5 papers
Chemical filters for ultra-high-throughput materials screening and generation
Kinga O. Mastej, Panyalak Detrattanawichai, Hyunsoo Park +3
Generative artificial intelligence is rapidly transforming materials design by enabling de novo exploration of immense chemical spaces. Yet a large proportion of AI-generated compo…
Continuous SUN (Stable, Unique, and Novel) Metric for Generative Modeling of Inorganic Crystals
Masahiro Negishi, Hyunsoo Park, Kinga O. Mastej +1
To address pressing scientific challenges such as climate change, increasingly sophisticated generative models are being developed to efficiently sample the large chemical space of…
Crystalyse: a multi-tool agent for materials design
Ryan Nduma, Hyunsoo Park, Aron Walsh
We present Crystalyse, an open, provenance-enforced scientific agent for computational materials design of inorganic crystals that orchestrates tools for compositional screening, c…
MLIP Arena: Advancing Fairness and Transparency in Machine Learning Interatomic Potentials via an Open, Accessible Benchmark Platform
Yuan Chiang, Tobias Kreiman, Christine Zhang +11
Machine learning interatomic potentials (MLIPs) have revolutionized molecular and materials modeling, but existing benchmarks suffer from data leakage, limited transferability, and…
Guiding Generative Models to Uncover Diverse and Novel Crystals via Reinforcement Learning
Hyunsoo Park, Aron Walsh
Discovering functional crystalline materials entails navigating an immense combinatorial design space. While recent advances in generative artificial intelligence have enabled the…