7 research outputs found
A RESTful API for exchanging Materials Data in the AFLOWLIB.org consortium
The continued advancement of science depends on shared and reproducible data.
In the field of computational materials science and rational materials design
this entails the construction of large open databases of materials properties.
To this end, an Application Program Interface (API) following REST principles
is introduced for the AFLOWLIB.org materials data repositories consortium.
AUIDs (Aflowlib Unique IDentifier) and AURLs (Aflowlib Uniform Resource
locator) are assigned to the database resources according to a well-defined
protocol described herein, which enables the client to access, through
appropriate queries, the desired data for post-processing. This introduces a
new level of openness into the AFLOWLIB repository, allowing the community to
construct high-level work-flows and tools exploiting its rich data set of
calculated structural, thermodynamic, and electronic properties. Furthermore,
federating these tools would open the door to collaborative investigation of
the data by an unprecedented extended community of users to accelerate the
advancement of computational materials design and development.Comment: 22 pages, 7 figure
AFLOW-ML: A RESTful API for machine-learning predictions of materials properties
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 achine
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.Comment: 10 pages, 2 figure
OPTIMADE, an API for exchanging materials data.
The Open Databases Integration for Materials Design (OPTIMADE) consortium has designed a universal application programming interface (API) to make materials databases accessible and interoperable. We outline the first stable release of the specification, v1.0, which is already supported by many leading databases and several software packages. We illustrate the advantages of the OPTIMADE API through worked examples on each of the public materials databases that support the full API specification
OPTIMADE, an API for exchanging materials data
: The Open Databases Integration for Materials Design (OPTIMADE) consortium has designed a universal application programming interface (API) to make materials databases accessible and interoperable. We outline the first stable release of the specification, v1.0, which is already supported by many leading databases and several software packages. We illustrate the advantages of the OPTIMADE API through worked examples on each of the public materials databases that support the full API specification
Obesity and depression: shared pathophysiology and translational implications
There is mounting evidence for a close relationship between obesity and depression. Depression is frequent in obese subjects and, in turn, obesity is associated with a greater risk of depression. Moreover, recent data suggest a role for obesity in treatment-resistant depression. While the association is bidirectional, the paths and mechanisms by which obesity can lead to depression appear to be particularly relevant to biological psychiatry, as they can provide new information on the pathophysiology and treatment of mood disorders. This chapter will review those pathophysiological pathways and processes that are shared by obesity and depression and that are likely to underlie the intricate relationship between the two disorders. Their potential translational implications and relevance to the development of personalized strategies for the treatment and management of depression will be further discussed