Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

12 September 2011

Papers citing "Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning"

50 / 268 papers shown

Title
Kohn-Sham equations as regularizer: building prior knowledge into machine-learned physics Li Li Stephan Hoyer Ryan Pederson Ruoxi Sun E. D. Cubuk Patrick F. Riley K. Burke AI4CE 94 125 0 17 Sep 2020
The role of feature space in atomistic learning Alexander Goscinski Guillaume Fraux G. Imbalzano Michele Ceriotti 26 29 0 06 Sep 2020
Multi-scale approach for the prediction of atomic scale properties Andrea Grisafi Jigyasa Nigam Michele Ceriotti 41 31 0 27 Aug 2020
Relevance of Rotationally Equivariant Convolutions for Predicting Molecular Properties Benjamin Kurt Miller Mario Geiger Tess E. Smidt Frank Noé 110 78 0 19 Aug 2020
Orbital Graph Convolutional Neural Network for Material Property Prediction M. Karamad Rishikesh Magar Yuting Shi Samira Siahrostami I. Gates A. Farimani 77 85 0 14 Aug 2020
A community-powered search of machine learning strategy space to find NMR property prediction models Lars A. Bratholm W. Gerrard Brandon M. Anderson Shaojie Bai Sunghwan Choi ... A. Torrubia Devin Willmott C. Butts David R. Glowacki Kaggle participants 46 17 0 13 Aug 2020
Machine Learning in Nano-Scale Biomedical Engineering Alexandros-Apostolos A. Boulogeorgos Stylianos E. Trevlakis Sotiris A. Tegos V. Papanikolaou G. Karagiannidis AI4CE 41 30 0 05 Aug 2020
DeePKS: a comprehensive data-driven approach towards chemically accurate density functional theory Yixiao Chen Linfeng Zhang Han Wang E. Weinan 76 73 0 01 Aug 2020
Coarse Graining Molecular Dynamics with Graph Neural Networks B. Husic N. Charron Dominik Lemm Jiang Wang Adria Pérez ... Yaoyi Chen Simon Olsson Gianni De Fabritiis Frank Noé C. Clementi AI4CE 125 162 0 22 Jul 2020
MathNet: Haar-Like Wavelet Multiresolution-Analysis for Graph Representation and Learning Xuebin Zheng Bingxin Zhou Ming Li Yu Guang Wang Junbin Gao 74 2 0 22 Jul 2020
OrbNet: Deep Learning for Quantum Chemistry Using Symmetry-Adapted Atomic-Orbital Features Zhuoran Qiao Matthew Welborn Anima Anandkumar F. Manby Thomas F. Miller AI4CE 91 218 0 15 Jul 2020
Atomistic Structure Learning Algorithm with surrogate energy model relaxation H. L. Mortensen S. A. Meldgaard M. K. Bisbo Mads-Peter V. Christiansen B. Hammer 15 17 0 15 Jul 2020
Machine learning for electronically excited states of molecules Julia Westermayr P. Marquetand 71 266 0 10 Jul 2020
Path Integral Based Convolution and Pooling for Graph Neural Networks Zheng Ma Junyu Xuan Yu Guang Wang Ming Li Pietro Lio GNN 89 56 0 29 Jun 2020
Committee neural network potentials control generalization errors and enable active learning Christoph Schran K. Brezina O. Marsalek 61 131 0 02 Jun 2020
Wavelet Scattering Networks for Atomistic Systems with Extrapolation of Material Properties Paul Sinz M. Swift Xavier Brumwell Jialin Liu K. Kim Y. Qi M. Hirn 55 12 0 01 Jun 2020
Semi-Supervised Hierarchical Drug Embedding in Hyperbolic Space Ke Yu Shyam Visweswaran Kayhan Batmanghelich BDL 37 29 0 01 Jun 2020
Machine Learning for Condensed Matter Physics Edwin Bedolla L. C. Padierna R. Castañeda-Priego AI4CE 80 68 0 28 May 2020
Machine learning and excited-state molecular dynamics Julia Westermayr P. Marquetand AI4CE 63 56 0 28 May 2020
Deep Learning and Knowledge-Based Methods for Computer Aided Molecular Design -- Toward a Unified Approach: State-of-the-Art and Future Directions Abdulelah S. Alshehri R. Gani Fengqi You AI4CE 98 86 0 18 May 2020
Learning the gravitational force law and other analytic functions Atish Agarwala Abhimanyu Das Rina Panigrahy Qiuyi Zhang MLT 43 0 0 15 May 2020
Ensemble Learning of Coarse-Grained Molecular Dynamics Force Fields with a Kernel Approach Jiang Wang Stefan Chmiela K. Müller Frank Noè C. Clementi 138 46 0 04 May 2020
Variational Integrator Graph Networks for Learning Energy Conserving Dynamical Systems Shaan Desai M. Mattheakis Stephen J. Roberts PINN AI4CE 66 12 0 28 Apr 2020
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 Yue Yang Y. Gao AI4CE 116 8 0 25 Apr 2020
Autonomous discovery in the chemical sciences part II: Outlook Connor W. Coley Natalie S. Eyke K. Jensen 91 175 0 30 Mar 2020
Predicting molecular dipole moments by combining atomic partial charges and atomic dipoles M. Veit D. Wilkins Yang Yang R. DiStasio Michele Ceriotti 78 94 0 27 Mar 2020
Representations of molecules and materials for interpolation of quantum-mechanical simulations via machine learning Marcel F. Langer Alex Goessmann M. Rupp AI4CE 73 99 0 26 Mar 2020
Predicting Elastic Properties of Materials from Electronic Charge Density Using 3D Deep Convolutional Neural Networks Yong Zhao Kunpeng Yuan Yinqiao Liu Steph-Yves M. Louis Ming Hu Jianjun Hu 55 26 0 17 Mar 2020
Automated discovery of a robust interatomic potential for aluminum Justin S. Smith B. Nebgen N. Mathew Jie Chen Nicholas Lubbers ... S. Tretiak H. Nam T. Germann S. Fensin K. Barros 46 83 0 10 Mar 2020
Lagrangian Neural Networks M. Cranmer S. Greydanus Stephan Hoyer Peter W. Battaglia D. Spergel S. Ho PINN 184 439 0 10 Mar 2020
ML4Chem: A Machine Learning Package for Chemistry and Materials Science Muammar El Khatib W. A. Jong VLM 35 6 0 02 Mar 2020
Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Data Marc Finzi Samuel Stanton Pavel Izmailov A. Wilson 140 324 0 25 Feb 2020
Structure-Property Maps with Kernel Principal Covariates Regression B. Helfrecht Rose K. Cersonsky Guillaume Fraux Michele Ceriotti 31 37 0 12 Feb 2020
Molecule Property Prediction and Classification with Graph Hypernetworks Eliya Nachmani Lior Wolf GNN 54 15 0 01 Feb 2020
Big-Data Science in Porous Materials: Materials Genomics and Machine Learning Kevin Maik Jablonka D. Ongari S. M. Moosavi B. Smit AI4CE 85 365 0 18 Jan 2020
Building high accuracy emulators for scientific simulations with deep neural architecture search M. F. Kasim D. Watson‐Parris L. Deaconu Sophy Oliver P. Hatfield ... S. Khatiwala J. Korenaga J. Topp-Mugglestone E. Viezzer S. Vinko AI4CE 54 95 0 17 Jan 2020
TeaNet: universal neural network interatomic potential inspired by iterative electronic relaxations So Takamoto S. Izumi Ju Li GNN 65 80 0 02 Dec 2019
Deep Density: circumventing the Kohn-Sham equations via symmetry preserving neural networks Leonardo Zepeda-Núnez Yixiao Chen Jiefu Zhang Weile Jia Linfeng Zhang Lin Lin 75 33 0 27 Nov 2019
Neural Network Based in Silico Simulation of Combustion Reactions Jinzhe Zeng Liqun Cao Mingyuan Xu Tong Zhu John Z. H. Zhang AI4CE 32 9 0 27 Nov 2019
Machine learning for protein folding and dynamics Frank Noé Gianni De Fabritiis C. Clementi AI4CE 121 138 0 22 Nov 2019
Machine learning for molecular simulation Frank Noé A. Tkatchenko K. Müller C. Clementi AI4CE 90 668 0 07 Nov 2019
Leveraging Legacy Data to Accelerate Materials Design via Preference Learning Xiaolin Sun Z. Hou Masato Sumita Shinsuke Ishihara Ryo Tamura Koji Tsuda 41 7 0 25 Oct 2019
Flow-based Alignment Approaches for Probability Measures in Different Spaces Tam Le Nhat Ho M. Yamada OT 404 0 0 10 Oct 2019
Generating valid Euclidean distance matrices Moritz Hoffmann Frank Noé 88 56 0 07 Oct 2019
Learning Everywhere: A Taxonomy for the Integration of Machine Learning and Simulations Geoffrey C. Fox S. Jha AI4CE 72 13 0 29 Sep 2019
Deep neural network solution of the electronic Schrödinger equation J. Hermann Zeno Schätzle Frank Noé 308 459 0 16 Sep 2019
Regression-clustering for Improved Accuracy and Training Cost with Molecular-Orbital-Based Machine Learning Lixue Cheng Nikola B. Kovachki Matthew Welborn Thomas F. Miller 82 45 0 04 Sep 2019
Gated Graph Recursive Neural Networks for Molecular Property Prediction Hiroyuki Shindo Yuji Matsumoto GNN 80 16 0 31 Aug 2019
Trees and Islands -- Machine learning approach to nuclear physics Nishchal R. Dwivedi 17 3 0 23 Jul 2019
Fast Haar Transforms for Graph Neural Networks Ming Li Zheng Ma Yu Guang Wang Xiaosheng Zhuang 83 77 0 10 Jul 2019