Find Academic Supervisors

• Quantum Chemistry
• Uncertainty Quantification
• Numerical Analysis
• +5 more

Virginie Ehrlacher Galland is a Professor at CERMICS (Centre d'Enseignement et de Recherche en Mathématiques et Calcul Scientifique) within École des Ponts ParisTech. Her expertise lies in applied mathematics, numerical analysis, and computational physics, with a focus on multiscale problems, quantum chemistry, and uncertainty quantification. She holds a PhD from CERMICS (2012) and a Habilitation (2020) from Université Paris-Dauphine. Her research interests include cross-diffusion systems, reduced basis methods, and optimal transport applications. Key contributions involve numerical methods for electronic structure calculations, homogenization techniques, and adaptive algorithms. She leads the ERC Starting Grant HighLEAP (2023-2028) and contributes to major projects like the ERC Synergy project EMC². Awards: Irène Joliot-Curie Prize (2023), Chevalier de l’Ordre National du Mérite (2025). Grants/Projects: ERC Starting Grant HighLEAP (PI), ERC Synergy EMC² (Member), ANR JCJC COMODO (PI). Her work bridges theoretical analysis and computational methods, addressing challenges in materials science, fluid dynamics, and machine learning applications.

Ioana IlieView profile

Assistant Professor

• Computational Chemistry
• Biophysics
• Peptide Therapeutics
• +3 more

Dr. Ioana Ilie is an Assistant Professor in Computational Chemistry at the van 't Hoff Institute for Molecular Sciences , part of the Faculty of Science at the University of Amsterdam . Her research focuses on developing multiscale computational tools to study polypeptide aggregation, design peptide-based therapeutics, and engineer smart biomaterials. She combines atomistic simulations, coarse-grained modeling, and machine learning to address challenges in neurodegenerative diseases and cancer. Dr. Ilie holds a PhD from the University of Twente (Netherlands), with postdoctoral training at the Technical University of Darmstadt (Germany) and the University of Zurich (Switzerland). She leads the Multiscale Simulation of Biomolecular Systems group , which aims to bridge computational and experimental approaches in biomedicine and materials science. Her lab has received a Career Development Award from the Synapsis Foundation (2022) and recognition in ChemComm 's Emerging Investigators edition. Her research spans amyloidogenic targets , cyclic peptide design , and coarse-grained modeling . She emphasizes interdisciplinary collaboration, leveraging computational methods to advance therapeutic strategies and biomaterial innovation. Her team's future goals include expanding machine learning applications and deepening international partnerships. For more information, visit her lab webpage or follow her on Twitter .

Chaitanya S. DeoView profile

Professor

• Nuclear Engineering
• Materials Science
• Computational Materials Science
• +3 more

Chaitanya S. Deo is a Professor in the Department of Nuclear and Radiological Engineering at Georgia Tech. He holds the Southern Nuclear Termed Professorship and specializes in computational materials science, focusing on radiation effects in nuclear materials. His research integrates statistical mechanics and solid mechanics to study structure-property relationships in materials for advanced nuclear systems. Dr. Deo earned his Ph.D. (2003) and M.S. (2000) in Nuclear Engineering from the University of Michigan, followed by a B.E. (1997) from the University of Pune, India. His career includes postdoctoral research at Los Alamos National Laboratory and collaborations with institutions like Princeton University and Sandia National Laboratories. His research emphasizes atomistic simulations (Monte Carlo, molecular dynamics) to address challenges in nuclear fuels and structural materials under irradiation. Current projects explore uranium-based fuel matrices, irradiation-induced defects in U-20%Zr, and high-temperature deformation mechanisms in irradiated materials. His work has been recognized through awards like the National Regulation Commission Faculty Development Grant (2008-2011). Graduate students collaborate with national labs such as Los Alamos and Idaho National Laboratory via internships. Research outcomes focus on advancing computational tools for predictive modeling in nuclear energy systems.

Peter BrommerView profile

Associate Professor

• Materials Science
• Condensed Matter Physics
• Computational Materials Science
• +3 more

Dr. Peter Brommer is an Associate Professor in the School of Engineering at the University of Warwick. He holds a Dipl.-Phys. and Dr. rer. nat. (PhD) and is a Fellow of the Higher Education Academy (FHEA). His research focuses on computational materials science, particularly nano-confined phase change materials, molecular dynamics simulations, and the development of interatomic potential tools like potfit . He leads an EPSRC-funded project on modeling nano-confined materials and collaborates with the University of Cambridge. His work integrates ab initio methods with scalable simulations for oxides and complex metallic alloys. Dr. Brommer’s teaching includes modules on dynamics of vibrating systems, planar structures, and MSc project supervision. He is affiliated with the University of Warwick’s School of Engineering, with previous roles at the Institute for Theoretical Atomic and Molecular Physics (ITAP) in Stuttgart and the Université de Montréal’s Physics department. His office is located in D208, and he is reachable via p.brommer@warwick.ac.uk . Research highlights include advancements in kinetic Monte Carlo methods ( k-ART ), graphene functionalization studies, and scalable MD techniques for long-range interactions. His tools, such as the bs_sc2pc band structure tool for CASTEP, enhance defect analysis in materials. He actively contributes to OpenKIM’s interatomic model infrastructure. Dr. Brommer’s work bridges computational methods with experimental insights, aiming to improve material design for nanoelectronics and energy applications. His research has been published in journals like Phys. Rev. B , J. Chem. Phys. , and Modell. Simul. Mater. Sci. Eng. .

Yashashree KulkarniView profile

Associate Professor

• Computational Mechanics
• Materials Science
• Nanocrystalline Materials
• +7 more

Yashashree Kulkarni is an Associate Professor in the Department of Mechanical Engineering at the Cullen College of Engineering, University of Houston. Her research focuses on computational mechanics, materials science, and the interplay between structure and properties in advanced materials. She investigates phenomena such as active matter, nanocrystalline materials, and defect-driven mechanical behavior. Her work spans topics including the mechanical behavior of nanocrystalline metals, the role of grain boundaries in strengthening, and the dynamics of active systems like biological membranes and soft matter. She employs atomistic simulations, statistical mechanics, and continuum theories to bridge microstructural details with macroscopic material performance. Recent publications highlight her contributions to understanding plasticity in Co-based intermetallics, thermal stability of nanocrystalline alloys, and the nonlinear curvature effects in active membranes. Her research has implications for developing high-strength, deformable materials and advancing models for soft matter systems. No specific scientific awards or grants are explicitly listed in the provided information. Her advisee list remains unreported here.

Leonid V. ZhigileiView profile

Professor

• Multiscale Modeling
• Nanomaterials
• Surface Processes
• +5 more

Leonid V. Zhigilei is a Professor of Materials Science and Engineering at the University of Virginia's School of Engineering and Applied Science. He serves as an academic advisor for MSE and is involved in Virginia Engineering Online Program. His research focuses on multiscale modeling of materials behavior, nanomaterials, laser-materials interactions, and surface science. He holds a B.S. from St. Petersburg State Technical University (1987), a Ph.D. from St. Petersburg State University and Tomsk State University (1991), and completed a postdoc at Penn State University (1995-2000). Research interests include computational materials science, catalysis, and nanomanufacturing. His work combines atomistic simulations with experimental insights to explore phenomena like phase explosions, nanoparticle formation, and thermal transport in advanced materials. Key grants include NSF-funded studies on laser-induced phase transitions and nanoparticle processing. Awards: Humboldt Research Award (2021), Fojtik-Henglein Prize (2021), NSF CAREER Award (2004) Courses Taught: MSE 3050, MSE 4592/6270, MSE 6020 Labs/Teams: Computational Materials Group Recent publications (2021–2025) investigate laser-driven material transformations, thermal conductivity in carbon nanotubes, and nanofiber mechanics. His work bridges computational modeling with practical applications in energy-efficient nanoparticle synthesis and advanced manufacturing.

Matthias MilitzerView profile

Professor

• Multi-scale Modelling
• Microstructure Evolution
• Physical Metallurgy
• +6 more

Prof. Matthias Militzer holds the Dofasco Chair in Advanced Steel Processing at the Department of Materials Engineering, Faculty of Applied Science, The University of British Columbia. He specializes in multi-scale modeling of microstructure evolution and physical metallurgy of advanced low-carbon steels, with interdisciplinary research in Cu interconnects for microelectronics. Research Interests: Multi-scale modeling of microstructure evolution during phase transformations Physical metallurgy of advanced high-strength steels (e.g., dual-phase steels, linepipe steels) Recrystallization and grain growth in electrodeposited Cu thin films Collaborations: McMaster University, ArcelorMittal Dofasco, Evraz, Dan Bizzotto (UBC), Ilya Elfimov (UBC) Recent Publications: Focus on phase transformations in Fe-Mn alloys, intercritical annealing, solute-interface interactions, and multi-scale models linking atomistic simulations to macroscopic process optimization in steels. Scientific Awards: Runner’s-up certificate as 'highly commended' in the James Clerk Maxwell Young Writers Prize (2013)

Nongnuch ArtrithView profile

Assistant Professor

• Machine Learning
• Computational Materials Science
• Energy Materials
• +8 more

N. (Nong) Artrith is an Assistant Professor at Utrecht University's Materials Chemistry and Catalysis group within the Debye Institute for Nanomaterials Science. She specializes in integrating machine learning with first-principles methods for energy materials discovery. PhD from Ruhr University Bochum (2013) under Prof. Jörg Behler Postdoc at MIT (2015) with Alexie M. Kolpak Research Scientist at Columbia University (2016-2019) Her research spans computational catalysis , amorphous materials , and electrochemical energy storage . Article analysis shows focus on: Neural network potential development Data-driven materials discovery Atomic-scale descriptor innovation Experiment-theory synergy in catalysis Awards Schlumberger Foundation Faculty for the Future fellow Scialog Fellow for Advanced Energy Storage She supervises PhD and master's students while leading the Atomic Energy Network software project and collaborating with the Columbia Center for Computational Electrochemistry .

Sergey KarabasovView profile

Professor

• Computational Fluid Dynamics
• Aeroacoustics
• Aerodynamics
• +5 more

Professor Sergey Karabasov is a leading academic in computational modeling and aeroacoustics at Queen Mary University of London’s School of Engineering and Materials Science . As Director of the Centre for Intelligent Transport , he bridges aerospace engineering with environmental technology and bioengineering. Education: PhD (1999, Moscow State University), DSc (2010, Keldysh Institute) Affiliations: Fellow of the Royal Aeronautical Society (FRAeS), Fellow of the Higher Education Academy (FHEA), Associate Fellow of AIAA (AFAIAA) Research Interests span multiscale fluid dynamics, computational aeroacoustics, and high-performance computing. His work addresses: Future Mobility: Noise reduction in urban air mobility and conventional aircraft Environmental Technologies: Turbulence modeling for renewable energy and climate systems Digital Twins: Physics-based and data-driven simulations for aerospace and bioengineering Article Trends focus on: Hybrid LES-acoustic models for jet noise Multiscale methods in nanofluidics and molecular systems GPU-accelerated algorithms (e.g., CABARET) for complex flows Climate dynamics (Southern Ocean jets, Chandler wobble) Scientific Awards include: Fellowships at Royal Aeronautical Society and Higher Education Academy Associate Fellowship at AIAA Guest Editor for Royal Society Phil.Trans. A theme issues (2014, 2019) Advising includes current PhD student Hussain Ali Abid and alumni working on: Jet noise optimization Graphene suspension rheology Hybrid molecular-continuum simulations Labs & Teams involve the Centre for Intelligent Transport , GPU-Prime.Ltd consultancy, and collaborations with institutions like Cambridge University and Keldysh Institute.

Stephen T. LamView profile

Assistant Professor

• Nuclear materials
• Advanced reactors
• Materials chemistry
• +10 more

Stephen T. Lam is an Assistant Professor of Chemical Engineering at the University of Massachusetts Lowell, where he also serves as Director of the Nuclear Engineering Program and RHSA Mentor within the Francis College of Engineering. His research focuses on accelerating materials development for nuclear and clean energy applications through computational methods. His educational background includes a Ph.D. in Nuclear Science and Engineering (2020) and an MS in Nuclear Science and Engineering (2017) from the Massachusetts Institute of Technology, and a BS in Chemical Engineering (2013) from the University of British Columbia. Prior to his academic career, Lam worked in the petroleum and chemical processing industries and holds a Professional Engineer license in Canada. Ph.D.: Nuclear Science and Engineering (2020), Massachusetts Institute of Technology MS: Nuclear Science and Engineering (2017), Massachusetts Institute of Technology BS: Chemical Engineering (2013), University of British Columbia Lam's research integrates multi-scale simulation, experimental validation, and data analytics to address materials challenges in nuclear and clean energy systems. His work spans molten salt chemistry for advanced reactors, neural network interatomic potentials, and fusion energy materials. He leads the Lam Research Group, which combines predictive simulation, data analytics, and experiments to accelerate materials development. His recent publications demonstrate expertise in molten salt structure, neural network potentials, and fusion materials, with emphasis on fluoride and chloride salt systems for nuclear applications. The research shows strong interdisciplinary collaboration across computational chemistry, materials science, and nuclear engineering. Among his notable recognitions are the Early Career Research Award (2024) from the U.S. Department of Energy and the Distinguished Faculty Advancement Award (2024) from the U.S. Nuclear Regulatory. He has also received multiple early-career research awards from national laboratories and professional societies. Early Career Research Award (2024), U.S. Department of Energy Distinguished Faculty Advancement Award (2024), U.S. Nuclear Regulatory Best Presentation (2017), Tokyo Institute of Technology Natural Sciences and Engineering Research Council of Canada Postgraduate Scholarship (2017) Lam actively mentors graduate students and postdoctoral researchers, with current advisees working on AI-assisted materials design, molten salt chemistry, and fusion energy applications. He teaches courses in nuclear materials, nuclear science and engineering, transport phenomena, and fundamentals of electricity. The Lam Research Group maintains strong collaborations with national laboratories and industry partners, focusing on developing computational tools to improve understanding of material properties and accelerate materials discovery for next-generation energy technologies.

• Structural Biology
• Computational Biology
• Molecular Biophysics
• +4 more

Bojan Zagrovic is a Professor at the Department of Structural and Computational Biology , University of Vienna. His research focuses on the interplay between protein structure, RNA interactions, and molecular dynamics, particularly in phase separation, enzyme mechanisms, and disease-related mutations. Key research areas: Structural Biology, Computational Biophysics, RNA-Protein Interactions, Phase Separation, and Protein Aggregation. Recent work includes studying FUS RGG3 phase separation, α-mannosidase pathophysiology, and SPOC domain interactions. His publications span molecular dynamics simulations, RNA modifications (e.g., N6-methyladenosine), and structural insights into ciliary proteins like CFAP410. He teaches courses in computational structural biology, molecular biophysics, and quantitative biology.

Chiheb Ben MahmoudView profile

Research Fellow

• Machine Learning
• Materials Science
• Computational Chemistry
• +3 more

Chiheb Ben Mahmoud is a Junior Research Fellow and Swiss National Science Foundation Postdoctoral Fellow in the Department of Chemistry at the University of Oxford, working under Prof. Volker Deringer. He holds a PhD in Materials Science and Engineering from the Swiss Federal Institute of Technology (EPFL) and Master’s degrees from Ecole CentraleSupélec (France) and the University of Paris-Saclay (France). His research focuses on applying machine learning to model macroscopic properties of materials, with a particular emphasis on atomistic simulations and computational materials science. Key research interests include graph neural networks for interatomic potentials, solid-state NMR predictions, and addressing data challenges in atomistic machine learning. His work bridges machine learning methodologies with fundamental materials science problems, such as charge density wave modulation and hot-electron dynamics. He collaborates with experts like Dr. Andy Anker and Dr. Dmytro, advancing interdisciplinary approaches to materials modeling. No scientific awards are explicitly mentioned in the provided text. His advisory and grant activities are not detailed here, though his postdoctoral position suggests affiliation with the Swiss National Science Foundation. He contributes to Prof. Deringer’s research group, focusing on cutting-edge computational techniques for materials discovery and analysis.

• Two-Dimensional Materials
• Nanoelectronics
• Catalysis
• +5 more

Dr. Mahdi Ghorbani-Asl is a Senior Researcher at the Institute of Ion Beam Physics and Materials Research at the Helmholtz Center Dresden-Rossendorf (HZDR), Germany. His research focuses on 2D materials, particularly their application in energy-efficient nanoelectronics, catalysis, and energy storage systems. He leads the COM2DMATER Group , which explores substituting silicon with novel 2D materials to address stability challenges in thin-film systems. Key research interests include Atomistic simulations of irradiation-induced phenomena Defect engineering in 2D materials Electronic and structural properties under ion/electron irradiation Material synthesis and characterization for quantum and energy devices Publications emphasize structural transformations, defect dynamics, and electronic property modulation in materials like MoS₂, MoTe₂, and CrSBr. His work bridges computational modeling (e.g., DFT) with experimental insights from TEM and in situ analysis. Awards: None mentioned explicitly in the provided texts. Grants/Advising: Actively recruiting PhD students via email for projects in 2D materials; no specific grants listed. Labs/Teams: Leads the COM2DMATER Group, collaborating with global institutions on materials discovery and device applications.

• Materials Science
• Computational Materials Design
• Hydrogen Embrittlement
• +3 more

Dr. Ali Tehranchi is a Researcher at the Max Planck Institute for Sustainable Materials in Düsseldorf, leading the Computational Phase Studies group within the Department of Computational Materials Design. His work focuses on atomistic mechanisms of hydrogen embrittlement, phase stability of intermetallics, and computational materials design. Education: 2017: Ph.D. in Mechanical Engineering, École Polytechnique Fédérale de Lausanne, Switzerland 2009: M.Sc. in Structural Engineering, Sharif University of Technology, Iran 2006: Dual B.Sc. in Civil Engineering and Petroleum Engineering, Sharif University of Technology, Iran Research Interests: Dr. Tehranchi investigates hydrogen effects on material properties, phase stability in alloys, and defect-driven phenomena using computational methods. His studies bridge atomistic simulations with macro-scale material behavior, addressing challenges in lightweight steels, magnesium alloys, and hydrogen embrittlement mechanisms. Publications: Over 15+ peer-reviewed articles focus on hydrogen-embrittlement mechanisms, defect phase diagrams, and computational modeling. Recent work explores boron segregation in steels, phase stability under alkaline conditions, and dislocation dynamics in magnesium-based materials. Affiliations: Active member of the Max Planck Alumni Network. Contact via tehranchi@... or visit his Google Scholar profile.

Feng WangView profile

Professor

• Theoretical and Computational Chemistry
• Molecular Spectroscopy
• Sustainable Aviation Fuels
• +7 more

Professor Feng Wang is a leading academic in theoretical and computational chemistry at the Department of Chemistry and Biotechnology, School of Science, Computing and Emerging Technologies, Swinburne University of Technology, Melbourne, Australia. She holds a PhD from the University of Newcastle and has held research positions at the University of Waterloo, University of Melbourne, and Swinburne. Education: PhD, The University of Newcastle, Australia MSc (Quantum Chemistry by Research), Sichuan University, China BSc (Physical Chemistry), Sichuan University, China Prof Cert (Teaching Practice, Higher Education), Swinburne University of Technology, Australia Her research spans molecular spectroscopy, sustainable aviation fuels, corrosion science, and computational drug design, with applications in energy, environment, and pharmaceuticals. She employs advanced methods like density functional theory (DFT), molecular dynamics, and machine learning to study molecular systems and materials. Her recent publications (2024–2025) reflect a strong focus on sustainable energy materials—particularly high-energy-density aviation fuels (e.g., JP-10, norbornyl derivatives), CO2 conversion, and battery aging—alongside computational drug discovery for SARS-CoV-2 and flavonoid-based antivirals. These works integrate quantum mechanics, NMR analysis, and AI-driven modeling. Scientific Awards and Honors: Fellow, Royal Australian Chemical Institute (RACI) Fellow, Australian Institute of Physics (AIP) Vice-Chancellor's Research Award, Swinburne University of Technology Professor Wang actively mentors PhD and Master’s students and leads nationally funded research projects. She serves on the Australian Research Council (ARC) College of Experts and has chaired the National Computational Merit Allocation Scheme (NCMAS). She evaluates proposals for international agencies including FRQNT (Canada), SFI (Ireland), FCT (Portugal), and SNSF (Switzerland). Her lab, the Digital Chemistry Research Laboratory, contributes to UN Sustainable Development Goals 7 and 13.