[HTML][HTML] Machine learning for advanced energy materials
The screening of advanced materials coupled with the modeling of their quantitative
structural-activity relationships has recently become one of the hot and trending topics in …
structural-activity relationships has recently become one of the hot and trending topics in …
[HTML][HTML] Scope of machine learning in materials research—A review
This comprehensive review investigates the multifaceted applications of machine learning in
materials research across six key dimensions, redefining the field's boundaries. It explains …
materials research across six key dimensions, redefining the field's boundaries. It explains …
Roadmap on machine learning in electronic structure
In recent years, we have been witnessing a paradigm shift in computational materials
science. In fact, traditional methods, mostly developed in the second half of the XXth century …
science. In fact, traditional methods, mostly developed in the second half of the XXth century …
The NOMAD laboratory: from data sharing to artificial intelligence
C Draxl, M Scheffler - Journal of Physics: Materials, 2019 - iopscience.iop.org
Abstract The Novel Materials Discovery (NOMAD) Laboratory is a user-driven platform for
sharing and exploiting computational materials science data. It accounts for the various …
sharing and exploiting computational materials science data. It accounts for the various …
DFT–kMC analysis for identifying novel bimetallic electrocatalysts for enhanced NRR performance by suppressing HER at ambient conditions via active-site …
As an alternative to the traditional Haber-Bosch process for ammonia synthesis under high
temperature and pressure, the electrochemical nitrogen reduction reaction (NRR) under …
temperature and pressure, the electrochemical nitrogen reduction reaction (NRR) under …
AFLOW-XtalFinder: a reliable choice to identify crystalline prototypes
The accelerated growth rate of repository entries in crystallographic databases makes it
arduous to identify and classify their prototype structures. The open-source AFLOW …
arduous to identify and classify their prototype structures. The open-source AFLOW …
OPTIMADE, an API for exchanging materials data
Abstract The Open Databases Integration for Materials Design (OPTIMADE) consortium has
designed a universal application programming interface (API) to make materials databases …
designed a universal application programming interface (API) to make materials databases …
Designing workflows for materials characterization
Experimental science is enabled by the combination of synthesis, imaging, and functional
characterization organized into evolving discovery loop. Synthesis of new material is …
characterization organized into evolving discovery loop. Synthesis of new material is …
Advanced modeling of materials with PAOFLOW 2.0: New features and software design
Recent research in materials science opens exciting perspectives to design novel quantum
materials and devices, but it calls for quantitative predictions of properties which are not …
materials and devices, but it calls for quantitative predictions of properties which are not …
Extensible Structure-Informed Prediction of Formation Energy with improved accuracy and usability employing neural networks
In the present paper, we introduce a new neural network-based tool for the prediction of
formation energies of atomic structures based on elemental and structural features of …
formation energies of atomic structures based on elemental and structural features of …