Quantum-chemical insights from deep tensor neural networks
Schütt, Kristof T., Arbabzadah, Farhad, Chmiela, Stefan, Müller, Klaus R., Tkatchenko, Alexandre·2017·Nature Communications
Cited by (1,001)
Items 401–500 of 1,001
- First-principles stability ranking of molecular crystal polymorphs with the DFT+MBD approachJohannes Hoja, Alexandre Tkatchenko · 2018 · Faraday Discussions
- Rotation Invariant Graph Neural Networks using Spin ConvolutionsMuhammed Shuaibi, Adeesh Kolluru, Abhishek Das, Aditya Grover, Anuroop Sriram, Zachary W. Ulissi, C. Lawrence Zitnick · 2021 · arXiv (Cornell University)
- Equivariant Graph Neural Networks for Toxicity PredictionCremer, Julian, Medrano Sandonas, Leonardo, Tkatchenko, Alexandre, Clevert, Djork-Arné, De Fabritiis, Gianni · 2023 · Chemical Research in Toxicology
- High-dimensional potential energy surfaces for molecular simulations: from empiricism to machine learningOliver T. Unke, Debasish Koner, Sarbani Patra, Silvan Käser, Markus Meuwly · 2020 · Machine Learning Science and Technology
- Capturing intensive and extensive DFT/TDDFT molecular properties with machine learningWiktor Pronobis, Kristof T. Schütt, Alexandre Tkatchenko, Klaus‐Robert Müller · 2018 · The European Physical Journal B
- Multi-body effects in a coarse-grained protein force fieldJiang Wang, Nicholas E. Charron, Brooke E. Husic, Simon Olsson, Frank Noé, Cecilia Clementi · 2021 · The Journal of Chemical Physics
- Deep learning metal complex properties with natural quantum graphsKneiding, Hannes, Lukin, Ruslan, Lang, Lucas, Reine, Simen, Pedersen, Thomas Bondo, De Bin, Riccardo, Balcells, David · 2023 · Digital Discovery
- Accelerating battery innovation: AI-powered molecular discoveryYuchen Gao, Xiang Chen, Yu‐Hang Yuan, Yao‐Peng Chen, Yi-Lin Niu, Nan Yao, Yan-Bin Gao, W.Z. Li, Qiang Zhang · 2025 · Chemical Society Reviews
- Attribute driven inverse materials design using deep learning Bayesian frameworkPiyush Tagade, Shashishekar P. Adiga, Shanthi Pandian, Min‐Sik Park, Krishnan S. Hariharan, Subramanya Mayya Kolake · 2019 · npj Computational Materials
- The message passing neural networks for chemical property prediction on SMILESJeonghee Jo, Bumju Kwak, Hyun-Soo Choi, Sungroh Yoon · 2020 · Methods
- Learning molecular energies using localized graph kernelsGrégoire Ferré, Terry Scot Haut, Kipton M. Barros · 2017 · The Journal of Chemical Physics
- Deep machine learning interatomic potential for liquid silicaI. A. Balyakin, Svetlana V. Rempel, Roman E. Ryltsev, Andrey A. Rempel · 2020 · Physical review. E
- Prediction of transition state structures of gas-phase chemical reactions via machine learningSunghwan Choi · 2023 · Nature Communications
- Deep machine learning potentials for multicomponent metallic melts: Development, predictability and compositional transferabilityRoman E. Ryltsev, N. M. Chtchelkatchev · 2021 · Journal of Molecular Liquids
- Potential Application of Machine-Learning-Based Quantum Chemical Methods in Environmental ChemistryDeming Xia, Jingwen Chen, Zhiqiang Fu, Tong Xu, Zhongyu Wang, Wenjia Liu, Hong‐Bin Xie, Willie J.G.M. Peijnenburg · 2022 · Environmental Science & Technology
- Accurate Binding Free Energy Method from End-State MD SimulationsEbru Akkus, Ömer Tayfuroğlu, Müslüm Yıldız, Abdulkadır Kocak · 2022 · Journal of Chemical Information and Modeling
- Deep Learning Model for Efficient Protein–Ligand Docking with Implicit Side-Chain FlexibilityMatthew R. Masters, Amr Hamed Mahmoud, Wei Yao, Markus A. Lill · 2023 · Journal of Chemical Information and Modeling
- A Generative Model for Molecular Distance GeometryGregor N. C. Simm, José Miguel Hernández-Lobato · 2019 · arXiv (Cornell University)
- Artificial neural network correction for density-functional tight-binding molecular dynamics simulationsJunmian Zhu, Van‐Quan Vuong, Bobby G. Sumpter, Stephan Irle · 2019 · MRS Communications
- HimGNN: a novel hierarchical molecular graph representation learning framework for property predictionHan Shen, Haitao Fu, Yuyang Wu, Ganglan Zhao, Zhenyu Song, Feng F. Huang, Zhongfei Mark Zhang, Shichao Liu, Wen Zhang · 2023 · Briefings in Bioinformatics
- Deep Learning for Optoelectronic Properties of Organic SemiconductorsChengqiang Lu, Qi Liu, Qiming Sun, Chang‐Yu Hsieh, Shengyu Zhang, Liang Shi, Chee‐Kong Lee · 2020 · The Journal of Physical Chemistry C
- Simulations of activities, solubilities, transport properties, and nucleation rates for aqueous electrolyte solutionsAthanassios Z. Panagiotopoulos · 2020 · The Journal of Chemical Physics
- Extensive deep neural networks for transferring small scale learning to large scale systemsKyle Mills, Kevin Ryczko, Iryna Luchak, Adam Domurad, Chris Beeler, Isaac Tamblyn · 2019 · Chemical Science
- Combined Deep Learning and Classical Potential Approach for Modeling Diffusion in UiO-66Siddarth Krishnaraja Achar, Jacob J. Wardzala, Leonardo Bernasconi, Linfeng Zhang, J. Karl Johnson · 2022 · Journal of Chemical Theory and Computation
- Detection of topological materials with machine learningNikolas H Claussen, Bogdan Andrei Bernevig, Nicolas Regnault · 2020 · Physical review. B./Physical review. B
- Convolutional Neural Networks for the Design and Analysis of Non-Fullerene AcceptorsShi-Ping Peng, Yi Zhao · 2019 · Journal of Chemical Information and Modeling
- A Perspective on Deep Learning for Molecular Modeling and SimulationsJun 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 A
- Learning together: Towards foundation models for machine learning interatomic potentials with meta-learningAlice E. A. Allen, Nicholas Lubbers, Sakib Matin, Justin S. Smith, Richard A. Messerly, Sergei Tretiak, Kipton M. Barros · 2024 · npj Computational Materials
- A deep neural network for molecular wave functions in quasi-atomic minimal basis representationMichael Gastegger, A. McSloy, M. Luya, K. T. Schütt, R. J. Maurer · 2020 · The Journal of Chemical Physics
- The Application of Convolutional Neural Networks (CNNs) to Recognize Defects in 3D-Printed PartsHao Wen, Chang Huang, Shengmin Guo · 2021 · Materials
- Interpretable and explainable predictive machine learning models for data-driven protein engineeringDavid Medina-Ortiz, Ashkan Khalifeh, Hoda Anvari-Kazemabad, Mehdi D. Davari · 2024 · Biotechnology Advances
- Machine Learning-Based Computational Design Methods for High-Entropy AlloysYan Ming Zhao, J. Y. Zhang, Peter Kaehuei Liaw, Tao Yang · 2025 · High Entropy Alloys & Materials
- Supervised Deep Learning in High Energy Phenomenology: a Mini Review*Murat Abdughani, Jie Ren, Lei Wu, Jin Min Yang, Jun Zhao · 2019 · Communications in Theoretical Physics
- Physical pooling functions in graph neural networks for molecular property predictionArtur M. Schweidtmann, Jan G. Rittig, Jana M. Weber, Martin Grohe, Manuel Dahmen, Kai O. Leonhard, Alexander Mitsos · 2023 · Computers & Chemical Engineering
- Machine Learning Models for Predicting Molecular UV–Vis Spectra with Quantum Mechanical PropertiesAndrew D. McNaughton, Rajendra Prasad Joshi, Carter R. Knutson, Fnu Anubhav, Kevin J Luebke, Jeremiah P. Malerich, Peter B. Madrid, Neeraj Kumar · 2023 · Journal of Chemical Information and Modeling
- Leveraging Machine Learning Potentials for In-Situ Searching of Active sites in Heterogeneous CatalysisXiran Cheng, Chenyu Wu, Jiayan Xu, Yulan Han, Wenbo Xie, P. Hu · 2024 · Precision Chemistry
- Prediction of Intramolecular Reorganization Energy Using Machine LearningŞule Atahan-Evrenk, F. Betul Atalay · 2019 · The Journal of Physical Chemistry A
- Catalytic materials and chemistry development using a synergistic combination of machine learning and ab initio methodsNilesh Varadan Orupattur, Samir H. Mushrif, Vinay Prasad · 2020 · Computational Materials Science
- MLSolvA: solvation free energy prediction from pairwise atomistic interactions by machine learningHyuntae Lim, YounJoon Jung · 2021 · Journal of Cheminformatics
- Monte Carlo Simulations of Au 38 (SCH 3 ) 24 Nanocluster Using Distance-Based Machine Learning MethodsAntti Pihlajamäki, Joonas Hämäläinen, Joakim Linja, P. Nieminen, Sami Malola, Tommi Kärkkäinen, Hannu Häkkinen · 2020 · The Journal of Physical Chemistry A
- Machine Learning in Nano-Scale Biomedical EngineeringAlexandros–Apostolos A. Boulogeorgos, Stylianos E. Trevlakis, Sotiris A. Tegos, Vasilis K. Papanikolaou, George K. Karagiannidis · 2020 · IEEE Transactions on Molecular Biological and Multi-Scale Communications
- Mol2Context-vec: learning molecular representation from context awareness for drug discoveryQiujie Lv, Guanxing Chen, Lu Zhao, Weihe Zhong, Calvin Yu‐Chian Chen · 2021 · Briefings in Bioinformatics
- Ensemble learning of coarse-grained molecular dynamics force fields with a kernel approachJiang Wang, Stefan Chmiela, Klaus‐Robert Müller, Frank Noé, Cecilia Clementi · 2020 · The Journal of Chemical Physics
- Artificial Intelligence for Autonomous Molecular Design: A PerspectiveRajendra Prasad Joshi, Neeraj Kumar · 2021 · Molecules
- MolRoPE-BERT: An enhanced molecular representation with Rotary Position Embedding for molecular property predictionYunwu Liu, Ruisheng Zhang, Tongfeng Li, Jing Jiang, Jun Ma, Ping Wang · 2022 · Journal of Molecular Graphics and Modelling
- Recent Applications of Machine Learning in Molecular Property and Chemical Reaction Outcome PredictionsShilpa Shilpa, Gargee Kashyap, Raghavan B. Sunoj · 2023 · The Journal of Physical Chemistry A
- Machine-Learned Fragment-Based Energies for Crystal Structure PredictionDavid McDonagh, Chris‐Kriton Skylaris, Graeme Matthew Day · 2019 · Journal of Chemical Theory and Computation
- Quantifying Chemical Structure and Machine‐Learned Atomic Energies in Amorphous and Liquid SiliconNoam Bernstein, Bishal Bhattarai, Gábor Cśanyi, David A. Drabold, Stephen R. Elliott, Volker L. Deringer · 2019 · Angewandte Chemie International Edition
- Predicting adsorption ability of adsorbents at arbitrary sites for pollutants using deep transfer learningZhilong Wang, Haikuo Zhang, Jiahao Ren, Xirong Lin, Tianli Han, Jinyun Liu, Jinjin Li · 2021 · npj Computational Materials
- Forecasting the outcome of spintronic experiments with Neural Ordinary Differential EquationsXing Chen, Flavio Abreu Araujo, Mathieu Riou, Jacob Torrejón, D. Ravelosona, Wang Kang, Weisheng S. Zhao, Julie Grollier, Damien Querlioz · 2022 · Nature Communications
- Towards robust explanations for deep neural networksAnn-Kathrin Dombrowski, Christopher J. Anders, Klaus‐Robert Müller, Pan Kessel · 2021 · Pattern Recognition
- Artificial Intelligence Effecting a Paradigm Shift in Drug DevelopmentMasturah Bte Mohd Abdul Rashid · 2020 · SLAS TECHNOLOGY
- Learning molecular potentials with neural networksHatice Gökcan, Olexandr Isayev · 2021 · Wiley Interdisciplinary Reviews Computational Molecular Science
- Spectroscopy from Machine Learning by Accurately Representing the Atomic Polar TensorPhilipp Schienbein · 2023 · Journal of Chemical Theory and Computation
- Improving accuracy of interatomic potentials: more physics or more data? A case study of silicaIvan Sergeyevich Novikov, Alexander V. Shapeev · 2018 · Materials Today Communications
- Improved accuracy and transferability of molecular-orbital-based machine learning: Organics, transition-metal complexes, non-covalent interactions, and transition statesTamara Husch, Jiace Sun, Lixue Cheng, Sebastian J. R. Lee, Thomas F. Miller · 2021 · The Journal of Chemical Physics
- Atomic permutationally invariant polynomials for fitting molecular force fieldsAlice E. A. Allen, Geneviève Dusson, Christoph Ortner, Gábor Cśanyi · 2020 · Machine Learning Science and Technology
- Learning Gradient Fields for Molecular Conformation GenerationChence Shi, Shitong Luo, Minkai Xu, Jian Tang · 2021 · arXiv (Cornell University)
- Efficient implementation of atom-density representationsFélix Musil, Max Veit, Alexander Goscinski, Guillaume Fraux, Michael J. Willatt, Markus Stricker, Till Junge, Michele Ceriotti · 2021 · The Journal of Chemical Physics
- Accurate Fourth-Generation Machine Learning Potentials by Electrostatic EmbeddingTsz Wai Ko, Jonas A. Finkler, Stefan Goedecker, Jörg Behler · 2023 · Journal of Chemical Theory and Computation
- Combining the Fragmentation Approach and Neural Network Potential Energy Surfaces of Fragments for Accurate Calculation of Protein EnergyZhilong Wang, Yanqiang Han, Jinjin Li, Xiao Dong He · 2020 · The Journal of Physical Chemistry B
- Molecular force fields with gradient-domain machine learning (GDML): Comparison and synergies with classical force fieldsHuziel E. Sauceda, Michael Gastegger, Stefan Chmiela, Klaus‐Robert Müller, Alexandre Tkatchenko · 2020 · The Journal of Chemical Physics
- 3D graph neural network with few-shot learning for predicting drug–drug interactions in scaffold-based cold start scenarioQiujie Lv, Jun Zhou, Ziduo Yang, Haohuai He, Calvin Yu‐Chian Chen · 2023 · Neural Networks
- Representations in neural network based empirical potentialsEkin D. Cubuk, Brad D. Malone, Berk Onat, Amos Waterland, Efthimios Kaxiras · 2017 · The Journal of Chemical Physics
- Group-theoretical high-order rotational invariants for structural representations: Application to linearized machine learning interatomic potentialAtsuto Seko, Atsushi Togo, Isao Tanaka · 2019 · Physical review. B./Physical review. B
- Transfer learning with graph neural networks for optoelectronic properties of conjugated oligomersChee‐Kong Lee, Chengqiang Lu, Yue Yu, Qiming Sun, Chang‐Yu Hsieh, Shengyu Zhang, Qi Liu, Liang Shi · 2021 · The Journal of Chemical Physics
- Machine learning accelerates quantum mechanics predictions of molecular crystalsYanqiang Han, Imran Ali, Zhilong Wang, Junfei Cai, Sicheng Wu, Jiequn Tang, Lin Zhang, Jiahao Ren, Rui Xiao, Qianqian Lu et al. · 2021 · Physics Reports
- Deep Learning of High-Order Interactions for Protein Interface PredictionLiu, Yi, Yuan, Hao, Cai, Lei, Ji, Shuiwang · 2020 · Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
- Machine Learning in Chemoinformatics and Medicinal ChemistryRaquel Rodríguez-Pérez, Filip N. Miljković, Jürgen Bajorath · 2022 · Annual Review of Biomedical Data Science
- Linking atomic structural defects to mesoscale properties in crystalline solids using graph neural networksZhenze Yang, Markus J. Buehler · 2022 · npj Computational Materials
- Design of polyimides with targeted glass transition temperature using a graph neural networkHaoke Qiu, Xuepeng Qiu, Xuemin Dai, Zhao‐Yan Sun · 2023 · Journal of Materials Chemistry C
- Transfer learning for chemically accurate interatomic neural network potentialsViktor Zaverkin, David Holzmüller, Luca Bonfirraro, Johannes Kästner · 2023 · Physical Chemistry Chemical Physics
- Accelerating Computation of Acidity Constants and Redox Potentials for Aqueous Organic Redox Flow Batteries by Machine Learning Potential-Based Molecular DynamicsFeng Wang, Ze-Bing Ma, Jun Jie Cheng · 2024 · Journal of the American Chemical Society
- Machine Learning for Accelerating Energy Materials Discovery: Bridging Quantum Accuracy with Computational EfficiencyKwang S. Kim · 2025 · Advanced Energy Materials
- Making the Coupled Cluster Correlation Energy Machine-LearnableJohannes T. Margraf, Karsten Reuter · 2018 · The Journal of Physical Chemistry A
- Exploiting Machine Learning to Efficiently Predict Multidimensional Optical Spectra in Complex EnvironmentsMichael S. Chen, Tim Joachim Zuehlsdorff, Tobias Morawietz, Christine M. Isborn, Thomas E. Markland · 2020 · The Journal of Physical Chemistry Letters
- Accurate Prediction of Aqueous Free Solvation Energies Using 3D Atomic Feature-Based Graph Neural Network with Transfer LearningDongdong Zhang, Song Xia, Yingkai Zhang · 2022 · Journal of Chemical Information and Modeling
- 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)
- Exploring chemical and conformational spaces by batch mode deep active learningViktor Zaverkin, David Holzmüller, Ingo Steinwart, Johannes Kästner · 2022 · Digital Discovery
- Convolutional neural networks for atomistic systemsKevin Ryczko, Kyle Mills, Iryna Luchak, Christa M. Homenick, Isaac Tamblyn · 2018 · Computational Materials Science
- General Atomic Neighborhood Fingerprint for Machine Learning-Based MethodsRohit Batra, Tran Doan Huan, Chiho Kim, James Chapman, Lihua Chen, Anand R. Chandrasekaran, Rampi Ramprasad · 2019 · The Journal of Physical Chemistry C
- Molecular machine learning with conformer ensemblesSimon Axelrod, Rafael Gómez‐Bombarelli · 2023 · Machine Learning Science and Technology
- Outsmarting Quantum Chemistry Through Transfer LearningJustin S Smith, Benjamin T. Nebgen, R.I. Zubatyuk, Nicholas Lubbers, Christian Devereux, Kipton M. Barros, Sergei Tretiak, Olexandr Isayev, Adrián E. Roitberg · 2018 · ChemRxiv
- Knowledge-Embedded Message-Passing Neural Networks: Improving Molecular Property Prediction with Human KnowledgeTatsuya HASEBE · 2021 · ACS Omega
- 3D-Scaffold: A Deep Learning Framework to Generate 3D Coordinates of Drug-like Molecules with Desired ScaffoldsRajendra Prasad Joshi, Niklas W. A. Gebauer, Mridula Bontha, Mercedeh Khazaieli, Rhema M. James, James Brown, Neeraj Kumar · 2021 · The Journal of Physical Chemistry B
- QDπ: A Quantum Deep Potential Interaction Model for Drug DiscoveryJinzhe Zeng, Yujun Tao, Timothy J. Giese, Darrin M. York · 2023 · Journal of Chemical Theory and Computation
- A deep learning framework for accurate reaction prediction and its application on high-throughput experimentation dataBaiqing Li, Shimin Su, Chan Zhu, Jie Lin, Xinyue Hu, Lebin Su, Zhunzhun Yu, Kuangbiao Liao, Hongming Chen · 2023 · Journal of Cheminformatics
- High-throughput quantum theory of atoms in molecules (QTAIM) for geometric deep learning of molecular and reaction propertiesSantiago Vargas, Winston Gee, Anastassia N. Alexandrova · 2024 · Digital Discovery
- Clinical translation of nanomedicine with integrated digital medicine and machine learning interventionsFarooq Ahmad, Tahir Muhmood · 2024 · Colloids and Surfaces B Biointerfaces
- Modeling protein–ligand interactions for drug discovery in the era of deep learningY.X. Wang, Yibo Li, Jiaxiao Chen, Luhua Lai · 2025 · Chemical Society Reviews
- Machine Learning Configuration Interaction for ab Initio Potential Energy CurvesJ. P. Coe · 2019 · Journal of Chemical Theory and Computation
- Graph convolutional neural networks with node transition probability-based message passing and DropNode regularizationTien Huu, Duc Minh Nguyen, Giannis Bekoulis, Adrian Munteanu, Nikos Deligiannis · 2021 · Expert Systems with Applications
- Chemistry-informed macromolecule graph representation for similarity computation, unsupervised and supervised learningSomesh Mohapatra, Joyce An, Rafael Gómez‐Bombarelli · 2022 · Machine Learning Science and Technology
- GRAPHAF: A FLOW-BASED AUTOREGRESSIVE MODEL FOR MOLECULAR GRAPH GENERATIONChence Shi · 2025 · arXiv (Cornell University)
- Lifelong Machine Learning PotentialsMarco Eckhoff, Markus Reiher · 2023 · Journal of Chemical Theory and Computation
- Advances in materials informatics: a reviewDawn Sivan, K. Satheesh Kumar, Aziman Abdullah, Veena Raj, Izan Izwan Misnon, Seeram Ramakrishna, Rajan Jose · 2024 · Journal of Materials Science
- Machine Learning Quantum Reaction Rate ConstantsEvan Komp, Stéphanie Valleau · 2020 · The Journal of Physical Chemistry A
- Machine Learning Applications for Chemical ReactionsSanggil Park, Herim Han, Hyungjun Kim, Sunghwan Choi · 2022 · Chemistry - An Asian Journal
- Identification of potential solid-state Li-ion conductors with semi-supervised learningForrest A. L. Laskowski, Daniel B. McHaffie, Kimberly A. See · 2023 · Energy & Environmental Science
- QupKake: Integrating Machine Learning and Quantum Chemistry for Micro-p K a PredictionsOmri D. Abarbanel, Geoffrey Hutchison · 2024 · Journal of Chemical Theory and Computation