Injecting Domain Knowledge from Empirical Interatomic Potentials to
Neural Networks for Predicting Material Properties

Injecting Domain Knowledge from Empirical Interatomic Potentials to Neural Networks for Predicting Material Properties

14 October 2022

Daniel S. Karls

George Karypis

Papers citing "Injecting Domain Knowledge from Empirical Interatomic Potentials to Neural Networks for Predicting Material Properties"

6 / 6 papers shown

Title
Transfer Learning for Molecular Property Predictions from Small Data Sets Thorren Kirschbaum A. Bande AI4CE 24 1 0 20 Apr 2024
Synthetic pre-training for neural-network interatomic potentials John L A Gardner Kathryn T. Baker Volker L. Deringer AI4CE 24 18 0 24 Jul 2023
Masked Autoencoders Are Scalable Vision Learners Kaiming He Xinlei Chen Saining Xie Yanghao Li Piotr Dollár Ross B. Girshick ViT TPM 305 7,434 0 11 Nov 2021
In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning Mamshad Nayeem Rizve Kevin Duarte Y. S. Rawat M. Shah 223 509 0 15 Jan 2021
E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials Simon L. Batzner Albert Musaelian Lixin Sun Mario Geiger J. Mailoa M. Kornbluth N. Molinari Tess E. Smidt Boris Kozinsky 203 1,238 0 08 Jan 2021
The Open Catalyst 2020 (OC20) Dataset and Community Challenges L. Chanussot Abhishek Das Siddharth Goyal Thibaut Lavril Muhammed Shuaibi ... Brandon M. Wood Junwoong Yoon Devi Parikh C. L. Zitnick Zachary W. Ulissi 229 503 0 20 Oct 2020