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6 papers
FastCSP: Accelerated Molecular Crystal Structure Prediction with Universal Model for Atoms
Vahe Gharakhanyan, Yi Yang, Luis Barroso-Luque +24
Molecular crystal structure prediction (CSP) is essential for applications in pharmaceuticals and organic electronics. However, CSP remains challenging and computationally intensiv…
Open Molecular Crystals 2025 (OMC25) Dataset and Models
Vahe Gharakhanyan, Luis Barroso-Luque, Yi Yang +16
The development of accurate and efficient machine learning models for predicting the structure and properties of molecular crystals has been hindered by the scarcity of publicly av…
Genetic Algorithm-Accelerated Computational Discovery of Liquid Crystal Polymers with Enhanced Optical Properties
Jianing Zhou, Yuge Huang, Arman Boromand +7
Liquid crystal polymers with exceptional optical properties are highly promising for next-generation virtual, augmented, and mixed reality (VR/AR/MR) technologies, serving as high-…
Predicting ionic conductivity in solids from the machine-learned potential energy landscape
Artem Maevskiy, Alexandra Carvalho, Emil Sataev +5
Discovering new superionic materials is essential for advancing solid-state batteries, which offer improved energy density and safety compared to the traditional lithium-ion batter…
Local-time formula for dissipation in solid ionic electrolytes
A. Rodin, B. A. Olsen, A. Ustyuzhanin +2
When ions move through solids, they interact with the solid's constituent atoms and cause them to vibrate around their equilibrium points. This vibration, in turn, modifies the pot…
On activation in solid ionic electrolytes
Keian Noori, Ben A. Olsen, Aleksandr Rodin
Ionic conductivity in solid electrolytes is commonly expected to exhibit Arrhenius dependence on temperature, determined by a well-defined activation energy. Consequently, a standa…