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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.

Hyung Sub SimView profile

Assistant Professor

• Combustion
• Carbon-Free Energy Conversion
• Rocket Propulsion
• +6 more

Hyung Sub Sim is an Assistant Professor in the Department of Aerospace Engineering at Sejong University, South Korea. Since 2022 he has led a research program centered on advanced combustion science, propulsion, and nano-engineered energy materials. His career spans academic, government, and industry roles, including positions at Pennsylvania State University, UCLA, Sandia National Laboratories, and the Agency for Defense Development. Education B.S., Chung-Ang University (2006) M.S., Chung-Ang University (2008) Ph.D., Pennsylvania State University (2016) Research Interests Professor Sim’s research integrates experiment, high-fidelity simulation, and machine learning to advance the fundamental understanding and practical deployment of: Combustion and carbon-free energy conversion Rocket propulsion and green propellants Optical diagnostics for reactive flows Nanoengineered energetic and catalytic materials Active combustion control strategies His work leverages state-of-the-art laser diagnostics (PLIF, schlieren) and reactive molecular-dynamics (ReaxFF) simulations to elucidate soot formation, ignition dynamics, and additive-enhanced fuel decomposition under high-pressure, high-temperature conditions. Publication Trends Across 31 publications (2013-2025) his output focuses on three dominant themes: (1) nanoparticle-laden hydrocarbon fuels for enhanced combustion and hypersonic propulsion, (2) soot and PAH formation mechanisms studied via high-speed optical diagnostics, and (3) computational materials design using ReaxFF for glass-ceramics, nanowires, and metal–graphene composites. A marked surge in 2020-2025 papers reflects intensified efforts in machine-learning-assisted combustion modeling and additive manufacturing of energetic nanomaterials. Scientific Awards Distinguished Paper Award, 38th International Symposium on Combustion (2022) Grants & Collaborations While specific grant numbers are not disclosed, his trajectory indicates sustained funding from Korean and U.S. defense agencies (ADD, Sandia), evidenced by joint publications with Sandia, UCLA, and Penn State teams. International collaborations span South Korea, USA, and Europe, focusing on next-generation propulsion systems and environmentally benign energetic materials. Laboratory & Teams At Sejong University he directs a combustion and propulsion laboratory equipped with high-pressure spray chambers, high-speed laser diagnostics, and GPU-accelerated ReaxFF simulation clusters. The group actively collaborates with the Engine Combustion Network (ECN) and maintains partnerships with Agency for Defense Development (ADD) for defense-oriented propulsion research.

Manuel PereiroView profile

Researcher

• Magnetic Topological Systems
• Atomistic Spin Dynamics
• Magnonics
• +6 more

Manuel Pereiro is a Researcher at the Department of Physics and Astronomy; Materials Theory , Uppsala University , Sweden. His work bridges condensed matter physics , magnetic materials , and computational modeling , with a focus on atomistic spin dynamics and ab-initio methods. He is a key member of the Atomistic Spin Dynamics (ASD) team , contributing to the development of the UppASD simulation code used globally. PhD in Physics, University of Santiago de Compostela (Spain), with over 55 high-impact publications Recipient of the Best PhD Thesis 2010 in the Faculty of Physics, Santiago de Compostela Awarded a European Thesis during postdoctoral work Secured Knut and Alice Wallenberg Foundation Grant (2018) for research on time crystals and topological magnetic materials Research Focus : Manuel specializes in magnetic topological systems , including skyrmions in kagome magnets and B20 compounds . His work explores ultrafast demagnetization driven by spin-polarized currents , quantum spin dynamics (e.g., magnon-magnon entanglement ), and non-collinear magnetism in van der Waals magnets and chiral materials . He combines Landau-Lifshitz-Gilbert equations with density functional theory to model spin-lattice interactions and predict novel magnetic phenomena. Collaborations and Impact : He collaborates internationally with groups at IFW Dresden (Germany), Federal University of Pará (Brazil), and University of South Florida (USA). His research targets energy-efficient electronics via magnonic logic devices and quantum vacuum energy extraction . His 15 most recent publications highlight advancements in spin-lattice coupling , skyrmion dynamics , and quantum entanglement in antiferromagnets . Scientific Awards : Best PhD Thesis Award, 2010 (University of Santiago de Compostela) European Thesis Award (postdoctoral period) Knut and Alice Wallenberg Foundation Grant (2018) Technological Contributions : Developed the UppASD code , enabling global research on atomistic spin dynamics. His methods integrate first-principles calculations with spin models to study magnetic materials without prior assumptions. This includes modeling non-collinear systems , strongly correlated materials , and time-crystal analogs .

Scott Thomas MilnerView profile

Professor

• Polymer Science
• Material Science
• Block Copolymers
• +5 more

Scott Thomas Milner serves as the William H. Joyce Chair Professor in the Department of Chemical Engineering within the College of Engineering at Pennsylvania State University. His research spans polymer physics, computational materials science, and electrocatalysis with over 198 publications and an h-index of 57. His research interests focus on polymer dynamics , block copolymer self-assembly , and molecular simulation techniques . Key areas include entanglement engineering in polymer melts, nanostructure formation in conjugated polymers, and ion transport mechanisms in membranes. His work frequently employs coarse-grained and atomistic simulations to bridge theoretical models with experimental characterization. Current research trends show increasing emphasis on electrocatalytic interfaces for energy applications structure-property relationships in organic photovoltaic materials computational design of polymer membranes for ion separation Scientific recognition includes: William H. Joyce Endowed Chair Professorship 14932 total citations (Scopus) Leadership in 14 NSF-funded projects since 1997 Milner directs multiple active grants including Nanostructure, ion affinity, and mobility in polymer membranes (NSF 2024-2027) and Combining DFT with classical simulations to predict solvation and cation effects (NSF 2024-2027). His team collaborates extensively with national laboratories and industry partners on polymer membrane development and electrocatalysis research, maintaining strong ties with the Materials Research Institute at Penn State. His laboratory specializes in computational polymer physics, utilizing advanced simulation strategies to predict polymer properties and nanostructure formation, with recent work focusing on conjugated polymer crystallinity and membrane ion transport mechanisms.

• Biophysics
• Computational Biology
• Molecular Dynamics Simulations
• +4 more

Grazia Cottone is an Associate Professor in the Department of Physics and Chemistry at the University of Palermo, Italy. She holds regular office hours on Mondays and Wednesdays from 3:00 PM to 5:00 PM at Studio 102, Dip. DIFC, Viale delle Scienze, Building 18, first floor. Her research focuses on computational and biophysical studies of protein structure and dynamics, particularly in amorphous matrices and ion channels. Her work employs molecular dynamics simulations, FTIR spectroscopy, and SAXS to investigate protein-matrix interactions, bioprotection mechanisms, and ion channel behavior. Articles from 2025-2022 highlight trends in biophysics, toxin research, and biopreservation. Current studies extend to medical physics applications like diffusion correction in hydrogel dosimeters and structural modeling of neurotoxins. Contact: grazia.cottone@unipa.it | Phone: +39 091 23891713

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.

Charlie HirstView profile

Assistant Professor

• Nuclear Materials
• Radiation Damage
• In Situ Experiments
• +3 more

Charlie Hirst is an Assistant Professor in the Department of Nuclear Engineering & Engineering Physics at the University of Wisconsin-Madison, where he leads the Wisconsin Ion Beam Laboratory's development of in situ ion irradiation experiments for nuclear fission and fusion materials research. His educational background includes: PhD in Nuclear Science and Engineering from Massachusetts Institute of Technology (2022) MEng from University of Oxford (2015) Dr. Hirst's research focuses on radiation damage mechanisms in nuclear materials through innovative in situ experiments combining mechanical testing, thermal analysis, and atomistic simulations. His work investigates defect evolution under coupled extremes of radiation, stress, and temperature to develop resilient materials for next-generation reactors, with emphasis on stored energy measurements , irradiation creep , and defect recovery stages . His publication portfolio shows strong interdisciplinary trends spanning nuclear forensics, superconducting magnet safety, and advanced characterization techniques. Key methodological themes include differential scanning calorimetry, machine learning analysis of microscopy data, and gradient irradiation experiments targeting both fission and fusion applications. His notable awards include: 2025 Steven J. and Teresa M. Zinkle Nuclear Materials Assistant Professorship 2020 MIT Exponent Fellowship 2019 ORNL MeV Summer School Best Team Project Multiple University of Oxford academic prizes (2012-2015) Industry-sponsored materials science awards from TATA Steel and Rolls Royce Dr. Hirst advises graduate researchers through N E 890 (Pre-Dissertator's Research) and N E 790 (Master's Research), while teaching core courses like N E 231 (Introduction to Nuclear Engineering). His research program integrates university resources with national laboratory collaborations for advanced materials testing. He directs the Wisconsin Ion Beam Laboratory's experimental development for mechanical testing and thermal analysis under irradiation, maintaining active partnerships with MIT, Oak Ridge National Laboratory, and international fusion research initiatives to advance nuclear materials science.

• Neutron Spectroscopy
• Condensed Matter Physics
• Quantum Chemistry
• +8 more

Mohamed Zbiri is a neutron spectroscopy scientist at the Institut Laue-Langevin (ILL) , specializing in condensed matter physics, quantum chemistry, and high-performance computing. His research combines neutron scattering measurements and atomistic simulations to study functional materials with societal impact, particularly in energy applications. Currently, he is a member of the IN5 direct-geometry cold-neutron disk chopper TOF spectrometer team and serves as Elected Secretary of College 7 (2025-present) at ILL, previously holding roles as Nominated Specialist (2015-2019) and Elected Secretary (2011-2015). Habilitation (HDR), Physics - University of Grenoble I (Joseph Fourier University) PhD, Chemistry - University of Freiburg, Switzerland Master, Astrophysics - University of Nice Sophia Antipolis and Côte d'Azur Observatory Bachelor, Physics - University Cadi Ayyad, Morocco His research interests span vibrational and structural dynamics of organic semiconductors , phonon-structure-magnetism interplay in correlated systems , and lattice dynamics in transition metal frameworks . He has contributed to neutron scattering studies on materials for greener hydrogen fuel production , plastic solar cells , and water-splitting photocatalysts . Zbiri also supervises PhD student Stella d'Ambrumenil and collaborates with institutions like Imperial College London , Delft University of Technology , and University of Oxford . He actively organizes scientific seminars and events at ILL and the UK Neutron and Muon Science and User Meeting (NMSUM).

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.

• Quantum mechanical simulations
• Machine learning
• Quantum computing
• +5 more

Houlong Zhuang is an Associate Professor in the School for Engineering of Matter, Transport and Energy at Arizona State University . He is also affiliated with the Center for Negative Carbon Emissions . His research focuses on quantum mechanical simulations, machine learning, and quantum computing applied to materials design and nanoscience. Ph.D., Materials Science and Engineering, Cornell University M.S., Materials Science and Engineering, Cornell University M.Phil., Mechanical Engineering, University of Hong Kong B.Eng., Materials Science and Engineering, Hunan University Dr. Zhuang's research spans quantum simulations for material discovery, machine learning-driven alloy design, and 2D nanomaterials engineering. He leverages computational physics and quantum computing to address challenges in energy storage and nanotechnology. His recent publications highlight trends in machine learning for materials design , quantum phase prediction , and hyperuniformity in disordered systems , with a focus on high-entropy alloys, 2D materials, and electrochemical applications. He is affiliated with the Center for Negative Carbon Emissions , which aligns with his work in sustainable materials and energy solutions.

• Deformation Behavior and Properties of Nanocrystalline Materials
• Irradiation Induced Damage of Structural Materials
• Atomistic and Multi-Scale Modeling of Crystal Defects
• +2 more

Ishraq Shabib serves as a Professor in the School of Engineering & Technology within the College of Science and Engineering. His office is located in ET 254, with office hours held Wednesdays from 11 a.m. to 1 p.m. His academic journey spans institutions across Bangladesh and Canada, establishing expertise in advanced materials research and engineering education. His educational foundation includes: Ph.D. in Mechanical Engineering from Carleton University, Canada (2009) M.A.Sc. in Mechanical Engineering from Carleton University, Canada (2005) B.S. in Mechanical Engineering from BUET, Bangladesh (2001) Professor Shabib's research centers on deformation mechanisms in nanocrystalline materials and radiation damage in structural alloys, with significant contributions to biomedical metallic glasses and additive manufacturing. His methodology integrates atomistic/molecular dynamics simulations with experimental validation through nanoindentation and electrochemical testing. Key focus areas include combinatorial development of Zr-Ti-based metallic glasses for implants, corrosion behavior of magnesium alloys, and radiation damage modeling in Fe-Cr nuclear materials. His teaching portfolio encompasses core mechanical engineering subjects including Dynamics, Finite Element Analysis, and Materials Science, bridging theoretical concepts with practical applications in advanced materials. Though no formal lab name is specified, his research group conducts computational and experimental work on nanoscale material behavior, with publications spanning high-impact journals in materials science and biomedical engineering. His collaborative approach is evident in multinational author teams across recent publications.

Benjamin AfflerbachView profile

Visiting Assistant Professor

• Computational Materials Science
• Machine Learning
• Atomistic Simulations
• +3 more

Dr. Benjamin Afflerbach serves as a Visiting Assistant Professor in the Department of Physics and Engineering at Fort Lewis College's College of Arts and Sciences, with his office located in Berndt Hall 605. His research bridges computational physics, materials engineering, and data science through innovative interdisciplinary approaches. His educational background includes: Ph.D. in Materials Science from the University of Wisconsin-Madison (2021) B.S. in Mechanical Engineering from Texas A&M University (2014) Dr. Afflerbach specializes in computational materials design using ab-initio and atomistic molecular dynamics simulations, with particular focus on bulk metallic glasses. He pioneers machine learning approaches for materials property prediction through accessible featurization techniques, creating synergies between traditional computational methods and modern data-driven modeling in materials informatics. His publication record reveals a clear trajectory toward integrating machine learning with materials discovery, particularly in predicting critical cooling rates for metallic glasses while simultaneously developing educational frameworks for undergraduate research engagement. As director of the Informatics Skunkworks program, Dr. Afflerbach has established a significant mentorship legacy: Program Scale: Impacted over 500 students through group project-based research Research Focus: Applied machine learning in materials informatics contexts Educational Model: Scalable apprenticeship framework for authentic research experiences The Informatics Skunkworks functions as an active undergraduate research laboratory within the Physics and Engineering department, operating as a collaborative hub for materials data science projects that connect classroom learning with real-world computational challenges.

Julien GuenoleView profile

Researcher

• Plasticity of crystalline materials
• Complex crystals and interfaces
• Multi-scale approaches
• +9 more

Julien Guenole is a CNRS Research Scientist (Chargé de recherche) at the LEM3 Laboratory (UMR CNRS 7239) in Metz, France, which is a joint research unit of the University of Lorraine, the National Centre for Scientific Research (CNRS), and Arts et Métiers ParisTech. He joined CNRS on January 1, 2020, after previously working at RWTH Aachen's Institute for Physical Metallurgy and Materials Physics (IMM). Guenole serves as Associate editor of Philosophical Magazine and was a Member of CoNRS (section 9) for 2024-2025. He is also Co-leader of Experimental and simulation challenges for the European COST MecaNano network. His research focuses on the plasticity of crystalline materials, complex crystals and their interfaces, multi-scale approaches, numerical nanomechanics, ion irradiation and induced defects . Guenole employs advanced computational methods including molecular dynamics, molecular statics, NEB calculations, and FFT-based approaches to bridge atomic-scale phenomena with continuum mechanics. Recent work includes developing generative machine learning approaches for microstructure design and predicting grain boundary segregation in magnesium alloys. His publication record shows consistent output in high-impact journals including Mechanics Research Communications, Communications Materials, Journal of Magnesium and Alloys, and Acta Materialia. His work frequently combines computational approaches with experimental validation, as seen in studies on metallic nanosponges and magnesium alloy plasticity. Associate editor of Philosophical Magazine Co-leader of Experimental and simulation challenges for European COST MecaNano network Member of CoNRS (section 9, 2024-2025) Supported by cluster IA ENACT (ANR - France 2030 funded project) As a supervisor, Guenole currently advises PhD student Badr LACHKAR on 'Generative Machine Learning for Microstructure Design: An Atomic-Scale-Informed Approach to Interfacial Engineering'. His lab provides opportunities for students to work at the cutting edge of computational materials science, with access to high-performance computing resources and connections to international research networks.

Bryan R. GoldsmithView profile

Associate Professor

• Computational Catalysis
• Machine Learning for Materials Science
• Electrocatalysis
• +7 more

Bryan R. Goldsmith is an Associate Professor of Chemical Engineering at the University of Michigan, Ann Arbor, where he also serves as Associate Chair for Graduate Education. He joined the Department in 2017 and was promoted to Associate Professor with tenure effective September 1, 2023. His academic journey includes a BS in Chemical Engineering from the University of California Riverside (2010) and a PhD in Chemical Engineering from the University of California Santa Barbara (2015), followed by a Humboldt Postdoctoral Fellowship at the Fritz Haber Institute of the Max Planck Society in Berlin, Germany. Prof. Goldsmith's research focuses on applying electronic-structure theory, molecular simulation, and data science tools to understand and design catalysts and materials for sustainable chemical conversion, pollution reduction, and energy generation/storage. His lab combines quantum mechanical modeling, molecular simulation, and machine learning to address major societal challenges in energy and the environment. His research spans four primary thrusts: (i) Thermo- and electro-catalysis for CO 2 and water pollution remediation; (ii) Machine learning to accelerate catalyst design and discovery; (iii) Catalysis and materials for renewable energy generation, storage, and use; and (iv) Electrocatalytic conversion of bio-oils for sustainable fuel production. These research areas directly address critical sustainability challenges through computational approaches. Prof. Goldsmith has received numerous prestigious awards including the NSF CAREER Award (2023), the 1938E Award from the College of Engineering (2023), and the ACS OpenEye Outstanding Junior Faculty Award in Computational Chemistry (2022). His research is supported by multiple federal agencies including the National Science Foundation, Department of Energy, Army Research Laboratory, and Office of Naval Research, as well as private corporations and foundations. NSF CAREER Award (2023): Supporting research on electrocatalytic nitrate reduction to ammonia 1938E Award (2023): Recognizing exceptional mentorship, teaching, and departmental contribution ACS OpenEye Award (2022): Honoring outstanding contributions to computational chemistry AIChE 35 under 35 Award (2020): In the Energy and Environment category Dow Corning Assistant Professorship (2020) Prof. Goldsmith has mentored an impressive number of students, including two post-doctoral researchers, nine PhD candidates, four master's students, and 19 undergraduate students. His teaching philosophy centers around project-based learning, active lecturing, and inclusive teaching methods. He has developed graduate and undergraduate data science and machine learning courses specifically for engineering students. The Goldsmith Lab is also committed to K-12 outreach and science communication, hosting educational events and contributing to various STEM initiatives.