Leveraging Diverse Chemical Datasets for Generalizable Atomic Property Prediction through Joint Multi-domain Pre-training

Core Concepts

Joint Multi-domain Pre-training (JMP) is an effective supervised pre-training strategy that leverages diverse chemical datasets to learn generalizable representations for accurate and efficient atomic property prediction across multiple domains.

Abstract

The paper introduces Joint Multi-domain Pre-training (JMP), a supervised pre-training strategy for atomic property prediction that simultaneously trains on multiple datasets from different chemical domains. The key insights are:

JMP frames each chemical dataset as a separate pre-training task within a multi-task learning framework, allowing a single model to learn from diverse data sources.

JMP employs several techniques to address the challenges of multi-task pre-training, including data normalization, temperature-based dataset sampling, structure-wise loss reduction, and effective regularization.

Evaluating on a comprehensive benchmark spanning small molecules, large molecules, and materials, JMP demonstrates significant performance improvements over training from scratch, setting or matching state-of-the-art on 34 out of 40 tasks.

JMP enables efficient scaling to larger models, overcoming the typical overfitting issues observed when training large models from scratch on small datasets. The pre-trained JMP models exhibit a 12x reduction in fine-tuning compute compared to training from scratch.

The authors conduct detailed ablation studies to understand the impact of various components of the JMP method, highlighting the importance of diverse multi-task pre-training and effective regularization strategies.

Overall, the paper presents a powerful pre-training approach that leverages the availability of large, diverse chemical datasets to advance the state-of-the-art in atomic property prediction, especially for low-data tasks and out-of-domain applications.

Stats

The combined pre-training dataset contains over 120M training examples with energy and force labels, with the majority (> 99%) coming from non-equilibrium structures.
The fine-tuning datasets range from 600 to 130,000 examples, covering small molecules, large molecules, and materials.

Quotes

"Foundation models have been transformational in machine learning fields such as natural language processing and computer vision. Similar success in atomic property prediction has been limited due to the challenges of training effective models across multiple chemical domains."
"By pre-training large models on diverse chemical data, we believe JMP represents an important step towards the goal of a universal ML potential, and that the continued growth of available data and compute power will only improve JMP's ability to learn transferable atomic representations."

Key Insights Distilled From

From Molecules to Materials: Pre-training Large Generalizable Models for Atomic Property Prediction

by Nima Shoghi,... at arxiv.org 05-07-2024

https://arxiv.org/pdf/2310.16802.pdf

Deeper Inquiries

How can the JMP approach be extended to incorporate self-supervised pre-training in addition to the supervised multi-task learning

To extend the JMP approach to incorporate self-supervised pre-training alongside supervised multi-task learning, we can introduce additional pre-training tasks that focus on self-supervised learning objectives. Self-supervised learning tasks can include predicting missing parts of molecular structures, reconstructing corrupted molecular graphs, or generating molecular embeddings based on context prediction. By integrating self-supervised tasks into the pre-training phase, the model can learn more robust and generalizable representations of atomic interactions. This hybrid approach would allow the model to leverage both labeled data from supervised tasks and unlabeled data from self-supervised tasks, enhancing its ability to capture underlying patterns in the data.

What are the potential limitations of the current JMP approach in terms of handling out-of-distribution data or rare chemical species

The current JMP approach may face potential limitations when handling out-of-distribution data or rare chemical species. One limitation is the risk of overfitting to the majority classes or chemical species present in the pre-training datasets, leading to reduced performance on out-of-distribution or rare data points during fine-tuning. To address this limitation, techniques such as data augmentation, transfer learning from related chemical species, or incorporating domain adaptation methods can be employed. Additionally, introducing diversity in the pre-training datasets by including a broader range of chemical species and structures can help the model generalize better to out-of-distribution data. Regularization techniques and model calibration strategies can also mitigate the impact of rare chemical species on model performance.

Could the JMP framework be adapted to enable few-shot learning or meta-learning for atomic property prediction tasks with limited data

Adapting the JMP framework to enable few-shot learning or meta-learning for atomic property prediction tasks with limited data involves designing specialized pre-training and fine-tuning strategies. For few-shot learning, the pre-training phase can focus on learning transferable features from a diverse set of chemical domains while fine-tuning can involve meta-learning techniques that leverage information from a few examples to adapt the model quickly to new tasks. Meta-learning algorithms like MAML (Model-Agnostic Meta-Learning) or Reptile can be integrated into the fine-tuning process to facilitate rapid adaptation to new atomic property prediction tasks with limited data. By incorporating few-shot learning and meta-learning principles, the JMP framework can enhance its ability to generalize to new tasks with minimal training data.