Rational solvent selection remains a significant challenge in process development. Here we describe a hybrid mechanistic - machine learning approach, geared towards automated process development workflow. A library of 459...YA is grateful to UCB Pharma for funding his PhD study. LC is grateful to BASF for part-funding her PhD study. This research was in part supported by the National Research Foundation, Prime Minister’s Office, Singapore under its CREATE programme
Automated chemical synthesis is one of the long-term goals pursued in the field of chemistry. In rec...
Machine learning (ML) is a broad, flexible suite of applied statistics tools combined with optimizat...
Machine learning-based tools are now capable of helping scientists design new molecules and synthesi...
Catalyst optimization for enantioselective transformations has traditionally relied on empirical eva...
Catalyst optimization for enantioselective transformations has traditionally relied on empirical eva...
Machine learning has been used to study chemical reactivity for a long time in fields such as physic...
The transformation of the chemical industry to renewable energy and feedstock supply requires new pa...
Chemical Product Engineering (CPE) is marked by numerous challenges, such as the complexity of the p...
Discovering chemicals with desired attributes is a long and painstaking process. Curated datasets co...
The prediction of compound properties from chemical structure is a main task for machine learning (M...
Academic and pharmaceutical industry research are both key for progresses in the field of molecular ...
Synthetic organic chemists face a dearth of challenges in the efficient construction of functional m...
Discovering new reactions, optimizing their performance, and extending the synthetically accessible ...
Machine learning enables computers to address problems by learning from data. Deep learning is a typ...
The benefits of using machine learning approaches in the design, optimisation and understanding of h...
Automated chemical synthesis is one of the long-term goals pursued in the field of chemistry. In rec...
Machine learning (ML) is a broad, flexible suite of applied statistics tools combined with optimizat...
Machine learning-based tools are now capable of helping scientists design new molecules and synthesi...
Catalyst optimization for enantioselective transformations has traditionally relied on empirical eva...
Catalyst optimization for enantioselective transformations has traditionally relied on empirical eva...
Machine learning has been used to study chemical reactivity for a long time in fields such as physic...
The transformation of the chemical industry to renewable energy and feedstock supply requires new pa...
Chemical Product Engineering (CPE) is marked by numerous challenges, such as the complexity of the p...
Discovering chemicals with desired attributes is a long and painstaking process. Curated datasets co...
The prediction of compound properties from chemical structure is a main task for machine learning (M...
Academic and pharmaceutical industry research are both key for progresses in the field of molecular ...
Synthetic organic chemists face a dearth of challenges in the efficient construction of functional m...
Discovering new reactions, optimizing their performance, and extending the synthetically accessible ...
Machine learning enables computers to address problems by learning from data. Deep learning is a typ...
The benefits of using machine learning approaches in the design, optimisation and understanding of h...
Automated chemical synthesis is one of the long-term goals pursued in the field of chemistry. In rec...
Machine learning (ML) is a broad, flexible suite of applied statistics tools combined with optimizat...
Machine learning-based tools are now capable of helping scientists design new molecules and synthesi...