A Spin-dependent Machine Learning Framework for Transition Metal Oxide Battery Cathode Materials

Owing to the trade-off between the accuracy and efficiency, machine-learning-potentials (MLPs) have been widely applied in the battery materials science, enabling atomic-level dynamics description for various critical processes. However, the challenge arises when dealing with complex transition metal (TM) oxide cathode materials, as multiple possibilities of d-orbital electrons localization often lead to convergence to different spin states (or equivalently local minimums with respect to the spin configurations) after ab initio self-consistent-field calculations, which causes a significant obstacle for training MLPs of cathode materials. In this work, we introduce a solution by incorporating an additional feature - atomic spins - into the descriptor, based on the pristine deep potential (DP) model, to address the above issue by distinguishing different spin states of TM ions. We demonstrate that our proposed scheme provides accurate descriptions for the potential energies of a variety of representative cathode materials, including the traditional Li$_x$TMO$_2$ (TM=Ni, Co, Mn, $x$=0.5 and 1.0), Li-Ni anti-sites in Li$_x$NiO$_2$ ($x$=0.5 and 1.0), cobalt-free high-nickel Li$_x$Ni$_{1.5}$Mn$_{0.5}$O$_4$ ($x$=1.5 and 0.5), and even a ternary cathode material Li$_x$Ni$_{1/3}$Co$_{1/3}$Mn$_{1/3}$O$_2$ ($x$=1.0 and 0.67). We highlight that our approach allows the utilization of all ab initio results as a training dataset, regardless of the system being in a spin ground state or not. Overall, our proposed approach paves the way for efficiently training MLPs for complex TM oxide cathode materials

Surface Adsorption-Mediated Ultrahigh Efficient Peptide Encapsulation with a Precise Ratiometric Control for Type 1 and 2 Diabetic Therapy

A surface adsorption strategy is developed to enable the engineering of microcomposites featured with ultrahigh loading capacity and precise ratiometric control of co-encapsulated peptides. In this strategy, peptide molecules (insulin, exenatide, and bivalirudin) are formulated into nanoparticles and their surface is decorated with carrier polymers. This polymer layer blocks the phase transfer of peptide nanoparticles from oil to water and, consequently, realizes ultrahigh peptide loading degree (up to 78.9%). After surface decoration, all three nanoparticles are expected to exhibit the properties of adsorbed polymer materials, which enables the co-encapsulation of insulin, exenatide, and bivalirudin with a precise ratiometric control. After solidification of this adsorbed polymer layer, the release of peptides is synchronously prolonged. With the help of encapsulation, insulin achieves 8 days of glycemic control in type 1 diabetic rats with one single injection. The co-delivery of insulin and exenatide (1:1) efficiently controls the glycemic level in type 2 diabetic rats for 8 days. Weekly administration of insulin and exenatide co-encapsulated microcomposite effectively reduces the weight gain and glycosylated hemoglobin level in type 2 diabetic rats. The surface adsorption strategy sets a new paradigm to improve the pharmacokinetic and pharmacological performance of peptides, especially for the combination of peptides.Peer reviewe

Credibility Test for Frequency Estimation of Sinusoid Using Chebyshev’s Inequality

Estimation of sinusoid frequency is a key research problem related to radar, sonar, and communication systems. The results of numerous investigations on frequency estimation have been reported in the literature. Nevertheless, to the best of our knowledge, none of them have dealt with credibility evaluation, which is used to decide whether an individual frequency estimate of the sinusoid is accurate or not. In this study, the credibility problem is modeled as a hypothesis test based on Chebyshev’s inequality (CI). The correlation calculated from the received signal and the reference signal generated according to the frequency estimate is used as a test statistic. A threshold is determined based on CI, and the analytical expression for the frequency estimation credibility detection performance is derived. Simulations show that the proposed method performs well even at low signal-to-noise ratios

Blind Frequency and Symbol Rate Estimation for MSK Signal under Low Signal-to-Noise Ratio ⋆

We investigate a novel blind estimator combination of carrier frequency and symbol rate of an complex MSK modulation under low signal-to-noise ratio (SNR). The carrier frequency is estimated by using Modified Rife frequency estimator at first and the referenced signal is constructed consequently. Then, the received signal is transformed to baseband by means of correlation with the referenced signal. The symbol rate is estimated by wavelet transform of the baseband signal at a suitable scale. Simulation results show that the proposed method is more accurate than the existing estimators when SNR is low