Merging Materials and Data Science: Opportunities, Challenges, and Education in Materials Informatics

Highly accurate machine learning prediction of crystal point groups for ternary materials from chemical formula

One of the most challenging problems in condensed matter physics is to predict crystal structure just from the chemical formula of the material. In this work, we present a robust machine learning (ML) predictor for the crystal point group of ternary materials (A\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_l$$\end{document}lB\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_m$$\end{document}mC\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_n$$\end{document}n) - as first step to predict the structure - with very small set of ionic and positional fundamental features. From ML perspective, the problem is strenuous due to multi-labelity, multi-class, and data imbalance. The resulted prediction is very reliable as high balanced accuracies are obtained by different ML methods. Many similarity-based approaches resulted in a balanced accuracy above 95% indicating that the physics is well captured by the reduced set of features; namely, stoichiometry, ionic radii, ionization energies, and oxidation states for each of the three elements in the ternary compound. The accuracy is not limited by the approach; but rather by the limited data points and we should expect higher accuracy prediction by having more reliable data.

Assessment of AMBER Force Fields for Simulations of ssDNA

Single-stranded DNA (ssDNA) plays an important role in biological processes and is used in DNA nanotechnology and other novel applications. Many important research questions can be addressed with molecular simulations of ssDNA molecules; however, no dedicated force field for ssDNA has been developed, and there is limited experimental information about ssDNA structures. This study assesses the accuracy and applicability of existing Amber force fields for all-atom simulations of ssDNA, such as ff99, bsc0, bsc1, and OL15, in implicit and explicit solvents via comparison to available experimental data, such as Forster resonance energy transfer and small angle X-ray scattering. We observed that some force fields agree better with experiments than others mainly due to the difference in parameterization of the propensity for hydrogen bonding and base stacking. Overall, the Amber ff99 force field in the IGB5 or IGB8 implicit solvent and the bsc1 force field in the explicit TIP3P solvent had the best agreement with experiment.