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 …
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 …
Opportunities and challenges for machine learning-assisted enzyme engineering
Enzymes can be engineered at the level of their amino acid sequences to optimize key
properties such as expression, stability, substrate range, and catalytic efficiency─ or even to …
properties such as expression, stability, substrate range, and catalytic efficiency─ or even to …
When Do Quantum Mechanical Descriptors Help Graph Neural Networks to Predict Chemical Properties?
Deep graph neural networks are extensively utilized to predict chemical reactivity and
molecular properties. However, because of the complexity of chemical space, such models …
molecular properties. However, because of the complexity of chemical space, such models …
Chemprop: a machine learning package for chemical property prediction
Deep learning has become a powerful and frequently employed tool for the prediction of
molecular properties, thus creating a need for open-source and versatile software solutions …
molecular properties, thus creating a need for open-source and versatile software solutions …
Temperature excavation to boost machine learning battery thermochemical predictions
Advancing battery technologies requires precise predictions of thermochemical reactions
among multiple components to efficiently exploit the stored energy and conduct thermal …
among multiple components to efficiently exploit the stored energy and conduct thermal …
Closed-loop transfer enables artificial intelligence to yield chemical knowledge
Artificial intelligence-guided closed-loop experimentation has emerged as a promising
method for optimization of objective functions,, but the substantial potential of this …
method for optimization of objective functions,, but the substantial potential of this …
Automation and machine learning augmented by large language models in a catalysis study
Y Su, X Wang, Y Ye, Y Xie, Y Xu, Y Jiang, C Wang - Chemical Science, 2024 - pubs.rsc.org
Recent advancements in artificial intelligence and automation are transforming catalyst
discovery and design from traditional trial-and-error manual mode into intelligent, high …
discovery and design from traditional trial-and-error manual mode into intelligent, high …
Merged-nets enumeration for the systematic design of multicomponent reticular structures
Rational design of intricate multicomponent reticular structures is often hindered by the lack
of suitable blueprint nets. We established the merged-net approach, proffering optimal …
of suitable blueprint nets. We established the merged-net approach, proffering optimal …
Opportunities for Machine Learning and Artificial Intelligence to Advance Synthetic Drug Substance Process Development
DJ Griffin, CW Coley, SA Frank… - … Process Research & …, 2023 - ACS Publications
The goals of this Perspective are threefold:(1) to inform a broad audience, including
machine learning (ML) and artificial intelligence (AI) academics and professionals, about …
machine learning (ML) and artificial intelligence (AI) academics and professionals, about …