The OpenLAM Challenges
Not on the map. Why?
Abstract (arXiv)
Inspired by the success of Large Language Models (LLMs), the development of Large Atom Models (LAMs) has gained significant momentum in scientific computation. Since 2022, the Deep Potential team has been actively pretraining LAMs and launched the OpenLAM Initiative to develop an open-source foundation model spanning the periodic table. A core objective is establishing comprehensive benchmarks for reliable LAM evaluation, addressing limitations in existing datasets. As a first step, the LAM Crystal Philately competition has collected over 19.8 million valid structures, including 1 million on the OpenLAM convex hull, driving advancements in generative modeling and materials science applications.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 4 of its 41 references that are in the map. First posted on arXiv 20 Jan 2025.
Builds on
4 of 41 references.
References · 4
- 2022MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fieldscited by 474
- 2020The Open Catalyst 2020 (OC20) Dataset and Community Challengescited by 246
- 2023Matbench Discovery -- A framework to evaluate machine learning crystal stability predictionscited by 35
- 2022DPA-1: Pretraining of Attention-based Deep Potential Model for Molecular Simulationcited by 15
Built on by
Citing papers per year: 2025: 1.
1 paper.