In computational material science, we need workflows for optimization surrogate models which requires high computation resources. I am actually concerned with why material science community is using FireWorks compared to highly supported workflow management package such as TensorFlow?
Tensorflow and FireWorks are different kinds of software. The "workflow management" features of Tensorflow are primarily designed to manage running Tensorflow itself, while FireWorks and related tools are designed to manage running other software.
Tensorflow is a library for machine learning. It can be used to develop, train and evaluate machine learning models and provides some tooling to build model and data pipelines. Some comparable software to Tensorflow are PyTorch, Flux.jl, and Aesara (formerly Theano). Environments like Wolfram Mathematica and MATLAB also have comparable machine learning tools.
On the other hand, FireWorks is a workflow management software. Some comparable software tools that are also general, not domain-specific are Luigi and Apache Airflow. These workflow management tools typically allow you to define and execute a directed graph of tasks with dependencies. The tasks can be anything from running code, shell scripts, running tasks that modify the workflow graph, and more. Workflow management tools are not used to build mathematical models, like the surrogate models you mentioned in your question.
In matter modeling, workflow management tools are used to orchestrate running a series of calculations in a specific order, enable passing relevant information between jobs, and to provide a high-level interface for managing job state. Some more matter modeling-specific examples are discussed here.