De nombreuses réactions chimiques peuvent produire des sous-produits indésirables, ce qui entraîne des étapes de purification additionnelles et génère des déchets non voulus. Ces étapes additionnelles consomment d'importantes quantités d'énergie, et les déchets peuvent avoir un impact considérable sur l'environnement. On peut modifier la chimie des réactions de façon à favoriser les produits désirables par […]

Canonical methods of molecular discovery and reaction optimization rely on “trial-and-error” approaches and slow experimentation with low discovery rates. By harnessing high-throughput experimentation (HTE) with machine learning (ML) methods, artificial intelligence (AI) and robotics, we have the potential to dramatically accelerate the discovery and preparation of next generation molecules and materials. We will extract, unify, […]

Canonical methods of molecular discovery and reaction optimization rely on “trial-and-error” approaches and slow experimentation with low discovery rates. By harnessing high-throughput experimentation (HTE) with machine learning (ML) methods, artificial intelligence (AI) and robotics, we have the potential to dramatically accelerate the discovery and preparation of next generation molecules and materials. We will extract, unify, […]

Many chemical reactions can produce unwanted byproducts which require additional purification steps and lead to unwanted waste. Additional purification steps consume large amounts of energy, and waste products can have a significant environmental impact. The chemistry can be modified so that the desired products are favored over the unwanted byproducts, and the mixing can be […]