Autonomous chemical experiments: Challenges and perspectives on establishing a self-driving lab
Conspectus We must accelerate the pace at which we make technological advancements to
address climate change and disease risks worldwide. This swifter pace of discovery requires …
address climate change and disease risks worldwide. This swifter pace of discovery requires …
Predictive chemistry: machine learning for reaction deployment, reaction development, and reaction discovery
The field of predictive chemistry relates to the development of models able to describe how
molecules interact and react. It encompasses the long-standing task of computer-aided …
molecules interact and react. It encompasses the long-standing task of computer-aided …
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 …
Bayesian optimization with known experimental and design constraints for chemistry applications
Optimization strategies driven by machine learning, such as Bayesian optimization, are
being explored across experimental sciences as an efficient alternative to traditional design …
being explored across experimental sciences as an efficient alternative to traditional design …
The future of self-driving laboratories: from human in the loop interactive AI to gamification
Recent developments in artificial intelligence (AI) and machine learning (ML), implemented
through self-driving laboratories (SDLs), are rapidly creating unprecedented opportunities …
through self-driving laboratories (SDLs), are rapidly creating unprecedented opportunities …
Atlas: a brain for self-driving laboratories
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 …
The promise of 3D printed solid polymer electrolytes for develo** sustainable batteries: A techno-commercial perspective
BR Alandur Ramesh, B Basnet, R Huang… - International Journal of …, 2024 - Springer
The year 1975 can be claimed to be the year of inception for the research and development
of solid polymer electrolytes (SPEs) for Lithium-Ion Batteries (LIB), when the ionic …
of solid polymer electrolytes (SPEs) for Lithium-Ion Batteries (LIB), when the ionic …
Automated electrolyte formulation and coin cell assembly for high-throughput lithium-ion battery research
Battery cell assembly and testing in conventional battery research is acknowledged to be
heavily time-consuming and often suffers from large cell-to-cell variations. Manual battery …
heavily time-consuming and often suffers from large cell-to-cell variations. Manual battery …
Inverse molecular design and parameter optimization with Hückel theory using automatic differentiation
Semiempirical quantum chemistry has recently seen a renaissance with applications in high-
throughput virtual screening and machine learning. The simplest semiempirical model still in …
throughput virtual screening and machine learning. The simplest semiempirical model still in …
Preference-Optimized Pareto Set Learning for Blackbox Optimization
Multi-Objective Optimization (MOO) is an important problem in real-world applications.
However, for a non-trivial problem, no single solution exists that can optimize all the …
However, for a non-trivial problem, no single solution exists that can optimize all the …