A3: Hardening Computational Materials-Science Workflows against Human Errors

@article{Carbone2023,

title = {Lightshow: a Python package for generating computational x-ray absorption spectroscopy input files},

author = {Matthew R. Carbone and Fanchen Meng and Christian Vorwerk and Benedikt Maurer and Fabian Peschel and Xiaohui Qu and Eli Stavitski and Claudia Draxl and John Vinson and Deyu Lu},

url = {https://doi.org/10.21105/joss.05182},

doi = {10.21105/joss.05182},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {Journal of Open Source Software},

volume = {8},

number = {87},

pages = {5182},

publisher = {The Open Journal},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Kuban2022,

title = {Similarity of materials and data-quality assessment by fingerprinting},

author = {Martin Kuban and Šimon Gabaj and Wahib Aggoune and Cecilia Vona and Santiago Rigamonti and Claudia Draxl},

url = {https://doi.org/10.1557/s43577-022-00339-w},

doi = {10.1557/s43577-022-00339-w},

issn = {1938-1425},

year  = {2022},

date = {2022-09-16},

urldate = {2022-09-16},

journal = {MRS Bulletin},

abstract = {Identifying similar materials (i.e., those sharing a certain 

property or feature) requires interoperable data of high quality. It 

also requires means to measure similarity. We demonstrate how a spectral 

fingerprint as a descriptor, combined with a similarity metric, can be 

used for establishing quantitative relationships between materials data, 

thereby serving multiple purposes. This concerns, for instance, the 

identification of materials exhibiting electronic properties similar to 

a chosen one. The same approach can be used for assessing uncertainty in 

data that potentially come from different sources. Selected examples 

show how to quantify differences between measured optical spectra or the 

impact of methodology and computational parameters on calculated 

properties, like the density of states or excitonic spectra. Moreover, 

combining the same fingerprint with a clustering approach allows us to 

explore materials spaces in view of finding (un)expected trends or 

patterns. In all cases, we provide physical reasoning behind the 

findings of the automatized assessment of data.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Kulik_2022,

title = {Roadmap on Machine learning in electronic structure},

author = {H J Kulik and T Hammerschmidt and J Schmidt and S Botti and M A L Marques and M Boley and M Scheffler and M Todorović and P Rinke and C Oses and A Smolyanyuk and S Curtarolo and A Tkatchenko and A P Bartók and S Manzhos and M Ihara and T Carrington and J Behler and O Isayev and M Veit and A Grisafi and J Nigam and M Ceriotti and K T Schütt and J Westermayr and M Gastegger and R J Maurer and B Kalita and K Burke and R Nagai and R Akashi and O Sugino and J Hermann and F Noé and S Pilati and Claudia Draxl and Martin Kuban and S Rigamonti and M Scheidgen and M Esters and D Hicks and C Toher and P V Balachandran and I Tamblyn and S Whitelam and C Bellinger and L M Ghiringhelli},

url = {https://doi.org/10.1088/2516-1075/ac572f},

doi = {10.1088/2516-1075/ac572f},

year  = {2022},

date = {2022-06-01},

urldate = {2022-06-01},

journal = {Electronic Structure},

volume = {4},

number = {2},

pages = {023004},

publisher = {IOP Publishing},

abstract = {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, are being 

complemented, extended, and sometimes even completely replaced by 

faster, simpler, and often more accurate approaches. The new approaches, 

that we collectively label by machine learning, have their origins in 

the fields of informatics and artificial intelligence, but are making 

rapid inroads in all other branches of science. With this in mind, this 

Roadmap article, consisting of multiple contributions from experts 

across the field, discusses the use of machine learning in materials 

science, and share perspectives on current and future challenges in 

problems as diverse as the prediction of materials properties, the 

construction of force-fields, the development of exchange correlation 

functionals for density-functional theory, the solution of the many-body 

problem, and more. In spite of the already numerous and exciting success 

stories, we are just at the beginning of a long path that will reshape 

materials science for the many challenges of the XXIth century.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{scheffler2022fair,

title = {FAIR data enabling new horizons for materials research},

author = {Matthias Scheffler and Martin Aeschlimann and Martin Albrecht and Tristan Bereau and Hans-Joachim Bungartz and Claudia Felser and Mark Greiner and Axel Groß and Christoph T Koch and Kurt Kremer and Wolfgang E Nagel and Markus Scheidgen and Christof Wöll and Claudia Draxl},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

journal = {Nature},

volume = {604},

number = {7907},

pages = {635–642},

publisher = {Nature Publishing Group UK London},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Kuban2022b,

title = {Density-of-states similarity descriptor for unsupervised learning from materials data},

author = {Martin Kuban and Santiago Rigamonti and Markus Scheidgen and Claudia Draxl},

url = {https://doi.org/10.1038/s41597-022-01754-z},

doi = {10.1038/s41597-022-01754-z},

issn = {2052-4463},

year  = {2022},

date = {2022-00-22},

urldate = {2022-00-22},

journal = {Scientific Data},

volume = {9},

number = {1},

pages = {646},

abstract = {We develop a materials descriptor based on the electronic density-of-states (DOS) and investigate the similarity of materials based on it. As an application example, we study the Computational 2D Materials Database (C2DB) that hosts thousands of two-dimensional materials with their properties calculated by density-functional theory. Combining our descriptor with a clustering algorithm, we identify groups of materials with similar electronic structure. We introduce additional descriptors to characterize these clusters in terms of crystal structures, atomic compositions, and electronic configurations of their members. This allows us to rationalize the found (dis)similarities and to perform an automated exploratory and confirmatory analysis of the C2DB data. From this analysis, we find that the majority of clusters consist of isoelectronic materials sharing crystal symmetry, but we also identify outliers, i.e., materials whose similarity cannot be explained in this way.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}