Dallas Trinkle is the Ivan Racheff Professor and Associate Head of the Department of Materials Science and Engineering at the University of Illinois, Urbana-Champaign. He holds a Ph.D. in Physics from Ohio State University (2003) and joined UIUC's faculty in 2006 after postdoctoral work at the Air Force Research Laboratory. Affiliations: Willett Faculty Scholar, NCSA Faculty Fellow, Center for Advanced Study Associate. Research Focus: Computational materials science, including defect properties (dislocations, point defects), mechanical behavior at atomic scales, and diffusion mechanisms using density-functional theory and machine learning. Key Achievements: NSF CAREER Award (2009), TMS Brimacombe Medal (2019), and over 150 peer-reviewed publications. His research group develops atomistic methods to study material defects and their impact on mechanical/thermal properties. Projects include magnesium alloy strengthening, hydrogen diffusion in palladium, and oxygen diffusion in titanium. They also create open-source computational tools like the magnesium solute database. Teaching: Courses on atomic-scale simulations, plasticity, and computational materials engineering (MSE 485, MSE 584, CSE 498 DM). Current Group:** Includes 7 Ph.D. students and a research scientist. Former advisees hold roles at institutions like MIT, Lawrence Berkeley Lab, and industry leaders such as MathWorks and General Motors.
Christopher A. Sutton is an Assistant Professor in the Department of Chemistry and Biochemistry at the University of South Carolina, affiliated with the McCausland College of Arts and Sciences. His research focuses on computational materials discovery, integrating machine learning and first-principles methods to design and understand functional materials for energy applications. Education : B.S., University of Central Arkansas, 2004–2008 Ph.D., Georgia Institute of Technology, 2009–2014 Research Interests : Sutton’s work emphasizes machine learning-driven materials design, electronic structure calculations, and high-throughput screening for energy storage, optoelectronics, and catalytic systems. His lab explores domains such as perovskites, battery materials, and defect engineering. Grants & Funding : DOE/HFTO (Co-PI): $1,000,000 NSF EPSCoR RII Track 1: $20,000,000 DOD/DEPSCOR: $600,000 Awards : Alexander von Humboldt Postdoctoral Fellowship (2016–2018) 67th Lindau Nobel Laureate Meeting Attendee (Chemistry) Recipient of multiple fellowships and scholarships Labs & Teams : The Sutton Lab at USC specializes in machine learning for quantum mechanical property prediction and computational materials discovery. Collaborations include experimentalists and AI experts to bridge theory and application.
Leon Wegner is a Professor in the Department of Civil Geological and Environmental Engineering at the University of Saskatchewan's College of Engineering. He holds a B.E. from the University of Saskatchewan, an M.A.Sc. from the Technical University of Nova Scotia, and a Ph.D. from MIT. His research focuses on structural engineering and materials, including fretting fatigue of bolted connections, structural health monitoring, cold weather masonry materials, composite materials mechanics, and cement-based materials. Current projects include developing antifreeze admixtures for subfreezing masonry construction and improving fatigue evaluation for potash mineshafts. He has openings for one PhD and two MSc students in industry-sponsored research on bolted steel connections. Education: B.E. (Saskatchewan) M.A.Sc. (Technical University of Nova Scotia) Ph.D. (MIT) Research Interests: Fretting Fatigue Structural Health Monitoring Cold-Weather Materials Composite Mechanics Cement Chemistry Recent Work Trends: His publications emphasize vibration-based damage detection for bridges, optimization of composite materials, and cold-weather construction solutions. He combines experimental and numerical approaches in projects like Diefenbaker Bridge monitoring and potash mine shaft evaluations. Grants and Advising: Supervises graduate students on industry-sponsored projects. His work aligns with infrastructure durability and material innovation in harsh environments.
Dr. Alice Allen is a Project Leader at the Max Planck Institute of Polymer Research , specializing in machine learning methods for molecular simulations. She holds a PhD in Physics from the University of Cambridge and completed her undergraduate degree in Physics at Imperial College London. Allen previously worked as a research associate at the University of Cambridge and the University of Luxembourg, followed by a postdoctoral position at Los Alamos National Laboratory. Education: BSc in Physics (Imperial College London) PhD in Physics (University of Cambridge) Her research focuses on developing machine learning models for interatomic potentials , enabling accurate and efficient simulations of reactive processes, biological molecules, and material properties. She has published extensively on topics such as permutationally invariant polynomials, data-driven force fields, and meta-learning approaches for foundation models in interatomic potential development. Recent publications highlight her work on integrating experimental data into machine learning potentials, enhancing the transferability of empirical valence bonds, and creating meta-learning frameworks for foundational interatomic models. These studies span applications in molecular dynamics , thermodynamics , and multi-scale simulations . Research Themes: Machine Learning for Atomistic Simulations Reactive Process Modeling Transferable Force Fields Interpretable Models for Material Properties Collaborative Meta-Learning Frameworks Alice Allen leads the Gräter Groups at the institute, where her team advances predictive methodologies for chemical reactions and material science applications. No specific scientific awards or student advisees are mentioned in the provided text.
Hanna Vehkamäki is a Professor at the Faculty of Science, University of Helsinki , and Vice Dean responsible for well-being, equality, bilingual affairs, and facilities/safety. She leads the Academy of Finland Center of Excellence VILMA (2022-2029) focused on molecular-level atmospheric transformations. Field of Science: Physical Sciences Email: hanna.vehkamaki@helsinki.fi Address: P.O. Box 64, Gustaf Hällströmin katu 2, 00014 Helsinki Her research bridges atmospheric science , physical chemistry , and computational modeling , with a focus on molecular cluster dynamics, ion-induced nucleation, and aerosol-cloud-climate interactions. She develops tools for molecular-level atmospheric simulations and machine learning applications in predicting particle formation. Recent publications highlight her work on APi-ToF mass spectrometer optimization , α-pinene ozonolysis mechanisms , and alkylammonium ion mobility . Her projects include VILMA (Virtual laboratory for molecular-level atmospheric transformations) and Atmospheric Mathematics . Scientific Awards : Finnish Aerosol Research Foundation Distinguished Scientist Award (2014) Magnus Ehrnrooth Foundation award (2010) Suomen Valkoisen Ruusun I luokan ritarimerkki (2022) NOSA Aerosologist Award (2014) University of Helsinki Maikki Friberg award (2015) She supervises Master’s/PhD theses (e.g., hydration layer simulations on K-feldspar) and participates in international conferences (e.g., ISSPIC XVIII, Gordon Research Seminar). Her grants include the Academy of Finland Center of Excellence and the Jane and Aatos Erkko Foundation project RESTART.
Erik Bekkers is an Associate Professor at the University of Amsterdam's Informatics Institute, leading research in the Machine Learning Lab (AMLab). His work bridges geometric mathematics and machine learning, focusing on developing robust and efficient deep learning architectures grounded in symmetry, equivariance, and physical principles. Education: PhD in Biomedical Engineering (cum laude) from Eindhoven University of Technology Previous Roles: Postdoctoral researcher in applied differential geometry at TU/e Department of Applied Mathematics His research spans: Group convolutional neural networks Symmetry-preserving representation learning Generative modeling on manifolds Physics-informed neural networks Medical imaging applications Recent publications emphasize geometric latent variable models, equivariant diffusion methods, and applications to molecular generation, medical imaging, and physics-driven AI. His team actively explores structure-preserving and self-supervised learning techniques. Scientific Awards MICCAI Young Scientist Award (2018) Philips Impact Award (MIDL 2018) NWO VENI grant: Context-Aware AI in Medical Imaging (2023) NWO VIDI grant: Neural Ideograms - Geometry-Grounded AI (2024) As co-founder of the ICML'24 GRaM workshop , he promotes geometry-grounded approaches in AI. His lab actively investigates geometric regularization, manifold-based PDE forecasting, and symmetry-aware generative methods.
John Q. Xiao is the UNIDEL Professor of Physics & Astronomy at the University of Delaware's College of Arts & Sciences. His research focuses on spintronic devices, magnetism in nanostructured materials, metamaterials, and high-frequency magnetic materials. He is affiliated with the Physics & Astronomy department and has contributed to groundbreaking work on magnetic tunnel junctions, magnon dynamics, and terahertz spintronic systems. Xiao's work integrates experimental and theoretical approaches, often involving advanced characterization techniques like thin-film deposition and time-resolved spectroscopy. Research Areas: Spintronics, Magnetism, Metamaterials, Nanostructured Composites Affiliations: UNIDEL Professor role emphasizing interdisciplinary research His studies explore phenomena such as spin-polarized transport, ultrafast demagnetization, and magnon-photon interactions. Recent projects include developing flexible magnetic composites for microwave absorption and investigating quantum effects in layered materials like Fe5GeTe2. Xiao collaborates on projects involving advanced materials for high-frequency electronics and quantum sensing applications. Xiao’s publications span topics from topological insulators to nanomaterial synthesis, with a focus on practical applications in energy-efficient devices and electromagnetic systems. His work is supported by grants addressing spin-orbit torques, magnonics, and novel 2D materials.
Varvara G. Kouznetsova is an Associate Professor in Multi-scale Mechanics of Solids at the Department of Mechanical Engineering of Eindhoven University of Technology (TU/e). Her roles include leading the Mechanics of Materials group and teaching courses such as Advanced Computational Continuum Mechanics and Material Models. She holds a PhD in Mechanical Engineering from TU/e and a degree in Applied Mathematics from Perm State Technical University, Russia. Prior to her current position, she was a Research Fellow at NIMR and M2i institutes and an Assistant Professor at TU/e from 2006 to 2018. Her research focuses on developing multi-scale techniques for materials ranging from advanced steels to metamaterials, emphasizing emergent phenomena across scales. Key interests include computational homogenization, wave propagation, and fracture mechanics. She has supervised 54 academic works and contributed to 160+ research outputs, including influential studies on metamaterials and multiscale analysis. Her recent articles explore topics like reduced-order modeling for elastomeric metamaterials, multiscale FEM-MD coupling for nanocrystalline metals, and acoustic metamaterial transient analysis. She collaborates internationally and maintains datasets on platforms like 4TU.Centre for Research Data. Courses taught include Computer-Aided Engineering and Composite Materials Design.
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.
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 .
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. .
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 .
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. 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.
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.
