Engineers develop a rapid screening system to test fracture resistance in billions of potential materials.

For engineers developing new materials or protective coatings, there are billions of different possibilities to sort through. Lab tests or even detailed computer simulations to determine their exact properties, such as toughness, can take hours, days, or more for each variation. Now, a new artificial intelligence-based approach developed at MIT could reduce that to a matter of milliseconds, making it practical to screen vast arrays of candidate materials.

The system, which MIT researchers hope could be used to develop stronger protective coatings or structural materials — for example, to protect aircraft or spacecraft from impacts — is described in a paper in the journal Matter, by MIT postdoc Chi-Hua Yu, civil and environmental engineering professor and department head Markus J. Buehler, and Yu-Chuan Hsu at the National Taiwan University.

The focus of this work was on predicting the way a material would break or fracture, by analyzing the propagation of cracks through the material’s molecular structure. Buehler and his colleagues have spent many years studying fractures and other failure modes in great detail, since understanding failure processes is key to developing robust, reliable materials. “One of the specialties of my lab is to use what we call molecular dynamics simulations, or basically atom-by-atom simulations” of such processes, Buehler says.

These simulations provide a chemically accurate description of how fracturing happens, he says. But it’s slow, because it requires solving equations of motion for every single atom. “It takes a lot of time to simulate these processes,” he says. The team decided to explore ways of streamlining that process, using a machine-learning system.

Screen Shot 2020 05 27 at 10.48.45 AM

The researchers ran hundreds of atom-by-atom simulations of the propagation of cracks through different kinds of layered material, to see which ones were most effective at stopping the cracks from making it all the way through the material. Shown here are a variety of simulation runs showing different outcomes.

Image courtesy of M. Hsu, C. Yu and M.J. Buehler

“We’re kind of taking a detour,” he says. “We’ve been asking, what if you had just the observation of how fracturing happens [in a given material], and let computers learn this relationship itself?” To do that, artificial intelligence (AI) systems need a variety of examples to use as a training set, to learn about the correlations between the material’s characteristics and its performance.

In this case, they were looking at a variety of composite, layered coatings made of crystalline materials. The variables included the composition of the layers and the relative orientations of their orderly crystal structures, and the way those materials each responded to fracturing, based on the molecular dynamics simulations. “We basically simulate, atom by atom, how materials break, and we record that information,” Buehler says.

The team used atom-by-atom simulations to determine how cracks propagate through different materials. This animation shows one such simulation, in which the crack propagates all the way through.

They painstakingly generated hundreds of such simulations, with a wide variety of structures, and subjected each one to many different simulated fractures. Then they fed large amounts of data about all these simulations into their AI system, to see if it could discover the underlying physical principles and predict the performance of a new material that was not part of the training set.

And it did. “That’s the really exciting thing,” Buehler says, “because the computer simulation through AI can do what normally takes a very long time using molecular dynamics, or using finite element simulations, which are another way that engineers solve this problem, and it’s very slow as well. So, this is a whole new way of simulating how materials fail.”

How materials fail is crucial information for any engineering project, Buehler emphasizes. Materials failures such as fractures are “one of the biggest reasons for losses in any industry. For inspecting planes or trains or cars, or for roads or infrastructure, or concrete, or steel corrosion, or to understand the fracture of biological tissues such as bone, the ability to simulate fracturing with AI, and doing that quickly and very efficiently, is a real game changer.”

The improvement in speed produced by using this method is remarkable. Hsu explains that “for single simulations in molecular dynamics, it has taken several hours to run the simulations, but in this artificial intelligence prediction, it only takes 10 milliseconds to go through all the predictions from the patterns, and show how a crack forms step by step.”

"Over the past 30 years or so there have been multiple approaches to model crack propagation in solids, but it remains a formidable and computationally expensive problem," says Pradeep Guduru, a professor of engineering at Brown University, who was not involved in this work. "By shifting the computational expense to training a robust machine-learning algorithm, this new approach can potentially result in a quick and computationally inexpensive design tool, which is always desirable for practical applications."

The method they developed is quite generalizable, Buehler says. “Even though in our paper we only applied it to one material with different crystal orientations, you can apply this methodology to much more complex materials.” And while they used data from atomistic simulations, the system could also be used to make predictions on the basis of experimental data such as images of a material undergoing fracturing.

“If we had a new material that we’ve never simulated before,” he says, “if we have a lot of images of the fracturing process, we can feed that data into the machine-learning model as well.” Whatever the input, simulated or experimental, the AI system essentially goes through the evolving process frame by frame, noting how each image differs from the one before in order to learn the underlying dynamics.

For example, as researchers make use of the new facilities in MIT.nano, the Institute’s facility dedicated to fabricating and testing materials at the nanoscale, vast amounts of new data about a variety of synthesized materials will be generated.

“As we have more and more high-throughput experimental techniques that can produce a lot of images very quickly, in an automated way, these kind of data sources can immediately be fed into the machine-learning model,” Buehler says. “We really think that the future will be one where we have a lot more integration between experiment and simulation, much more than we have in the past.”

The system could be applied not just to fracturing, as the team did in this initial demonstration, but to a wide variety of processes unfolding over time, he says, such as diffusion of one material into another, or corrosion processes. “Anytime where you have evolutions of physical fields, and we want to know how these fields evolve as a function of the microstructure,” he says, this method could be a boon.

The research was supported by the U.S. Office of Naval Research and the Army Research Office.back to newsletter

David L. Chandler | MIT News Office
May 20, 2020

Video: A Quantum Mechanic’s Quest for the Perfect Conductor. MIT Assistant Professor of Physics Joseph Checkelsky designs new materials with remarkable conductive properties, by harnessing quantum mechanics and ancient tiling geometries first described by Archimedes. Here he bops superconducting magnets around like air hockey pucks and reveals how his group turned theory into matter. This new class of materials could one day help remedy energy waste caused by resistance and heat build-up in electrical devices and throughout the grid. Produced by the Museum of Science in collaboration with the Center for Integrated Quantum Materials, with support from the National Science Foundation (Award #1231319). Directed by Carol Lynn Alpert. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation, CIQM, or the Museum of Science. Filmed at the Museum of Science Boston, April 7, 2018.

Thursday, 16 August 2018 15:27

Advisory Board Meeting

The external advisory board dinner will be held on October 9, 2019. Immediately following the Materials Day Poster Session.

Location: MIT Student Center, West Lounge
Cocktails will start at 6:30pm.

The advisory board meeting will be held on October 10, 2019.

Location: Bush Room, Building 10-105
8:30am - 4:30pm



Thursday, 16 August 2018 15:21

Poster Instructions

Materials Day is scheduled for October 9, 2019

Poster Setup will be in the Student Center - La Sala de Puerto Rico


REGISTRATION IS NOW CLOSED, however if you'd still like to present a poster, please feel free to show up with your poster and you will be assigned a poster board.

A board measuring 4 ft high x 6 ft wide will be provided for each poster. If additional space or utilities will be required (for models, demonstrations, prototypes, 3-D displays, etc.), please contact Maria Aglietti at 617-253-6472 or This email address is being protected from spambots. You need JavaScript enabled to view it. to let us know as soon as possible.

Please include the MRL logo on the top left side of your poster. Download the MRL logo.

Posters may be set up between 12:00 pm and 3:00 pm the day of the event. Individuals are expected to be with and remain with their poster during the Poster Session, from 4:00-6:00pm.

You must also submit an electronic copy of your poster, to be posted on our website for later viewing. Submit the electronic copy via email to Maria Aglietti (This email address is being protected from spambots. You need JavaScript enabled to view it.) by 12:00 noon on Monday, October 7, 2019.

 



Thursday, 16 August 2018 15:20

Speakers

MATERIALS DAY SPEAKERS 

AGENDA
REGISTER

Carl Thompson

Welcome
&
Introduction


Carl V. Thompson
Professor, Materials Science & Engineering
and Director,
Materials Research Laboratory

Brian Storey

Accelerating Materials Design and Discovery for Electric Vehicles


Brian Storey
Director, Accelerated Materials
Design & Discovery

TOYOTA Research Institute

Elsa Olivetti

Text and Data Mining for Material Synthesis


Elsa Olivetti
Associate Professor
Materials Science & Engineering, MIT
Rafael Gomez-Bombarelli

Learning Matter: Materials Design Through Atomistic Simulations and Machine Learning


Rafael Gomez-Bombarelli
Assistant Professor
Materials Science & Engineering, MIT
Klavs
Advancing Chemical Development Through Process Intensification, Automation, and Machine Learning


Klavs F. Jensen
Professor
Chemical Engineering and
Materials Science & Engineering, MIT
Abstract & Bio
Ju Li
Elastic Strain Engineering for Unprecedented Properties


Ju Li
Professor
Nuclear Science & Engineering and
Materials Science and Engineering, MIT
Abstract & Bio
JJ
Machine Learning in Optics: From Spectrum Reconstruction to Metasurface Design


Juejun Hu
Associate Professor
Materials Science & Engineering, MIT
Abstract & Bio
asu ozdaglar
Computing at MIT


Asu Ozdaglar
Professor & Department Head
Electrical Engineering & Computer Science, MIT
Abstract & Bio

Brian Storey

Dr. Brian Storey
Director, Accelerated Materials Design & Discovery

Toyota Research Institute


Keynote:
Accelerating Materials Design and Discovery for Electric Vehicles


Elsa Olivetti

Elsa Olivetti
Associate Professor
Department of Materials Science & Engineering, MIT

Text and Data Mining for Material Synthesis


Bombarelli Rafael Gomez-Bombarelli
Assistant Professor

Department of Materials Science & Engineering, MIT

Learning matter: Materials Design Through Atomistic Simulations and Machine Learning


Klavs Klavs F. Jensen
Professor
Department of Chemical Engineering and
Department of Materials Science & Engineering, MIT

Advanced Chemical Development Through Process Intensification, Automation, and Machine Learning


Ju Li Ju Li
Professor
Department of Nuclear Science & Engineering and
Department of Materials Science & Engineering, MIT


Elastic Strain Engineering for Unprecedented Properties


JJ

Juejun Hu
Associate Professor
Department of Materials Science & Engineering, MIT

Machine Learning in Optics: From Spectrum Reconstruction to Metasurface Design


asu ozdaglar

Asu Ozdaglar
Professor & Department Head
Department of Electrical Engineering & Computer Science, MIT

Computing at MIT


CarlCarl V. Thompson
Director
Materials Research Laboratory
Stavros Salapatas Professor of Materials Science & Engineering, MIT

Save

Save

Save

Save

Save

Thursday, 16 August 2018 15:20

Agenda

Machine Learning in Materials Research  

 MATERIALS DAY AGENDA

October 9, 2019
MIT, Kresge Theatre (W16)

Register

8:00am

Registration
Kresge Lobby, MIT Bldg. W16
8:45-9:00am


Welcome and Overview
Professor Carl V. Thompson
Director, Materials Research Laboratory, MIT
Session I:
9:00-9:30am

 

Keynote: 
Accelerating Materials Design and Discovery for Electric Vehicles
Dr. Brian Storey
Director, Accelerated Materials Design & Discovery, TOYOTA Research Institute
9:30-10:00am


Text and Data Mining for Material Synthesis
Associate Professor Elsa Olivetti
Department of Materials Science & Engineering, MIT

10:00-10:30am


Advancing Chemical Development Through Process Intensification, Automation, and Machine Learning
Professor Klavs F. Jensen

Departments of Chemical Engineering and Materials Science & Engineering, MIT

10:30-11:00am
BREAK
11:00-12:00pm Poster Previews: 2-minute talks by selected poster presenters

12:00-1:30pm

Lunch
Stratton Student Center, 3rd Floor

Twenty Chimneys/Mezzanine Lounge (Building W20)
Session II:
1:30-1:50pm


Computing at MIT
Professor Asu Ozdaglar

Department Head, Electrical Engineering & Computer Science, MIT
1:50-2:20pm


Machine Learning in Optics: From Spectrum Reconstruction to Metasurface Design
Associate Professor Juejun (JJ) Hu
Department of Materials Science & Engineering, MIT
2:20-2:50pm



Elastic Strain Engineering for Unprecedented Properties
Professor Ju Li

Departments of Nuclear Science & Eng. and Materials Science & Engineering, MIT


2:50-3:20pm



Learning matter: Materials Design Through Atomistic Simulations and Machine Learning
Assistant Professor Rafael Gomez-Bombarelli

Department of Materials Science & Engineering, MIT

3:20-3:30pm



Session Wrap Up
Professor Carl V. Thompson
Director, Materials Research Laboratory, MIT


3:35-5:30pm



Poster Session and Social
La Sala de Puerto Rico, 2nd Floor
Stratton Student Center (Building W20) 

5:30pm
Poster Awards
5:45pm
Adjourn
Thursday, 16 August 2018 15:19

Abstract

Abstract: The theme of this year’s meeting will largely be focused on imaging-enabled nanoscale research on the structure, properties and processing of materials. Invited speakers will describe new tools and methods for atomic-scale structural and chemical characterization of materials, and application of these methods to optimization of processing and properties of materials for a wide range of applications. Results from imaging-based in situ studies of vapor- and liquid-phase processes for synthesis of nanostructured materials and in situ studies of nano- and micro-scale phenomena that can be used to engineer properties of bulk materials will be presented. Development of compact high-brilliance X-ray sources that can provide synchrotron-level materials analyses with laboratory-scale systems will also be discussed. Studies of nanoscale electronic, photonic, mechanical and catalytic properties of materials will be included and discussion of prospects for development of new state-of-the-art tools and methods for imaging-based and x–ray based materials research will be featured.

We are no longer accepting registrations but you are welcome to register in person on the day of the event. Lunch will only be provided to people who pre-registered.

SPEAKERS     

Monday, 23 July 2018 21:40

Pulling drinking water out of thin air

Powered only by solar energy, a new device developed at MIT could provide relief to regions where water is scare.

With droughts plaguing much of the western United States and millions of people across the globe living without access to safe water, the need for technologies that produce clean water is greater than ever. The key, according to Evelyn Wang, the Gail E. Kendall Professor and department head for MIT’s Department of Mechanical Engineering, is in the very air we breathe.

Video by: John Freidah

"Water vapor is all around us in the air, even in arid conditions,” explains Wang. She and her team in MIT’s Device Research Laboratory have developed a device that can tap into this abundant resource and literally pull water out of thin air.

The key to the process is a powder that desiccates the air, attracting vapor directly to the porous matrix at the base of the device’s main chamber like a sponge. The vapor is then condensed into liquid and can be collected as usable water – even in dry atmospheres with as low as 20 percent humidity.

The entire process of converting the water vapor found in air into potable water can be done using only the power of the sun. “The device is completely passive,” says Wang. “There is no need to use outside power supplies which can help keep the device low-cost and efficient.”

Keeping costs low and efficiency high is one of Wang’s central goals. “We hope to develop a device that provides relief to the millions of people living in communities that lack the infrastructure needed to provide access to clean drinking water or those living in regions plagued by drought,” adds Wang.

During a field test in Tempe, Arizona, earlier this year, a small proof-of-concept prototype of the device extracted a quarter-liter of water per day per kilogram of the absorbent powder. The researchers hope to increase this output by further tailoring the powder and optimizing the device.

If the production capacity of the device can be increased, Wang’s research could have a tangible impact in places experiencing water scarcity — even in the driest of conditions.back to newsletter

Mary Beth O'Leary, Department of Mechanical Engineering
MIT News Office, July 23, 2018

Headed by Carl Thompson, the newly formed Materials Research Laboratory opens up opportunities for industrial partnerships.

Inside a high-performance integrated circuit, the copper wiring is tens of nanometers in diameter, with a coating that is a few nanometers thick. “If you took all this wiring and connected it and stretched it out, it would be about 20 kilometers long,” says Carl Thompson, professor of materials science and engineering. “And it all has to work, and it has to work for years.”

That’s just one sample, from his own work, of the challenges MIT’s enormous spectrum of materials research – ranging from quantum devices all the way to buildings and roads. “There’s one researcher in metallurgy who makes objects that weigh a ton, in the same laboratory where people make objects that weigh nanograms,” Thompson notes.

Formed in 2017 by combining two longstanding MIT centers, the Materials Research Laboratory [MRL] acts as an umbrella for this work. About 70 faculty are directly involved in the MRL. The total materials research community at MIT includes about 150 faculty, from all departments in the School of Engineering and many in the School of Science.

More videos.

Materials research spans many disciplines, and projects often bring together researchers with very different sets of expertise, Thompson says. He emphasizes that the MRL’s strengthened ability to foster and accelerate such interdisciplinary work will boost partnerships with industry, where interdisciplinary collaborations are a norm.

Incentives for collaborations

Corporate connections have been central to Thompson’s own research, which focuses primarily on making thin films, micromaterials, and nanomaterials and integrating them into microelectronic and microelectromechanical devices.

“I’ve found that I can have impact on real systems that people can buy only by being deeply involved with industry,” Thompson says. “Industry partnerships have informed not only my research but my teaching, because I can talk about why some of the more fundamental problems in materials science and engineering are very important in applications that we all depend on.”

“It’s incredibly important for students and postdocs to interact with industry, and to understand the real problems and the real constraints,” he adds. “Many things sound great in the laboratory, and many of them are great, and eventually will become part of devices and systems. But there are many steps in between, and it’s very important for everybody in an academic community to understand that.”

Thompson’s research also underlines the necessity for cross-discipline collaborations – for instance, in his current research on thin-film batteries.

“There are projections that by 2025 there will be hundreds of billions of sensors out there in the Internet of Things, and we can't do that if we have to change the batteries on all of those all the time,” he remarks. “If you can make them with batteries and an energy source, then they can be autonomous, so you don't need to ever change the battery.”

His group seeks not only to develop thin film battery materials but to integrate these materials with other components such as circuits, sensors and microelectromechanical devices.

“There’s a relationship between how you make the materials, what their structure is, and the performance of not only the material in the device but also the device itself,” Thompson says. “That work is very highly collaborative with people in other disciplines, such as electrical engineering and mechanical engineering. Materials research is critical; chemistry and physics are critical. So is understanding the factors that lead to the failure of batteries, and a mathematician here at MIT in collaboration with engineers and physical scientists has made a very important contribution to that topic.”

“In batteries, a small interdisciplinary working group has blossomed into an area of great expertise that is very highly interactive with industry,” he says. “Now the MRL is ideally positioned to help make collaborations like this happen.”

Carl Thompson ILP Sella
CARL THOMPSON
Photo, David Sella.

Merging into the MRL

The MRL combines MIT’s long-established Materials Processing Center [which was funded by industry, government agencies, and foundations] with the Center for Materials Science and Engineering [which performed basic science with experimental facilities supported by the National Science Foundation]. Geoffrey Beach, associate professor of materials science and engineering, is MRL co-director.

“One of the main reasons we did the merger was so that we could do all these complementary activities together,” Thompson says. “Academics tend to work in silos, and you want to take people out of them to see how what they do is relevant to applications that other people do. MIT is very good about that. But the MRL, which takes the two communities together, will be an even better place to make those matches.”

Importantly, the MRL is also tightly joined to the new MIT.nano facility, a 200,000-square-foot center for nanoscience and nanotechnology scheduled to open this summer, designed as a global powerhouse for research expertise and equipment. MRL researchers will be able to leverage the newly assembled MIT.nano resources that are unique within academia, Thompson says.

Even more broadly, Thompson and his colleagues are using MIT’s convening power to provide leadership outside the Institute as well. One set of efforts will be workshops in industrial sectors such as aerospace and microelectronics, which will bring companies, academics, and often government agencies to discuss research opportunities and current development challenges.

Other projects will build consortia designed to create a sustained mechanism for companies to collaborate to support pre-competitive research that benefits them all. For example, one existing consortium studies the use of carbon nanotubes to create stronger and lighter aircraft fuselage materials.

On a larger scale, MRL can sponsor meetings with industry, academia, and government to address global challenges, such as sustainable materials processing and supply of critical materials. “For instance, cobalt is mined primarily in the Congo, which is not a good situation on many levels, but are there alternatives?” Thompson says. “And how can you make material with lower energy costs, not only in making the material but over the period of its use? How do you make it in a way that doesn't affect the environment? And how do you recycle the materials?”

“There's been a real renaissance in looking at these questions, at the same times in the same laboratories where people are doing fundamental innovations at the atomic scale.,” Thompson says. “That's one of the exciting aspects of materials research.”

back to newsletterEric Bender, MIT ILP 
June 5, 2018

Feldspar Process Ciceri Allanore Web

Chemistry World featured an article Oct. 10, 2017, on Associate Professor of Metallurgy Antoine Allanore’s work to produce potassium fertilizer from potassium feldspar using an efficient hydrothermal process.

The scientific paper by by research scientist Davide Ciceri, visiting engineer Marcelo de Oliveira and Allanore, in Green Chemistry is free to access until November 20, 2017.

back to newsletter

Read more.

 

 

 

 

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Contact Us

MIT Materials Research Laboratory
77 Massachusetts Avenue, 13-2106
Cambridge, MA 02139
617-253-5179
Email: mit-mrl@mit.edu