Publications

@article{Kulik2022,

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 Todorovi\'{c} and P Rinke and C Oses and A Smolyanyuk and S Curtarolo and A Tkatchenko and A P Bart\'{o}k 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 Sch\"{u}tt 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 No\'{e} and S Pilati and C Draxl and M 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},

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

year  = {2022},

date = {2022-06-01},

urldate = {2022-06-01},

journal = {Electron. Struct.},

volume = {4},

number = {2},

pages = {023004},

publisher = {IOP Publishing},

abstract = {\<jats:title\>Abstract\</jats:title\>\<jats:p\>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.\</jats:p\>},

keywords = {Machine learning potential, Materials informatics, Review},

pubstate = {published},

tppubtype = {article}

}

@article{Reis2021,

title = {Machine-Learning-Guided Discovery of ^{19}F MRI Agents Enabled by Automated Copolymer Synthesis},

author = {Marcus Reis and Filipp Gusev and Nicholas G. Taylor and Sang Hun Chung and Matthew D. Verber and Yueh Z. Lee and Olexandr Isayev and Frank A. Leibfarth},

doi = {10.1021/jacs.1c08181},

issn = {1520-5126},

year  = {2021},

date = {2021-10-27},

urldate = {2021-10-27},

journal = {J. Am. Chem. Soc.},

volume = {143},

number = {42},

pages = {17677--17689},

publisher = {American Chemical Society (ACS)},

abstract = {Modern polymer science suffers from the curse of multidimensionality. The large chemical space imposed by including combinations of monomers into a statistical copolymer overwhelms polymer synthesis and characterization technology and limits the ability to systematically study structure\textendashproperty relationships. To tackle this challenge in the context of 19F magnetic resonance imaging (MRI) agents, we pursued a computer-guided materials discovery approach that combines synergistic innovations in automated flow synthesis and machine learning (ML) method development. A software-controlled, continuous polymer synthesis platform was developed to enable iterative experimental\textendashcomputational cycles that resulted in the synthesis of 397 unique copolymer compositions within a six-variable compositional space. The nonintuitive design criteria identified by ML, which were accomplished by exploring \<0.9% of the overall compositional space, lead to the identification of \>10 copolymer compositions that outperformed state-of-the-art materials.},

keywords = {Materials informatics, Science automation},

pubstate = {published},

tppubtype = {article}

}

@article{Fronzi2021,

title = {Active Learning in Bayesian Neural Networks for Bandgap Predictions of Novel Van der Waals Heterostructures},

author = {Marco Fronzi and Olexandr Isayev and David A. Winkler and Joseph G. Shapter and Amanda V. Ellis and Peter C. Sherrell and Nick A. Shepelin and Alexander Corletto and Michael J. Ford},

doi = {10.1002/aisy.202100080},

issn = {2640-4567},

year  = {2021},

date = {2021-08-02},

journal = {Advanced Intelligent Systems},

volume = {3},

number = {11},

publisher = {Wiley},

abstract = {\<jats:sec\>\<jats:label /\>\<jats:p\>The bandgap is one of the most fundamental properties of condensed matter. However, an accurate calculation of its value, which could potentially allow experimentalists to identify materials suitable for device applications, is very computationally expensive. Here, active machine learning algorithms are used to leverage a limited number of accurate density functional theory calculations to robustly predict the bandgap of a very large number of novel 2D heterostructures. Using this approach, a database of ≈2.2 million bandgap values for various novel 2D van der Waals heterostructures is produced.\</jats:p\>\</jats:sec\>},

keywords = {Materials informatics},

pubstate = {published},

tppubtype = {article}

}