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 …
Controllable Data Generation by Deep Learning: A Review
Designing and generating new data under targeted properties has been attracting various
critical applications such as molecule design, image editing and speech synthesis …
critical applications such as molecule design, image editing and speech synthesis …
Leveraging large language models for predictive chemistry
Abstract Machine learning has transformed many fields and has recently found applications
in chemistry and materials science. The small datasets commonly found in chemistry …
in chemistry and materials science. The small datasets commonly found in chemistry …
Artificial intelligence for science in quantum, atomistic, and continuum systems
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …
ChemOS 2.0: An orchestration architecture for chemical self-driving laboratories
Summary Self-driving laboratories (SDLs), which combine automated experimental
hardware with computational experiment planning, have emerged as powerful tools for …
hardware with computational experiment planning, have emerged as powerful tools for …
Delocalized, asynchronous, closed-loop discovery of organic laser emitters
Contemporary materials discovery requires intricate sequences of synthesis, formulation,
and characterization that often span multiple locations with specialized expertise or …
and characterization that often span multiple locations with specialized expertise or …
Balancing computational chemistry's potential with its environmental impact
Computational chemistry techniques offer tremendous potential for accelerating the
discovery of sustainable chemical processes and reactions. However, the environmental …
discovery of sustainable chemical processes and reactions. However, the environmental …
Inducing point allocation for sparse Gaussian processes in high-throughput Bayesian optimisation
Sparse Gaussian processes are a key component of high-throughput Bayesian optimisation
(BO) loops; however, we show that existing methods for allocating their inducing points …
(BO) loops; however, we show that existing methods for allocating their inducing points …
Position paper: Bayesian deep learning in the age of large-scale ai
In the current landscape of deep learning research, there is a predominant emphasis on
achieving high predictive accuracy in supervised tasks involving large image and language …
achieving high predictive accuracy in supervised tasks involving large image and language …
Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI
In the current landscape of deep learning research, there is a predominant emphasis on
achieving high predictive accuracy in supervised tasks involving large image and language …
achieving high predictive accuracy in supervised tasks involving large image and language …