A universal strategy for the creation of machine learning-based atomistic force fields
Published 2017 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
A universal strategy for the creation of machine learning-based atomistic force fields
Authors
Keywords
-
Journal
npj Computational Materials
Volume 3, Issue 1, Pages -
Publisher
Springer Nature
Online
2017-09-13
DOI
10.1038/s41524-017-0042-y
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Machine learning of accurate energy-conserving molecular force fields
- (2017) Stefan Chmiela et al. Science Advances
- Rational Co-Design of Polymer Dielectrics for Energy Storage
- (2016) Arun Mannodi-Kanakkithodi et al. ADVANCED MATERIALS
- From Organized High-Throughput Data to Phenomenological Theory using Machine Learning: The Example of Dielectric Breakdown
- (2016) Chiho Kim et al. CHEMISTRY OF MATERIALS
- Machine learning for atomic forces in a crystalline solid: Transferability to various temperatures
- (2016) Teppei Suzuki et al. INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY
- Machine Learning Assisted Predictions of Intrinsic Dielectric Breakdown Strength of ABX3 Perovskites
- (2016) Chiho Kim et al. Journal of Physical Chemistry C
- Machine Learning Force Fields: Construction, Validation, and Outlook
- (2016) V. Botu et al. Journal of Physical Chemistry C
- Moment Tensor Potentials: A Class of Systematically Improvable Interatomic Potentials
- (2016) Alexander V. Shapeev MULTISCALE MODELING & SIMULATION
- Computational predictions of energy materials using density functional theory
- (2016) Anubhav Jain et al. Nature Reviews Materials
- Machine Learning Strategy for Accelerated Design of Polymer Dielectrics
- (2016) Arun Mannodi-Kanakkithodi et al. Scientific Reports
- Gaussian approximation potentials: A brief tutorial introduction
- (2015) Albert P. Bartók et al. INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY
- Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials
- (2015) A.P. Thompson et al. JOURNAL OF COMPUTATIONAL PHYSICS
- Machine Learning for Quantum Mechanical Properties of Atoms in Molecules
- (2015) Matthias Rupp et al. Journal of Physical Chemistry Letters
- Learning scheme to predict atomic forces and accelerate materials simulations
- (2015) V. Botu et al. PHYSICAL REVIEW B
- Alignment of the diamond nitrogen vacancy center by strain engineering
- (2014) Todd Karin et al. APPLIED PHYSICS LETTERS
- Adaptive machine learning framework to accelerateab initiomolecular dynamics
- (2014) Venkatesh Botu et al. INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY
- Accuracy and transferability of Gaussian approximation potential models for tungsten
- (2014) Wojciech J. Szlachta et al. PHYSICAL REVIEW B
- Rational design of all organic polymer dielectrics
- (2014) Vinit Sharma et al. Nature Communications
- Accelerating materials property predictions using machine learning
- (2013) Ghanshyam Pilania et al. Scientific Reports
- Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons
- (2010) Albert P. Bartók et al. PHYSICAL REVIEW LETTERS
- Kernel methods in machine learning
- (2008) Thomas Hofmann et al. ANNALS OF STATISTICS
Find Funding. Review Successful Grants.
Explore over 25,000 new funding opportunities and over 6,000,000 successful grants.
ExploreAdd your recorded webinar
Do you already have a recorded webinar? Grow your audience and get more views by easily listing your recording on Peeref.
Upload Now