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"

26 / 26 papers shown

Title
Optimal Invariant Bases for Atomistic Machine Learning Alice Allen Emily Shinkle Roxana Bujack Nicholas Lubbers 37 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 52 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 32 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 28 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 24 21 0 03 Mar 2023
HOAX: A Hyperparameter Optimization Algorithm Explorer for Neural Networks Albert S. Thie M. Menger S. Faraji 14 0 0 01 Feb 2023
Synthetic data enable experiments in atomistic machine learning John L A Gardner Z. Beaulieu Volker L. Deringer 34 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 43 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 37 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 47 441 0 15 Jun 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 43 4 0 04 Feb 2022
Graph Neural Networks Accelerated Molecular Dynamics Zijie Li Kazem Meidani Prakarsh Yadav A. Farimani GNN AI4CE 21 53 0 06 Dec 2021
Geometric Transformer for End-to-End Molecule Properties Prediction Yoni Choukroun Lior Wolf AI4CE ViT 25 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 23 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 174 246 0 01 May 2021
A Universal Framework for Featurization of Atomistic Systems Xiangyun Lei A. Medford AI4CE 21 19 0 04 Feb 2021
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 22 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 17 257 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 9 78 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 22 93 0 10 Jan 2019
Quantum-chemical insights from interpretable atomistic neural networks Kristof T. Schütt M. Gastegger A. Tkatchenko K. Müller AI4CE 23 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 25 600 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 18 63 0 19 Dec 2017