Self-driving laboratories for chemistry and materials science
Self-driving laboratories (SDLs) promise an accelerated application of the scientific method.
Through the automation of experimental workflows, along with autonomous experimental …
Through the automation of experimental workflows, along with autonomous experimental …
14 examples of how LLMs can transform materials science and chemistry: a reflection on a large language model hackathon
Large-language models (LLMs) such as GPT-4 caught the interest of many scientists.
Recent studies suggested that these models could be useful in chemistry and materials …
Recent studies suggested that these models could be useful in chemistry and materials …
In pursuit of the exceptional: Research directions for machine learning in chemical and materials science
Exceptional molecules and materials with one or more extraordinary properties are both
technologically valuable and fundamentally interesting, because they often involve new …
technologically valuable and fundamentally interesting, because they often involve new …
Preventing cation intermixing enables 50% quantum yield in sub-15 nm short-wave infrared-emitting rare-earth based core-shell nanocrystals
Short-wave infrared (SWIR) fluorescence could become the new gold standard in optical
imaging for biomedical applications due to important advantages such as lack of …
imaging for biomedical applications due to important advantages such as lack of …
Smart Dope: a self‐driving fluidic lab for accelerated development of doped perovskite quantum dots
Metal cation‐doped lead halide perovskite (LHP) quantum dots (QDs) with
photoluminescence quantum yields (PLQYs) higher than unity, due to quantum cutting …
photoluminescence quantum yields (PLQYs) higher than unity, due to quantum cutting …
Autonomous, multiproperty-driven molecular discovery: From predictions to measurements and back
A closed-loop, autonomous molecular discovery platform driven by integrated machine
learning tools was developed to accelerate the design of molecules with desired properties …
learning tools was developed to accelerate the design of molecules with desired properties …
Machine intelligence-accelerated discovery of all-natural plastic substitutes
One possible solution against the accumulation of petrochemical plastics in natural
environments is to develop biodegradable plastic substitutes using natural components …
environments is to develop biodegradable plastic substitutes using natural components …
Performance metrics to unleash the power of self-driving labs in chemistry and materials science
AA Volk, M Abolhasani - Nature Communications, 2024 - nature.com
With the rise of self-driving labs (SDLs) and automated experimentation across chemical
and materials sciences, there is a considerable challenge in designing the best autonomous …
and materials sciences, there is a considerable challenge in designing the best autonomous …
Using Data-Driven Learning to Predict and Control the Outcomes of Inorganic Materials Synthesis
EM Williamson, RL Brutchey - Inorganic Chemistry, 2023 - ACS Publications
The design of inorganic materials for various applications critically depends on our ability to
manipulate their synthesis in a rational, robust, and controllable fashion. Different from the …
manipulate their synthesis in a rational, robust, and controllable fashion. Different from the …
Self-driving laboratories to autonomously navigate the protein fitness landscape
Protein engineering has nearly limitless applications across chemistry, energy and
medicine, but creating new proteins with improved or novel functions remains slow, labor …
medicine, but creating new proteins with improved or novel functions remains slow, labor …