Transferability of Machine Learning Models for Predicting Raman Spectra.

Theoretical prediction of vibrational Raman spectra enables a detailed interpretation of experimental spectra, and the advent of machine learning techniques makes it possible to predict Raman spectra while achieving a good balance between efficiency and accuracy. However, the transferability of machine learning models across different molecules remains poorly understood. This work proposed a new strategy whereby machine learning-based polarizability models were trained on similar but smaller alkane molecules to predict spectra of larger alkanes, avoiding extensive first-principles calculations on certain systems. Results showed that the developed polarizability model for alkanes with a maximum of nine carbon atoms can exhibit high accuracy in the predictions of polarizabilities and Raman spectra for the n-undecane molecule (11 carbon atoms), validating its reasonable extrapolation capability. Additionally, a descriptor space analysis method was further introduced to evaluate the transferability, demonstrating potentials for accurate and efficient Raman predictions of large molecules using limited training data labeled for smaller molecules.

Tensorial Properties via the Neuroevolution Potential Framework: Fast Simulation of Infrared and Raman Spectra.

Infrared and Raman spectroscopy are widely used for the characterization of gases, liquids, and solids, as the spectra contain a wealth of information concerning, in particular, the dynamics of these systems. Atomic scale simulations can be used to predict such spectra but are often severely limited due to high computational cost or the need for strong approximations that limit the application range and reliability. Here, we introduce a machine learning (ML) accelerated approach that addresses these shortcomings and provides a significant performance boost in terms of data and computational efficiency compared with earlier ML schemes. To this end, we generalize the neuroevolution potential approach to enable the prediction of rank one and two tensors to obtain the tensorial neuroevolution potential (TNEP) scheme. We apply the resulting framework to construct models for the dipole moment, polarizability, and susceptibility of molecules, liquids, and solids and show that our approach compares favorably with several ML models from the literature with respect to accuracy and computational efficiency. Finally, we demonstrate the application of the TNEP approach to the prediction of infrared and Raman spectra of liquid water, a molecule (PTAF-), and a prototypical perovskite with strong anharmonicity (BaZrO3). The TNEP approach is implemented in the free and open source software package gpumd, which makes this methodology readily available to the scientific community.

Phase regulation enabling dense polymer-based composite electrolytes for solid-state lithium metal batteries.

Solid polymer electrolytes with large-scale processability and interfacial compatibility are promising candidates for solid-state lithium metal batteries. Among various systems, poly(vinylidene fluoride)-based polymer electrolytes with residual solvent are appealing for room-temperature battery operations. However, their porous structure and limited ionic conductivity hinder practical application. Herein, we propose a phase regulation strategy to disrupt the symmetry of poly(vinylidene fluoride) chains and obtain the dense composite electrolyte through the incorporation of MoSe2 sheets. The electrolyte with high dielectric constant can optimize the solvation structures to achieve high ionic conductivity and low activation energy. The in-situ reactions between MoSe2 and Li metal generate Li2Se fast conductor in solid electrolyte interphase, which improves the Coulombic efficiency and interfacial kinetics. The solid-state Li||Li cells achieve robust cycling at 1 mA cm-2, and the Li||LiNi0.8Co0.1Mn0.1O2 full cells show practical performance at high rate (3C), high loading (2.6 mAh cm-2) and in pouch cell.

Spontaneously Restoring Specific Bioaffinity of RGD in Linear RGD-containing Peptides by Conjugation with Zwitterionic Dendrimers.

Peptides are more versatile than small molecule drugs, but their specific bioaffinities are usually lower than their original native proteins because of the loss of preferred conformations. To overcome this key obstacle, we demonstrated a hydrogen bond-induced conformational constraint method to enhance the specific bioaffinities of peptides to achieve a high success rate by using linear RGD-containing peptides as a model of bioactive peptides. By performing molecular simulation, we found that the chemically immobilized linear CRGDS via cysteine (C) at the N-terminus on zwitterionic PAMAM G-5 can not only spontaneously restore the natural conformation of the RGD segment through the assistance of the dynamic hydrogen bond from serine (S) at the C-terminus of the peptide, but it can also narrow the distribution of all possible conformations. Consequently, the conjugates showed comparable or even better high affinity than native proteins without the use of conventional, labor-intensive, synthesis-based structure search methods to construct a binding conformation. In addition, the conjugates showed globular protein-like characteristics chemically, physically, and physiologically. They exhibited not only high efficacy and biosafety both in vitro and in vivo, but they also showed extremely high thermostability even upon boiling in a solution. This approach offers great design flexibility for reviving functional peptides without impairing their high specific affinity for their targets. STATEMENT OF SIGNIFICANCE: In this work, we developed a swift approach to spontaneously restore the natural conformation of a linear peptide from a nature protein and thus enhance its specific bioaffinity instead of constructing a binding conformation by the labor-intensive, synthesis-based structure search method. In details, our new approach involves dynamically constraining the linear peptide on a zwitterionic PAMAM G-5 surface by a combination of chemical bonding at one terminus and dynamic hydrogen bonding at the other terminus of the linear peptide. The zwitterionic background offers abundant interaction sites for hydrogen bonding as well as resistance to nonspecific interactions. This approach fully restores the specific bioaffinity of RGD segments on a zwitterionic PAMAM G-5 through only one conjugation point at the C-terminus of the peptide. Moreover, the bioaffinity of all three types of RGD-containing peptides is successfully restored, which indicates the high rate of success of this approach in affinity restoring.