Challenges in natural sciences can often be phrased as optimization problems. Machine
learning techniques have recently been applied to solve such problems. One example in …

Generating new molecules with specified chemical and biological properties via generative
models has emerged as a promising direction for drug discovery. However, existing …

The discovery of novel materials with desired properties is essential to the advancements of
energy-related technologies. Despite the rapid development of computational infrastructures …

Predictive materials design has rapidly accelerated in recent years with the advent of large-
scale resources, such as materials structure and property databases generated by ab initio …

The chemical space for designing materials is practically infinite. This makes disruptive
progress by traditional physics-based modeling alone challenging. Yet, training data for …

One of the major applications of generative models for drug discovery targets the lead-
optimization phase. During the optimization of a lead series, it is common to have scaffold …

Predicting the synthesizability of hypothetical crystals is challenging because of the wide
range of parameters that govern materials synthesis. Yet, exploring the exponentially large …

De novo design seeks to generate molecules with required property profiles by virtual
design-make-test cycles. With the emergence of deep learning and neural generative …

Evolutionary design has gained significant attention as a useful tool to accelerate the design
process by automatically modifying molecular structures to obtain molecules with the target …

Finding new superconductors with a high critical temperature (T c) has been a challenging
task due to computational and experimental costs. We present a diffusion model inspired by …