AFLOW-ML: A RESTful API for machine-learning predictions of materials properties

TL;DR

AFLOW-ML offers an accessible RESTful API that simplifies the integration of machine learning models for predicting various materials properties, thereby accelerating materials research and development.

Contribution

It introduces a cloud-based API that streamlines access to AFLOW's machine learning models, reducing technical barriers for users.

Findings

Provides predictions for electronic, thermal, and mechanical properties

Enables integration into diverse workflows

Facilitates faster materials discovery

Abstract

Machine learning approaches, enabled by the emergence of comprehensive databases of materials properties, are becoming a fruitful direction for materials analysis. As a result, a plethora of models have been constructed and trained on existing data to predict properties of new systems. These powerful methods allow researchers to target studies only at interesting materials $\unicode{x2014}$ neglecting the non-synthesizable systems and those without the desired properties $\unicode{x2014}$ thus reducing the amount of resources spent on expensive computations and/or time-consuming experimental synthesis. However, using these predictive models is not always straightforward. Often, they require a panoply of technical expertise, creating barriers for general users. AFLOW-ML (AFLOW $\underline{\mathrm{M}}$achine $\underline{\mathrm{L}}$earning) overcomes the problem by streamlining the use of the machine learning methods developed within the AFLOW consortium. The framework provides an open RESTful API to directly access the continuously updated algorithms, which can be transparently integrated into any workflow to retrieve predictions of electronic, thermal and mechanical properties. These types of interconnected cloud-based applications are envisioned to be capable of further accelerating the adoption of machine learning methods into materials development.