Artificial intelligence advances materials manufacturing

A research team from MIT, the University of Massachusetts at Amherst, and the University of California, Berkeley hopes to address the gap in materials science automation with a new artificial intelligence system that mines and produces specific materials through research papers. Source: Chelsea Turner/MIT Research efforts such as the Materials Genome Initiative and the Materials Project in recent years have produced a plethora of computational tools used to design new applications in a range of applications, from energy and electronics to aviation and civil engineering. Material. But the process of developing these materials still relies on a combination of experience, intuition, manual work, and literature reviews. A research team from MIT, the University of Massachusetts at Amherst, and the University of California, Berkeley hopes to address the gap in materials science automation with a new artificial intelligence system that mines and produces specific materials through research papers. Elsa Olivetti, assistant professor of energy research in MIT's Department of Materials Science and Engineering (DMSE) in Ridgefield, Atlantic said:“Computational materials scientists are already“what are we going to do”great progress has been made. But precisely because of these achievements, the problem has become“However, what should I do now?”The researchers envisioned a database of material preparation methods drawn from millions of papers. Scientists and engineers simply enter the name of the target material and any other criteria (precursor material, reaction conditions, manufacturing process), and the system suggests a suitable preparation scheme. To achieve this, Olivetti and colleagues developed a research paper that can analyze The only learning system that can deduce which part of the paper contains the material preparation method and classify the keywords according to the preparation steps: target material name, quantity, equipment name, operating conditions, descriptive adjectives, etc. They are in the latest“material chemistry”A paper published in the journal demonstrates that intelligent learning systems can infer general properties of material classes from the extracted data, such as the temperature required for material synthesis, or the performance of different materials prepared with different physical methods due to changes in preparation conditions. specific characteristics. Olivetti is the lead author of the paper, along with MIT graduate student Edward Kim, DMSE postdoc Kevin Huang, UMass Amherst computer scientists Adam Saunders and Andrew McCallum, and Berkeley Dean and Professor of Materials Science and Engineering Gerbrand Cede . Filling in the gaps Researchers build their systems with a combination of supervised and unsupervised AI learning techniques.“Supervision”It means that the test data fed into the system is manually annotated; the system will try to find correlations between the original data and the annotated data.“Unregulated”This means that the test data is unlabeled, and the system learns to cluster data classes together based on structural similarity. Because the extraction of material preparation methods is an entirely new field, Olivetti and her colleagues do not have the luxurious annotated datasets that different research teams have accumulated over the years. So they can only annotate their data from their own roughly 100+ papers. The AI ​​learning criterion is a very small dataset, and to improve it, they used an algorithm developed by Google called Word2vec. Word2vec can look at the context in which a word appears, that is, the syntactic structure in a sentence and other words around it, and group words with similar contexts together. So, for example, if a paper contains“We heat titanium tetrachloride to 500°C”sentence, another paper contains“Sodium hydroxide heated to 500°C”sentence, Word2vec will“Titanium tetrachloride”and“sodium hydroxide”The two keywords are combined together. With the use of Word2vec, intelligent learning systems can infer the label of any given word and the keyword classification it applies to, so researchers can build their system around roughly 640,000 papers instead of just 100 papers, So researchers can expand their datasets as much as possible. Tip of the Iceberg However, because they have no criteria for evaluating performance on unlabeled data, the accuracy of their test system can only rely on labeled data. In these tests, the system was able to identify paragraphs containing keywords with 99 percent accuracy and label words within those paragraphs with 86 percent accuracy. The researchers hope that the next work will further improve the accuracy of the system, so they are exploring a series of deep learning techniques that can further generalize the composition of material preparation methods. The ultimate goal is to automatically design materials containing existing literature. described preparation method. Much of Olivetti's previous research has focused on finding more cost-effective and environmentally responsible ways to produce useful materials, so she hopes a database of materials preparation methods can help with this project. Fred, Linda R. Wud and Ram Seshadri, professors of materials science at UC Santa Barbara, said:“This is landmark work. The authors take on the difficult and ambitious challenge of exploring strategies for the preparation of new materials through artificial intelligence methods. This work demonstrates the power of AI learning. But the only certainty is that the ultimate judgement of success is whether the practicality of the method will persuade practitioners to abandon the more primitive methods.”The article comes from the phys website, the original title is Artificial intelligence aids materials fabrication, and is compiled by Materials Science and Technology Online.
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