Researchers from MIT investigated the scaling behavior of large chemical language models, including generative pre-trained transformers (GPT) for chemistry and graph neural network force fields (GNNs). They introduced the concept of neural scaling, examining the impact of model and data size on pre-training loss. The study also explored hyperparameter optimization using a technique called Training Performance Estimation (TPE). Overall, the research provides insights into resource efficiency in deep learning applications for chemistry.
MIT Research Explores Scaling of Deep Learning Models for Chemistry
A recent study conducted by researchers from MIT investigates the scaling behavior of large chemical language models, specifically focusing on generative pre-trained transformers (GPT) for chemistry (ChemGPT) and graph neural network force fields (GNNs). The study introduces the concept of neural scaling, which characterizes the performance of models in terms of loss scaling as a power law based on factors like model parameters, dataset size, and compute resources. The goal of the research is to provide insights into resource allocation for improving pre-training loss.
ChemGPT: Improving Chemical Language Modeling
The researchers developed ChemGPT, a GPT-3-style model based on GPT-Neo, specifically designed for chemical language modeling. The model uses a tokenizer for self-referencing embedded strings (SELFIES) representations of molecules. It is pre-trained on molecules from PubChem, and the study explores the impact of dataset and model size on pre-training loss.
GNNs for Molecular Geometry and Structure
In addition to language models, the paper also addresses graph neural network force fields (GNNs) for tasks related to molecular geometry and three-dimensional structure. The study considers four types of GNNs with varying model architectures and evaluates their capacity in terms of depth and width during neural-scaling experiments.
Efficient Hyperparameter Optimization
The paper introduces a technique called Training Performance Estimation (TPE) to handle hyperparameter optimization (HPO) for deep chemical models. This technique, adapted from computer vision architectures, utilizes training speed to estimate performance across different domains and model/dataset sizes. The paper provides details on the experimental settings, including the use of NVIDIA Volta V100 GPUs, PyTorch, and distributed data-parallel acceleration for model implementation and training.
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