Members

Nicola earned a BS and a MS in Physics from the University of Padova (where people of the caliber of Galileo once taught!) in 2013, and spent a year in the UK as part of the ERASMUS scholarship program. He then was awarded a scholarship in the EPSRC Centre for Doctoral Training on Theory and Simulation of Materials at Imperial College London, where he earned a MSc and a PhD in Physics. Nicola joined the MIR lab at its very origin as a Postdoctoral Fellow. Currently he is a research engineer at Bosch, USA and a visiting associate at MIR lab. His research interests are at the interface between computational condensed matter physics and materials science, and include ionic transport and energy storage, membrane permeation, and mechanical properties of polymers. Among others, he has received the Materials Design Research Prize, the Johnson-Matthey Prize for the Best PhD, and the Blackett Laboratory Industry Club Thesis Prize. Outside the academic walls, his interests include, but are not limited to, cooking, chess, triathlon, and hiking.

nmolinari@seas.harvard.edu

Francesco earned his MSc in Mechanical Engineering from Politecnico di Torino in 2017. He later continued his research activity in the group "Theory and Simulation of Materials" in Lausanne, where he obtained his PhD in Materials Science in 2022. His research interests encompass a broad category of topics, including the theory and simulation of thermal transport, advanced electronic structure techniques, optical properties of qubits, and quantum computing. In 2021, he undertook a secondment at IBM Zurich aimed at developing algorithms for solving quantum chemistry problems on current superconducting quantum devices.

In 2022, Francesco joined the MIR group as a Postdoctoral Fellow, funded by the Swiss National Science Foundation Fellowship (SNSF), to develop a theory for the quantum motion of nuclei in crystals. This work aims to study ultrafast transient phenomena induced by irradiating materials with strong laser pulses in the THz range.

Norma earned her MSc in Physics from the University of Cagliari in 2018, conducting her thesis at EPFL, Lausanne, as an INSPIRE Potentials – MARVEL Master's Fellow in the "Theory and Simulation of Materials" group. Continuing at EPFL, Norma received her Ph.D. in Materials Science in 2023, specializing in "Phonons, Electron-Phonon Coupling, and Charge Transport in Low-Dimensional Materials". Her research focuses on the theory and simulation of low-dimensional materials, investigating how dimensionality affects vibrational properties, electron-phonon interactions, and transport applications. Norma aims to leverage dimensionality to engineer and design novel materials, employing a combination of analytical modeling, accurate first-principles calculations and high-throughput techniques. In 2023, she joined the MIR group as a Postdoctoral Fellow, integrating her expertise with the group's machine learning capabilities. Norma's goal is to develop a strategy that combines first-principles predictions with scalable methods for transport property predictions of large and complex heterostructures.

Blake is a graduate student in Applied Physics at Harvard University. His research interests intersect condensed matter theory, materials science, and developing efficient computational techniques for such problems. Currently, he is developing machine learning frameworks for coarse graining of molecular dynamics simulations. He is supported be a NASA Space Technology Graduate Research Opportunity (NSTGRO) award.

bduschatko@g.harvard.edu

Menghang (David) is a PhD student in Applied Physics at Harvard. He is interested in applying Machine Learning (ML) methods developed in the MIR group to study specific physics problems and exploring the potential of symmetry in ML. Before joining Harvard, he obtained his B.S. in Physics from the College of Creative Studies at UC Santa Barbara, where he worked on computational astrophysics and particle theory. During his free time, he enjoys outdoor activities and tries to become a better pilot in New England. He is from Qingdao, a beautiful seaside city in China.

Chuin Wei is a PhD student in Applied Physics. His research interests lie in improving the accuracy and speed of atomistic materials modelling techniques through machine learning. Prior to joining MIR, Chuin Wei spent a year as a senior research assistant at the Singapore University of Technology and Design after obtaining his MPhil in Materials Science and BA in Physics from the University of Cambridge.

Clare is a PhD student in Material Science & Mechanical Engineering. Her research interest lies at the intersection of chemistry, materials science, and computational simulation methods. Clare obtained her BSc degree in Chemistry from Imperial College London. Before joining MIR, she worked as a research engineer for a year at the Institute of High Performance Computing (IHPC), Singapore. Clare is supported by the National Science Scholarship (BS-PhD) from the Agency for Science, Technology and Research (A*STAR).

clarexie@fas.harvard.edu

Laura is a PhD student in Material Science and Mechanical Engineering. She is interested in understanding the reaction mechanisms that control electrolyte decomposition and interphase formation in all-solid-state batteries. Before joining MIR, she obtained her B.S. in Physics from the University of Michigan, where she studied two-dimensional atomic crystals. Additionally, she worked at Lawrence Berkeley National Laboratory, where she developed kinetic Monte Carlo simulations to study the solid-electrolyte interphase in Li-ion batteries.

Marc is a PhD student in Materials Science & Mechanical Engineering. He is interested in developing methods to accelerate the sampling of rare events in molecular dynamics simulations using machine learned force fields. He obtained his B.S. in Chemical Physics and M.S. in Materials Science and Engineering from Tufts University, where he used molecular dynamics and machine learning methods to predict the structures of cyclic peptides. 

Yuqian is a PhD student in Chemistry & Chemical Biology (CCB). She is interested in applying the Machine Learning Force Field (MLFF) methods developed in the MIR group to explore the mechanisms behind chemical systems, particularly in catalysis and surface chemistry. Before joining Harvard, she earned her Bachelor's degree from the University of Science and Technology of China (USTC), Yan Jici Talent Program. During her undergraduate studies, her research primarily focused on DFT calculations on two-dimensional materials. Outside of computation, she enjoys singing, dancing, and is a big fan of K-pop. She also likes traveling, discovering beautiful scenes and delicious food (especially desserts and ice cream) in different cities.

Constance is a PhD student in Biophysics and a member of the Therapeutics Graduate Program at Harvard Medical School. She applies computational approaches to study the conformations and properties of biomolecules and is also a member of the Arthanari lab at HMS/DFCI. Prior to starting her PhD, she completed two years of study at the University of Cambridge, followed by two years of study at Harvard where she graduated with concurrent A.B. and A.M. degrees in Chemistry. Her research is supported by a Hertz Fellowship.

ckraay@g.harvard.edu  

Ulrik is a PhD student in Applied Mathematics. He is interested in high performance machine learning for solving high dimensional problems. Before coming to Harvard, Ulrik obtained his Master's in Applied Mathematics from the Norwegian University of Science and Technology, where he studied numerically solving fractional partial differential equations.

Mohamed is a PhD student in Applied physics. He is interested in developing methods for electronic structure theory leveraging machine learning methods and insights from many-body physics. He is also interested in applying machine-learning force fields to the study of ionic transport and charged defects. Before joining MIR, he obtained his MSc in Physics from Ecole Polytechnique Fédérale de Lausanne (EPFL) in the Chair of Atomic Scale Simulation where he studied vertex corrections to GW methods. He had obtained his BSc in Physics and Mechanical Engineering from the American University in Cairo in the Materials Theory Group where he studied the transport of charged defects under high electric fields. Outside work, he has a keen interest in history, philosophy, and fictional and fantasy novels. He enjoys hiking, recreationally playing squash, and tennis.

Richard Hu is a PhD student in Applied Mathematics whose work centers on uncovering new perspectives for interpreting machine learning algorithms within scientific applications. By deriving insights from these methods, he aims to enhance both the efficiency and understanding of physical systems. Originally from Shanghai, China, he obtained a B.Sc. in Applied Mathematics - Computer Science and a B.A. in Physics from Brown University. Outside the physical sciences, he also has a devoted interest in historical linguistics, especially concerning the change of language sounds.

Gabriel is a PhD student in Materials Science and Engineering at MIT, with a dual appointment at Harvard. His research focuses on frameworks for extending machine learning interatomic potentials to large-scale atomistic simulations, aiming to bridge the gap between quantum accuracy and macroscopic scales. Prior to joining the group, he obtained his Master's degree in Materials Science from École Polytechnique Fédérale de Lausanne (EPFL), while holding an Excellence Fellowship, and a B.S. in Theoretical Physics at the Federal University of ABC (Brazil). Outside of the lab, he enjoys rock climbing, sailing, and film photography.

Lorenzo is a junior pursuing a BA in Physics. Originally from the alpine region of Aosta Valley, he is interested in employing the computational methods developed by the MIR to investigate the properties of dislocations within materials at the atomic level.

When he is not studying, you can find him exploring Massachusetts on his road bike or tinkering with anything mechanical!

Calista is a sophomore studying Applied Math and Physics. An Arizona native, she is interested in researching and developing ways that machine learning methods can be applied to advance our understanding of materials at the molecular level. She has previously studied the integration of 2-dimensional materials into thin-film transistors and solar cells along with their applications in energy conservation. When not debugging code, she enjoys playing tennis and learning new songs on the violin and piano.

Johnny is a junior at Harvard studying Physics. He is currently working on prediction techniques for large-scale material systems. In the past he has done computational modeling for Janus TMD bilayers.

Outside work, Johnny enjoys traveling, basketball, and trap shooting. He is from Cedar Park, Texas.

Matthew has been a Faculty Coordinator with SEAS since 2017, and an administrative Team Lead since 2022. He has helped support the Kozinsky/MIR group as of 2023, in addition to the Weitz and Spaepen groups. A Malden resident originally from Brockton, he received his BA in Theatre and English from Northeastern University in 2009. Outside of Harvard, he works as a professional theatre and voice over actor. At home, Matthew is a slightly-more-than-casual chef, board game fanatic, horror aficionado, and Peter Cushing devotee.