock, Max Hoffman, Na Le Dang, Dakota L. Folmsbee, Luke A. Langkamp, Geoffrey Hutchison, Neeraj Kumar, Kathryn Sarullo, S. Joshua Swamidass · 2021 · The Journal of Physical Chemistry A
82. Low-cost prediction of molecular and transition state partition functions via machine learning
    Evan Komp, Stéphanie Valleau · 2022 · Chemical Science
83. Investigation of the structure-odor relationship using a Transformer model
    Xiaofan Zheng, Yoichi Tomiura, Kenshi Hayashi · 2022 · Journal of Cheminformatics
84. Machine Learning Frontier Orbital Energies of Nanodiamonds
    Thorren Kirschbaum, Börries von Seggern, Joachim Dzubiella, Annika Bande, Frank Noé · 2023 · Journal of Chemical Theory and Computation
85. Uncovering the relationship between point-of-interests-related human mobility and socioeconomic status
    Dong Li, Jiming Liu · 2019 · Telematics and Informatics
86. Gradient domain machine learning with composite kernels: improving the accuracy of PES and force fields for large molecules
    Kasra Asnaashari, Roman V. Krems · 2021 · Machine Learning Science and Technology
87. QML Based Community Detection in the realm of Social Network Analysis
    Sana Akbar, Sri Khetwat Saritha · 2020 · International Conference on Computing, Communication and Networking Technologies (ICCCNT)
88. Machine learning in computational chemistry
    Lin Fang Shen, Luyang Jia, Diandong Tang, Zhan‐Yun Zhang, Ding Peng · 2022 · Scientia Sinica Chimica
89. Demand Forecasting from Spatiotemporal Data with Graph Networks and Temporal-Guided Embedding
    Doyup Lee, Suehun Jung, Yeongjae Cheon, Dongil Kim, Seungil You · 2019 · arXiv (Cornell University)
90. The ANI-1ccx and ANI-1x Data Sets, Coupled-Cluster and Density Functional Theory Properties for Molecules
    Justin S. Smith, R.I. Zubatyuk, Benjamin T. Nebgen, Nicholas Lubbers, Kipton M. Barros, Adrián E. Roitberg, Olexandr Isayev, Sergei Tretiak · 2019 · ChemRxiv
91. ANI neural network potentials for small molecule p K a prediction
    Ross James Urquhart, Alexander van Teijlingen, Tell Tuttle · 2024 · Physical Chemistry Chemical Physics
92. Multikernel similarity‐based clustering of amorphous systems and machine‐learned interatomic potentials by active learning
    Firas Shuaib, Guido Ori, Philippe Thomas, Olivier Masson, Assil Bouzid · 2024 · Journal of the American Ceramic Society
93. Stable molecular dynamics simulations of halide perovskites from a temperature-ensemble gradient-domain machine learning approach
    Oz Yosef Mendelsohn, Michal Hartstein, Stefan Chmiela, Alexandre Tkatchenko, Leeor Kronik · 2025 · Chemical Physics Letters
94. Spillover Dynamics in Heterogeneous Catalysis on Singe‐Atom Alloys: A Theoretical Perspective
    Sutao Lin, Rui Xiong, Jun Chen, Sen Lin · 2025 · Wiley Interdisciplinary Reviews Computational Molecular Science
95. Breaking the Barriers of Molecular Dynamics With Deep‐Learning: Opportunities, Pitfalls, and How to Navigate Them
    Klara Bonneau, Aldo S. Pasos-Trejo, Michael Plainer, Luca Sagresti, Jacopo Venturin, Iryna Zaporozhets, Alessandro Caruso, Edoardo Rolando, Andrea Guljas, Leon Klein et al. · 2026 · Wiley Interdisciplinary Reviews Computational Molecular Science
96. Graph neural networks for molecular dynamics simulations
    Mohd Ahsan, Chinmai Pindi, Souvik Sinha, Amun C. Patel, Giulia Palermo · 2026 · Current Opinion in Structural Biology
97. Predicting Experimental Heats of Formation via Deep Learning with Limited Experimental Data
    GuanYa Yang, Wai Yuet Chiu, Jiang Wu, Yi Ren Zhou, ShuGuang Chen, WeiJun Zhou, Jiaqi Fan, GuanHua Chen · 2022 · The Journal of Physical Chemistry A
98. GNN-assisted phase space integration with application to atomistics
    Shashank Saxena, Jan-Hendrik Bastek, Miguel Spínola, Prateek Gupta, Dennis M. Kochmann · 2023 · Mechanics of Materials
99. Intelligent performance inference: A graph neural network approach to modeling maximum achievable throughput in optical networks
    Robin Matzner, Ruijie Luo, Georgios Zervas, Polina Bayvel · 2023 · APL Machine Learning
100. An interpretable graph representation learning model for accurate predictions of drugs aqueous solubility
     Qiufen Chen, Yuewei Zhang, Peng Gao, Jun Zhang · 2023 · Artificial Intelligence Chemistry