80 research outputs found
Comparison of computational methods for the electrochemical stability window of solid-state electrolyte materials
Superior stability and safety are key promises attributed to all-solid-state
batteries (ASSBs) containing solid-state electrolyte (SSE) compared to their
conventional counterparts utilizing liquid electrolyte. To unleash the full
potential of ASSBs, SSE materials that are stable when in contact with the low
and high potential electrodes are required. The electrochemical stability
window is conveniently used to assess the SSE-electrode interface stability. In
the present work, we review the most important methods to compute the SSE
stability window. Our analysis reveals that the stoichiometry stability method
represents a bridge between HOMO-LUMO method and phase stability method (grand
canonical phase diagram). Moreover, we provide computational implementations of
these methods for SSE material screening. We compare their results for the
relevant Li- and Na-SSE materials LGPS, LIPON, LLZO, LLTO, LATP, LISICON, and
NASICON, and we discuss their relation to published experimental stability
windows
Language models in molecular discovery
The success of language models, especially transformer-based architectures,
has trickled into other domains giving rise to "scientific language models"
that operate on small molecules, proteins or polymers. In chemistry, language
models contribute to accelerating the molecule discovery cycle as evidenced by
promising recent findings in early-stage drug discovery. Here, we review the
role of language models in molecular discovery, underlining their strength in
de novo drug design, property prediction and reaction chemistry. We highlight
valuable open-source software assets thus lowering the entry barrier to the
field of scientific language modeling. Last, we sketch a vision for future
molecular design that combines a chatbot interface with access to computational
chemistry tools. Our contribution serves as a valuable resource for
researchers, chemists, and AI enthusiasts interested in understanding how
language models can and will be used to accelerate chemical discovery.Comment: Under revie
The Role of AI in Driving the Sustainability of the Chemical Industry
Sustainability is here to stay. As businesses migrate away from fossil fuels and toward renewable sources, chemistry will play a crucial role in bringing the economy to a point of net-zero emissions. In fact, chemistry has always been at the forefront of developing new or enhanced materials to fulfill societal demands, resulting in goods with appropriate physical or chemical qualities. Today, the main focus is on developing goods and materials that have a less negative impact on the environment, which may include (but is not limited to) leaving behind smaller carbon footprints.
Integrating data and AI can speed up the discovery of new eco-friendly materials, predict environmental impact factors for early assessment of new technological integration, enhance plant design and management, and optimize processes to reduce costs and improve efficiency, all of which contribute to a more rapid transition to a sustainable system. In this perspective, we hint at how AI technologies have been employed so far first, at estimating sustainability metrics and second, at designing more sustainable chemical processes
Tools for Synthesis Planning, Automation, and Analytical Data Analysis
Computer-aided synthesis design, automation, and analytics assisted by machine learning are promising resources in the researcher’s toolkit. Each component may alleviate the chemist from routine tasks, provide valuable insights from data, and enable more informed experimental design. Herein, we highlight selected works in the field and discuss the different approaches and the problems to which they may apply. We emphasize that there are currently few tools with a low barrier of entry for non-experts, which may limit widespread integration into the researcher’s workflow
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