159 citations · 418 across the 29 of their papers we have counts for
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From Natural Language to Materials Discovery:The Materials Knowledge Navigation Agent
Genmao Zhuang, Amir Barati Farimani
Accelerating the discovery of high-performance materials remains a central challenge across energy, electronics, and aerospace technologies, where traditional workflows depend heav…
Smooth Dynamic Cutoffs for Machine Learning Interatomic Potentials
Kevin Han, Haolin Cong, Bowen Deng +1
Machine learning interatomic potentials (MLIPs) have proven to be wildly useful for molecular dynamics simulations, powering countless drug and materials discovery applications. Ho…
Coupled Diffusion-Encoder Models for Reconstruction of Flow Fields
AmirPouya Hemmasian, Amir Barati Farimani
Data-driven flow-field reconstruction typically relies on autoencoder architectures that compress high-dimensional states into low-dimensional latent representations. However, clas…
LinkD: AutoRegressive Diffusion Model for Mechanical Linkage Synthesis
Yayati Jadhav, Amir Barati Farimani
Designing mechanical linkages to achieve target end-effector trajectories presents a fundamental challenge due to the intricate coupling between continuous node placements, discret…
Image2Gcode: Image-to-G-code Generation for Additive Manufacturing Using Diffusion-Transformer Model
Ziyue Wang, Yayati Jadhav, Peter Pak +1
Mechanical design and manufacturing workflows conventionally begin with conceptual design, followed by the creation of a computer-aided design (CAD) model and fabrication through m…
Meta-Learning for Cross-Task Generalization in Protein Mutation Property Prediction
Srivathsan Badrinarayanan, Yue Su, Janghoon Ock +3
Protein mutations can have profound effects on biological function, making accurate prediction of property changes critical for drug discovery, protein engineering, and precision m…