APEX: an automated cloud-native material property explorer

IF 9.4 1区材料科学 Q1 CHEMISTRY, PHYSICAL

npj Computational Materials Pub Date : 2025-04-02 DOI:10.1038/s41524-025-01580-y

Zhuoyuan Li, Tongqi Wen, Yuzhi Zhang, Xinzijian Liu, Chengqian Zhang, A. S. L. Subrahmanyam Pattamatta, Xiaoguo Gong, Beilin Ye, Han Wang, Linfeng Zhang, David J. Srolovitz

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Abstract

The ability to rapidly evaluate materials properties through atomistic simulation approaches is the foundation of many new artificial intelligence-based approaches to materials identification and design. This depends on the availability of accurate descriptions of atomic bonding and an efficient means for determining materials properties. We present an efficient, robust platform for calculating materials properties from a wide-range of atomic bonding descriptions, i.e., APEX, the Alloy Property Explorer. APEX enables the rapid evolution of interatomic potential development and optimization, which is of particular importance in fine-tuning new classes of general AI-based foundation models for applications in materials science and engineering. APEX is an open-source, extendable, cloud-native platform for material property calculations using a range of atomistic simulation methodologies that effectively manages diverse computational resources and is built upon user-friendly features including automatic results visualization, a web-based platform and a NoSQL database client. It is designed for expert and non-specialist users, lowering the barrier to entry for interdisciplinary research within an “AI for Materials” framework. We describe the foundation and use of APEX, as well as provide two examples of its application to properties of titanium and 179 metals and alloys for a wide-range of bonding descriptions.

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来源期刊

npj Computational Materials Mathematics-Modeling and Simulation

CiteScore

15.30

自引率

5.20%

发文量

229

审稿时长

6 weeks

期刊介绍： npj Computational Materials is a high-quality open access journal from Nature Research that publishes research papers applying computational approaches for the design of new materials and enhancing our understanding of existing ones. The journal also welcomes papers on new computational techniques and the refinement of current approaches that support these aims, as well as experimental papers that complement computational findings. Some key features of npj Computational Materials include a 2-year impact factor of 12.241 (2021), article downloads of 1,138,590 (2021), and a fast turnaround time of 11 days from submission to the first editorial decision. The journal is indexed in various databases and services, including Chemical Abstracts Service (ACS), Astrophysics Data System (ADS), Current Contents/Physical, Chemical and Earth Sciences, Journal Citation Reports/Science Edition, SCOPUS, EI Compendex, INSPEC, Google Scholar, SCImago, DOAJ, CNKI, and Science Citation Index Expanded (SCIE), among others.