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
IRNet: A General Purpose Deep Residual Regression Framework for Materials Discovery Dipendra Jha Logan T. Ward Zijiang Yang C. Wolverton Ian Foster W. Liao A. Choudhary Ankit Agrawal PINN 48 43 0 07 Jul 2019
Generative Models for Automatic Chemical Design Daniel Schwalbe-Koda Rafael Gómez-Bombarelli MedIm AI4CE 87 81 0 02 Jul 2019
Unifying machine learning and quantum chemistry -- a deep neural network for molecular wavefunctions Kristof T. Schütt M. Gastegger A. Tkatchenko K. Müller R. Maurer AI4CE 98 390 0 24 Jun 2019
Protocol for implementing quantum nonparametric learning with trapped ions Dan-Bo Zhang Shi-Liang Zhu Zhenghan Wang 42 14 0 08 Jun 2019
Cormorant: Covariant Molecular Neural Networks Brandon M. Anderson Truong-Son Hy Risi Kondor 141 426 0 06 Jun 2019
Hamiltonian Neural Networks S. Greydanus Misko Dzamba J. Yosinski PINN AI4CE 145 900 0 04 Jun 2019
Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules Niklas W. A. Gebauer M. Gastegger Kristof T. Schütt 164 211 0 02 Jun 2019
A survey on Big Data and Machine Learning for Chemistry J. F. Rodrigues L. Florea Maria Cristina Ferreira de Oliveira D. Diamond Osvaldo N. Oliveira AI4CE 64 6 0 23 Apr 2019
DScribe: Library of Descriptors for Machine Learning in Materials Science Lauri Himanen M. Jäger Eiaki V. Morooka F. F. Canova Y. S. Ranawat D. Gao Patrick Rinke A. Foster 69 591 0 18 Apr 2019
Fast, accurate, and transferable many-body interatomic potentials by symbolic regression Alberto Hernandez Adarsh Balasubramanian Fenglin Yuan Simon Mason Tim Mueller 74 70 0 01 Apr 2019
An Atomistic Machine Learning Package for Surface Science and Catalysis M. H. Hansen J. A. G. Torres P. C. Jennings Ziyun Wang Jacob R. Boes Osman G. Mamun T. Bligaard 72 19 0 01 Apr 2019
Measuring the Similarity between Materials with an Emphasis on the Materials Distinctiveness Tran-Thai Dang T. Pham H. Kino T. Miyake H. Dam 368 0 0 23 Mar 2019
Atomistic structure learning M. Jørgensen H. L. Mortensen S. A. Meldgaard E. L. Kolsbjerg Thomas L. Jacobsen K. H. Sørensen B. Hammer AI4CE 56 37 0 27 Feb 2019
A Universal Density Matrix Functional from Molecular Orbital-Based Machine Learning: Transferability across Organic Molecules Lixue Cheng Matthew Welborn Anders S. Christensen Thomas F. Miller 82 95 0 10 Jan 2019
Learning formation energy of inorganic compounds using matrix variate deep Gaussian process Saket V Mishra P. Tagade 13 0 0 22 Dec 2018
Machine Learning for Molecular Dynamics on Long Timescales Frank Noé AI4CE 79 32 0 18 Dec 2018
Learning representations of molecules and materials with atomistic neural networks Kristof T. Schütt A. Tkatchenko K. Müller NAI 45 13 0 11 Dec 2018
Machine Learning of coarse-grained Molecular Dynamics Force Fields Jiang Wang Simon Olsson C. Wehmeyer Adria Pérez Nicholas E. Charron Gianni De Fabritiis Frank Noe C. Clementi AI4CE 86 407 0 04 Dec 2018
Boltzmann Generators -- Sampling Equilibrium States of Many-Body Systems with Deep Learning Frank Noé Simon Olsson Jonas Köhler Hao Wu 92 26 0 04 Dec 2018
Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models Daniil Polykovskiy Alexander Zhebrak Benjamín Sánchez-Lengeling Sergey Golovanov Oktai Tatanov ... Simon Johansson Hongming Chen Sergey I. Nikolenko Alán Aspuru-Guzik Alex Zhavoronkov ELM 301 663 0 29 Nov 2018
Steerable Wavelet Scattering for 3D Atomic Systems with Application to Li-Si Energy Prediction Xavier Brumwell Paul Sinz K. Kim Y. Qi M. Hirn 46 8 0 21 Nov 2018
Independent Vector Analysis for Data Fusion Prior to Molecular Property Prediction with Machine Learning Zois Boukouvalas Daniel C. Elton Peter W. Chung M. Fuge 69 9 0 01 Nov 2018
Compressing physical properties of atomic species for improving predictive chemistry John E. Herr Kevin J Koh Kun Yao John A. Parkhill AI4CE 55 20 0 31 Oct 2018
Band gap prediction for large organic crystal structures with machine learning B. Olsthoorn R. Geilhufe S. Borysov A. Balatsky AI4CE 66 75 0 30 Oct 2018
Active Learning of Uniformly Accurate Inter-atomic Potentials for Materials Simulation Linfeng Zhang De-Ye Lin Han Wang R. Car E. Weinan 77 340 0 28 Oct 2018
Generating equilibrium molecules with deep neural networks Niklas W. A. Gebauer M. Gastegger Kristof T. Schütt BDL 81 38 0 26 Oct 2018
Analysis of Atomistic Representations Using Weighted Skip-Connections K. Nicoli Pan Kessel M. Gastegger Kristof T. Schütt 51 0 0 23 Oct 2018
Prediction of Atomization Energy Using Graph Kernel and Active Learning Yu-Hang Tang W. A. Jong 30 29 0 16 Oct 2018
Machine learning electron correlation in a disordered medium Jianhua Ma Puhan Zhang Yaohua Tan Avik W. Ghosh Gia-Wei Chern AI4CE 32 15 0 04 Oct 2018
Discriminative Learning of Similarity and Group Equivariant Representations Shubhendu Trivedi 41 1 0 30 Aug 2018
Hierarchical Visualization of Materials Space with Graph Convolutional Neural Networks T. Xie Jeffrey C. Grossman AI4CE 42 65 0 09 Jul 2018
Quantum-chemical insights from interpretable atomistic neural networks Kristof T. Schütt M. Gastegger A. Tkatchenko K. Müller AI4CE 86 32 0 27 Jun 2018
Clebsch-Gordan Nets: a Fully Fourier Space Spherical Convolutional Neural Network Risi Kondor Zhen Lin Shubhendu Trivedi 95 272 0 24 Jun 2018
Neural Message Passing with Edge Updates for Predicting Properties of Molecules and Materials Peter Bjørn Jørgensen K. Jacobsen Mikkel N. Schmidt 63 82 0 08 Jun 2018
Solid Harmonic Wavelet Scattering for Predictions of Molecule Properties Michael Eickenberg Georgios Exarchakis M. Hirn S. Mallat L. Thiry 79 71 0 01 May 2018
Conditional molecular design with deep generative models Seokho Kang Kyunghyun Cho BDL 246 184 0 30 Apr 2018
Spherical CNNs Taco S. Cohen Mario Geiger Jonas Köhler Max Welling 190 908 0 30 Jan 2018
Less is more: sampling chemical space with active learning Justin S. Smith B. Nebgen Nicholas Lubbers Olexandr Isayev A. Roitberg 96 627 0 28 Jan 2018
Metadynamics for Training Neural Network Model Chemistries: a Competitive Assessment John E. Herr Kun Yao R. McIntyre David W Toth John A. Parkhill 61 63 0 19 Dec 2017
WACSF - Weighted Atom-Centered Symmetry Functions as Descriptors in Machine Learning Potentials M. Gastegger Ludwig Schwiedrzik Marius Bittermann Florian Berzsenyi P. Marquetand 57 242 0 15 Dec 2017
DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics Han Wang Linfeng Zhang Jiequn Han E. Weinan AI4CE 92 1,274 0 11 Dec 2017
Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics C. Wehmeyer Frank Noé AI4CE BDL 179 362 0 30 Oct 2017
Hierarchical modeling of molecular energies using a deep neural network Nicholas Lubbers Justin S. Smith K. Barros AI4CE BDL 92 272 0 29 Sep 2017
Enhanced Quantum Synchronization via Quantum Machine Learning F. A. Cárdenas-López M. Sanz J. C. Retamal Enrique Solano 27 12 0 25 Sep 2017
Learning Graph-Level Representation for Drug Discovery Junying Li Deng Cai Xiaofei He GNN AI4CE 94 117 0 12 Sep 2017
$Machine learning \& artificial intelligence in the quantum domain$ Machine learning \& artificial intelligence in the quantum domain Vedran Dunjko Hans J. Briegel 96 347 0 08 Sep 2017
ANI-1: A data set of 20M off-equilibrium DFT calculations for organic molecules Justin S. Smith Olexandr Isayev A. Roitberg 74 5 0 16 Aug 2017
Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanics Linfeng Zhang Jiequn Han Han Wang R. Car E. Weinan 76 1,163 0 30 Jul 2017
SchNet: A continuous-filter convolutional neural network for modeling quantum interactions Kristof T. Schütt Pieter-Jan Kindermans Huziel Enoc Sauceda Felix Stefan Chmiela A. Tkatchenko K. Müller 170 1,088 0 26 Jun 2017
Direct Mapping Hidden Excited State Interaction Patterns from ab initio Dynamics and Its Implications on Force Field Development Fang Liu Likai Du Dongju Zhang Jun Gao 57 19 0 28 May 2017