Zobrazeno 1 - 10
of 60
pro vyhledávání: '"Magar, Rishikesh"'
Autor:
Ock, Janghoon, Badrinarayanan, Srivathsan, Magar, Rishikesh, Antony, Akshay, Farimani, Amir Barati
Adsorption energy is a reactivity descriptor that must be accurately predicted for effective machine learning (ML) application in catalyst screening. This process involves determining the lowest energy across various adsorption configurations on a ca
Externí odkaz:
http://arxiv.org/abs/2401.07408
With the rise of Transformers and Large Language Models (LLMs) in Chemistry and Biology, new avenues for the design and understanding of therapeutics have opened up to the scientific community. Protein sequences can be modeled as language and can tak
Externí odkaz:
http://arxiv.org/abs/2310.19915
With the emergence of Transformer architectures and their powerful understanding of textual data, a new horizon has opened up to predict the molecular properties based on text description. While SMILES are the most common form of representation, they
Externí odkaz:
http://arxiv.org/abs/2310.03030
Recently, the remarkable capabilities of large language models (LLMs) have been illustrated across a variety of research domains such as natural language processing, computer vision, and molecular modeling. We extend this paradigm by utilizing LLMs f
Externí odkaz:
http://arxiv.org/abs/2308.16259
Metal-Organic Frameworks (MOFs) are materials with a high degree of porosity that can be used for applications in energy storage, water desalination, gas storage, and gas separation. However, the chemical space of MOFs is close to an infinite size du
Externí odkaz:
http://arxiv.org/abs/2210.14188
Machine learning (ML) models have been widely successful in the prediction of material properties. However, large labeled datasets required for training accurate ML models are elusive and computationally expensive to generate. Recent advances in Self
Externí odkaz:
http://arxiv.org/abs/2205.01893
Publikováno v:
Published in Journal of Chemical Information and Modeling, 2022
Deep learning has been a prevalence in computational chemistry and widely implemented in molecule property predictions. Recently, self-supervised learning (SSL), especially contrastive learning (CL), gathers growing attention for the potential to lea
Externí odkaz:
http://arxiv.org/abs/2202.09346
Autor:
Magar, Rishikesh, Wang, Yuyang, Lorsung, Cooper, Liang, Chen, Ramasubramanian, Hariharan, Li, Peiyuan, Farimani, Amir Barati
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 chemical structure property prediction, datasets with sufficient sample
Externí odkaz:
http://arxiv.org/abs/2111.15112
The increased presence of advanced sensors on the production floors has led to the collection of datasets that can provide significant insights into machine health. An important and reliable indicator of machine health, vibration signal data can prov
Externí odkaz:
http://arxiv.org/abs/2010.02146
Autor:
Gare, Gautam Rajendrakumar, Li, Jiayuan, Joshi, Rohan, Vaze, Mrunal Prashant, Magar, Rishikesh, Yousefpour, Michael, Rodriguez, Ricardo Luis, Galeotti, John Micheal
We present W-Net, a novel Convolution Neural Network (CNN) framework that employs raw ultrasound waveforms from each A-scan, typically referred to as ultrasound Radio Frequency (RF) data, in addition to the gray ultrasound image to semantically segme
Externí odkaz:
http://arxiv.org/abs/2008.12413