4 papers
PXtal: Learning to Align Powder X-Ray Diffraction and Crystal Structures under Information Asymmetry across Modalities
Zhuoran Yang, Christopher M. Collins, Bei Peng +3
Scientific multimodal learning commonly assumes that paired views are comparably informative. Powder X-ray diffraction (PXRD) makes this mismatch explicit: compressing a three-dime…
Introducing physics-informed generative models for targeting structural novelty in the exploration of chemical space
Andrij Vasylenko, Federico Ottomano, Christopher M. Collins +3
Discovering materials with new structural chemistry is key to achieving transformative functionality. Generative artificial intelligence offers a scalable route to propose candidat…
MACS: Multi-Agent Reinforcement Learning for Optimization of Crystal Structures
Elena Zamaraeva, Christopher M. Collins, George R. Darling +8
Geometry optimization of atomic structures is a common and crucial task in computational chemistry and materials design. Following the learning to optimize paradigm, we propose a n…
Inferring energy-composition relationships with Bayesian optimization enhances exploration of inorganic materials
Andrij Vasylenko, Benjamin Asher, Chris C. Collins +4
Computational exploration of the compositional spaces of materials can provide guidance for synthetic research and thus accelerate the discovery of novel materials. Most approaches…