230 citations · 362 across the 11 of their papers we have counts for
11 papers
Multi-Peptide: Multimodality Leveraged Language-Graph Learning of Peptide Properties
Srivathsan Badrinarayanan, Chakradhar Guntuboina, Parisa Mollaei +1
Peptides are essential in biological processes and therapeutics. In this study, we introduce Multi-Peptide, an innovative approach that combines transformer-based language models w…
Large Language Model Agent as a Mechanical Designer
Yayati Jadhav, Amir Barati Farimani
Conventional mechanical design follows an iterative process in which initial concepts are refined through cycles of expert assessment and resource-intensive Finite Element Method (…
IDP-Bert: Predicting Properties of Intrinsically Disordered Proteins (IDP) Using Large Language Models
Parisa Mollaei, Danush Sadasivam, Chakradhar Guntuboina +1
Intrinsically Disordered Proteins (IDPs) constitute a large and structure-less class of proteins with significant functions. The existence of IDPs challenges the conventional notio…
FaultFormer: Pretraining Transformers for Adaptable Bearing Fault Classification
Anthony Zhou, Amir Barati Farimani
The growth of global consumption has motivated important applications of deep learning to smart manufacturing and machine health monitoring. In particular, analyzing vibration data…
GPCR-BERT: Interpreting Sequential Design of G Protein Coupled Receptors Using Protein Language Models
Seongwon Kim, Parisa Mollaei, Akshay Antony +2
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 scientif…
PeptideBERT: A Language Model based on Transformers for Peptide Property Prediction
Chakradhar Guntuboina, Adrita Das, Parisa Mollaei +2
Recent advances in Language Models have enabled the protein modeling community with a powerful tool since protein sequences can be represented as text. Specifically, by taking adva…
Physics Informed Token Transformer for Solving Partial Differential Equations
Cooper Lorsung, Zijie Li, Amir Barati Farimani
Solving Partial Differential Equations (PDEs) is the core of many fields of science and engineering. While classical approaches are often prohibitively slow, machine learning model…
Crystal Twins: Self-supervised Learning for Crystalline Material Property Prediction
Rishikesh Magar, Yuyang Wang, Amir Barati Farimani
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 elus…
Dominant motion identification of multi-particle system using deep learning from video
Yayati Jadhav, Amir Barati Farimani
Identifying underlying governing equations and physical relevant information from high-dimensional observable data has always been a challenge in physical sciences. With the recent…
Deep Reinforcement Learning Optimizes Graphene Nanopores for Efficient Desalination
Yuyang Wang, Zhonglin Cao, Amir Barati Farimani
Two-dimensional nanomaterials, such as graphene, have been extensively studied because of their outstanding physical properties. Structure and geometry optimization of nanopores on…
Machine learning force fields and coarse-grained variables in molecular dynamics: application to materials and biological systems
Paraskevi Gkeka, Gabriel Stoltz, Amir Barati Farimani +14
Machine learning encompasses a set of tools and algorithms which are now becoming popular in almost all scientific and technological fields. This is true for molecular dynamics as…