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 …
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 …
Simulation intelligence: Towards a new generation of scientific methods
The original" Seven Motifs" set forth a roadmap of essential methods for the field of scientific
computing, where a motif is an algorithmic method that captures a pattern of computation …
computing, where a motif is an algorithmic method that captures a pattern of computation …
From characterization to discovery: artificial intelligence, machine learning and high-throughput experiments for heterogeneous catalyst design
J Benavides-Hernández, F Dumeignil - ACS Catalysis, 2024 - ACS Publications
This review paper delves into synergistic integration of artificial intelligence (AI) and
machine learning (ML) with high-throughput experimentation (HTE) in the field of …
machine learning (ML) with high-throughput experimentation (HTE) in the field of …
Machine learning–accelerated design and synthesis of polyelemental heterostructures
In materials discovery efforts, synthetic capabilities far outpace the ability to extract
meaningful data from them. To bridge this gap, machine learning methods are necessary to …
meaningful data from them. To bridge this gap, machine learning methods are necessary to …
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 …
Bayesian optimization with adaptive surrogate models for automated experimental design
Bayesian optimization (BO) is an indispensable tool to optimize objective functions that
either do not have known functional forms or are expensive to evaluate. Currently, optimal …
either do not have known functional forms or are expensive to evaluate. Currently, optimal …
Modern applications of machine learning in quantum sciences
In these Lecture Notes, we provide a comprehensive introduction to the most recent
advances in the application of machine learning methods in quantum sciences. We cover …
advances in the application of machine learning methods in quantum sciences. We cover …
Bayesian optimization of nanoporous materials
Nanoporous materials (NPMs) could be used to store, capture, and sense many different
gases. Given an adsorption task, we often wish to search a library of NPMs for the one with …
gases. Given an adsorption task, we often wish to search a library of NPMs for the one with …
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 …