جست‌وجوی استاد و پژوهشگر

• Machine Learning in Computational Chemistry
• Theoretical Chemistry
• Excited-State Molecular Dynamics
• +۱۰ مورد دیگر

Junior Professor Dr. Julia Westermayr leads the Theoretical Chemistry of Materials Design group at the Wilhelm-Ostwald-Institute for Physical and Theoretical Chemistry (Leipzig University). Her interdisciplinary research bridges machine learning , quantum chemistry , and materials science to advance molecular simulations and reaction mechanism discovery. Academic rank: Assistant Professor (Junior Professor) Research focus: AI-driven excited-state dynamics, interatomic potentials, CO₂ conversion, and photocatalysis Key collaborators: Bell Flavors & Fragrances GmbH, ScaDS.AI, TU Berlin, University of Vienna Her team develops transferable ML models for nonadiabatic molecular dynamics , enabling long-timescale simulations of photodriven processes at metal surfaces and solvent environments . Recent work includes equivariant neural networks for UV absorption spectra and generative AI for molecular design . The group actively trains PhD students like Daniel Bitterlich, Peter Fichtelmann, and Robin Curth, while hosting international researchers from institutions like Bologna and Vienna. Research trends span Computational Chemistry (15/15 articles), with subfields including Excited-State Nonadiabatic Dynamics , Interatomic Potential Modeling , Photochemistry , Semiconductor Design , Reaction Mechanism Discovery , and ML-Augmented Quantum Simulations . The group participates in major scientific collaborations (DFG Cluster of Excellence, ScaDS.AI) and industry partnerships (Bell Flavors & Fragrances GmbH). They host regular research stays (e.g., Sascha Mausenberger from Vienna) and student internships , while maintaining active presence at conferences like PsiK2025 .

Chris Bartelمشاهده پروفایل

استادیار

• Computational Materials Science
• Solid-state Materials
• Quantum Chemistry
• +۱۱ مورد دیگر

Chris Bartel is an Assistant Professor in the Department of Chemical Engineering and Materials Science at the University of Minnesota's College of Science and Engineering. He leads the Design of Materials on Computers (DMC) Lab, focusing on computational approaches to materials discovery for sustainable energy applications. His research spans computational materials science, quantum chemistry, and machine learning, with specific interest in solid-state materials for batteries, photovoltaics, catalysts, and ceramics. Bartel's group integrates electronic structure theory, thermodynamics, and data science to develop predictive models for material properties and degradation mechanisms. Analysis of his recent publications (2024-2025) reveals a strong focus on generative materials discovery, solid-state reaction mechanisms, battery materials design, and the application of machine learning to materials science problems. His work frequently appears in high-impact journals including Nature, Science Advances, and ACS Energy Letters, often in collaboration with leading researchers like Gerbrand Ceder. Prof. Bartel actively mentors a diverse research group including postdoctoral researchers and undergraduate students. His lab philosophy emphasizes teamwork and mentorship, with a specific commitment to enabling careers in science and engineering for those from historically excluded backgrounds. The DMC Lab's mission centers on two goals: making a difference in mitigating climate change effects and multiplying impact by helping all group members realize their potential. The Design of Materials on Computers Lab represents a vibrant research environment at the forefront of computational materials discovery, combining theoretical approaches with practical applications to address critical energy challenges.

Bryan R. Goldsmithمشاهده پروفایل

دانشیار

• Computational Catalysis
• Machine Learning for Materials Science
• Electrocatalysis
• +۷ مورد دیگر

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.