Aluminum doping improves the energetics of lithium, sodium, and magnesium storage in silicon

While Si is an effective insertion type anode for Li-ion batteries, crystalline Si has been shown to be unsuitable for Na and Mg storage due, in particular, to insufficient binding strength. It has recently been reported that Si nanowires could be synthesized with high-concentration (several atomic %) and dispersed Al doping. Here we show based on density functional theory calculations that Al doping significantly improves the energetics for Na and Mg insertion, specifically, making it thermodynamically favored versus vacuum reference states. For high Al concentrations, the energy of Mg in Al-doped Si approaches the cohesive energy of Mg. However, the migration barriers for the diffusion of Li (0.57-0.70 eV), Na (1.07-1.19 eV) and Mg (0.97-1.18 eV) in Al-doped Si are found to remain about as high as in pure Si, likely preventing effective electrochemical sodiation and magnesiation

Understanding the difference in cohesive energies between alpha and beta tin in DFT calculations

The transition temperature between the low-temperature alpha phase of tin to beta tin is close to the room temperature (Tab =13C), and the difference in cohesive energy of the two phases at 0 K of about dEcoh=0.02 eV/atom is at the limit of the accuracy of DFT (density functional theory) with available exchange-correlation functionals. It is however critically important to model the relative phase energies correctly for any reasonable description of phenomena and technologies involving these phases, for example, the performance of tin electrodes in electrochemical batteries. Here, we show that several commonly used and converged DFT setups using the most practical and widely used PBE functional result in dEcoh of about 0.04 eV/atom, with different types of basis sets and with different models of core electrons (all-electron or pseudopotentials of different types), which leads to a significant overestimation of Tab. We show that this is due to the errors in relative positions of s and p -like bands, which, combined with different populations of these bands in alpha and beta Sn, leads to overstabilization of alpha tin. We show that this error can be effectively corrected by applying a Hubbard +U correction to s -like states, whereby correct cohesive energies of both alpha and beta Sn can be obtained with the same computational scheme. We quantify for the first time the effects of anharmonicity on dEcoh and find that it is negligible.Comment: 7 pages, 5 figure

Highly Accurate Local Pseudopotentials of Li, Na, and Mg for Orbital Free Density Functional Theory

We present a method to make highly accurate pseudopotentials for use with orbital-free density functional theory (OF-DFT) with given exchange-correlation and kinetic energy functionals, which avoids the compounding of errors of Kohn-Sham DFT and OF-DFT. The pseudopotentials are fitted to reference (experimental or highly accurate quantum chemistry) values of interaction energies, geometries, and mechanical properties, using a genetic algorithm. This can enable routine large-scale ab initio simulations of many practically relevant materials. Pseudopotentials for Li, Na, and Mg resulting in accurate geometries and energies of different phases as well as of vacancy formation and bulk moduli are presented as examples.Comment: 15 pages, 1 figure, 3 table

Materials Screening for the Discovery of New Half-Heuslers: Machine Learning versus Ab Initio Methods

Machine learning (ML) is increasingly becoming a helpful tool in the search for novel functional compounds. Here we use classification via random forests to predict the stability of half-Heusler (HH) compounds, using only experimentally reported compounds as a training set. Cross-validation yields an excellent agreement between the fraction of compounds classified as stable and the actual fraction of truly stable compounds in the ICSD. The ML model is then employed to screen 71,178 different 1:1:1 compositions, yielding 481 likely stable candidates. The predicted stability of HH compounds from three previous high throughput ab initio studies is critically analyzed from the perspective of the alternative ML approach. The incomplete consistency among the three separate ab initio studies and between them and the ML predictions suggests that additional factors beyond those considered by ab initio phase stability calculations might be determinant to the stability of the compounds. Such factors can include configurational entropies and quasiharmonic contributions.Comment: 11 pages, 5 figures, 2 table

COMPARATIVE COMPUTATIONAL STUDIES OF LI, NA, AND MG INSERTION IN ELEMENTAL GROUP IV MATERIALS AND OXIDES: MATERIAL CHOICES FOR POST-LITHIUM BATTERIES

Ph.DDOCTOR OF PHILOSOPH

AFLOW-ML: A RESTful API for machine-learning predictions of materials properties

Machine learning approaches, enabled by the emergence of comprehensive databases of materials properties, are becoming a fruitful direction for materials analysis. As a result, a plethora of models have been constructed and trained on existing data to predict properties of new systems. These powerful methods allow researchers to target studies only at interesting materials \unicode{x2014} neglecting the non-synthesizable systems and those without the desired properties \unicode{x2014} thus reducing the amount of resources spent on expensive computations and/or time-consuming experimental synthesis. However, using these predictive models is not always straightforward. Often, they require a panoply of technical expertise, creating barriers for general users. AFLOW-ML (AFLOW $\underline{\mathrm{M}}$achine $\underline{\mathrm{L}}$earning) overcomes the problem by streamlining the use of the machine learning methods developed within the AFLOW consortium. The framework provides an open RESTful API to directly access the continuously updated algorithms, which can be transparently integrated into any workflow to retrieve predictions of electronic, thermal and mechanical properties. These types of interconnected cloud-based applications are envisioned to be capable of further accelerating the adoption of machine learning methods into materials development.Comment: 10 pages, 2 figure

Vibrational properties of metastable polymorph structures by machine learning

Despite vibrational properties being critical for the ab initio prediction of the finite temperature stability and transport properties of solids, their inclusion in ab initio materials repositories has been hindered by expensive computational requirements. Here we tackle the challenge, by showing that a good estimation of force constants and vibrational properties can be quickly achieved from the knowledge of atomic equilibrium positions using machine learning. A random-forest algorithm trained on only 121 metastable structures of KZnF$_3$ reaches a maximum absolute error of 0.17 eV/$\textrm\AA^2$ for the interatomic force constants, and it is much less expensive than training the complete force field for such compound. The predicted force constants are then used to estimate phonon spectral features, heat capacities, vibrational entropies, and vibrational free energies, which compare well with the ab initio ones. The approach can be used for the rapid estimation of stability at finite temperatures.Comment: 18 pages, 4 figure