ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2408.05215
  4. Cited By
Physics-Informed Weakly Supervised Learning for Interatomic Potentials

Physics-Informed Weakly Supervised Learning for Interatomic Potentials

23 July 2024
Makoto Takamoto
Viktor Zaverkin
Mathias Niepert
    AI4CE
ArXivPDFHTML

Papers citing "Physics-Informed Weakly Supervised Learning for Interatomic Potentials"

2 / 2 papers shown
Title
Gaussian Moments as Physically Inspired Molecular Descriptors for
  Accurate and Scalable Machine Learning Potentials
Gaussian Moments as Physically Inspired Molecular Descriptors for Accurate and Scalable Machine Learning Potentials
Viktor Zaverkin
Johannes Kastner
34
67
0
15 Sep 2021
The Open Catalyst 2020 (OC20) Dataset and Community Challenges
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
226
503
0
20 Oct 2020
1