15 research outputs found
Replication Data for: Description of the Martensitic Transformation Resulting From the Minimization of Atomic Displacements
The files present the evolution of the crystal structure during the martensitic transformation in the POSCAR format
The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalysts
The development of machine learning models for electrocatalysts requires a
broad set of training data to enable their use across a wide variety of
materials. One class of materials that currently lacks sufficient training data
is oxides, which are critical for the development of Oxygen Evolution Reaction
(OER) catalysts. To address this, we developed the Open Catalyst 2022 (OC22)
dataset, consisting of 62,331 Density Functional Theory (DFT) relaxations
(~9,854,504 single point calculations) across a range of oxide materials,
coverages, and adsorbates. We define generalized total energy tasks that enable
property prediction beyond adsorption energies; we test baseline performance of
several graph neural networks; and we provide pre-defined dataset splits to
establish clear benchmarks for future efforts. In the most general task,
GemNet-OC sees a ~32% improvement in energy predictions when combining the
chemically dissimilar Open Catalyst 2020 Dataset (OC20) and OC22 datasets via
fine-tuning. Similarly, we achieved a ~19% improvement in total energy
predictions on OC20 and a ~9% improvement in force predictions in OC22 when
using joint training. We demonstrate the practical utility of a top performing
model by capturing literature adsorption energies and important OER scaling
relationships. We expect OC22 to provide an important benchmark for models
seeking to incorporate intricate long-range electrostatic and magnetic
interactions in oxide surfaces. The dataset and baseline models are open
sourced, and a public leaderboard has been made available to encourage
continued community developments on the total energy tasks and data.Comment: 48 pages, 14 figure