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Matbench Discovery -- A framework to evaluate machine learning crystal
  stability predictions

Matbench Discovery -- A framework to evaluate machine learning crystal stability predictions

28 August 2023
Janosh Riebesell
Rhys E. A. Goodall
Philipp Benner
Chiang Yuan
Bowen Deng
A. Lee
Anubhav Jain
Kristin A. Persson
    OOD
ArXivPDFHTML

Papers citing "Matbench Discovery -- A framework to evaluate machine learning crystal stability predictions"

9 / 9 papers shown
Title
MatterTune: An Integrated, User-Friendly Platform for Fine-Tuning Atomistic Foundation Models to Accelerate Materials Simulation and Discovery
MatterTune: An Integrated, User-Friendly Platform for Fine-Tuning Atomistic Foundation Models to Accelerate Materials Simulation and Discovery
Lingyu Kong
Nima Shoghi
Guoxiang Hu
Pan Li
Victor Fung
21
0
0
14 Apr 2025
SymmCD: Symmetry-Preserving Crystal Generation with Diffusion Models
SymmCD: Symmetry-Preserving Crystal Generation with Diffusion Models
Daniel Levy
Siba Smarak Panigrahi
Sékou-Oumar Kaba
Qiang Zhu
Kin Long Kelvin Lee
Mikhail Galkin
Santiago Miret
Siamak Ravanbakhsh
83
11
0
05 Feb 2025
Establishing baselines for generative discovery of inorganic crystals
N. Szymanski
Christopher J. Bartel
30
1
0
04 Jan 2025
Predicting ionic conductivity in solids from the machine-learned potential energy landscape
Predicting ionic conductivity in solids from the machine-learned potential energy landscape
Artem Maevskiy
Alexandra Carvalho
Emil Sataev
Volha Turchyna
Keian Noori
Aleksandr Rodin
A. H. Castro Neto
Andrey E. Ustyuzhanin
29
0
0
11 Nov 2024
Learning Ordering in Crystalline Materials with Symmetry-Aware Graph
  Neural Networks
Learning Ordering in Crystalline Materials with Symmetry-Aware Graph Neural Networks
Jiayu Peng
James K. Damewood
Jessica Karaguesian
Jaclyn R. Lunger
Rafael Gómez-Bombarelli
AI4CE
32
2
0
20 Sep 2024
Interpolation and differentiation of alchemical degrees of freedom in
  machine learning interatomic potentials
Interpolation and differentiation of alchemical degrees of freedom in machine learning interatomic potentials
Juno Nam
Rafael Gómez-Bombarelli
AI4CE
19
4
0
16 Apr 2024
E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate
  Interatomic Potentials
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
183
1,218
0
08 Jan 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
202
370
0
20 Oct 2020
MoleculeNet: A Benchmark for Molecular Machine Learning
MoleculeNet: A Benchmark for Molecular Machine Learning
Zhenqin Wu
Bharath Ramsundar
Evan N. Feinberg
Joseph Gomes
C. Geniesse
Aneesh S. Pappu
K. Leswing
Vijay S. Pande
OOD
152
1,748
0
02 Mar 2017
1