Degree

​Ph.D., 2021, University of Colorado Boulder, USA

Research Interests

• Computational Chemistry
• Machine Learning
• Homogenous Catalysis

Biography

Dr. Mohammed Alkhater is an Assistant Professor in the Department of Chemical Engineering. He earned his Ph.D. in 2022 from the University of Colorado Boulder. His research interests encompass computational chemistry, homogeneous catalysis, and machine learning. In his work, Dr. Alkhater employs a variety of techniques to evaluate and enhance Density Functional Theory (DFT) methods, applying them to simulate diverse chemical systems. Additionally, he utilizes machine learning algorithms to model the properties of molecular systems. Research in Dr. Alkhater's group offers an enriching experience, blending both chemistry and computer simulations. Students who graduate from this group are anticipated to be experts in quantum chemical calculations as well as machine learning techniques.

Degree

​Ph.D., 2021, University of Colorado Boulder, USA

Research Interests

• Computational Chemistry
• Machine Learning
• Homogenous Catalysis

Biography

Dr. Mohammed Alkhater is an Assistant Professor in the Department of Chemical Engineering. He earned his Ph.D. in 2022 from the University of Colorado Boulder. His research interests encompass computational chemistry, homogeneous catalysis, and machine learning. In his work, Dr. Alkhater employs a variety of techniques to evaluate and enhance Density Functional Theory (DFT) methods, applying them to simulate diverse chemical systems. Additionally, he utilizes machine learning algorithms to model the properties of molecular systems. Research in Dr. Alkhater's group offers an enriching experience, blending both chemistry and computer simulations. Students who graduate from this group are anticipated to be experts in quantum chemical calculations as well as machine learning techniques.

Degree

​Ph.D., 2021, University of Colorado Boulder, USA

Research Interests

• Computational Chemistry
• Machine Learning
• Homogenous Catalysis

Biography

Dr. Mohammed Alkhater is an Assistant Professor in the Department of Chemical Engineering. He earned his Ph.D. in 2022 from the University of Colorado Boulder. His research interests encompass computational chemistry, homogeneous catalysis, and machine learning. In his work, Dr. Alkhater employs a variety of techniques to evaluate and enhance Density Functional Theory (DFT) methods, applying them to simulate diverse chemical systems. Additionally, he utilizes machine learning algorithms to model the properties of molecular systems. Research in Dr. Alkhater's group offers an enriching experience, blending both chemistry and computer simulations. Students who graduate from this group are anticipated to be experts in quantum chemical calculations as well as machine learning techniques.

Degree

​Ph.D., 2021, University of Colorado Boulder, USA

Research Interests

• Computational Chemistry
• Machine Learning
• Homogenous Catalysis

Biography

Dr. Mohammed Alkhater is an Assistant Professor in the Department of Chemical Engineering. He earned his Ph.D. in 2022 from the University of Colorado Boulder. His research interests encompass computational chemistry, homogeneous catalysis, and machine learning. In his work, Dr. Alkhater employs a variety of techniques to evaluate and enhance Density Functional Theory (DFT) methods, applying them to simulate diverse chemical systems. Additionally, he utilizes machine learning algorithms to model the properties of molecular systems. Research in Dr. Alkhater's group offers an enriching experience, blending both chemistry and computer simulations. Students who graduate from this group are anticipated to be experts in quantum chemical calculations as well as machine learning techniques.

Degree

​Ph.D., 2021, University of Colorado Boulder, USA

Research Interests

• Computational Chemistry
• Machine Learning
• Homogenous Catalysis

Biography

Dr. Mohammed Alkhater is an Assistant Professor in the Department of Chemical Engineering. He earned his Ph.D. in 2022 from the University of Colorado Boulder. His research interests encompass computational chemistry, homogeneous catalysis, and machine learning. In his work, Dr. Alkhater employs a variety of techniques to evaluate and enhance Density Functional Theory (DFT) methods, applying them to simulate diverse chemical systems. Additionally, he utilizes machine learning algorithms to model the properties of molecular systems. Research in Dr. Alkhater's group offers an enriching experience, blending both chemistry and computer simulations. Students who graduate from this group are anticipated to be experts in quantum chemical calculations as well as machine learning techniques.

Degree

​Ph.D., 2021, University of Colorado Boulder, USA

Research Interests

• Computational Chemistry
• Machine Learning
• Homogenous Catalysis

Biography

Dr. Mohammed Alkhater is an Assistant Professor in the Department of Chemical Engineering. He earned his Ph.D. in 2022 from the University of Colorado Boulder. His research interests encompass computational chemistry, homogeneous catalysis, and machine learning. In his work, Dr. Alkhater employs a variety of techniques to evaluate and enhance Density Functional Theory (DFT) methods, applying them to simulate diverse chemical systems. Additionally, he utilizes machine learning algorithms to model the properties of molecular systems. Research in Dr. Alkhater's group offers an enriching experience, blending both chemistry and computer simulations. Students who graduate from this group are anticipated to be experts in quantum chemical calculations as well as machine learning techniques.

Degree

​Ph.D., 2021, University of Colorado Boulder, USA

Research Interests

• Computational Chemistry
• Machine Learning
• Homogenous Catalysis

Biography

Dr. Mohammed Alkhater is an Assistant Professor in the Department of Chemical Engineering. He earned his Ph.D. in 2022 from the University of Colorado Boulder. His research interests encompass computational chemistry, homogeneous catalysis, and machine learning. In his work, Dr. Alkhater employs a variety of techniques to evaluate and enhance Density Functional Theory (DFT) methods, applying them to simulate diverse chemical systems. Additionally, he utilizes machine learning algorithms to model the properties of molecular systems. Research in Dr. Alkhater's group offers an enriching experience, blending both chemistry and computer simulations. Students who graduate from this group are anticipated to be experts in quantum chemical calculations as well as machine learning techniques.