arXiv subjects

Efrain Rodriguez

Publications and source records attributed to Efrain Rodriguez.

4 recordsLinked to original sources

arXiv2022

A Low-Cost Robot Science Kit for Education with Symbolic Regression for Hypothesis Discovery and Validation

The next generation of physical science involves robot scientists - autonomous physical science systems capable of experimental design, execution, and analysis in a closed loop. Such systems have shown real-world success for scientific exploration and discovery, including the first discovery of a best-in-class material. To build and use these systems, the next generation workforce requires expertise in diverse areas including ML, control systems, measurement science, materials synthesis, decision theory, among others. However, education is lagging. Educators need a low-cost, easy-to-use platform to teach the required skills. Industry can also use such a platform for developing and evaluating autonomous physical science methodologies. We present the next generation in science education, a kit for building a low-cost autonomous scientist. The kit was used during two courses at the University of Maryland to teach undergraduate and graduate students autonomous physical science. We discuss its use in the course and its greater capability to teach the dual tasks of autonomous model exploration, optimization, and determination, with an example of autonomous experimental "discovery" of the Henderson-Hasselbalch equation.

cond-mat.mtrl-sci↗

arXiv2018

Manipulating surface magnetic order in iron telluride

Control of emergent magnetic orders in correlated electron materials promises new opportunities for applications in spintronics. For their technological exploitation, it is important to understand the role of surfaces and interfaces to other materials and their impact on the emergent magnetic orders. Here, we demonstrate for iron telluride, the nonsuperconducting parent compound of the iron chalcogenide superconductors, determination and manipulation of the surface magnetic structure by low-temperature spin-polarized scanning tunneling microscopy. Iron telluride exhibits a complex structural and magnetic phase diagram as a function of interstitial iron concentration. Several theories have been put forward to explain the different magnetic orders observed in the phase diagram, which ascribe a dominant role either to interactions mediated by itinerant electrons or to local moment interactions. Through the controlled removal of surface excess iron, we can separate the influence of the excess iron from that of the change in the lattice structure.

cond-mat.str-el↗

arXiv2017

Frustrated magnetism in tetragonal CoSe, analogue to superconducting FeSe

Recently synthesized metastable tetragonal CoSe, isostructural to the FeSe superconductor, offers a new avenue for investigating systems in close proximity to the iron-based superconductors. We present magnetic and transport property measurements on powders and single crystals of CoSe. High field magnetic susceptibility measurements indicate a suppression of the previously reported 10 K ferromagnetic transition with the magnetic susceptibility exhibiting time-dependence below the proposed transition. Dynamic scaling analysis of the time-dependence yields a critical relaxation time of $\tau^{*} = 0.064 \pm 0.008 $ s which in turn yields an activation energy of $E_{a}^{*}$ = 14.84 $\pm$ 0.59 K and an ideal glass temperature $T_{0}^{*}$ = 8.91 $\pm$ 0.09 K from Vogel-Fulcher analysis. No transition is observed in resistivity and specific heat measurements, but both measurements indicate that CoSe is metallic. These results are interpreted on the basis of CoSe exhibiting frustrated magnetic ordering arising from competing magnetic interactions. Arrott analysis of single crystal magnetic susceptibility has indicated that the magnetic moments lie in the $ab$-plane so frustration may arise from intralayer magnetic fluctuations as well as interlayer coupling. The results have implications for understanding the superconductivity in the iron chalcogenide systems as well as utilizing CoSe as a host for chemical and physical manipulation to tune and explore emergent phenomena within an expanding new class of transition metal chalcogenides.

cond-mat.supr-con↗

arXiv2017

Machine learning modeling of superconducting critical temperature

Superconductivity has been the focus of enormous research effort since its discovery more than a century ago. Yet, some features of this unique phenomenon remain poorly understood; prime among these is the connection between superconductivity and chemical/structural properties of materials. To bridge the gap, several machine learning schemes are developed herein to model the critical temperatures ($T_{\mathrm{c}}$) of the 12,000+ known superconductors available via the SuperCon database. Materials are first divided into two classes based on their $T_{\mathrm{c}}$ values, above and below 10 K, and a classification model predicting this label is trained. The model uses coarse-grained features based only on the chemical compositions. It shows strong predictive power, with out-of-sample accuracy of about 92%. Separate regression models are developed to predict the values of $T_{\mathrm{c}}$ for cuprate, iron-based, and "low-$T_{\mathrm{c}}$" compounds. These models also demonstrate good performance, with learned predictors offering potential insights into the mechanisms behind superconductivity in different families of materials. To improve the accuracy and interpretability of these models, new features are incorporated using materials data from the AFLOW Online Repositories. Finally, the classification and regression models are combined into a single integrated pipeline and employed to search the entire Inorganic Crystallographic Structure Database (ICSD) for potential new superconductors. We identify more than 30 non-cuprate and non-iron-based oxides as candidate materials.

cond-mat.supr-con↗