887 citations · 1k across the 10 of their papers we have counts for
10 papers
MOFGPT: Generative Design of Metal-Organic Frameworks using Language Models
Srivathsan Badrinarayanan, Rishikesh Magar, Akshay Antony +2
The discovery of Metal-Organic Frameworks (MOFs) with application-specific properties remains a central challenge in materials chemistry, owing to the immense size and complexity o…
AdditiveLLM: Large Language Models Predict Defects in Additive Manufacturing
Peter Pak, Amir Barati Farimani
In this work we investigate the ability of large language models to predict additive manufacturing defect regimes given a set of process parameter inputs. For this task we utilize…
Adsorb-Agent: Autonomous Identification of Stable Adsorption Configurations via Large Language Model Agent
Janghoon Ock, Radheesh Sharma Meda, Tirtha Vinchurkar +2
Adsorption energy is a key reactivity descriptor in catalysis. Determining adsorption energy requires evaluating numerous adsorbate-catalyst configurations, making it computational…
ThermoPore: Predicting Part Porosity Based on Thermal Images Using Deep Learning
Peter Myung-Won Pak, Francis Ogoke, Andrew Polonsky +5
We present a deep learning approach for quantifying and localizing ex-situ porosity within Laser Powder Bed Fusion fabricated samples utilizing in-situ thermal image monitoring dat…
Multimodal Language and Graph Learning of Adsorption Configuration in Catalysis
Janghoon Ock, Srivathsan Badrinarayanan, Rishikesh Magar +2
Adsorption energy is a reactivity descriptor that must be accurately predicted for effective machine learning (ML) application in catalyst screening. This process involves determin…
Pour me a drink: Robotic Precision Pouring Carbonated Beverages into Transparent Containers
Feiya Zhu, Shuo Hu, Letian Leng +3
With the growing emphasis on the development and integration of service robots within household environments, we will need to endow robots with the ability to reliably pour a varie…
AugLiChem: Data Augmentation Library of Chemical Structures for Machine Learning
Rishikesh Magar, Yuyang Wang, Cooper Lorsung +4
Machine learning (ML) has demonstrated the promise for accurate and efficient property prediction of molecules and crystalline materials. To develop highly accurate ML models for c…
Molecular Contrastive Learning of Representations via Graph Neural Networks
Yuyang Wang, Jianren Wang, Zhonglin Cao +1
Molecular Machine Learning (ML) bears promise for efficient molecule property prediction and drug discovery. However, labeled molecule data can be expensive and time-consuming to a…
Thermal Boundary Conductance of Two-Dimensional Interfaces
Saurabh V. Suryavanshi, Alexander J. Gabourie, Amir Barati Farimani +1
Understanding the thermal properties of two-dimensional (2D) materials and devices is essential for thermal management of 2D applications. Here we perform molecular dynamics simula…
Thermal Transport in MoS from Molecular Dynamics using Different Empirical Potentials
Ke Xu, Alexander J. Gabourie, Arsalan Hashemi +7
Thermal properties of molybdenum disulfide (MoS) have recently attracted attention related to fundamentals of heat propagation in strongly anisotropic materials, and in the con…