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 X—MRS/The Kavli Foundation Frontiers of Materials

Deji Akinwande, University of Texas at Austin

Unconventional Applications of Atomic Materials from Nonvolatile Electronics to Wearable Health and Ion Transport

Written by Sophia Chen

So-called two-dimensional (2D) materials, which consist of a single layer of atoms, offer many desirable properties for use in electronics, according to Deji Akinwande of the University of Texas at Austin. Akinwande made the case for this emerging category of materials during his Symposium X talk on Monday, December 2, titled “Unconventional Applications of Atomic Materials from Nonvolatile Electronics to Wearable Health and Ion Transport.” He presented research on the use of these materials in applications ranging from nanoelectronics to bioelectronics to energy.

To give the audience an intuitive conception of these materials, Akinwande likened 2D materials to a single sheet of paper in a stack. “In the x-y plane, they're very strongly bonded…But out of the plane, they're very weak,” he explained. These materials, removed from a stack one sheet at a time, have already proven useful in commercial applications. Examples of 2D materials include graphene, found in pencil lead, hexagonal boron nitride, found in makeup, and molybdenum disulfide, which is used as a dry lubricant in vehicles.

Akinwande first discussed the application of 2D molybdenum disulfide (MoS₂) for building a new type of computer known as a neuromorphic computer. The architecture of a neuromorphic computer emulates the human brain, where information is encoded and transported by “neurons” that connect to each other via “synapses” in imitation of human brain biology. This is in contrast to typical computers used today, which store its memory separately from where it computes, known as von Neumann architecture. Proponents of neuromorphic computers say that these machines offer higher energy efficiency than conventional computers, which could help solve the growing energy footprint of information technology.

Akinwande’s research involved developing the MoS₂ as a material for a memristor, which is a component of a neuromorphic computer for storing and computing data. The memristor cycles between two different resistances like a fast switch. As a thin crystalline material, MoS₂ tends to have fewer defects than metal oxides, which are the currently most popular material for memristors. Akinwande also said that the engineering of MoS₂ memristors has increased the number of times they can cycle from hundreds to millions. (See also: https://www.nature.com/articles/s41565-020-00789-w and https://onlinelibrary.wiley.com/doi/full/10.1002/advs.202406703 )

He also discussed projects involving using 2D materials for electrodes in wearable health technology. These technologies make use of the fact that the human body is full of ions, which means you can measure voltages and currents and resistances in the human body correlated with human health.

These graphene electrodes, known as electronic tattoos, are thinner than other materials for wearable electrodes, such as thin metal: electronic tattoos. This makes it conform better to the skin, improving the signal-to-noise ratio. In addition, they designed the tattoos so that the wearer does not feel their presence. They performed a demonstration where they placed the graphene tattoo on a person’s eyelids to measure EOG, the electrical signal that emanates from your eyes. From their studies, “we can conclude that the graphene gives comparable signal fidelity and in some cases even superior signal fidelity to the commercial standard,” he said. They have also used these electrodes to measure blood pressure. Unlike the standard cuff, their wearable device can measure blood pressure with “every beat of the heart,” he says, leading to about 100,000 data points per day.

Akinwande ended his talk discussing the use of 2D materials in fuel cells. Strategies include mixing the 2D materials to create semi-permeable membranes in the cells which allow for proton conduction inside the cell while blocking undesired reactants.

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 X—MRS/The Kavli Foundation Frontiers of Materials

Juan J. de Pablo, New York University

AI-Enabled Design of Sustainable Polymeric Materials

Written by Molly McDonough

Plastics have been being sold, used, and researched since their advent during the Industrial Revolution in the 1800s. Now, we find plastics everywhere, from our clothes to airplanes’ coatings. In 2024, over 220 million tons of plastic waste will be generated, and as many of us know, plastic pollution is a large concern. Plastic pollution can find its way into drinking water, food, soil, and our bodies, threatening the health of humans, other animals, and the environment. Juan J. de Pablo’s research into the artificial intelligence (AI) design of polymetric materials aims to find more sustainable polymer materials to help mitigate the issues caused by plastic pollution. However, the major constraint on this development is the already existing industrial processes corporations worldwide have implemented to manufacture plastic. It would be too expensive and time-consuming to deploy new manufacturing methods for sustainable polymers, so de Pablo seeks to find new polymetric materials that can fit into existing industrial processes. Additionally, de Pablo aims to create new materials with the same or better properties, costing the same or less than conventional plastics like polylactic acid (PLA), polyethylene, and polyester.

De Pablo’s group looks for sustainable polymer candidates by developing tools to predict the rheology of polymers and to design polymers with particular rheological responses. This approach begins by designing new monomer molecules with targeted equilibrium processes. The first step in this process was asking if scientists could use existing large language models (LLMs) such as ChatGPT and retrain them using existing polymer databases such as simplified molecular-input line-entry systems (SMILES) to predict properties of molecules. Unfortunately, current-generation LLMs lack the domain-specific fine-tuning for this application, impacting their ability to understand and predict chemical language accurately. At best, ChatGPT could predict chemical language with a 20% success rate, which is not high enough to be reliable to conduct experiments based on it.

Instead, de Pablo implemented a different approach, using variational autoencoders (VAE). VAE combines known chemistry in the form of functional groups with graph neural networks (GNNs). Then, the GNNs are applied to encode information at two levels, atom graph and motif graph, to encode the structure of the polymer fully. Next, they implement multilayer perception for hierarchical connections between the graphs. After this, the decoder operates in an autoregressive manner. Finally, the model reconstructs the molecule by iteratively generating a new motif and connecting it to the partial molecule built thus far. This process resulted in models that are chemically meaningful; despite using unlabeled data (unsupervised learning), the model is able to cluster molecules based on their chemistry. This allowed de Pablo to simultaneously model the density, cohesive energy, radius of gyration, heat of vaporization, glass transition temperature, and isothermal compressibility, which are all important parameters in creating new, usable, polymetric materials while using a small dataset of only 600 materials. There are still two challenges with this approach: (1) being that it only simulates micromonomers, meaning that there are millions of ways to arrange them that yield different properties, and (2) the model can predict materials that are not thermodynamically stable or able to be synthesized.

As such, de Pablo went back to the drawing board and decided to implement this machine learning framework in conjunction with a dataset of polymers. Here, his research team uses a database of standard polymer structures, each with primitive units of 4 or fewer elements, and N = 400 along the backbone. They find here that after about 500 iterations through polymer structures, the model is highly optimized, likely due to the dependence on property and chemical complexity. This machine learning framework can then be easily repurposed for design, and Bayesian optimization yields numerous successful candidates in a space not included in the training data.

The last portion of de Pablo’s work focuses on creating a database of dimers, trimers, some oligomers, and probabilistic representations, combined with experimental data to create a multiscale approach to predicting rheology and processing techniques of polymer materials. This database aims to integrate all atom scale, coarse-grained, and slip spring simulations to fit experimental data and yield new potential polymer materials. De Pablo closed his talk by discussing using this framework to design degradable polymers. The model takes a simple polyethylene and intercalate monomers susceptible to degradation. This allows de Pablo to determine how degradable monomers behave in existing polymer structures, allowing us to understand if the monomers remain degradable when integrated into a larger system. This work extends existing machine learning frameworks to discover new materials that have the potential to solve critical issues in plastics manufacturing and recycling.

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 X—MRS/The Kavli Foundation Frontiers of Materials

Michelle Simmons, University of New South Wales

Engineering Qubits in Silicon with Atomic Precision

Written by Andrew M. Fitzgerald

Renowned quantum physicist and 2018 Australian of the Year Michelle Simmons gave an exciting lecture focused on recent advancements in silicon-based quantum computing. As the CEO and founder of Silicon Quantum Computing and the director of the Australian Research Council’s Centre of Excellence for Quantum Computation & Communication Technology, Simmons highlighted many achievements and contributions that she and her team have made in engineering atomic-scale qubits as they set records in coherence times and fidelity.

Simmons outlined her team’s progress in addressing the main challenges of scalability, precision, and efficiency in quantum computing. By using silicon as a manufacturable platform, they have achieved a high level of control over qubit design. This includes sub-nanometer precision in placing phosphorus atoms, ensuring identical quantum dot sizes, and enabling rapid (as well as efficient) scaling of qubit counts. These advancements allow for highly coherent, stable, and fast qubits, which are critical for building a large-scale, error-corrected quantum computer.

In her lecture, Simmons also showcased a few quantum devices that she and her team have been developing. One device, a quantum machine learning accelerator, involved a quantum feature generator that outperforms classical methods, demonstrating quantum computing’s potential to make a large impact in the computational world. Another device, an analog simulator, carries out simulations of topological states and condensed matter phenomena, such as metal-insulator transitions, suggesting that quantum computing will impact fields beyond quantum computing, including condensed matter physics. Overall, these devices emphasize the ability of atomic-scale engineering to push the field of quantum research forward.

Simmons further detailed her team’s achievements in quantum computing. Qubit gates operating at ~99% fidelity and a Grover’s algorithm with a world record efficiency of 98.87% demonstrate the positive impact of her materials-first approach. These results position silicon-based qubits as a leading platform for scalable quantum systems. Moreover, Simmons emphasized that her team can design, fabricate, and test quantum devices within a week, an exciting accomplishment that accelerates the device development timeline. This capability allows for rapid iteration and positions her group as a leader in the quantum computing research field.

Looking ahead, Simmons expressed optimism about the timeline for achieving large-scale quantum computers. With the right materials and techniques, her team projects that a fully error-corrected quantum computer could be realized by 2033. This prediction reflects the great amount of progress made by researchers under her leadership.

In addition to groundbreaking advancements in quantum technology, Simmons’ work offers significant contributions to condensed matter physics by studying the interactions between qubits and their environment. This dual focus not only accelerates the development of quantum computing but also enhances our understanding of materials behaviors at the atomic scale. Globally recognized as a pioneer in quantum technology, Simmons has pushed the boundaries of what is possible in the field.

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 X—MRS/The Kavli Foundation Frontiers of Materials

Tonio Buonassisi, Massachusetts Institute of Technology

Finding a Path from “Promise” to “Performance”—Toward Realizing Novel Materials Predicted via Generative AI

Written by Sophia Chen

It’s all the rage: artificial intelligence—from generative models to automated laboratories—will help researchers find novel and useful materials faster. But Tonio Buonassisi, a mechanical engineer at Massachusetts Institute of Technology, knows that to get to that place, the field has its work cut out for it.

“This is not your normal tech-vangelist talk where I'm going to go up and show you how great AI is and how autonomous labs are going to rock the world,” said Buonassisi during Thursday’s Symposium X. “Yes, I do believe that. And I also believe that the path to getting there is a hard one.”

In Thursday’s Symposium X, titled “Finding a Path from ‘Promise’ to ‘Performance’—Toward Realizing Novel Materials Predicted via Generative AI,” Buonassisi outlined his perspectives on the state of automation and automated materials discovery, as well as the role of AI in these processes.

Buonassisi focused on pragmatic challenges. The adoption of AI will involve growing pains, he says. He described the trajectory as curve, where the field will hit what feels like unproductive bottlenecks: “You're putting in a lot of investment and not getting out much return. So this talk is about a lot of that part of the curve, and creating community around getting us over that part.”

Currently, the materials community has already adopted automation in a variety of settings. Scientists at the University of Liverpool, for example, use co-bots, or robots designed to work in close proximity to humans. Researchers at Boston University have also developed a robot arm that coordinates with 3D printers to manipulate materials samples.

Working at Singapore’s Agency for Science, Technology, and Research, Buonassisi started a program known as Accelerated Materials Development for Manufacturing, where they adopted a method known as “islands of automation.” To do this, instead of aiming to create a fully autonomous materials discovery process, they focused on accelerating “specific parts of the process and […] strategically targeted those points in the workflow that require more time, or that suffer from irreproducibility due to human factors,” he said.

The adoption of AI, which largely comprises models known as neural network, presents many challenges for the field, Buonassisi described in the talk. For example, the field needs to better identify which problems are most appropriate for autonomous or automated systems. On top of that, they need to increase the odds that experiments can actually synthesize and test an AI-predicted material. He listed several practical questions in the use of AI: for example, how should researchers choose a neural network architecture? How do they select, obtain, and validate training data? How should researchers represent materials to the neural network? How do they diagnose issues when things are going wrong? How should they select candidate materials that the neural network generates? How should they quantify the uniqueness or the novelty of the AI-generated materials? “These are […] examples of some of the things that, in my opinion, would yield truly high-impact papers, if you're a graduate student, postdoc, or early-stage career professor,” said Buonassisi. Using AI to discover unusual materials presents a particular challenge, he said. Experts have concluded that they would probably not have been able to predict the cuprates, high-temperature superconductors, given today’s AI tools and data prior to 1987 when researchers discovered the material.

The field should also critically examine the social structures that drive materials research, Buonassisi said. Individuals face different incentives than those of academia as an institution, and that can present a tension. While Buonassisi says this is a systemic problem and does not promise any solutions, he proposes a different academic structure in which the total number of students and postdocs are reduced. As they become more senior, they begin to manage “undergraduate researchers and staff who can augment their capabilities, who can complement some of their weaknesses in computation automation, in domain expertise,” he said.

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.

Printing Technologies for Energy Sustainability and a Habitable Mars

MRS TV pops into Ying Dao's Symposium X lecture to find out more about her work in printing technologies to sustain humanity in the event of severe climate events.

Symposium X—MRS/The Kavli Foundation Frontiers of Materials

Ying Diao, University of Illinois at Urbana-Champaign

Printing Polymer Electronics for Sustainable Earth and Habitable Mars

Written by Sophia Chen

During Ying Diao’s talk on Wednesday, she showed an image of a printer roll—not of paper, but semiconductors. “Organic electronics can be made […] akin to the way we make newspapers,” she said. These methods, which fall under the technique of 3D printing, promise cheap, high-throughput, and on-demand production. Consequently, researchers are investigating methods and materials for applications ranging from solar power to agriculture. Diao discussed the state of the technology in her talk, titled “Printing Polymer Electronics for Sustainable Earth and Habitable Mars.”

For their printed electronics, Diao’s laboratory uses conjugated polymers. While conjugated polymers are inherently semiconductors, when doped, these organic molecules can be as conductive as metals. Such organic molecules are already used in wearable electronics such as the organic light-emitting diodes in smartwatches. Researchers can modulate these materials’ conductivity over 14 orders of magnitude, she said. They have also recently used three-dimensional conjugated polymers to create structural color, where an object’s color derives from light interference with microscale or nanoscale structures. (Many animals, such as butterflies, exhibit structural color.) They also discover and design new materials using both physics-based approaches as well as artificial-intelligence-aided approaches.

Diao believes the future generation of organic electronics materials will be semiconductors that are chiral. The structure of chiral materials exhibits either right-handedness or left-handedness, meaning they lack mirror symmetry. (A helix is an example of a chiral structure.) Chiral structures are common in nature, such as in chlorophyll. The chlorophyll’s chirality makes charge transport much more efficient during photosynthesis. Chiral organic semiconductors could offer similar advantages. In recent work, her team found that they could create helical organic semiconductors through 3D printing. The chirality emerged by adjusting the flow rate and concentration of the material during printing.

Notably, when Diao and her group analyzed the material, they found that it exhibited chirality on multiple scales—from the micron-scale to the nanometer-scale. “We have a helix within a helix within a helix,” she said. This nested helicity also occurs in collagen.

Chiral organic semiconductors would be well-suited for various next-generation electronics, said Diao. For example, when hit with light, chiral molecules sustain excitons longer than planar molecules, a quality useful for solar cells.  They could also be useful for spintronics.

Diao ended her talk discussing a prototype device using printed electronics to aid agriculture. They designed the device with futuristic missions for inhabiting Mars in mind. The device consists of a stretchable sensor for monitoring the growth rate for plants. Printed electronics are promising for extraterrestrial applications because they are lightweight and high performance, she said.

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 X—MRS/The Kavli Foundation Frontiers of Materials

Carolyn R. Duran, Apple Inc.

Oh, The Things We Build! What Materials Research Looks Like at Apple

Written by Molly McDonough

How does a $2 trillion company, like Apple, develop the best materials they can for the products they make? This question is exactly what Carolyn Duran, Senior Director, Product Integrity at Apple, discussed during her talk Oh, The Things We Build! What Materials Research Looks Like at Apple. Within Apple’s hardware engineering teams there is significant focus on the impact of materials on product development. Teams, including Duran’s, work to improve the durability, recyclability, and reusability of the other two billion Apple products that are currently in use. Duran’s talk focused on three materials use cases: glass in iPhone screens, aluminum used for MacBook casings, and plastics used in keyboards.

Testing for materials durability for glass in iPhone screens focuses primarily on determining how the glass fractures, and how to reduce the likelihood of fracture. The failure analysis falls into four broad categories: mechanical, optical, scratch, and coating durability. By tuning the crystallinity of the glass through glass cooling and promoting nucleation and crystal growth through the annealing process, the microstructure of the cover glass can be modified to decrease the likelihood of fracture. Additionally, the team at Apple worked with corporate partners, like Corning, to optimize the chemical strengthening process of the glass. By using ion exchange, one can encourage compressive stress in the glass at the surface, leading to the closure of fractures. This work resulted in a four times reduction in failures in the field with ceramic glass going from iPhone 11 to iPhone 12.

Next, Duran focused on how the alloy compositions of aluminum used in MacBook laptops have changed over the years to improve durability while maintaining the look and feel Apple users know and love. Apple uses 6000 series aluminum alloys for their MacBook products, which are fabricated using an extrusion/sheet process followed by precipitation strengthening. This is followed by aluminum anodization, which is an aqueous electrochemical process that oxidizes the surface to form an amorphous Al₂O₃. This leaves the surface porous, alloying it to be dyed to various colors.

The alloy team set out to find a new aluminum alloy that was more durable than the previous version by leveraging computational materials science to analyze material yield and solubility for hundreds of alloys. From the hundreds of alloys, the team picked the 10 best alloy options. From this, two alloys made it to product testing, and additional modifications were made following this testing. The new material passed Apple’s qualification process in less than six months, and led to a 30% increase in strength.

Lastly, Duran discussed how Apple improves plastics used in keyboards. Apple’s keyboard keys consist of four layers: top hard coat for protection, a color coat, a base coat for opacity, and a tinted diffuse substrate layer for glyph color and light scattering. Quality and durability issues commonly arise in keyboards, like top coat staining, glyph transmissivity, and side wall light leakage. The top coat staining issue was reduced by testing various solvent- and water-based solutions. The team found that solvent-based low volatile organic compounds showed the lowest color change due to chemical staining.

A large part of the materials development at Apple also focuses on minimizing the environmental impact of manufacturing Apple products. The company focuses primarily on reducing its impact on the climate, utilizing resources that can be recycled, and focusing on smart chemistry, meaning using chemicals that won’t end up as “forever chemicals” in the environment. For example, Apple has reduced the amount of CO₂ produced from their anodization process by over 72% by utilizing ELYSIS™ technology. Apple has also transitioned to utilizing more recycled and reusable materials, including reducing the plastic in their packaging by 18% since 2015.

Apple has put a large stake into using materials science and engineering to solve real-world problems for their customers.

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 X—MRS/The Kavli Foundation Frontiers of Materials

Keith A. Brown (Boston University), John Dunlap (UES, Inc. and Air Force Research Laboratory), Robert Epps (National Renewable Energy Laboratory), Jason Hattrick-Simpers (University of Toronto) and Kiran Vaddi (University of Washington)

How to Build A Self-Driving Lab

Written by Sophia Chen

John Dunlap's PhD research could be tedious. The chemist, now working with UES, Inc., and Air Force Research Laboratory (AFRL) contractor in Ohio, was developing polymer-coated quantum dots for biomedical applications at the University of South Carolina. The synthesis process was partially automated, but on many days, he would have to sit around and wait to press a button every 15 minutes. To test the samples, he would have to walk down three floors to use the instruments. And then he would do this over and over again, zeroing in on the recipe he sought. "It was a lot of blood, sweat, and tears on my end," he told the crowd during Thursday's Symposium X - MRS/The Kavli Foundation Frontiers of Materials.

By the time Dunlap obtained his PhD degree in 2022, materials science researchers had begun adopting new automation strategies that pushes the exhausting repetitive laboratory work to robots and computers. During the panel discussion on “How To Build A Self-Driving Lab,” Dunlap, along with Keith A. Brown of Boston University, Jason Hattrick-Simpers of the University of Toronto, Kiran Vaddi of the University of Washington, and Robert Epps of the National Renewable Energy Laboratory, discussed their experiences developing and using laboratory techniques that do not require any human intervention.

Not all lab processes are suitable for full automation. It may not be worth it to automate experiments that are too short or too long, said Brown. “There’s a sweet spot in terms of experiment length,” he says.

In 2016, Brown’s group began developing a self-driving lab based on 3D-printing for making and testing mechanical materials that can efficiently absorb energy. These materials might be useful in designing helmets or the crumple zone of a car, for example. Brown’s system consists of six 3D printers arranged in a circle with a robotic arm in the middle, along with instruments that can weigh and perform compression tests on the 3D-printed elements. They’ve named it Mama Bear, which stands for “Mechanics of Additively Manufactured Architectures Bayesian Experimental Autonomous Researcher.”

Brown’s research team tasked Mama Bear with finding a 3D-printed structure with optimal energy absorption efficiency. “Over the span of about two years of continuous study, we’re able to find structures that exceeded the limits that have been previously found,” he said.

Dunlap, collaborating with AFRL, now has a fully automated continuous flow setup for synthesizing small molecules and modifying polymers. This self-driving setup can explore solid state reactions and photochemical reactions, among others, and perform NMR measurements for testing. The researchers are moving toward updating the setup to be capable of high-throughput experiments.

The panelists emphasized the importance of using modular machines that run on open-source software. This allows researchers to assemble and customize components to make a self-driving lab for their scientific needs. Vaddi cautioned that the commercially available machines he uses for designing colloidal nanoparticles are becoming increasingly less open-source and more expensive. “We are trying to move away from these highly expensive systems and build low-cost modular hardware,” he said.

Hattrick-Simpers talked about how to assemble a team with the necessary skills and mindset to build these self-driving labs. People can sometimes have a “trust barrier” to automation, and it’s crucial that team members fully buy into the concept. Otherwise “you're not going to make a lot of progress,” he said.

In addition, building a self-driving lab requires an interdisciplinary team with “broad range and skill set,” said Epps. Hattrick-Simpers advised researchers to be realistic about what a single person can do. “You can’t expect one person to build the AI … and have the bandwidth to become a subject matter expert,” he said.

Several panelists have moved beyond simply showing that their systems work. Brown and Hattrick-Simpers are developing user facilities at Boston University and the University of Toronto, their respective institutions.

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 X—MRS/The Kavli Foundation Frontiers of Materials

Giulia Grancini, Università degli Studi di Pavia

Hybrid Perovskite Solar Cells—A Game Changer for Near-Future Photovoltaics

Written by Elizabeth Wilson

In the past decade, perovskites have emerged as a promising material for solar cells. Current silicon-based solar cell production consumes lots of energy and is technologically intensive.

Perovskite solar cells sound almost too good to be true: with efficiencies of up to 26%, they self assemble from solutions, and production is scalable and less expensive. They're also recyclable and use 90% less energy in manufacturing compared with silicon-based cells. However, they have serious drawbacks that have so far thwarted industrial progress. They are unstable in moisture and heat, they have short lifetimes, and they can possibly release lead as they degrade.

At Wednesday's Symposium X—MRS/The Kavli Foundation Frontiers of Materials, Giulia Grancini, at the Universita degli Studi di Pavia, described her research group’s advances in hybrid perovskite solar cell designs.

A typical three-dimensional design consists of perovskite crystals and organic compounds. Scientists have found that a two-dimensional perovskite structure is more stable in water. But its efficiency is only 15%.

Grancini has been experimenting with hybrid perovskite solar cell designs that combine two-dimensional and three-dimensional perovskite structures, in an attempt to increase both efficiency and stability.

Much attention is now focused on the interface between layers of 2D and 3D materials, where the 2D layer protects the more efficient 3D layer from moisture in a phenomenon known as surface passivation. The industry standard lifetime for solar cells is about 25 years; Grancini's hybrid remained stable over a year of accelerated aging tests, which is more than 25 actual years.

Recently, Grancini's group has been trying to understand how crystal orientation affects charge transport. The 2D perovskites form vertical columns aligned perpendicular to the substrate, which boosts charge transport.

Grancini hopes that new stability breakthroughs will come within a few years, moving the technology towards industrial use.

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.