Computer-aided design of novel molecules and compounds is a challenging task that can
be addressed with quantum computing (QC) owing to its notable advances in optimization …

The optimal design of compounds through manipulating properties at the molecular level is
often the key to considerable scientific advances and improved process systems …

The design of molecules and materials with tailored properties is challenging, as candidate
molecules must satisfy multiple competing requirements that are often difficult to measure or …

Combining quantum chemistry characterizations with generative machine learning models
has the potential to accelerate molecular discovery. In this paradigm, quantum chemistry …

Generative molecular design strategies have emerged as promising alternatives to trial-and-
error approaches for exploring and optimizing within large chemical spaces. To date …

This work presents a proof-of-concept study in artificial-intelligence-assisted (AI-assisted)
chemistry where a machine-learning-based molecule generator is coupled with density …

Domain-aware artificial intelligence has been increasingly adopted in recent years to
expedite molecular design in various applications, including drug design and discovery …

The inverse design of novel molecules with a desirable optoelectronic property requires
consideration of the vast chemical spaces associated with varying chemical composition …

Deep generative chemistry models emerge as powerful tools to expedite drug discovery.
However, the immense size and complexity of the structural space of all possible drug-like …

The discovery of high-performance functional materials is crucial for overcoming technical
issues in modern industries. Extensive efforts have been devoted toward accelerating and …