Find Academic Supervisors

• Multiscale Modeling
• Machine Learning
• Surface Science
• +4 more

Karsten Reuter is a Director at the Fritz-Haber-Institut der Max-Planck-Gesellschaft in Berlin. His research spans multiple disciplines within computational chemistry and materials science, with a strong focus on heterogeneous catalysis, surface science, and energy conversion processes. With over 32,000 citations and an h-index of 91, he is a highly influential researcher in his field. Dr. Reuter's research interests primarily revolve around multiscale modeling , machine learning applications in materials science , surface science , heterogeneous catalysis , and energy conversion . His work bridges theoretical approaches with practical applications in catalysis and materials design. He has made significant contributions to the development of computational methods for studying surface reactions and catalytic processes at the atomic level. An analysis of his recent publications reveals a strong trend toward integrating machine learning with traditional computational chemistry methods. His work spans from fundamental surface science studies to practical applications in energy conversion and storage. The publications cover diverse subfields including catalysis modeling, quantum chemistry methods, surface adsorption phenomena, and materials informatics. His research increasingly focuses on multiscale approaches that connect atomic-scale simulations with macroscopic phenomena. Dr. Reuter has received numerous citations for his methodological contributions, particularly in kinetic Monte Carlo simulations, first-principles thermodynamics, and the development of computational frameworks for studying heterogeneous catalysis. His work on the 'ab initio molecular simulations with numeric atom-centered orbitals' has been particularly influential with over 3,000 citations. Throughout his career, Dr. Reuter has collaborated extensively with leading researchers in computational chemistry and materials science, including Matthias Scheffler, Volker Blum, and Harald Oberhofer. His research has been supported by various grants focused on advancing computational methods for materials discovery and catalysis research.

Yu Fen HsiaoView profile

Part-Time Lecturer

• Chinese Language Instruction
• Materials Science
• Nanotechnology
• +4 more

Yu Fen Hsiao is a Part-Time Lecturer in Chinese language instruction at George Mason University since Fall 2021, teaching Mandarin from a background at Sun Yat-sen University (Taiwan). She has prior teaching experience at Linnaeus University (Sweden), Trinity University of Asia (Philippines), and Manila Science High School. Education : Implied expertise in materials science through publications, though formal degrees are not specified in the provided text. Research Interests : Span materials science, focusing on nanocrystalline alloys, grain boundary diffusion, atomistic simulations, and thermodynamic stabilization of microstructures. Her work bridges mechanical engineering and computational modeling. Publications highlight trends in nanotechnology, grain boundary dynamics, and machine learning potentials for materials modeling. Scientific awards include the NSF-BSF grant for metallic nanoparticle research.

Yessica GomezView profile

Assistant Professor

• Computational Biology
• Structural Biology
• Biophysics
• +5 more

Dr. Yessica Gomez serves as an Assistant Professor in the Department of Pharmaceutical Chemistry at the University of California San Francisco (UCSF) College of Pharmacy . As the first teaching faculty member in her department, she directs the first-year Respiratory Theme integrated core course and acts as department liaison to the PharmD program. Her research focuses on protein-membrane interactions , RNA folding dynamics , and computational biophysics , bridging experimental data with theoretical modeling. PhD in Biophysics (UCSF, 2023) BS in Biochemistry and BA in Physics (California State University Long Beach, 2016) Her work employs Molecular Dynamics Simulations and Monte Carlo Methods to study complex biological systems, particularly focusing on membrane protein insertion and neurodegenerative disease mechanisms . Through collaborative research with teams across multiple departments including Biochemistry and Pharmacology, she investigates synaptic vesicle transporters and ribosomal RNA structures . Scientific contributions include: Real-time simulation of α-synuclein membrane interactions (2020) Groundbreaking work on RNA pseudoknot folding (2017) Innovations in apolipoprotein modeling (2016-2020)

• Rheology
• Polymer Science
• Computational Modeling
• +3 more

Katerina Foteinopoulou is a Full Professor at the Universidad Politécnica de Madrid (UPM), affiliated with the Department of Industrial Chemical Engineering and Environmental Engineering and the University Institute of Optoelectronic Systems and Microtechnology (ISOM). Her research spans multiscale modeling of complex fluids, focusing on non-Newtonian rheology, viscoelasticity, and industrial applications. PhD in Chemical Engineering Postdoctoral experience at University of Patras and UPM Recipient of Ramón y Cajal fellowship (2010) Principal Investigator for Spanish Ministry-funded projects (2012-2016) Her work bridges atomistic-level entanglement networks in polymers with macroscopic rheological properties, aiming to validate novel constitutive equations. She applies these methods to medical challenges like ultrasound contrast agents, drug delivery, and shock-wave lithotripsy. Scientific Contributions: Co-authored 24 peer-reviewed publications (h-index 14), presented at over 30 conferences, and actively reviews for five journals. She has participated in EU projects (DEFSAM, MNIBS, PMILS, Korrigan), national initiatives (MOBILIFE, NAFCA, FEMCOP), and industrial collaborations (Procter & Gamble). Leadership Roles: Member of ISOM's Semiconductor Devices Research Group since 2009. Served as evaluator for ANEP and the Ramón y Cajal/Juan de La Cierva programs (2015-2016).

• Polymer Science
• Molecular Simulation
• Monte Carlo Methods
• +7 more

Nikolaos Karagiannis serves as Professor at the Technical University of Madrid (UPM) within the Department of Industrial Chemical Engineering and Environmental Engineering, and is a member of the Institute for Optoelectronic Systems and Microtechnology (ISOM) and its Semiconductor Devices Research Group. His research pioneers molecular simulations of synthetic/biological polymers, specializing in atomistic-level Monte Carlo algorithms for studying barrier properties, rheology, thermodynamics, and phase behavior of complex macromolecular systems. This work enables computer-aided design of advanced materials including colloidal-based systems, liquid crystal biosensors, and nanoconfined thin films, with significant contributions to the Simu-D simulation software suite. Professor Karagiannis has coordinated four and participated in twelve competitive projects including EC initiatives (PMILS, KORRIGAN, MNIBS), Spanish programs (NAFCA, MOSDOP, HIMOBIOS), and industrial collaborations with Dow Chemicals, BP-Amoco, Rhodia, and Borealis. He has directed five research fellowships, four diploma theses, thirteen master's theses, and two doctoral theses while currently supervising two master's and three doctoral candidates. His scientific recognition includes: Distinguished scientist award from Hellenic Ministry of Defense (2005) Ramón y Cajal fellowship from Spanish Ministry of Science (2009) Outstanding Reviewer award from American Physical Society (2023) He serves as founding member of the APS Soft Matter topical group, academic editor for Crystals journal, and board director of polyhub network. With over 350 reviews for 50+ journals, he maintains active roles evaluating for ANECA, CSIC, FONDECYT, and Marie-Sklodowska-Curie programs.

Julian BraunView profile

Assistant Professor

• Elasticity
• Nonlinear Problems
• Crystal Defects
• +5 more

Julian Braun is an Assistant Professor in the Department of Mathematics at Heriot-Watt University, Edinburgh, within the School of Mathematical and Computer Sciences. His research focuses on the mathematical analysis of multiscale models in solid mechanics, particularly the coupling of atomistic and continuum frameworks for crystalline materials. His primary research domains include: Elasticity and nonlinear problems Crystal defects and boundary conditions Convergence analysis and asymptotic expansions Atomistic-to-continuum coupling methods Multiscale simulation techniques Mathematical derivation of reduced models Analysis of his 2020-2025 publications reveals consistent emphasis on rigorous mathematical treatment of crystalline defects, with particular attention to boundary condition formulations, far-field expansions, and convergence properties in multiscale simulations. His work bridges abstract mathematical theory with practical computational mechanics challenges in materials science. Publications appear in high-impact venues including SIAM Journal on Numerical Analysis and Archive for Rational Mechanics and Analysis, demonstrating significant contributions to numerical analysis and continuum mechanics with cumulative citations exceeding 20 in Scopus-indexed literature.

Dionisios MargetisView profile

Professor

• Mathematical Modeling & Applied Analysis
• Non-Equilibrium Statistical Mechanics
• Evolution of Materials Surfaces & Interfaces with Microstructures
• +5 more

Dionisios Margetis is a Professor in the Department of Mathematics at the University of Maryland, College Park. He is affiliated with the Institute for Physical Science and Technology (IPST), Center for Scientific Computation and Mathematical Modeling (CSCAMM), and Maryland NanoCenter. His research lies at the intersection of applied mathematics, theoretical physics, and materials science, focusing on connecting continuum laws (e.g., PDEs) to microscopic models in classical and quantum systems. Education: Diploma (summa cum laude), Electrical Engineering, National Technical University of Athens (1992) SM, Applied Physics, Harvard University (1994) PhD, Applied Physics, Harvard University (1999) Current research interests include mathematical modeling of materials surfaces/interfaces, plasmonics on 2D materials, quantum dynamics of Bose-Einstein condensation, quantum information, classical wave diffraction, and crystal growth under extreme pressure. Application areas span nanoscale light propagation, surface evolution in epitaxy, quantum computing decoherence, and microscale effects at large scales. Selected scientific awards: NSF CAREER Award (2009-2014), two RASA Awards (2013-14, 2018-19), Dean’s Excellence in Teaching Award (2011), and early career prizes from IEEE and Sigma Xi. He has organized numerous workshops on multiscale modeling, quantum systems, and non-equilibrium dynamics. Students & Mentees: Advisees include PhD graduates in Applied Mathematics, Physics, and Mathematics, many of whom now hold academic or research positions. Undergraduate mentees from NSF REU programs worked on topics like Schrödinger equation solutions and crystal-step interactions. Collaborators & Labs: Key collaborators include researchers from MIT, Harvard, Duke, and NIST. He contributes to interdisciplinary teams within the University of Maryland’s CSCAMM and NanoCenter, integrating computational methods with experimental validation.

Ryo KobayashiView profile

Associate Professor

• Computational Materials Science
• Molecular Dynamics Simulation
• Solid State Ionics
• +4 more

Ryo Kobayashi serves as Associate Professor in the Department of Applied Physics within the Graduate School of Engineering at Nagoya Institute of Technology. His research focuses on computational materials science with emphasis on molecular dynamics simulations for solid-state ionics and battery materials development. His work bridges fundamental atomic-scale phenomena with practical energy storage applications. Doctor of Science from Chiba University (2001) Assistant Professor at Nagoya Institute of Technology (2006-2023) Associate Professor at Nagoya Institute of Technology (2023-present) Kobayashi's research centers on computational materials design, particularly using molecular dynamics to investigate ion transport mechanisms in solid electrolytes, ferroelectric materials, and cementitious systems. His work develops high-throughput methods for force field parameterization and examines nanoscale phenomena in battery materials. Recent studies analyze grain boundary effects in ionic conductors, polarization switching in ferroelectrics, and dehydration processes in construction materials. His publication record shows strong focus on solid-state ionics and computational materials science, with significant contributions to understanding Li/Na-ion transport mechanisms in next-generation battery materials. Recent work combines machine learning potentials with traditional molecular dynamics to accelerate materials discovery. Award for Encouragement of Research in Materials Science (2011) Kobayashi leads multiple competitive research grants including MEXT Innovative Areas projects on local ionic conductivity analysis in solid electrolytes and reaction force field development for electrode interfaces. His teaching includes Solid State Physics II, Quantum Mechanics Seminar, and Applied Physics Experiments. He actively contributes to industry through workshops on materials informatics for corporate researchers and educational outreach to high school students on nanoscale computer simulations. His laboratory maintains active collaborations with national research institutions and develops computational tools like the fitpot program for force field optimization and pmd for molecular dynamics simulations.

Aurora ClarkView profile

Professor

• Computational Chemistry
• Molecular Dynamics Simulations
• Data Science in Chemistry
• +7 more

Aurora Clark is a Professor in the Department of Chemistry at Washington State University (2005-2022), with a Ph.D. from Indiana University (2003) and postdoctoral experience at Los Alamos National Laboratory (2003-2005). Her interdisciplinary research bridges computational chemistry, data science, and applied mathematics to understand complex chemical systems across multiple scales. Educational Background: Ph.D. in Chemistry, Indiana University (2003) Director's Postdoctoral Fellow, Los Alamos National Laboratory , Theory Division (2003-2005) Research Interests: Clark's laboratory employs atomistic simulations (ab-initio and classical molecular dynamics) combined with data science to characterize complex solutions and interfaces. Her work advances graph theory , topological data analysis , and machine learning to develop predictive models linking molecular-scale behavior to macroscopic properties. Key areas include: Industrial Processing Chemistry: Structure and reactivity of concentrated electrolytes for materials production, separations, and battery technologies Environmental and Health Applications: Interfacial chemistry for drug purification, environmental remediation, and green separations Interdisciplinary Mathematics: Novel applications of graph theory and topology in chemistry, including spectral graph theory and persistent homology Scientific Awards and Honors: Fellow, American Physical Society (2021) Fellow, American Association for the Advancement of Science (2019) Fellow, American Chemical Society (2017) WSU Young Faculty Performance Award WSU Mid-Career Award WSU Professional Service Award WSU Undergraduate Research Excellence Mentor Award Volunteer of the Year , Washington Idaho Border Section of ACS ACS HP Junior Faculty Award ACS PROGRESS/Dreyfus Lectureship Leadership and Service: Clark has held numerous leadership positions including Chair of the DOE Council on Chemical Sciences, Geosciences and Biosciences (2017-2021), Director of the WSU Center for Institutional Research Computing (2016-2022), and Director of the WSU-PNNL Nuclear Science and Technology Institute (2016-2019). She serves on editorial boards of Journal of Physical Chemistry , Journal of Chemical Physics , Solvent Extraction and Ion Exchange , and Computational and Theoretical Chemistry . Research Impact: Her recent publications (2022-2024) demonstrate cutting-edge work in surface chemistry, nanoparticle interactions, aluminate chemistry, and advanced mathematical methods for chemical analysis, with applications ranging from environmental cleanup to advanced materials design.

Yi LiuView profile

Assistant Professor

• geometric deep learning
• AI for Science
• LLMs for Science
• +3 more

Yi Liu is an Assistant Professor of Data Science jointly appointed in the Department of Applied Mathematics & Statistics and the Department of Computer Science at Stony Brook University. Previously, he served as an Assistant Professor in the Department of Computer Science at Florida State University. His academic background includes a Ph.D. in Computer Science from Texas A&M University (2022), an M.E. in Biomedical Engineering (2015), and a B.E. in Electronic Engineering (2012), both from the University of Science and Technology of China. Dr. Liu's research focuses on cutting-edge developments in artificial intelligence and computational science, with particular emphasis on: Geometric deep learning architectures for molecular and material systems AI-driven scientific discovery (AI4Science) Large language models for scientific applications 3D graph neural networks for molecular modeling Neural operators for physical system simulations His recent publications demonstrate a strong focus on developing novel AI methodologies for scientific applications, particularly in computational chemistry and materials science. Research trends include geometric deep learning for 3D molecular graphs, neural operators for physical modeling, explainable AI for chemical systems, and transformer-based approaches for material property prediction. The work consistently bridges fundamental AI research with applications in drug discovery, materials design, and scientific computing.

Armand SolderaView profile

Professor

• Soft Matter Physics
• Molecular Modeling
• Phase Transitions
• +3 more

Armand Soldera is a Full Professor at the Department of Chemistry of the Université de Sherbrooke. His research focuses on multi-scale studies of soft matter and molecular modeling techniques such as ab initio, atomistic, and mesoscopic methods. Education: M. Sc. in Physics (D.E.A), Strasbourg, France (1988) Ph.D. in Molecular Physical Chemistry, Strasbourg, France (1992) His current research explores phase transitions in polymers and molecular glasses, crystalline polymer melting, liquid crystalline phases (Sm C), spinodal decomposition in copolymers, and LCST polymers. Contact: Armand.Soldera@USherbrooke.ca | Phone: 819-821-8000 ext. 67011

Manuel FriedrichView profile

Professor

• Calculus of Variations
• Multiscale Methods
• Free-discontinuity Problems
• +7 more

Manuel Friedrich is a Professor at FAU Erlangen-Nürnberg , where he leads the research group in Applied Analysis. Previously, he held a Junior Professorship at the University of Münster and was a Postdoc at Universität Wien. His work bridges the Calculus of Variations , Multiscale Methods , and Materials Science , with a focus on Free-discontinuity Problems , Atomistic Models , and Geometric Rigidity . Education : PhD in TopMath elite graduate program (University of Augsburg, 2015), Masters and Bachelors in Mathematics (TU München). Research : He explores variational models for brittle fracture , crystallization in lattices , and geometric stability of nanostructures , often collaborating with Ulisse Stefanelli and others. Awards : Recipient of the Feodor Lynen Fellowship (2015-2018) from the Alexander von Humboldt Foundation. Students : Collaborates with researchers like Leonard Kreutz , Lennart Machill , and Konstantinos Zemas on projects involving graphene ripples and viscoelastic plate theory . Grants : Funded by DFG grants for fracture models in SBD (2019-2021) and metric gradient flows in solid mechanics (2021-2024).

Sara KadkhodaeiView profile

Assistant Professor

• Computational Materials Science
• First-Principles Computational Methods
• Data-Driven Materials Discovery
• +7 more

Sara Kadkhodaei is an Assistant Professor in the Department of Civil and Materials Engineering at the University of Illinois Chicago (UIC) College of Engineering. She leads the Computational Materials Research Lab (CMRL), where her work focuses on developing advanced computational frameworks to bridge atomic-level interactions to macroscopic material properties. Her research spans: First-principles and atomistic computational methods Data-driven materials discovery High-throughput computational frameworks for novel material development Recent work trends highlight her expertise in machine learning applications for materials science, defect thermodynamics, and nanoscale energy systems. CMRL's research has been published in prestigious journals like Science , Nano Energy , and Physical Review Materials , covering topics from high-entropy oxide nanoribbons to CALPHAD thermodynamics. The lab's activities include collaborations with the Chicago DOE Alliance Center and contributions to the ASM Materials Genome Toolkit project. Sara's team has received recognition in teaching (2025 College of Engineering Teaching Award) and mentored junior researchers like Kritik Jain, a Gold Award winner at the 2025 Illinois Junior Academy of Science State Science Fair.

Stefan BoreschView profile

Professor

• Computational Biology
• Chemistry
• Molecular Dynamics
• +10 more

Stefan Boresch is a Professor at the Department of Computational Biological Chemistry within the Faculty of Chemistry at the University of Vienna. His research focuses on understanding soft matter properties through atomistic simulations, particularly targeting peptides and proteins, with a strong emphasis on free energy difference calculations using molecular dynamics. Develops and optimizes alchemical free energy methods for industrial applications Applies neural network potentials (NNP) to replace traditional force fields in simulations Collaborates with companies like Exscientia and Recursion Addresses limitations of current NNP approaches in condensed phases His work bridges computational and experimental approaches, aiming to provide microscopic insights into chemical and biological questions. He has contributed to CHARMM software developments and organized major conferences like the 2015 CHARMM meeting in Vienna. Boresch also served as scientific editor for Martin Karplus' autobiography Facetten meines Lebens and authored an obituary for his mentor. He presented at the 2024 ACS Spring Meeting and the 2024 Workshop on Free Energy Methods in Drug Design, with resources like slides and recordings publicly available.

Joshua LequieuView profile

Assistant Professor

• Computational Materials Science
• Soft Matter
• Biomaterials
• +4 more

Joshua Lequieu is an Assistant Professor in the Department of Chemical and Biological Engineering at Drexel University. His research focuses on developing predictive multi-scale models of nano-structured soft matter, bridging atomistic, coarse-grained, and field-theoretic simulations to address challenges in materials science and biological systems. He holds a Ph.D. in Molecular Engineering from the University of Chicago (2017), an M.S. in Chemical Engineering from the University of Wisconsin-Madison (2013), and a B.S. in Chemical Engineering from Cornell University (2010). Education : Ph.D., Molecular Engineering, University of Chicago (2017) M.S., Chemical Engineering, University of Wisconsin-Madison (2013) B.S., Chemical Engineering, Cornell University (2010) Dr. Lequieu’s research spans block polymer self-assembly, nanoporous materials, and chromatin structure prediction. His lab develops novel simulation techniques combining field-theoretic and molecular dynamics methods to model polymer systems across length scales, with applications in energy, human health, and advanced materials. Recent work explores molecular chirality and non-equilibrium processing for expanding block copolymer phase diversity. Scientific trends in his publications include multi-scale modeling of soft materials, computational approaches for chromatin organization, and simulation algorithm development. His 2025 articles on AB2 star polymers and Bayesian optimization highlight these themes. Scientific Awards : NSF CAREER Award (2024) Charles E. Kaufman Foundation New Investigator Award (2021) Edward J. Kramer Prize in Materials (2019) William Rainey Harper Dissertation Fellowship (2017) Genentech George Scheele Award (2008) Dr. Lequieu advises graduate students in chemical engineering and biological materials, with a lab actively pursuing computational tool development for field-theoretic simulations (OpenFTS) and chromatin modeling. His group has secured grants including the NSF CAREER award to advance chemically specific polymer models. The Lequieu Lab generates open-source simulation software (OpenFTS, fieldkit) and maintains active collaborations in computational materials science and chromatin biology.