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

نویسندگان
چکیده

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

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

Eric Gossett, 2 Cormac Toher, 2 Corey Oses, 2 Olexandr Isayev, Fleur Legrain, 5 Frisco Rose, 2 Eva Zurek, Jesús Carrete, Natalio Mingo, Alexander Tropsha, and Stefano Curtarolo 2, 8, ∗ Department of Mechanical Engineering and Materials Science, Duke University, Durham, North Carolina 27708, USA Center for Materials Genomics, Duke University, Durham, North Carolina 27708, USA Laboratory for Mole...

متن کامل

Protocols and Structures for Inference: A RESTful API for Machine Learning

Diversity in machine learning APIs (in both software toolkits and web services), works against realising machine learning’s full potential, making it difficult to draw on individual algorithms from different products or to compose multiple algorithms to solve complex tasks. This paper introduces the Protocols and Structures for Inference (PSI) service architecture and specification, which prese...

متن کامل

Accelerating materials property predictions using machine learning

The materials discovery process can be significantly expedited and simplified if we can learn effectively from available knowledge and data. In the present contribution, we show that efficient and accurate prediction of a diverse set of properties of material systems is possible by employing machine (or statistical) learning methods trained on quantum mechanical computations in combination with...

متن کامل

AFLUX: The LUX materials search API for the AFLOW data repositories

Article history: Received 29 March 2017 Accepted 29 April 2017 Automated computational materials science frameworks rapidly generate large quantities of materials data for accelerated materials design. In order to take advantage of these large databases, users should have the ability to efficiently search and extract the desired data. Therefore, we have extended the data-oriented AFLOW-reposito...

متن کامل

A RESTful API for exchanging materials data in the AFLOWLIB.org consortium

http://dx.doi.org/10.1016/j.commatsci.2014.05.014 0927-0256/ 2014 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/3.0/). ⇑ Corresponding author. E-mail address: [email protected] (S. Curtarolo). 1 On leave from the Physics Department, NRCN, Israel. Richard H. Taylor , Frisco Rose , Cormac Toher , Ohad Levy , K...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: Computational Materials Science

سال: 2018

ISSN: 0927-0256

DOI: 10.1016/j.commatsci.2018.03.075