Blog

Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules

Designing molecules that have desired properties is a long and difficult process. To obtain a new molecule that can be used in some application, scientists must use their creativity and domain knowledge to propose many new molecules, synthesize them and test them for the given application. Moreover, human creativity often has its limits in the number and diversity of ideas that it can generate.

Continue reading →

Accelerated discovery of metallic glasses through iteration of machine learning and high-throughput experiments

Metallic glasses are amorphous alloys of metals and metalloids. They usually have properties that are very different from crystalline alloys: exceptional mechanical performances (eg. yield strength and wear resistance) and sometimes improved corrosion resistance or high magnetic permeability.

Continue reading →

Optimizing Chemical Reactions with Deep Reinforcement Learning

Optimizing chemical reactions is a very common task for chemists. It usually aims at maximizing the yield or selectivity of a reaction in order to get the most possible product from some raw material.

Continue reading →

Launching ChemIntelligence

Artificial intelligence (AI) will change the way we make chemistry and materials and make it faster, by better targeting experiments that we run in the laboratory. This will allow to increase the return on investment of chemistry and materials R&D.

Continue reading →