Hierarchical modeling of molecular energies using a deep neural network

29 September 2017

Papers citing "Hierarchical modeling of molecular energies using a deep neural network"

31 / 31 papers shown

Title
Optimal Invariant Bases for Atomistic Machine Learning Alice Allen Emily Shinkle Roxana Bujack Nicholas Lubbers 42 0 0 30 Mar 2025
FastCHGNet: Training one Universal Interatomic Potential to 1.5 Hours with 32 GPUs Yuanchang Zhou Siyu Hu Chen Wang Lin-Wang Wang Guangming Tan Weile Jia AI4CE GNN 56 0 0 30 Dec 2024
FeNNol: an Efficient and Flexible Library for Building Force-field-enhanced Neural Network Potentials Thomas Plé Olivier Adjoua Louis Lagardère Jean‐Philip Piquemal 49 8 0 02 May 2024
Predicting Properties of Periodic Systems from Cluster Data: A Case Study of Liquid Water Viktor Zaverkin David Holzmüller Robin Schuldt Johannes Kastner 30 15 0 03 Dec 2023
Denoise Pretraining on Nonequilibrium Molecules for Accurate and Transferable Neural Potentials Yuyang Wang Chang Xu Zijie Li A. Farimani AAML AI4CE 34 21 0 03 Mar 2023
HOAX: A Hyperparameter Optimization Algorithm Explorer for Neural Networks Albert S. Thie M. Menger S. Faraji 27 0 0 01 Feb 2023
Reconstructing Kernel-based Machine Learning Force Fields with Super-linear Convergence Stefan Blücher Klaus-Robert Muller Stefan Chmiela 21 4 0 24 Dec 2022
Synthetic data enable experiments in atomistic machine learning John L A Gardner Z. Beaulieu Volker L. Deringer 37 6 0 29 Nov 2022
Extreme Acceleration of Graph Neural Network-based Prediction Models for Quantum Chemistry Hatem Helal J. Firoz Jenna A. Bilbrey M. M. Krell Tom Murray Ang Li S. Xantheas Sutanay Choudhury GNN 49 5 0 25 Nov 2022
Artificial Intelligence in Material Engineering: A review on applications of AI in Material Engineering Lipichanda Goswami Manoj Deka Mohendra Roy AI4CE 42 19 0 15 Sep 2022
MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields Ilyes Batatia D. P. Kovács G. Simm Christoph Ortner Gábor Csányi 47 445 0 15 Jun 2022
So3krates: Equivariant attention for interactions on arbitrary length-scales in molecular systems J. Frank Oliver T. Unke Klaus-Robert Muller 27 43 0 28 May 2022
EcoFlow: Efficient Convolutional Dataflows for Low-Power Neural Network Accelerators Lois Orosa Skanda Koppula Yaman Umuroglu Konstantinos Kanellopoulos Juan Gómez Luna Michaela Blott K. Vissers O. Mutlu 46 4 0 04 Feb 2022
Graph Neural Networks Accelerated Molecular Dynamics Zijie Li Kazem Meidani Prakarsh Yadav A. Farimani GNN AI4CE 32 53 0 06 Dec 2021
Geometric Transformer for End-to-End Molecule Properties Prediction Yoni Choukroun Lior Wolf AI4CE ViT 30 16 0 26 Oct 2021
Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments Viktor Zaverkin David Holzmüller Ingo Steinwart Johannes Kastner 31 19 0 20 Sep 2021
SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effects Oliver T. Unke Stefan Chmiela M. Gastegger Kristof T. Schütt H. E. Sauceda K. Müller 177 248 0 01 May 2021
A Universal Framework for Featurization of Atomistic Systems Xiangyun Lei A. Medford AI4CE 23 19 0 04 Feb 2021
Directed Graph Attention Neural Network Utilizing 3D Coordinates for Molecular Property Prediction Chao Qian Yunhai Xiong Xiang Chen 13 13 0 01 Dec 2020
Machine Learning Force Fields Oliver T. Unke Stefan Chmiela H. E. Sauceda M. Gastegger I. Poltavsky Kristof T. Schütt A. Tkatchenko K. Müller AI4CE 34 888 0 14 Oct 2020
Accelerating Finite-temperature Kohn-Sham Density Functional Theory with Deep Neural Networks J. Ellis Lenz Fiedler G. Popoola N. Modine J. A. Stephens A. Thompson A. Cangi S. Rajamanickam AI4CE 26 40 0 10 Oct 2020
Heterogeneous Molecular Graph Neural Networks for Predicting Molecule Properties Zeren Shui George Karypis 29 62 0 26 Sep 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 24 214 0 15 Jul 2020
Machine learning for electronically excited states of molecules Julia Westermayr P. Marquetand 27 258 0 10 Jul 2020
InteractionNet: Modeling and Explaining of Noncovalent Protein-Ligand Interactions with Noncovalent Graph Neural Network and Layer-Wise Relevance Propagation Hyeoncheol Cho E. Lee I. Choi GNN FAtt 25 4 0 12 May 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 19 79 0 10 Mar 2020
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 27 93 0 10 Jan 2019
Three-Dimensionally Embedded Graph Convolutional Network (3DGCN) for Molecule Interpretation Hyeoncheol Cho I. Choi 30 55 0 24 Nov 2018
Quantum-chemical insights from interpretable atomistic neural networks Kristof T. Schütt M. Gastegger A. Tkatchenko K. Müller AI4CE 33 31 0 27 Jun 2018
Less is more: sampling chemical space with active learning Justin S. Smith B. Nebgen Nicholas Lubbers Olexandr Isayev A. Roitberg 33 602 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 20 63 0 19 Dec 2017