COMBINING ARTIFICIAL INTELLIGENCE WITH COMBINATORI
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Posted On: 09/18/2017 10:50:42 PM
Re: Puravida19 #7829
COMBINING ARTIFICIAL INTELLIGENCE WITH COMBINATORIAL X-RAY DIFFRACTION ENABLES RAPID PHASE MAPPING OF NEW MATERIALS
JCAP’s high-throughput team has partnered with computer scientists from Cornell University to develop a new method for rapid construction of phase diagrams using data from JCAP’s collaboration with Stanford Synchrotron Radiation Lightsource Laboratory.
Suram, S. K. et al. Automated Phase Mapping with AgileFD and its Application to Light Absorber Discovery in the V-Mn-Nb Oxide System. ACS Combinatorial Science, 19(1), 37-46 DOI: 10.1021/acscombsci.6b00153 (2017)
A recent article led by S. Suram details the development of an algorithm that captures the complexity of material phase behavior. The algorithm is called AgileFD and is the first scalable phase mapping algorithm for combinatorial XRD data that models alloying-based peak shifting and adheres to Gibbs’ phase rules. AgileFD enabled characterization of the previously-unknown phase behavior of V-Mn-Nb oxides where the identification of eight phases, including phases with significant alloying, generated the first phase map for this pseudoternary system. When the AgileFD solutions are combined with bad-gap energies from automated Tauc analysis and high-throughput UV-Vis spectroscopy data, the researchers could identify band-gap tuning of nearly 0.2 eV in the MnV2O6 crystal structure as a function of lattice parameters and vanadium composition in the energy range suitable for solar applications.
The article was recognized as the ACS Editor’s Choice®, is the cover article in January’s issue of ACS Combinatorial Science, and demonstrates how the multidisciplinary effort in material and computer science accelerates discovery of materials that are critical to emerging technologies such solar-fuel generation.
https://solarfuelshub.org/012317-jcap-ai
JCAP’s high-throughput team has partnered with computer scientists from Cornell University to develop a new method for rapid construction of phase diagrams using data from JCAP’s collaboration with Stanford Synchrotron Radiation Lightsource Laboratory.
Suram, S. K. et al. Automated Phase Mapping with AgileFD and its Application to Light Absorber Discovery in the V-Mn-Nb Oxide System. ACS Combinatorial Science, 19(1), 37-46 DOI: 10.1021/acscombsci.6b00153 (2017)
A recent article led by S. Suram details the development of an algorithm that captures the complexity of material phase behavior. The algorithm is called AgileFD and is the first scalable phase mapping algorithm for combinatorial XRD data that models alloying-based peak shifting and adheres to Gibbs’ phase rules. AgileFD enabled characterization of the previously-unknown phase behavior of V-Mn-Nb oxides where the identification of eight phases, including phases with significant alloying, generated the first phase map for this pseudoternary system. When the AgileFD solutions are combined with bad-gap energies from automated Tauc analysis and high-throughput UV-Vis spectroscopy data, the researchers could identify band-gap tuning of nearly 0.2 eV in the MnV2O6 crystal structure as a function of lattice parameters and vanadium composition in the energy range suitable for solar applications.
The article was recognized as the ACS Editor’s Choice®, is the cover article in January’s issue of ACS Combinatorial Science, and demonstrates how the multidisciplinary effort in material and computer science accelerates discovery of materials that are critical to emerging technologies such solar-fuel generation.
https://solarfuelshub.org/012317-jcap-ai
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