Data-Driven Discovery in Mineralogy: Recent Advances in Data Resources, Analysis, and Visualization
Sign Up to like & getrecommendations! Published in 2019 at "Engineering"
DOI: 10.1016/j.eng.2019.03.006
Abstract: Abstract Large and growing data resources on the diversity, distribution, and properties of minerals are ushering in a new era of data-driven discovery in mineralogy. The most comprehensive international mineral database is the IMA database,… read more here.
Keywords: discovery mineralogy; driven discovery; data resources; data driven ... See more keywords
Data-Driven Discovery of Photoactive Quaternary Oxides Using First-Principles Machine Learning
Sign Up to like & getrecommendations! Published in 2019 at "Chemistry of Materials"
DOI: 10.1021/acs.chemmater.9b01519
Abstract: We present a low-cost, virtual high-throughput materials design workflow and use it to identify earth-abundant materials for solar energy applications from the quaternary oxide chemical space. A statistical model that predicts bandgap from chemical composition… read more here.
Keywords: discovery photoactive; machine learning; driven discovery; data driven ... See more keywords
Genomics-Driven Discovery of Chlorinated Cyclic Hexapeptides Ulleungmycins A and B from a Streptomyces Species.
Sign Up to like & getrecommendations! Published in 2017 at "Journal of natural products"
DOI: 10.1021/acs.jnatprod.7b00660
Abstract: Analysis of the genome sequence of Streptomyces sp. KCB13F003 showed the presence of a cryptic gene cluster encoding flavin-dependent halogenase and nonribosomal peptide synthetase. Pleiotropic approaches using multiple culture media followed by LC-MS-guided isolation and… read more here.
Keywords: cyclic hexapeptides; driven discovery; hexapeptides ulleungmycins; discovery chlorinated ... See more keywords
Data-driven discovery of Green’s functions with human-understandable deep learning
Sign Up to like & getrecommendations! Published in 2022 at "Scientific Reports"
DOI: 10.1038/s41598-022-08745-5
Abstract: There is an opportunity for deep learning to revolutionize science and technology by revealing its findings in a human interpretable manner. To do this, we develop a novel data-driven approach for creating a human–machine partnership… read more here.
Keywords: data driven; green functions; deep learning; driven discovery ... See more keywords
Robust data-driven discovery of governing physical laws with error bars
Sign Up to like & getrecommendations! Published in 2018 at "Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences"
DOI: 10.1098/rspa.2018.0305
Abstract: Discovering governing physical laws from noisy data is a grand challenge in many science and engineering research areas. We present a new approach to data-driven discovery of ordinary differential equations (ODEs) and partial differential equations… read more here.
Keywords: driven discovery; data driven; robust data; error bars ... See more keywords
Data-driven discovery of partial differential equation models with latent variables
Sign Up to like & getrecommendations! Published in 2019 at "Physical review. E"
DOI: 10.1103/physreve.100.022219
Abstract: In spatially extended systems, it is common to find latent variables that are hard, or even impossible, to measure with acceptable precision but are crucially important for the proper description of the dynamics. This substantially… read more here.
Keywords: discovery partial; partial differential; driven discovery; equation ... See more keywords
Data-Driven Discovery of Active Nematic Hydrodynamics.
Sign Up to like & getrecommendations! Published in 2022 at "Physical review letters"
DOI: 10.1103/physrevlett.129.258001
Abstract: Active nematics can be modeled using phenomenological continuum theories that account for the dynamics of the nematic director and fluid velocity through partial differential equations (PDEs). While these models provide a statistical description of the… read more here.
Keywords: hydrodynamics; discovery active; data driven; active nematic ... See more keywords
Navigating the Ti-C-O and Al-C-O ternary systems through theory-driven discovery
Sign Up to like & getrecommendations! Published in 2021 at "Physical Review Materials"
DOI: 10.1103/physrevmaterials.5.123801
Abstract: Joseph R. Nelson, 2, ∗ Richard J. Needs, and Chris J. Pickard 2 Department of Materials Science and Metallurgy, University of Cambridge, 27 Charles Babbage Road, Cambridge CB3 0FS, United Kingdom Advanced Institute for Materials… read more here.
Keywords: cambridge; navigating ternary; theory driven; systems theory ... See more keywords
Data-Driven Discovery of Block-Oriented Nonlinear Models Using Sparse Null-Subspace Methods.
Sign Up to like & getrecommendations! Published in 2020 at "IEEE transactions on cybernetics"
DOI: 10.1109/tcyb.2020.3015705
Abstract: This article develops an identification algorithm for nonlinear systems. Specifically, the nonlinear system identification problem is formulated as a sparse recovery problem of a homogeneous variant searching for the sparsest vector in the null subspace.… read more here.
Keywords: discovery block; block oriented; subspace; driven discovery ... See more keywords