Welcome to the web page of Prof. Aldo Romero's group.
Eberly Family Distinguished Professor, Physics and Astronomy Department, West Virginia University.
Director of Research Computing at West Virginia University https://research.wvu.edu/tools/research-computing/high-performance-computing
Contact: Office: 304-2936317 | Cell: 304-4357440 | Email: alromero at mail.wvu.edu
Education:
Ph.D. Physics, University of California, San Diego, 1998.
Ph.D. Chemistry, University of California, San Diego, 1998.
Summary
Dr. Aldo Humberto Romero leads a multidisciplinary research group at West Virginia University that integrates computational materials science, artificial intelligence, high-performance computing, and interdisciplinary applications. His team investigates materials across length scales—ranging from nanostructures and disordered systems to one-, two-, and three-dimensional crystalline solids—using state-of-the-art ab initio and many-body theory methods. Their goal is to predict, analyze, and understand materials at the atomic and molecular levels, providing insights that accelerate discovery in physics, chemistry, and engineering. Beyond traditional condensed matter physics, Romero’s group actively incorporates AI-driven approaches, optimization algorithms, and advanced data science methods to address complex questions in materials design, electronic structure, and functional properties. These innovations have extended naturally into interdisciplinary collaborations, applying computational models and machine learning techniques to challenges in forensic science, biology, genetics, and computer science. The group’s research has wide-ranging applications in energy storage, next-generation electronics, spintronics, and quantum technologies, and their open-source software tools are used by researchers worldwide. Working with more than 20 graduate students and 4 postdoctoral researchers, Romero emphasizes mentorship, cross-disciplinary training, and global collaboration. Partnering with scientists in the United States, Europe, and Latin America, his team not only advances fundamental science but also strengthens the role of computation and AI as a bridge across disciplines and industries.
Expertise
- High-Performance Computing: Proficiency in C, C++, Python, and Fortran, with a focus on computational efficiency in parallel environments (CPU and GPU).
- Data Science: Skilled in Python libraries like Pandas, Matplotlib, SciPy, and NumPy, as well as statistical computing in R and Julia. He is also expert in many of the different existing packages used for neural network training such as PyTorch, Tensorflow, Keras,
- Software Development: Creator of several GitHub-hosted packages, including ABINIT (density functional theory and many body theory), PyChemia (high-throughput), PyProcar (band and fermi energy electronic structure analysis), MechElastic (elastic properties), TDEP (anharmonic thermal properties), and DMFTwDFT (electronic structure from dynamical mean field theory).
- Multidisciplinary research: collaborations with other departments, mostly on artificial intelligence applications, such as Forensics Science, Biology, Data Science, and Genetics.