Finding new materials has traditionally been guided by intuition and trial and error,” said Turab Lookman, a physicist and materials scientist in the Physics of Condensed Matter and Complex Systems group at Los Alamos National Laboratory. “But with increasing chemical complexity, the combination possibilities become too large for trial-and-error approaches to be practical.”
Computational and theoretical materials science is playing an increasingly important role in advancing the search for novel materials and understanding the properties of existing ones. Computational research uses complex models in a variety of ways, all of which advance materials science and engineering. Modern computational hardware and software enable faculty to create “virtual laboratories,” where materials are tested and properties predicted computationally. “Problems that used to take years to solve can now be solved in a month,” says Srikanth Patala, a materials science and engineering researcher at NC State.
Integrated Computational Materials Engineering (ICME) is an emerging discipline which seeks to accelerate the development of new materials by linking materials models of the manufacturing process to the composition and structure and hence properties of the material, enabling the rapid design and development of materials for specific applications at low cost. ICME can rapidly focus the search for new, optimised materials in the most promising regions of multidimensional materials space and provide decision support to reduce empiricism. While ICME requires a fundamental understanding of materials structure, properties and performance, the aim is to develop a comprehensive computational model to simulate the design and manufacture of new materials and products.
“These computational models can help researchers understand the outcome of an experiment, identify the most promising avenues for future experiments, and give us insight into processes that can’t be easily explored in the lab,” says NC State researcher Don Brenner, a pioneer in the field of computational materials research who has been publishing in the field since the late 1980s. “For example, computational research helps us understand the behavior of materials in nuclear reactors, which are exposed to high levels of heat and radiation.”
“And we can now use models to design new materials that have a specific set of characteristics for use in any given application,” Patala says.
Databases and computations can help find answers. “We do quantum mechanical-level calculations of materials, calculations sophisticated enough that we can actually predict the properties of a possible new material on a computer before it’s ever made in a laboratory,” says Chris Wolverton, a materials scientist at Northwestern University who runs the Open Quantum Materials Database. (Other major databases include the Materials Project and the Materials Cloud.) The databases aren’t complete, but they’re growing, and already giving us exciting discoveries.
Nicola Marzari, a researcher at Switzerland’s École Polytechnique Fédérale de Lausanne, used databases to find 3D materials that can be peeled apart to create 2D materials of just one layer. One example of this is the much-hyped graphene, which consists of a single sheet of graphite, the material in a pencil. Like graphene, these 2D materials could have extraordinary properties, like strength, that they don’t have in their 3D form.
Marzari’s team had an algorithm sift through information from several databases. Starting from more than 100,000 materials, the algorithm eventually found about 2,000 materials that could be peeled into one layer, according to the paper Marzari published last month in Nature Nanotechnology. Marzari, who runs Materials Cloud, says these materials are a “treasure trove” because many have properties that could improve electronics. Some conduct electricity very well, some can convert heat into water, some absorb energy from the Sun: They could be useful for semiconductors in computers or batteries, so the next step is to investigate these possible properties more closely.
Marzari’s work is one example of how scientists are using databases to predict which compounds might create new and exciting materials. Those predictions, however, still need to be confirmed in a lab. And Marzari still had to tell his algorithm to follow certain rules, like looking for weak chemical bonds. Artificial intelligence can create a shortcut: Instead of programming specific rules, scientists can tell AI what they want to create — like a superstrong material — and the AI will tell the scientists the best experiment to run to make the new material.
Computational materials screening and targeted experiments reveal promising nitride semiconductors
Fumiyasu Oba and colleagues at Tokyo Institute of Technology and Kyoto University have used simulations to identify previously undiscovered semiconductors with promising attributes for optical and electronic applications. They used calculations to screen a set of compounds for potential semiconductor candidates. The study identified 11 previously unreported materials, including the particularly promising compound calcium zinc nitride (CaZn2N2).
The discovery of new semiconducting materials is a scientifically and technologically important issue; Increasingly sophisticated electronic devices, such as smartphones and laptops, are raising demand for semiconductors with wider ranges of properties. Silicon and, to a lesser degree, germanium are the foundations of almost all electronic devices that govern modern life. However, these materials are not suited for optoelectronic applications, such as LEDs for TV or mobile phone screens.
Here, the materials gallium nitride (GaN) and indium nitride (InN) dominate currently, but the discovery of new nitrides could pave the way to new applications. Nitrides tend to be chemically stable and can be readily made with existing techniques. Nitrogen is also a widely abundant and environmentally friendly element, but, at present, the nitrides used in industry are largely limited to gallium and indium compounds.
Calcium zinc nitride for optoelectronic applications
CaZn2N2 has not been reported previously but was identified by the researchers using their materials discovery computational approach. They were also able to predict the correct synthesis conditions for CaZn2N2. Synthesis of the material using high-pressure techniques confirmed the hypothesised properties and also revealed red luminescence even at room temperature; thereby validating the study’s approach.
The paper also shows that other earth-abundant materials, such as calcium magnesium nitride, can be used to tune the electrical properties of CaZn2N2, further increasing the eligibility of this material for use in devices.
As Oba and colleagues conclude, “The present study demonstrates accelerated materials discovery via cutting-edge computational screening followed by targeted experiments.
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