Set your schedule for the 2024 MRS Spring Meeting & Exhibit – a hybrid event! #S24MRS

Time and time again, multidisciplinary research is touted as essential to innovation. That is why, on April 22-26, 2024, researchers working in seemingly unrelated fields will gather in Seattle, Washington to promote, share, and discuss issues and developments across disciplines at the 2024 MRS Spring Meeting & Exhibit. The virtual portion of the Meeting is May 7-9, 2024.

Click here to view the full program

Tutorial sessions can be viewed here

Don’t miss Special Events!

MRS Meeting Scene—Call for Reporters and Bloggers

Graduate students and post-docs who are interested in contributing to the 2024 MRS Spring Meeting Scene newsletter and the Meeting Blog, either in person or virtually, are encouraged to apply.

Reporters will be required to attend talks in a variety of symposia and write brief summaries (100-250 words) of four talks each day; bloggers will be required to post at least five items per day and also tweet about their experiences at the Meeting. Reporters and Bloggers will have their registration reimbursed up to the Student Rate. In addition, in-person volunteers will receive a stipend of $50 toward their onsite expenses.

To apply, please send an email to [email protected] stating your qualifications and your reasons for wanting to report or blog for us. We need only six reporters and two bloggers, so we will not be able to accept everyone who applies. Apply now! We look forward to hearing from you.

MRS Meeting Scene | Call for Reporters and Bloggers

2023 MRS Fall Meeting Best Poster Awards

Selected by the Meeting Chairs on the basis of the poster’s technical content, appearance, graphic excellence and presentation quality (not necessarily equally weighted).


Monday poster winners: Andrea Corazza (University of Basel), Fan Feng (The University of Melbourne), Xiaolin Guo (University of Louisville), Sangmin Song (Korea Institute of Science and Technology, Seoul National University), Taemin Kim (Korea Advanced Institute of Science and Technology). 


Tuesday  poster winners: Hyuk Jae (Gwangju Institute of Science and Technology), Ana Palacios Saura (Helmholtz-Zentrum Berlin für Materialien und Energie, Freie Universität Berlin), Anna Goestenkors (Washington University in St. Louis), Áine Coogan (Trinity College Dublin, The University of Dublin), Kayla Hellikson (Texas A&M University). 


Wednesday poster winners: Andre Niyongabo Rubungo (Princeton University), Ahyoung Jeong (Sungkyunkwan University), Marios Constantinou (University of Cyprus), Shawn Michael Maguire (Princeton University), Ross Kerner (National Renewable Energy Laboratory), Chenyang Shi (PNNL). 

Symposium QT03: Higher-Order Topological Structures in Real Space—From Charge to Spin

Laura Bégon-Lours, IBM Research & ETH Zürich

Ferroelectric Hafnia Superlattices for Bio-Inspired Computing OnDemand

Written by Matthew Nakamura

Laura Bégon-Lours of IBM Research & ETH Zürich delivered an insightful talk emphasizing the intersection of artificial intelligence (AI) and environmental sustainability. She acknowledged AI’s aptitude in rapid learning and prediction and highlighted the imperative for energy-efficient solutions to align with greenhouse gas emission goals. Bégon-Lours detailed the innovative use of ferroelectric materials, specifically HZO-SL (ferroelectric HfO2/ZrO2 superlattices), in crafting synaptic weights for in-memory deep neural networks. Overcoming challenges such as large voltages and footprint issues, the research team achieved sub-volt programming and significantly reduced the footprint. The integration of HZO-SL devices into the Back-End-Of-Line of CMOS was successfully demonstrated, showcasing remarkable properties, including a large On/Off ratio, ultra-fast switching, linear readout, and high endurance. This work presents a promising outlook in applying these advancements in hardware for artificial neural networks, thereby supporting bio-inspired computing. The talk signifies a crucial step toward deploying AI responsibly utilizing cutting-edge technologies.

Symposium DS06: Integrating Machine Learning with Simulations for Accelerated Materials Modeling

Sergei Manzhos, Tokyo Institute of Technology

Neural Networks with Optimized Neuron Activation Functions and Without Nonlinear Optimization or How to Prevent Overfitting, Cut CPU Cost and Get Physical Insight All at Once

Written by Matthew Nakamura

Sergei Manzhos, a professor at Tokyo Institute of Technology, explained the challenges and innovations in applying neural networks (NN) to materials science and computational chemistry. Emphasizing NN's vital role in diverse applications, Manzhos highlighted their expressive power and generality, albeit at the expense of CPU-intensive parameter optimization and susceptibility to overfitting. Addressing these issues, he proposed a method involving rule-based parameter definitions, eliminating the need for nonlinear optimization. Additionally, optimal neuron activation functions tailored to specific neurons were introduced, enhancing NN's expressiveness. By leveraging additive Gaussian process regression, Manzhos demonstrated a novel approach combining NN's power with linear regression’s robustness. Notably, the method showcased resistance to overfitting with an increased number of neurons. The talk underscored the versatility of this approach, facilitating insights in physics and computational chemistry through modified parameter rules.

Symposium DS06: Integrating Machine Learning with Simulations for Accelerated Materials Modeling

Pinar Acar, Virginia Tech

Materials Informatics for Computational and Machine Learning (ML)-Assisted Design: An Overview for Polycrystalline Metals and Mechanical Metamaterials

Written by Matthew Nakamura

Pinar Acar of Virginia Tech provided a comprehensive overview of computational methods developed by her group for optimizing metals and metamaterials at the micro-scale. The presentation began by outlining numerical approaches to assess the crystallographic texture and grain topology of polycrystalline metals, alongside a shape descriptor method for modeling mechanical metamaterials. These computational characterization techniques were seamlessly integrated into homogenization schemes for deriving mechanical properties. Acar then delved into the challenges posed by manufacturing-related uncertainties and defects, emphasizing the importance of design under uncertainty formulations. Acar discussed strategies for addressing forward and inverse design problems to enhance the elasto-plastic properties of materials. Notably, she talked about the integration of artificial intelligence/machine learning techniques into physics-informed materials models for accelerating design processes, showcasing applications in both conventional and additive manufacturing. The talk concluded with demonstrations of ML-driven design approaches for polycrystalline metals and mechanical metamaterials.

Symposium QT03: Higher-Order Topological Structures in Real Space—From Charge to Spin

Yukako Fujishiro, RIKEN Center for Emergent Matter Science

Topological Phase Transitions in Chiral Magnets

Written by Matthew Nakamura

Yukako Fujishiro of RIKEN Center for Emergent Matter Science presented her work on topological chiral crystals, exploring their fascinating spin textures and multi-fold Weyl Fermions, promising unique electromagnetic responses. The discussion centered on exotic phase transitions observed in systems, highlighting transitions between skymion and emergent magnetic monopole. Additionally, attention was drawn to manganese germanide’s distinctive transport properties linked to the nontrivial unwinding process of emergent magnetic monopole under a magnetic field. Notably, Fujishiro presented recent research on high-pressure manipulation of multi-fold Weyl Fermions in B20-type magnets, resulting in a metal-to-insulator transition and magnetic quantum criticality with unconventional magneto-transport properties. The talk illuminated the intriguing realms of topological chiral crystals and their potential applications in diverse physical phenomena.

Thank you!

While the 2023 MRS Fall Meeting & Exhibit came to conclusion with the end of The Virtual Experience on December 7th, Meeting content will be available online to registered participants through the end of January 2024.

Our congratulations go to the 2023 MRS Fall Meeting Chairs Derya Baran, Alexandra Boltasseva, Julien Pernot, Kristofer Reyes, and Jonathan Rivnay for putting together an excellent technical program along with various special events. MRS would also like to thank all the Symposium Organizers and Session Chairs for their part in the success of this Meeting. A thank you goes to the Exhibitors, Symposium Support, and to the sponsors of the Meeting and of the special events and activities.

Contributors to news on the 2023 MRS Fall Meeting & Exhibit include Meeting Scene reporters  Birgül Akolpoglu, Sophia Chen, Alison Hatt, Corrisa Heyes, Ankita Mathur, Matt Nakamura, Mruganka Parasnis, MD Afzalur Rab, Rahul Rao, Vineeth Venugopal, and Elizabeth Wilson; bloggers Cecilia Hong and Utkarsh Misra; and graphic artist Stephanie Gabborin; with newsletter production by Jason Zimmerman.

Thank you for subscribing to the MRS Meeting Scene newsletters. We hope you enjoyed reading them and continue your subscription as we launch into the 2024 MRS Spring Meeting & Exhibit. The conversation already started at #S24MRS! We welcome your comments and feedback.

Symposium DS04: Accelerating Data-Driven Materials Research for Energy Applications

Roman Garnett, Washington University in St. Luis

Active Search for Efficient Discovery of Visible Light-Activated Azoarene Photoswitches with Long Half-Lives

Written by Matthew Nakamura and Md Afzalur Rab

A photoswitch is a kind of molecule that can transform its structure, geometry, and chemical properties when the molecule is excited with electromagnetic radiation. In this talk, Roman Garnett from Washington University of St. Luis discussed an uncommon statistical method called “active search”— a variant of Bayesian optimization—to discover potential azoarene photoswitches.

In statistics, sequential analysis is a kind of hypothetical testing where the sample size is not known in advance. So the data are tested as they are collected and sampling is stopped according to some predefined rules. Bayesian optimization is a type of sequential analysis, where the size of samples are taken as undefined. Bayesian optimization is useful to test black box type functions where only inputs and outputs are known but intermediate processes are unknown.

Garnett highlighted the challenge of identifying rare, valuable subsets of photoswitches within a vast pool of possibilities and introduced nonmyopic-yet-efficient policies to address this complexity. Garnett emphasized the significance of active search in optimizing the discovery process, particularly where optimizing specific properties is crucial to overall performance of new materials. The discussion also showcased a successful application of active search to the discovery of photoswitches with desirable properties. Overall, Garnett’s talk provided valuable insights into the potential of intelligent experimental design utilizing active search to enhance the efficiency of discovery processes in various scientific domains.

Symposium SB08: Bio-Based Polymers and Composites for Sustainable Manufacturing

Mitra Ganewatta, Ingevity Corporation, University of South Carolina, Sandia National Laboratories

Industry–Academic Partnerships: Valorizing Lignin Through De-Aromatization and COOH Functionalization

Written by Mruganka Parasnis

Commercial lignin can improve the economic viability of lignocellulosic biorefineries. Depolymerization of lignin monomers is not viable. A new approach is chelator mediated by Fenton chemistry. In Mitra Ganewatta’s work, lignin was obtained from paper pulp and was sulphonated to different polymer products used in agriculture in making powders, granules, etc. The mechanism of action is due to the ionic repulsion polymers of the coated particles that eventually undergo steric stabilization. Sandia labs produce oxidized lignin and a CMF process optimization where the aromaticity, molecular weight and COOH functionality was controlled. Ingevity Corporation tested the products on a large scale and technoeconomic analysis was performed for commercial application. Researchers at the University of South Carolina studied heavy metal removal for different heavy metals such as Co, Cu, and Zn. Thus, Fenton chemistry was optimized successfully for tailoring -COOH groups and molecular weight. The lignin feedstock purity was critical for high performance.