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 …
Engineering a Sustainable Future: Harnessing Automation, Robotics, and Artificial Intelligence with Self-Driving Laboratories
The accelerating depletion of natural resources undoubtedly demands a radical
reevaluation of research practices addressing the escalating climate crisis. From traditional …
reevaluation of research practices addressing the escalating climate crisis. From traditional …
Data-driven development of an oral lipid-based nanoparticle formulation of a hydrophobic drug
Due to its cost-effectiveness, convenience, and high patient adherence, oral drug
administration normally remains the preferred approach. Yet, the effective delivery of …
administration normally remains the preferred approach. Yet, the effective delivery of …
A Sober Look at LLMs for Material Discovery: Are They Actually Good for Bayesian Optimization Over Molecules?
Automation is one of the cornerstones of contemporary material discovery. Bayesian
optimization (BO) is an essential part of such workflows, enabling scientists to leverage prior …
optimization (BO) is an essential part of such workflows, enabling scientists to leverage prior …
Atlas: a brain for self-driving laboratories
R Hickman, M Sim, S Pablo-García, I Woolhouse… - 2023 - chemrxiv.org
Self-driving laboratories (SDLs) are next-generation research and development platforms for
closed-loop, autonomous experimentation that combine ideas from artificial intelligence …
closed-loop, autonomous experimentation that combine ideas from artificial intelligence …
Towards Data-driven Development of Advanced Drug Formulations Leveraging Machine Learning and Experimental Automation
Z Bao - 2024 - search.proquest.com
The majority of new drugs fail during clinical trials, primarily due to ineffective performance,
unacceptable side effects, and poor drug-like properties. Typically, many of these issues can …
unacceptable side effects, and poor drug-like properties. Typically, many of these issues can …
Global and preference-based optimization using surrogate-based methods
M Zhu - 2024 - e-theses.imtlucca.it
This thesis explores methodologies in black-box and preference-based optimization,
addressing three key research questions. Firstly, it introduces a semi-automated calibration …
addressing three key research questions. Firstly, it introduces a semi-automated calibration …