相关论文: Accelerating Cathode Material Discovery through Ab…

Ab initio structure prediction methods have been nowadays widely used as powerful tools for structure searches and material discovery. However, they are generally restricted to small systems owing to the heavy computational cost of…

LiMn2O4 (LMO), cathodes present large stability when cycled in aqueous electrolytes, contrasting its behavior in conventional organic electrolytes in Lithium-ion batteries (LIBs). To elucidate the mechanisms underlying this distinctive…

We created a computational workflow to analyze the potential energy surface (PES) of materials using machine-learned interatomic potentials in conjunction with the minima hopping algorithm. We demonstrate this method by producing a…

Advances in lithium-metal anodes have inspired interest in discovery of Li-free cathodes, most of which are natively found in their charged state. This is in contrast to today's commercial lithium-ion battery cathodes, which are more stable…

AI-driven materials discovery that couples automated experimentation with algorithmic decision-making requires process aware recipe to property predictors that are accurate, calibrated, and physically admissible. We approach this as a…

Multivalent batteries are an energy storage technology with the potential to surpass lithium-ion batteries, however their performance has been limited by the low voltages and poor solid-state ionic mobility of available cathodes. A…

Rechargeable Zn batteries with aqueous electrolytes have been considered as promising alternative energy storage technology, with various advantages such as low cost, high volumetric capacity, environmentally friendly, and high safety.…

Recent advances in generative models have introduced a new paradigm for the inverse design of inorganic materials, enabling the discovery of new crystalline structures with desired properties. However, existing generative models focus…

In this study, we propose a novel microstructure-sensitive Bayesian optimization (BO) framework designed to enhance the efficiency of materials discovery by explicitly incorporating microstructural information. Traditional materials design…

Due to the vast chemical space, discovering materials with a specific function is challenging. Chemical formulas are obligated to conform to a set of exacting criteria such as charge neutrality, balanced electronegativity, synthesizability,…

Data-driven materials design often encounters challenges where systems require or possess qualitative (categorical) information. Metal-organic frameworks (MOFs) are an example of such material systems. The representation of MOFs through…

The key challenge in advancing multivalent-ion batteries lies in finding suitable intercalation hosts. Open-tunnel oxides, featuring one-dimensional channels or nanopores, show promise for enabling effective ion transport. However, the vast…

Employing two-dimensional (2D) materials as anodes for lithium-ion batteries (LIBs) is believed to be an effective approach to meet the growing demands of high-capacity next-generation LIBs. In this work, the first-principles density…

With the importance of Li-ion and emerging alternative batteries to our electric future, predicting new sustainable materials, electrolytes and complete cells that safely provide high performance, long life, energy dense capability is…

CO2 reduction requires efficient catalysts, yet materials discovery remains bottlenecked by 10-20 year development cycles requiring deep domain expertise. This paper demonstrates how large language models can assist the catalyst discovery…

Critical to the development of improved solid oxide fuel cell (SOFC) technology are novel compounds with high oxygen reduction reaction (ORR) catalytic activity and robust stability under cathode operating conditions. Approximately 2145…

Electrides, with their excess electrons distributed in crystal cavities playing the role of anions, exhibit a variety of unique properties which make these materials desirable for many applications in catalysis, nonlinear optics and…

The efficiency of active learning (AL) approaches to identify materials with desired properties relies on the knowledge of a few parameters describing the property. However, these parameters are unknown if the property is governed by a high…

To aid in the automation of inorganic materials synthesis, we introduce an algorithm (ARROWS3) that guides the selection of precursors used in solid-state reactions. Given a target phase, ARROWS3 iteratively proposes experiments and learns…

First-principles based crystal structure prediction (CSP) methods have revealed an essential tool for the discovery of new materials. However, in solids close to displacive phase transitions, which are common in ferroelectrics,…