Amir Barati Farimani

About

Amir Barati Farimani is an Associate Professor of Mechanical Engineering at Carnegie Mellon University, with courtesy appointments in Biomedical Engineering, Chemical Engineering, and Machine Learning. He received his Ph.D. in mechanical science and engineering from the University of Illinois at Urbana-Champaign in 2015, where his thesis focused on detecting and sensing biological molecules using nanopores, utilizing atomistic simulations to understand DNA sensing and detection physics. Following his doctoral studies, he conducted postdoctoral research at Stanford University in Professor Vijay Pande's lab, where he combined machine learning and molecular dynamics to study conformational changes in G-Protein Coupled Receptors, specifically Mu-Opioid Receptors, to elucidate their free energy landscape and activation mechanisms. His research group, the Mechanical and Artificial Intelligence Laboratory (MAIL) at Carnegie Mellon University, is broadly interested in applying machine learning, data science, and molecular dynamics simulations to health and bio-engineering problems. The lab is multidisciplinary, integrating expertise from mechanical, computer science, bio-engineering, physics, material, and chemical engineering fields. The mission of the lab is to bring state-of-the-art machine learning algorithms into mechanical engineering, developing data-driven models that incorporate physics to improve the accuracy of predictive models. His work involves multi-scale simulations…

Research topics

• Computer Science
• Machine Learning
• Artificial Intelligence
• Chemistry
• Mathematics
• Computational chemistry
• Algorithm
• Organic chemistry
• Materials science
• Mathematical analysis

Selected publications

• Molecular contrastive learning of representations via graph neural networks

  Nature Machine Intelligence · 2022 · 775 citations

  Senior authorCorresponding
  DOI

• A Review on Challenges and Successes in Atomic-Scale Design of Catalysts for Electrochemical Synthesis of Hydrogen Peroxide

  ACS Catalysis · 2020 · 488 citations

  Hydrogen peroxide is a valuable chemical oxidant with a wide range of applications in a variety of industrial processes, especially in water sanitization. Electrochemical synthesis of hydrogen peroxide (H2O2) through a two-electron oxygen reduction reaction (2e-ORR) or a two-electron water oxidation reaction (2e-WOR) has emerged as an appealing process for onsite production of this chemically valuable oxidant. On-site produced H2O2 can be applied for wastewater treatment in remote locations or a…

  DOI

• Machine learning force fields and coarse-grained variables in molecular\n dynamics: application to materials and biological systems

  Journal of Chemical Theory and Computation · 2020 · 224 citations

  Machine learning encompasses a set of tools and algorithms which are now\nbecoming popular in almost all scientific and technological fields. This is\ntrue for molecular dynamics as well, where machine learning offers promises of\nextracting valuable information from the enormous amounts of data generated by\nsimulation of complex systems. We provide here a review of our current\nunderstanding of goals, benefits, and limitations of machine learning\ntechniques for computational studies on atomis…

  DOI

• StressGAN: A Generative Deep Learning Model for Two-Dimensional Stress Distribution Prediction

  Journal of Applied Mechanics · 2021 · 129 citations

  Abstract Using deep learning to analyze mechanical stress distributions is gaining interest with the demand for fast stress analysis. Deep learning approaches have achieved excellent outcomes when utilized to speed up stress computation and learn the physical nature without prior knowledge of underlying equations. However, most studies restrict the variation of geometry or boundary conditions, making it difficult to generalize the methods to unseen configurations. We propose a conditional genera…

  DOI

• MOFGPT: Generative Design of Metal–Organic Frameworks using Language Models

  Journal of Chemical Information and Modeling · 2025-08-28 · 15 citations

  articleOpen accessSenior authorCorresponding

  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 of their structural design space. Conventional computational screening techniques such as molecular simulations and density functional theory (DFT), while accurate, are computationally prohibitive at scale. Machine learning offers an exciting alternative by leveraging data-driven approaches to accelerate materials disco…

  PublisherOA PDFDOI

Recent grants

• CDS&E: Collaborative Research: Deep learning enhanced parallel computations of fluid flow around moving boundaries on binarized octrees

  NSF · $245k · 2020–2023

Frequent coauthors

• N. R. Aluru

  Walker (United States)

  36 shared

• Francis Ogoke

  34 shared

• Yuyang Wang

  30 shared

• Rishikesh Magar

  Carnegie Mellon University

  30 shared

• Zhonglin Cao

  Guizhou Minzu University

  24 shared

• Kazem Meidani

  20 shared

• Zijie Li

  Institute of High Energy Physics

  18 shared

• Jack Beuth

  17 shared

Education

• Ph.D., Mechanical Science and Engineering

  University of Illinois at Urbana-Champaign

  2015

Awards & honors

• 2023 Engineering Faculty Awards

Similar researchers at Carnegie Mellon University

• Ignacio Grossmannh-117
• Neil Donahueh-116
• Lorenz Bieglerh-92