Thank you!

The 2024 MRS Fall Meeting & Exhibit came to conclusion on December 6, 2024.

Our congratulations go to the 2024 MRS Fall Meeting Chairs Philippe Bergonzo, SEKI Diamond USA; Ageeth Bol, University of Michigan, USA; Keith A. Brown, Boston University, USA; Alessandro Molle, Consiglio Nazionale delle Ricerche, Italy; and Winston Tumps Ireeta, Makerere University, Uganda. 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 Symposium Support, and to the sponsors of the Meeting and of the special events and activities, and to the Exhibitors whose commitment and enthusiasm made the Materials Science Exhibit a success.

Contributors to news on the 2024 MRS Fall Meeting & Exhibit include Meeting Scene reporters Sophia Chen, Andrew M. Fitzgerald, Molly McDonough, Jun Meng, In Young Park, Ethan To, and Ashleigh K Wilson; bloggers Gabriele Kalantaite and Rhys Otten; 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 2025 MRS Spring Meeting & Exhibit. The conversation already started at #S25MRS! We welcome your comments and feedback.

Symposium X—MRS/The Kavli Foundation Frontiers of Materials

Mitra Taheri, Johns Hopkins University

Teaching Machines to Solve Global Challenges, One Atom at a Time

Written by Sophia Chen

In 2011, the US federal government launched its Materials Genome Initiative, a program whose goal was to accelerate materials discovery. To that end, the community developed so-called “high-throughput” methods that used data-heavy computational simulations to inform human-led experiments. Researchers have also worked to make various steps of the materials discovery process autonomous, with the aid of machine learning tools.

However, focusing solely on the speed of discovery can exacerbate or ignore other global problems, such as climate change, according to Mitra Taheri of Johns Hopkins University. It’s time for the field to consider a more holistic approach to materials discovery, as Taheri discussed in Thursday’s Symposium X, titled, “Teaching Machines to Solve Global Challenges, One Atom at a Time.” “We need to start making better decisions about how we set up autonomous labs,” she said.

Taheri highlighted the field’s problematic reliance on rare earth metals. Her group focused on cobalt, which is used in many types of so-called Heusler alloys, a class of materials with desirable magnetic properties. Researchers are investigating cobalt-containing Heusler alloys for spintronics, an emerging information technology that could be more energy-efficient than current computers.

However, most cobalt originally comes from mines in the Democratic Republic of the Congo, where they employ workers, including children, to perform dangerous work in an environment full of toxins for the equivalent of dollars per day. Taheri thinks that materials scientists need to consider this human cost in their research.

To this end, Taheri’s group used high-throughput methods to discover Heusler alloys with the desired magnetic properties, but with little or no cobalt. Using characterization methods such as the Magneto-Optical Kerr Effect and Extended X-ray Absorption Fine Structure, they found several promising candidate materials. In addition, the high-throughput methods allowed them to “reduce the time to […] less than a day versus months,” said Taheri.

In addition, the field may find it fruitful to take more “good enough” attitude toward materials discovery. “How many of you […] have come home from vacation and eaten something that you really didn't want to eat just because you were hungry and you didn't go to the store?” she asked the audience. Spaghetti sauce without herbs may not be delicious, but it still feeds you. 

Taheri’s group took this attitude to a project to discover high-entropy alloys. Instead of using the more precise density functional theory to simulate candidate materials, they looked at using graph neural network, a machine learning method that is less precise but quicker than DFT. “We were able to discover a number of alloys that actually matched the prediction,” she says.

Using faster but less precise methods could help researchers move toward autonomous laboratories that can “learn on the fly” and make decisions in real time, she says.

Symposium X—MRS/The Kavli Foundation Frontiers of Materials features lectures aimed at a broad audience to provide meeting attendees with an overview of leading-edge topics.

Symposium EN10: Critical Materials for Energy—Extraction, Functionality and Recycling

Marie Bermeo, Lund University

Semiconductor Nanowire-supported Palladium Nanocatalysts 
Written by In Young Park 

Nanocatalysts, combining the benefits of homogeneous and heterogeneous systems, have emerged as powerful tools in catalysis. Bermeo offers a novel nanocatalyst platform which utilizes palladium (Pd) nanoparticles supported by semiconductor gallium phosphide (GaP) nanowires, leveraging the nanowires’ structure to reduce nanoparticle aggregation and maximize active site exposure. Pd facilitates key reactions like hydrogenation, while GaP enhances nanoparticle dispersion and biocompatibility, creating a synergistic effect. The fabrication process involves nanowire synthesis via metal-organic vapor phase epitaxy and spark ablation for particle generation. These versatile techniques allow precise control over the nanowire morphology and particle features such as composition, density and particle size. These nanocatalysts demonstrate superior turnover frequency (TOF) and optimized styrene formation, with future research focusing on reducing particle synthesis time and elucidating the nanowire’s role in catalytic mechanisms. 

Symposium SB01: Electrifying Biomaterials—Frontiers of Biohybrid Devices

Ye Ji Kim, Massachusetts Institute of Technology

Magnetoelectric Nanodiscs for Treatment of Parkinsonian Behavior in Mice

Written by Ethan To

Parkinson’s disease is the most common neurodegenerative disorder in the world, characterized by the loss of dopaminergic neurons in the mid-brain region resulting in resting tremors, bradykinesia, slowed gait, and postural instability. The current gold standard of surgical treatment is use of subthalamic nucleus deep brain stimulation (STN DBS), which effectively controls tremors but is highly invasive, risky, and limited to select patients. To address these challenges, Ye Ji Kim and colleagues at MIT are developing a new approach to stimulate the STN DBS using magnetoelectric nanodiscs (MENDs). These nanoscale transducers convert magnetic fields into electric polarization, allowing non-invasive stimulation of deep brain regions. By synthesizing anisotropic core–double-shell Fe₃O₄–CoFe₂O₄–BaTiO₃ hexagonal nanodiscs, Kim demonstrates enhanced electrical potential generated by individual MENDs, achieving remote control of reward and motor behavior in mice at low concentrations. Ultimately, this promising research is paving the way to novel neuroprotective therapies using non-invasive MENDs for deep brain stimulation.

Symposium CH03: Towards Quantitative Characterization of Soft Materials by Scanning Probe Microscopy—Beyond Imaging

Madeline Buxton, Georgia Institute of Technology

Surface Properties of Two-Dimensional Materials with In-Depth Nano Surface Characterization

Written by Andrew M. Fitzgerald

Madeline Buxton from Georgia Institute of Technology has used advanced atomic force microscopy (AFM) techniques to look into the surface properties of two-dimensional (2D) materials. Buxton’s work looks into the fundamental characteristics of 2D flakes like Ti₃C₂T_x MXene and graphene oxide functionalized with dopamine (and other molecules), which have applications in lightweight composites, electronics, and sensing technologies. The thin-film materials were synthesized with Langmuir-Blodgett deposition. The AFM techniques that Buxton employed for this study include topography, quantitative nanomechanical measurement (QNM), Kelvin-probe microscopy (KPFM), and Nano-IR AFM. These techniques provided quantitative insights into the mechanical, electrical, and chemical properties of the 2D flakes, and by comparing the effects of chemical surface modifications, Buxton demonstrated how surface functionalization can be used to tune the properties of these materials to values that might be desired in their applications.

Symposium EN05: Electrodes for Chemical and Energy Conversion Technologies

Joakim Halldin Stenlid, NASA Ames Research Center 

Role of surface roughening in enhancing selectivity of copper for CO2 electroreduction 
Written by In Young Park 

Efficient and selective carbon dioxide (CO₂) reduction is critical for a sustainable carbon cycle, with copper (Cu) remaining the only metal catalyst capable of producing significant multicarbon (C₂⁺) products like ethylene and ethanol. Roughened copper surfaces—achieved through methods like sputtering or electropolishing—have shown enhanced selectivity for multicarbon products, but understanding the origin of this improvement is key to developing practical catalysts. This novel work by Dr. Stenlid and his team models roughened copper surfaces derived from cuprous oxide using a hybrid approach combining empirical medium theory (EMT), semilocal density functional theory (DFT) using the revised Perdew-Burke-Ernzerhof (RPBE) functional, and grand canonical potential DFT. Using the alpha parameter scheme to link local structure, site stability, and adsorption energies, the study generates selectivity maps capturing trends for polycrystalline copper surfaces, identifying "sweet spots" for multicarbon selectivity. Results reveal a broad distribution of active sites on roughened surfaces, far from idealized structures, highlighting the potential of macroscopic roughness to control atomic-scale catalytic activity. Future work aims to explore the impact of intensified roughening methods on catalytic performance, advancing the design of efficient carbon dioxide reduction catalysts. 

Forum on the Future of Synthesis

Joseph Montoya, Toyota Research Institute

Successes and Vision for Practical Materials Discovery

Written by Jun Meng

The Forum on the Future of Synthesis at the MRS 2024 Fall Meeting highlighted how artificial intelligence (AI) and machine learning (ML) can reshape materials discovery—but with measured optimism. Joseph Montoya from the Toyota Research Institute explored the ongoing challenge of turning AI-predicted materials into real-world discoveries.

While advances in data science have accelerated structure predictions since 2019, Montoya emphasized that synthesizing even one novel material remains a challenging process. He noted that several AI-predicted materials have been attempted in the lab, but 5 out of 6 failed to be successfully synthesized. However, Montoya stressed that failure in synthesis doesn’t negate the potential of these materials—it often depends on the precise conditions under which they are made.

A notable example was Ca1.6​RuOx​, which degraded quickly during testing, revealing unexpected challenges. Montoya introduced a synthesis theory called PIRO, a strategy to bridge theory and practice by incorporating thermodynamic data and phase stability into experimental workflows.

This iterative approach, he explained, holds promise for accelerating materials’ synthesizability, allowing a vast number of candidate materials to be screened and refined in practice. AI in materials discovery is still evolving, but forums like this are paving the way toward actionable breakthroughs.

Symposium EN05: Electrodes for Chemical and Energy Conversion Technologies

Joseph H. Montoya, Toyota Research Institute 

Practical Materials AI for Improving Electrochemical Stability 
Written by In Young Park 

As we transition to cleaner energy, the need for electrochemically stable and durable materials is greater than ever, requiring a deeper integration of theory and practice. Pourbaix diagrams, essential for understanding phase stability and electrochemical properties, now incorporate multi-element systems using voltage as a convex hull, expanding analysis from three to eight or more components. The SCAN functional has significantly enhanced density functional theory (DFT) accuracy without requiring empirical corrections, surpassing traditional methods like Perdew-Burke-Ernzerhof (PBE). In battery research, tools like Toyota Research Institute’s novel platform automate cycling experiments and use machine learning to predict performance degradation, optimizing durability. For catalysts, high-throughput Inductively Coupled Plasma Mass Spectrometry (ICP-MS) and automated systems reveal oxygen reduction reaction (ORR) mechanisms, while nanoprinted particle libraries map electrochemical activity on a large scale. These advancements bridge theoretical understanding with practical testing, addressing critical challenges in battery and fuel cell technologies as we strive for a more sustainable energy future. 

Symposium SF03: Materials for Robotics

Zenghao Zhang, University of Michigan

Magnetoactive Janus Particle Swarms for Information Display, Memory and Encryption

Written by Ethan To

Magnetic microrobots have gained significant interest in the robotics community because they offer untethered actuation, fast response, flexible control, and are easy to miniaturize. While actuation of a single magnetic robot is relatively straightforward, the design and control of robotic swarms to achieve programmed synchronized and asynchronous motions remains a challenge. Zenghao Zhang and colleagues at the University of Michigan are developing magnetoactive Janus particles (MAJPs), microrobots with programmable magnetization that rotate under magnetic fields and exhibit dynamic color changes for versatile applications. The MAJPs are engineered with tunable structures and properties, enabling precise swarming behavior and reversible switching mechanisms. These capabilities allow for versatile and programmable functions like dynamic displays, memory storage, and information encryption in soft, wearable devices. By harnessing the unique swarming behaviors of MAJPs in applied magnetic fields, this technology paves the way for reconfigurable display systems and multifunctional devices, bridging the gap between soft robotics and responsive metamaterials. Overall, MAJPs represent a significant step toward next-generation soft physical computing devices, opening new horizons in adaptive, intelligent materials and wearable tech.

Symposium MT04: Next-Generation AI-Catalyzed Scientific Workflow for Digital Materials Discovery

Bowen Deng, University of California, Berkeley

Potential Energy Surface Softening in Universal Machine Learning Interatomic Potentials

Written by Jun Meng

Universal machine learning interatomic potentials (uMLIPs) are revolutionizing atomic-scale simulations by scaling quantum chemical accuracy to complex, large-scale systems. Bowen Deng from the University of California, Berkeley, explored both the promise and limitations of the top-tier uMLIPs, such as M3GNet, CHGNet, and MACE-MP-0, when applied to challenging out-of-distribution (OOD) environments like defects, surfaces, and ion migration barriers.

Deng highlighted a common issue in these models: potential energy surface (PES) softening, where energy and force predictions are systematically underpredicted. Surprisingly, this error can often be corrected by fine-tuning the models with as little as a constant correction factor, demonstrating the efficiency and adaptability of uMLIPs.

This work underscores the importance of systematic error analysis in next-generation materials modeling. By addressing PES softening, uMLIPs need to be optimized for diverse applications.