GPU-accelerated approximate kernel method for quantum machine learning
Published 2022 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
GPU-accelerated approximate kernel method for quantum machine learning
Authors
Keywords
-
Journal
JOURNAL OF CHEMICAL PHYSICS
Volume 157, Issue 21, Pages 214801
Publisher
AIP Publishing
Online
2022-11-15
DOI
10.1063/5.0108967
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Permutationally invariant polynomial regression for energies and gradients, using reverse differentiation, achieves orders of magnitude speed-up with high precision compared to other machine learning methods
- (2022) Paul L. Houston et al. JOURNAL OF CHEMICAL PHYSICS
- Machine learned interatomic potentials using random features
- (2022) Gurjot Dhaliwal et al. npj Computational Materials
- E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials
- (2022) Simon Batzner et al. Nature Communications
- High-throughput predictions of metal–organic framework electronic properties: theoretical challenges, graph neural networks, and data exploration
- (2022) Andrew S. Rosen et al. npj Computational Materials
- Challenges for machine learning force fields in reproducing potential energy surfaces of flexible molecules
- (2021) Valentin Vassilev-Galindo et al. JOURNAL OF CHEMICAL PHYSICS
- Machine learning meets chemical physics
- (2021) Michele Ceriotti et al. JOURNAL OF CHEMICAL PHYSICS
- Efficient implementation of atom-density representations
- (2021) Félix Musil et al. JOURNAL OF CHEMICAL PHYSICS
- Ab Initio Machine Learning in Chemical Compound Space
- (2021) Bing Huang et al. CHEMICAL REVIEWS
- Introduction: Machine Learning at the Atomic Scale
- (2021) Michele Ceriotti et al. CHEMICAL REVIEWS
- Gaussian Process Regression for Materials and Molecules
- (2021) Volker L. Deringer et al. CHEMICAL REVIEWS
- Properties of α-Brass Nanoparticles II: Structure and Composition
- (2021) Jan Weinreich et al. Journal of Physical Chemistry C
- Performant implementation of the atomic cluster expansion (PACE) and application to copper and silicon
- (2021) Yury Lysogorskiy et al. npj Computational Materials
- Linear Atomic Cluster Expansion Force Fields for Organic Molecules: Beyond RMSE
- (2021) Dávid Péter Kovács et al. Journal of Chemical Theory and Computation
- Properties of α-Brass Nanoparticles. 1. Neural Network Potential Energy Surface
- (2020) Jan Weinreich et al. Journal of Physical Chemistry C
- TorchANI: A Free and Open Source PyTorch-Based Deep Learning Implementation of the ANI Neural Network Potentials
- (2020) Xiang Gao et al. Journal of Chemical Information and Modeling
- Extending the Applicability of the ANI Deep Learning Molecular Potential to Sulfur and Halogens
- (2020) Christian Devereux et al. Journal of Chemical Theory and Computation
- Exploring chemical compound space with quantum-based machine learning
- (2020) O. Anatole von Lilienfeld et al. Nature Reviews Chemistry
- Retrospective on a decade of machine learning for chemical discovery
- (2020) O. Anatole von Lilienfeld et al. Nature Communications
- Operators in quantum machine learning: Response properties in chemical space
- (2019) Anders S. Christensen et al. JOURNAL OF CHEMICAL PHYSICS
- sGDML: Constructing accurate and data efficient molecular force fields using machine learning
- (2019) Stefan Chmiela et al. COMPUTER PHYSICS COMMUNICATIONS
- SchNet – A deep learning architecture for molecules and materials
- (2018) K. T. Schütt et al. JOURNAL OF CHEMICAL PHYSICS
- Alchemical and structural distribution based representation for universal quantum machine learning
- (2018) Felix A. Faber et al. JOURNAL OF CHEMICAL PHYSICS
- wACSF—Weighted atom-centered symmetry functions as descriptors in machine learning potentials
- (2018) M. Gastegger et al. JOURNAL OF CHEMICAL PHYSICS
- Neural networks vs Gaussian process regression for representing potential energy surfaces: A comparative study of fit quality and vibrational spectrum accuracy
- (2018) Aditya Kamath et al. JOURNAL OF CHEMICAL PHYSICS
- Towards exact molecular dynamics simulations with machine-learned force fields
- (2018) Stefan Chmiela et al. Nature Communications
- ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost
- (2017) J. S. Smith et al. Chemical Science
- Quantum-chemical insights from deep tensor neural networks
- (2017) Kristof T. Schütt et al. Nature Communications
- OpenMM 7: Rapid development of high performance algorithms for molecular dynamics
- (2017) Peter Eastman et al. PLoS Computational Biology
- Machine learning of accurate energy-conserving molecular force fields
- (2017) Stefan Chmiela et al. Science Advances
- Efficient non-parametric fitting of potential energy surfaces for polyatomic molecules with Gaussian processes
- (2016) Jie Cui et al. JOURNAL OF PHYSICS B-ATOMIC MOLECULAR AND OPTICAL PHYSICS
- Gaussian approximation potentials: A brief tutorial introduction
- (2015) Albert P. Bartók et al. INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY
- Quantum chemistry structures and properties of 134 kilo molecules
- (2014) Raghunathan Ramakrishnan et al. Scientific Data
- On representing chemical environments
- (2013) Albert P. Bartók et al. PHYSICAL REVIEW B
- Commentary: The Materials Project: A materials genome approach to accelerating materials innovation
- (2013) Anubhav Jain et al. APL Materials
- Atom-centered symmetry functions for constructing high-dimensional neural network potentials
- (2011) Jörg Behler JOURNAL OF CHEMICAL PHYSICS
- Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons
- (2010) Albert P. Bartók et al. PHYSICAL REVIEW LETTERS
- Permutationally invariant potential energy surfaces in high dimensionality
- (2009) Bastiaan J. Braams et al. INTERNATIONAL REVIEWS IN PHYSICAL CHEMISTRY
Find Funding. Review Successful Grants.
Explore over 25,000 new funding opportunities and over 6,000,000 successful grants.
ExploreAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started