相关论文: Integrating electronic structure into generative m…

The participation of oxygen or other anionic species in redox activities in cathode materials for lithium and sodium-ion battery systems is known to play a role in governing the useful capacity of these batteries. Directly probing anionic…

The use of eco-friendly materials for the environment has been addressed as a critical issue in the development of systems for renewable energy applications. In this regard, the investigation of organic photovoltaic (OPV) molecules for the…

Solid-state ion conductors hold promise as next generation battery materials. To realize their full potential, an understanding of atomic-scale ion conduction mechanisms is needed, including ionic and electronic degrees of freedom.…

Optimization of the formation step in lithium-ion battery manufacturing is challenging due to limited physical understanding of solid electrolyte interphase formation and the long testing time (~100 days) for cells to reach the end of life.…

Organic semiconductors are indispensable for today's display technologies in form of organic light emitting diodes (OLEDs) and further optoelectronic applications. However, organic materials do not reach the same charge carrier mobility as…

A new electronic structure model is developed in which the ground state energy of a molecular system is given by a Hartree-Fock-like expression with parametrized one- and two-electron integrals over an extended (minimal + polarization) set…

Thermal batteries based on a reversible adsorption-desorption of a working fluid rather than the conventional vapor compression is a promising alternative to exploit waste thermal energy for heat reallocation. In this context, there is an…

The class of 2D carbon allotropes has garnered significant attention due to its exceptional optoelectronic and mechanical properties, crucial for diverse device applications, such as energy storage. This study employs density functional…

We introduce ELECTRAFI, a fast, end-to-end differentiable model for predicting periodic charge densities in crystalline materials. ELECTRAFI constructs anisotropic Gaussians in real space and exploits their closed-form Fourier transforms to…

We study the effect of non-Markovianity in the charging process of an open-system quantum battery. We employ a collisional model framework, where the environment is described by a discrete set of ancillary systems and memory effects in the…

Inverse design of materials with desired properties is currently laborious and heavily relies on intuition of researchers through a trial-and-error process. The massive combinational spaces due to the constituent elements and their…

In the quest to increase battery performance, nanostructuring battery electrodes gives access to architectures with electrical conductivity and solid-state diffusion regimes not accessible with traditional electrodes based on aggregated…

A common thread in designing electrochemically-based renewable energy devices comprises materials that exploit nano-scale morphologies, e.g., supercapacitors, batteries, fuel cells, and bulk heterojunction organic photovoltaics. In these…

We investigate the interfacial wave coupling dynamics in liquid metal batteries and their effects to the battery's operation safety. Similar to aluminum reduction cells, liquid metal batteries can be highly susceptible to…

Despite their rich information content, electronic structure data amassed at high volumes in $ab$ $initio$ molecular dynamics simulations are generally under-utilized. We introduce a transferable high-fidelity neural network representation…

Rechargeable aqueous zinc-ion batteries (RAZIBs) attract considerable scientific and commercial interest for deployment in grid-scale energy storage due to higher safety and lower manufacturing cost when compared to lithium-ion batteries.…

Mechanical metamaterials leverage geometric design to achieve unconventional properties, such as high strength at low density, efficient wave guiding, and complex shape morphing. The ability to control shape changes builds on the complex…

In addition to the forward inference of materials properties using machine learning, generative deep learning techniques applied on materials science allow the inverse design of materials, i.e., assessing the…

The reconstruction of 3D microstructures from 2D slices is considered to hold significant value in predicting the spatial structure and physical properties of materials.The dimensional extension from 2D to 3D is viewed as a highly…

The ability to readily design novel materials with chosen functional properties on-demand represents a next frontier in materials discovery. However, thoroughly and efficiently sampling the entire design space in a computationally tractable…