COMPUTATIONAL MATERIALS SCIENCE XI

By P. Ruggerone, V. Fiorentini, F. Meloni. 2004

137 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/dc0182f78a165c24d99f7b9003ab430772cab896

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Citations in the corpus

Advanced capabilities for materials modelling with Quantum ESPRESSO (2017, methodology): “For such operators, there exists a second recursive algorithm to compute the resolvent – pseudoHermitian Lanczos algorithm – which is numerically more stable and requires only half the numbers of operations per Lanczos step [24, 118].”
Comprehensive search for topological materials using symmetry indicators (2018, unknown): “221 AgSc[905],AgY[906],Ag3In[907],AlNTi3[908],AsSc[909],AsY[910],AuSc[905],AuY[911],Au3Li[912] BPbSc3[913],BPd3Sc[914],BPd3Y[914],BPt3Y[914] BaCd[915],BaHg[916],CCaPd3[917],CInY3[918],CTlY3[919] C20[920],Ca[921],CaTe[922],Ca3NTl[923],Ca3Pb[924],CdSr[925],CsIO3[926],HfPd[927] Hg3Zr[927],InNTi3[928],I”
Machine learning with force-field inspired descriptors for materials: fast screening and mapping energy landscape (2018, methodology): “enhance interpretability of the ML models we chose gradient boosting decision trees (GBDT) method [25].”
Tunable magnetic properties of transition metal doped MoS 2 (2014, methodology): “A unique and relatively accurate U-parametrization could be possible if the chosen Uparameters for the cation d- and anion p-orbitals would have been fitted to two experimental properties of the material, namely the band gap and a spectral or structural property[17, 22].”
Crystal structure representations for machine learning models of formation energies (2015, methodology): “We note briefly that the python materials genomics (pytmatgen) open source library [36] contains a similar matrix as an intermediate step in the calculation of the full Ewald sum energy.”
Atomistic spin model simulations of magnetic nanomaterials (2013, methodology): “Today most magnetic modelling is performed using numerical micromagnetics in finite-difference[37] and finiteelement[38, 39] flavours.”
Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis (2012, methodology): “The Materials Project is based on the high-throughput framework developed by Jain et al. [6] and subsequently extended by collaborators at the Lawrence Berkeley Laboratory and National Energy Research Scientific Computing Center (NERSC).”
Bypassing the Kohn-Sham equations with machine learning (2016, background): “Such calculations are playing a key role in the materials genome initiative[5], at least for weakly correlated materials[6].”