Prediction of stable Li-Sn compounds: boosting ab initio searches with neural network potentials

Abstract

The Li-Sn binary system has been the focus of extensive research because it features Li-rich alloys with potential applications as battery anodes. Our present re-examination of the binary system with a combination of machine learning and ab initio methods has allowed us to screen a vast configuration space and uncover a number of overlooked thermodynamically stable alloys. At ambient pressure, our evolutionary searches identified an additional stable Li3Sn phase with a large BCC-based hR48 structure and a possible high-T LiSn4 ground state. By building a simple model for the observed and predicted Li-Sn BCC alloys we constructed an even larger viable hR75 structure at an exotic 19:6 stoichiometry. At 20 GPa, low-symmetry 11:2, 5:1, and 9:2 phases found with our global searches destabilize previously proposed phases with high Li content. The findings showcase the appreciable promise machine-learning interatomic potentials hold for accelerating ab initio prediction of complex materials.

Thermodynamic stability of Li–B–C compounds from first principles

“…50,51 The slight overbinding of bcc-Li with respect to molecular Li 2 by 0.03 eV per Li in our default DFT approximation would result in a small shift in the boundary, D log 10 ðP=P 0 Þ % 0:03=0:07 log 10 ðeÞ % 0.19 at 800 K. Assessment of discrepancies between DFT and experiment for solid state phases is more challenging because measurements are usually conducted at elevated temperatures. 52 The typical differences of up to B20 meV per atom between DFT flavors in the calculation of relative energies 3,12,52 and the neglected configurational entropy term of similar values (Fig. 3(b)) could be amplified to a sizable 0.2 eV per Li upon rescaling to be per Li atom.…”

Machine learning search for stable binary Sn alloys with Na, Ca, Cu, Pd, and Ag

“…Unfortunately, the high cost of ab initio calculations limits the scope of unconstrained searches. Evaluation of structure stability with less expensive and fairly accurate machine learning potentials (MLPs) has shown great promise for accelerating ab initio searches [16] but successful predictions of stable compounds remain scarce [17][18][19][20]. In particular, our recent re-examination of the Li-Sn binary with a MLP has uncovered several stable alloys with large unit cells not detected in ab initio searches [19].…”

Understanding Charge Storage Mechanisms for Amorphous MoSnSe1.5S1.5 Nanoflowers in Alkali‐Ion Batteries

“…For NIBs, the peaks of Sn−S and Sn−Se remained after full discharge, and Na−Sn (2.68 Å) indicated that amorphous MSSS would undergo an alloying reaction at low potentials with Na‐ions. [ 25b ] The reaction was also reversible for amorphous MSSS in NIBs, as only two peaks of Sn−S and Sn−Se were observed after full charging, as shown in Figure 5f.…”

“…From the reported simulations, the shortest length of Li−Sn can range from 2.5 to 2.61 Å. [ 25 ] Although no significant electron transfer was observed in the XANES spectra, Li x Sn was formed after full discharging. For the fully charged state, the peaks mainly consisted of Sn−S and Sn−Se.…”

“…In several studies, x for Li x Sn is up to 4.25 after fully lithiation. [ 25b,27 ] Therefore, the total number of electrons transferred in LIBs is 10.25, which makes the theoretical capacity of MoSnSe 1.5 S 1.5 721 mAh g −1 in LIBs. However, the practical capacities (>800 mAh g −1 ) are obviously higher than theoretical capacities.…”