793 citations · 1.7k across the 34 of their papers we have counts for
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The Open DAC 2025 Dataset for Sorbent Discovery in Direct Air Capture
Anuroop Sriram, Logan M. Brabson, Xiaohan Yu +12
Identifying useful sorbent materials for direct air capture (DAC) from humid air remains a challenge. We present the Open DAC 2025 (ODAC25) dataset, a significant expansion and imp…
Insights into CO dimerization at electrified Cu interfaces from large-scale machine learning simulations
Sushree Jagriti Sahoo, Mikael Maraschin, Joel B Varley +7
Catalysis at solid-liquid interfaces underpins many energy technologies, yet ab initio simulations that capture interfacial dynamics remain prohibitively expensive. Here we introdu…
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…
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…
UMA: A Family of Universal Models for Atoms
Brandon M. Wood, Misko Dzamba, Xiang Fu +15
The ability to quickly and accurately compute properties from atomic simulations is critical for advancing a large number of applications in chemistry and materials science includi…
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-…
The Open Molecules 2025 (OMol25) Dataset, Evaluations, and Models
Daniel S. Levine, Muhammed Shuaibi, Evan Walter Clark Spotte-Smith +20
Machine learning (ML) models hold the promise of transforming atomic simulations by delivering quantum chemical accuracy at a fraction of the computational cost. Realization of thi…
All-atom Diffusion Transformers: Unified generative modelling of molecules and materials
Chaitanya K. Joshi, Xiang Fu, Yi-Lun Liao +4
Diffusion models are the standard toolkit for generative modelling of 3D atomic systems. However, for different types of atomic systems -- such as molecules and materials -- the ge…