Julia is from the well-defined region of central New Jersey and earned her B.S. in Materials Science and Engineering (MSE), with an additional major in Physics, from Carnegie Mellon University in 2016. She is a computational materials scientist, having studied density functional theory approximations applied to crystal structure prediction, phase stability and electrochemical performance of disordered electrodes, and the theory and application of the cluster expansion method applied to ionic systems. She earned her Ph.D. in MSE from University of California, Berkeley in 2022 while supported by the National Defense Science and Engineering Graduate Fellowship (2016-2019), and joined MIR as a HUCE Fellow to study the solvation thermodynamics and transport of charged species in non-aqueous liquids for applications in battery recycling, among others. She likes running, volleyball, wine, Berkeley Bowl, and finding new ways to lower her carbon footprint.
Jingxuan joined MIR in 2022 as a SEAS Fellow to study the Li ion transport and the interfacial reactions in solid state lithium battery. He is also interested in lattice dynamics and thermal transport in thermoelectric (TE) materials. He got his B.S. (2014) and M.S. (2016) in Materials Science and Engineering from Tsinghua University, China, where he did synthesis, characterization and DFT simulations of TE materials. He earned his Ph.D. in Mechanical Engineering and Materials Science from Duke University in 2022, studying atomic dynamics in TE and solid state electrolyte materials via neutron/x-ray scattering and computational modeling. Food and games are his favorite things. He wants to stay indoors, but his dog Cola (Lab & Pitbull mix) forces him to do outdoor activities like running and hiking. He is originally from Beijing, China.
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.
Zac earned his MSci in Chemistry with Molecular Physics from Imperial College London (ICL) in 2017. He continued on at ICL in the Theory and Simulation of Materials Centre for Doctoral Training, where he obtained an MSc in Physics (2018) and his PhD in Materials Science (2022). His research interests include theory and simulation of concentrated electrolytes, in the bulk and at electrified interfaces, with a particular focus on the role of ionic aggregation and solvation; strong electron interactions and broken symmetry phases in 2D moiré materials, which was the focus of his PhD and first postdoctoral position in the National Graphene Institute at the University of Manchester; and DFT calculations of charged molecules/defects interacting with graphene multilayers. Zac joined the MIR group in 2022 as a Postdoctoral Fellow to perform large scale atomistic simulations, using the methods developed within the group to obtain accurate and data efficient machine learned interatomic potentials, on various systems in the area of energy materials, such as polymer electrolytes for batteries and barocaloric materials. In his spare time, he enjoys cycling, bouldering, hiking, skiing, and making pasta.
Alby Musaelian is a graduate student in applied math interested in method development for atomic-scale simulation. He is supported by the Department of Energy's Computational Science Graduate Fellowship (CSGF).
Anders is an Applied Physics graduate student. He is interested in the development of new and more general force fields for molecular dynamics simulations. Before joining MIR, he obtained his B.Sc. and M.Sc. from the University of Oslo, where he studied creep processes in silica asperities using molecular dynamics. In his spare time, he enjoys skeet shooting.
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.
Cameron is a graduate student in the Department of Chemistry and Chemical Biology. He is interested in using novel methods developed in the MIR group to study and screen materials for reactions in heterogeneous catalysis. Before joining Harvard, he received his MPhil degree from the University of Cambridge on a Churchill Scholarship, and B.S. degrees in Chemistry and Physics from the University of Utah. He is from Boise, Idaho. Outside of research, he enjoys mountain biking, hiking, and walking around Cambridge. He is supported by the National Science Foundation’s Graduate Research Fellowship Program (NSF-GRFP).
Clare Yijia Xie
Clare is a PhD student in Material Science & Mechanical Engineering. Her research interest lies at the intersection of theoretical & physical 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).
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.
Jenny is a PhD student in applied physics. She is interested in using computational techniques to predict transport in bulk materials. As a developer of the Phoebe code, she studies the impact of different scattering processes on thermal/electrical conductivities and thermoelectric properties. Jenny completed her B.S. in physics at Rutgers University and is a recipient of the DOE Computational Science Graduate Fellowship (CSGF).
Kyle is a PhD student in Applied Physics. His primary research interest is developing more accurate and efficient electronic structure methods, particularly density functionals, using analytical theory and machine learning. He grew up in the San Francisco Bay Area and obtained his B.S. in Chemistry from UC Berkeley.
Kehang is a Physics grad student. His interests focus on how to use AI/ML to solve problems in material science and have applications. He got his B.S. in Physics (2021) in USTC, where he did quantum computing and condensed matter theory. During his senior year, he joined Nobel laureate Frank Wilczek’s group and work on exotic materials with emergent symmetry. He always like to challenge himself. That’s why he loves almost every outdoor activities, like skiing/ hiking/ kayaking.
Simon is a PhD student in Applied Mathematics at Harvard. His interests lie primarily at the intersection of deep learning and physics, with a particular focus on symmetry in deep learning. Before joining Harvard, he worked on machine learning at MIT and on the NASA mission SOFIA. In his free time, you can find him playing soccer, hiking, and swimming. He comes to Harvard having finished his Master's at MIT. He is originally from beautiful Illertissen, Germany.
Yu is a PhD student in Applied Physics. She is originally from Chongqing, China. In 2018, she got her Bachelor's degree from the School of Mathematical Sciences, Peking University. Now, she is working on implementing machine learning methods for molecular dynamics.
(How to pronounce her name:
Yu Xie: /jü ʃe/)