Self-Conditioned Denoising for Atomistic Representation Learning
arXiv:2603.17196v1 Announce Type: new Abstract: The success of large-scale pretraining in NLP and computer vision has catalyzed growing efforts to develop analogous foundation models for the physical sciences. However, pretraining strategies using atomistic data remain underexplored. To date, large-scale supervised pretraining on DFT force-energy labels has provided the strongest performance gains to downstream property prediction, out-performing existing methods of self-supervised learning (SSL) which remain limited to ground-state geometries, and/or single domains of atomistic data. We address these shortcomings with Self-Conditioned Denoising (SCD), a backbone-agnostic reconstruction objective that utilizes self-embeddings for conditional denoising across any domain of atomistic data, including small molecules, proteins, periodic materials, and 'non-equilibrium' geometries. When controlled for backbone architecture and pretraining dataset, SCD significantly outperforms previous SSL
arXiv:2603.17196v1 Announce Type: new Abstract: The success of large-scale pretraining in NLP and computer vision has catalyzed growing efforts to develop analogous foundation models for the physical sciences. However, pretraining strategies using atomistic data remain underexplored. To date, large-scale supervised pretraining on DFT force-energy labels has provided the strongest performance gains to downstream property prediction, out-performing existing methods of self-supervised learning (SSL) which remain limited to ground-state geometries, and/or single domains of atomistic data. We address these shortcomings with Self-Conditioned Denoising (SCD), a backbone-agnostic reconstruction objective that utilizes self-embeddings for conditional denoising across any domain of atomistic data, including small molecules, proteins, periodic materials, and 'non-equilibrium' geometries. When controlled for backbone architecture and pretraining dataset, SCD significantly outperforms previous SSL methods on downstream benchmarks and matches or exceeds the performance of supervised force-energy pretraining. We show that a small, fast GNN pretrained by SCD can achieve competitive or superior performance to larger models pretrained on significantly larger labeled or unlabeled datasets, across tasks in multiple domains. Our code is available at: https://github.com/TyJPerez/SelfConditionedDenoisingAtoms
Executive Summary
This article presents Self-Conditioned Denoising (SCD), a novel backbone-agnostic reconstruction objective for atomistic representation learning. SCD utilizes self-embeddings for conditional denoising across various domains of atomistic data, outperforming previous self-supervised learning (SSL) methods and matching or exceeding the performance of supervised force-energy pretraining. The approach is demonstrated to achieve competitive or superior performance to larger models pretrained on larger labeled or unlabeled datasets across tasks in multiple domains. The authors' code is available for public access, enabling further research and development. This study has significant implications for the development of foundation models in the physical sciences, particularly in the areas of materials science and computational chemistry.
Key Points
- ▸ SCD is a novel backbone-agnostic reconstruction objective for atomistic representation learning.
- ▸ SCD utilizes self-embeddings for conditional denoising across various domains of atomistic data.
- ▸ SCD outperforms previous SSL methods and matches or exceeds the performance of supervised force-energy pretraining.
Merits
Strength in Self-Supervised Learning
SCD presents a significant advancement in self-supervised learning for atomistic representation, offering a versatile and effective approach to learning from unlabeled data. This strength enables researchers to explore the physical sciences without relying on expensive and time-consuming labeling processes.
Scalability and Efficiency
The ability of SCD to achieve competitive or superior performance to larger models pretrained on larger labeled or unlabeled datasets highlights its scalability and efficiency. This merit has significant implications for practical applications, particularly in areas where computational resources are limited.
Demerits
Limited Domain Generalization
While SCD demonstrates impressive performance across various domains, its ability to generalize to entirely new domains remains unclear. Further research is necessary to fully understand the limitations of SCD in this regard.
Dependence on Backbone Architecture
SCD's backbone-agnostic nature is a significant advantage, but its performance may still be influenced by the choice of backbone architecture. More research is needed to fully understand this relationship and optimize SCD for various backbones.
Expert Commentary
The article presents a significant advancement in self-supervised learning for atomistic representation, offering a versatile and effective approach to learning from unlabeled data. The study's findings have significant implications for the development of foundation models in the physical sciences, enabling researchers to learn from unlabeled data and apply their knowledge to various domains. However, further research is necessary to fully understand the limitations of SCD, particularly in terms of its ability to generalize to entirely new domains. Additionally, the study highlights the potential for SCD to facilitate the development of generalizable models in areas such as materials science and computational chemistry.
Recommendations
- ✓ Further research is necessary to fully understand the limitations of SCD and optimize its performance for various backbone architectures.
- ✓ The study's findings should be replicated and validated in various domains to fully understand the generalizability of SCD.
