10x
faster
design
Generative Modeling: Expanding creativity in molecular design.
*Initial material concept ideation demonstrated in partnership with Nagase.
You may have heard of AI engines that can draw realistic images of landscapes or portraits of people that don't exist. These are called generative models, and rather than use them to create imaginary things, we’ve adapted this technology to design new materials at unprecedented speeds.
Generative modeling “flips” the traditional design process. Rather than going through cycles of invention then testing, researchers simply decide what characteristics they want a new material to have, and AI pulls from massive amounts of data to reverse-engineer molecules that fit the description. In a matter of hours, an AI engine can draft thousands of designs. This significantly expands the scope of possibility researchers can explore.
How it works
Scientists define the challenge. Generative models create every possible solution to pick from.
Materials design is a complex combinatorial problem. Even small molecules made of only a few atoms have hundreds of possible combinations, making the entire space of materials almost inconceivably vast.
AI, however, is especially suited to handle huge sets of possibilities and permutations. We use data from a Deep Search → database (augmented with AI-simulated data →) to train our generative model. From analyzing this data, the model is able to comprehend the relationship between molecules and their properties—it understands the “rules” of molecule design.
Next, researchers input desired properties—performance, safety, sustainability, etc.—and in what target ranges. Researchers can also add design constraints like radioactivity or chemical stability thresholds.
The generative model then sets to work, calculating and reconciling thousands of atomic configurations to produce an exhaustive set of designs that satisfy the parameters. It’s now up to the researchers to curate the results. Using a combination of human expertise and AI, they can whittle the outputs down to the best candidates before even setting foot in a lab.
Figure G1.
01
Analyzing existing materials
AI analyzes a dataset of known molecules to understand the relationship between molecular formations and their resulting properties.
02
Exploring variations
Once researchers set the output parameters (target values), the generative model uses the thousands of molecular “formulas” it’s derived from the data to generate an exhaustive set of novel combinations.
Figure G2.
03
Unique molecules designed
The generative model has effectively turned existing molecules into “guidelines” to create a batch of novel molecules with the same attributes.
Designing a new material can now happen in a matter of hours. Generative models account for the exponential number of permutations and calculations required to draft a molecule —a process that once
took researchers months— to efficiently create thousands of new molecular designs. This lets scientists explore a wider range of possibilities, including ones they hadn’t thought of before.
Models for
Materials
