Amir Barati Farimani received his Ph.D. in 2015 in mechanical science and engineering from the University of Illinois at Urbana-Champaign. His Ph.D. thesis was titled “Detecting and Sensing Biological Molecules using Nanopores.” He extensively used atomistic simulations to shed light on the DNA sensing and detection physics of biological and solid state nanopores. Right after that, he joined Professor Vijay Pande’s lab at Stanford. During his post-doc, he combined machine learning and molecular dynamics to elucidate the conformational changes of G-Protein Coupled Receptors (GPCRs). He specifically was focused on Mu-Opioid Receptors to elucidate their free energy landscape and their activation mechanism and pathway.

The Barati Farimani’s lab, the Mechanical and Artificial Intelligence laboratory (MAIL), at Carnegie Mellon University is broadly interested in the application of machine learning, data science, and molecular dynamics simulations to health and bio-engineering problems. The lab is inherently a multidisciplinary group bringing together researchers with different backgrounds and interests, including mechanical, computer science, bio-engineering, physics, material, and chemical engineering. The mission is to bring the state-of-the-art machine learning algorithm to mechanical engineering. Traditional mechanical engineering paradigms use only physics-based rules and principles to model the world, which does not include the intrinsic noise/stochastic nature of the system. To this end, the lab is developing the algorithms that can infer, learn, and predict the mechanical systems based on data. These data-driven models incorporate the physics into learning algorithms to build more accurate predictive models. They use multi-scale simulation (CFD, MD, DFT) to generate the data.

212 Scaife Hall
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Amir Barati Farimani
Mechanical and AI Lab

Using Machine Learning and AI to Scale Up Additive Manufacturing

A new material for water desalination

The Role of AI and Machine Learning in Mechanical Engineering

The intersection of AI & Mechanical Engineering


2015 Ph.D., Mechanical Science and Engineering, University of Illinois at Urbana-Champaign

Media mentions

CMU Engineering

Training robotic arms with a hands-off approach

Researchers at Carnegie Mellon University recently trained a robotic arm with human movements generated by artificial intelligence.

Mechanical Engineering

More MOFs, less problems

CMU researchers introduce MOFormer, a machine learning model that can achieve higher accuracy on prediction tasks than leading models without explicitly relying on 3D atomic structures.

Tech Explorist

Barati Farimani to work on self-charging power sources

MechE’s Amir Barati Farimani was mentioned in Tech Explorist as a contributor for new research on self-charging power sources for space applications.

CMU Engineering

Machine learning fights global warming

Researchers at Carnegie Mellon University have introduced a machine learning model to identify ionic liquid molecules to make identifying candidates for CO2 storage easier.

CMU Engineering

Machine learning gets smarter to speed up drug discovery

This self-supervised learning framework is better at predicting molecular properties than other machine learning models because it can use large amounts of unlabeled data that other models can’t.

CMU Engineering

Using AI to provide the world with drinking water

Amir Barati Farimani is seeking new possibilities in water purification through using AI agents in the desalination process.

The Academic Minute

CMU Engineering week on The Academic Minute

August 16 begins Carnegie Mellon Engineering week on National Public Radio’s (NPR) The Academic Minute. Each day, a different professor will discuss interesting facets of their research. The faculty lineup includes: Daniel Armanios, Amir Barati Farimani, Bin He, Destenie Nock, and Larry Pillegi.

Mechanical Engineering

Using deep learning to research material transport in the brain

Understanding the causes of degenerative diseases like Alzheimer's, Huntington's, and Parkinson's will require the meticulous investigation into the complex, branch-like neurite networks of the brain. Machine learning can be an efficient, highly accurate part of this process.

CMU Engineering

Pushing through nanopores: Genetic sequencing with MXene

MXene—a single layer, two-dimensional nanomaterial—shows potential for solid-state, nanopore-based DNA sequencing. This could lead to efficient, rapid diagnostics and personalized medicine.


Barati Farimani quoted on predictive drone swarms

MechE’s Amir Barati Farimani was quoted in WIRED about predictively-controlled drone swarms. The drones were able to adjust their trajectory based on how they expect neighboring drones to move, rather than merely reacting to them through a “predictive” algorithm. This represents a step toward a goal of fully-autonomous drone swarms.

CMU Engineering

Order up! AI finds the right material

Amir Barati Farimani has improved an algorithm to predict a material’s properties.

Pittsburgh Health Data Alliance

Jayan and Barati Farimani featured on norovirus project

MechE’s Reeja Jayan and Amir Barati Farimani were featured in the Pittsburgh Health Data Alliance newsletter on their Norovirus Sensor project.