The rise of machine learning: from material design to biomedicine, quantum computing... To industrial applications

Heart of machine 2021-09-14 22:47:03

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 The rise of machine learning : From material design to biomedicine 、 Quantum computing .... Then to industrial applications

Machine learning has great potential in accelerating material research . Many fields of material science benefit from its applications , However, some challenges remain , Will the field be like the hype surrounding it , Remains to be seen .

The machine age is coming . When we put forward the focus of machine learning in Material Science , We know very well that the algorithm can write a reasonable opening editorial for it . After all , It won't be the first time to write an article , Or in this case , It's not even the first time to write a Book .

 The rise of machine learning : From material design to biomedicine 、 Quantum computing .... Then to industrial applications

You can ask Alexa or Siri, It will use its machine learning algorithm to find some articles for you about the benefits and dangers of artificial intelligence . Based on your past searches and their interest in you , It may continue to speculate whether there is too much hype about the ability of machine learning tools to surpass human beings .

However , without doubt , Machine learning is affecting all areas of Science , Material science is no exception . stay 《Nature Reviews Materials》 Magazine 2021 year 8 month , The first 6 volume The first 8 The focus of this issue and other articles in the magazine , This paper discusses how machine learning promotes material research , What progress can we actually expect , And what researchers should pay attention to to to ensure that their machine learning algorithms work in the way they are designed .

 The rise of machine learning : From material design to biomedicine 、 Quantum computing .... Then to industrial applications

Machine learning and emerging materials intelligent ecosystem

To set the scene , From Argonne National Laboratory 、 Georgia institute of technology (Georgia Institute of Technology) Of scientists in 《Nature Reviews Materials》 The magazine is entitled 《Emerging materials intelligence ecosystems propelled by machine learning》 An overview of . The key components of machine learning in materials science are reviewed , From data acquisition and management protocol to independent experiment strategy . The imminent challenges were discussed , This includes the standardization protocol 、 The need for open sharing of benchmark data sets and machine learning code and data .

 The rise of machine learning : From material design to biomedicine 、 Quantum computing .... Then to industrial applications

Thesis link :https://www.nature.com/articles/s41578-020-00255-y

Machine learning and material design

Machine learning toolbox has been effectively applied in almost all topics of material science . It is used in the design of photonic devices ,

Researchers in the Department of electrical engineering at Stanford University wrote a paper entitled 《Deep neural networks for the evaluation and design of photonic devices》 This is described in detail in the review of .

In this review , The researchers showed how the deep neural network configured as a discriminant network learns from the training set and runs as a high-speed proxy electromagnetic solver . This paper studies how to learn the geometric features of device distribution in deep generation network , Even configured as a powerful global optimizer . The basic data science concept constructed in the context of photonics is discussed , Including network training process 、 Division of different network categories and architectures , And dimensionality reduction .

 The rise of machine learning : From material design to biomedicine 、 Quantum computing .... Then to industrial applications

Thesis link :https://www.nature.com/articles/s41578-020-00260-1

Just like Rutgers University (The State University of New Jersey)Adam Gormley And Princeton University Michael Webb In a review 《Machine learning in combinatorial polymer chemistry》 As described in , Machine learning can control the complex structure of Polymers — The ability of functional landscape can be used to design high-performance polymers .

 The rise of machine learning : From material design to biomedicine 、 Quantum computing .... Then to industrial applications


Thesis link :https://www.nature.com/articles/s41578-021-00282-3

Another example is Brigham Young University (Brigham Young University,BYU)Gus Hart And colleagues at 《Machine learning for alloys》 As discussed in the comments , When applied to alloys , Machine learning has promoted the progress of material optimization from metallic glass to high entropy alloys and structural materials .

This paper summarizes the research status of machine learning driven alloys , The methods and applications in this field are discussed , The theoretical prediction and experimental verification are summarized . It is foreseen that the cooperation between machine learning and alloys will lead to the design of new and improved systems .

 The rise of machine learning : From material design to biomedicine 、 Quantum computing .... Then to industrial applications

Thesis link :https://www.nature.com/articles/s41578-021-00340-w

Besides , Machine learning accelerates and improves the synthesis of nanoparticles with various properties , University of Toronto Eugenia Kumacheva And his colleagues published a review 《Nanoparticle synthesis assisted by machine learning》. This review discusses the methods that can be used in the synthesis of nanoparticles ML Algorithm , The key methods of collecting large data sets are introduced . Studied ML Guided semiconductor 、 Metal 、 Synthesis of carbon based and polymer nanoparticles , Finally, we discuss ML Current limitations in the development of assisted nanoparticle synthesis 、 Advantages and Prospects .

 The rise of machine learning : From material design to biomedicine 、 Quantum computing .... Then to industrial applications

Thesis link :https://www.nature.com/articles/s41578-021-00337-5

Machine learning and biomedicine

As in the United States Simons The foundation Flatiron As the researchers at the Institute discussed in their comments , Machine learning has also become a key tool for interrogating complex and large biomedical data sets , Ability to study multicellular complexity and develop personalized therapies .

First , This paper briefly introduces the key single-cell and whole tissue methods that allow the study of tissue specificity , then , Two kinds of methods based on machine learning are introduced , They can be used to analyze 、 Model and interpret the experimental data of these methods . Last , Combined with high resolution 、 Large scale multiomics data sets and interpretable machine learning models , The future possibility of examining multicellular complexity is prospected .

 The rise of machine learning : From material design to biomedicine 、 Quantum computing .... Then to industrial applications

Thesis link :https://www.nature.com/articles/s41578-021-00339-3

Machine learning and computing sustainability applications

Machine learning can be applied to very different problems , Therefore, machine learning can promote the cross integration between disciplines . In the past decade , The Institute for computational sustainability has developed artificial intelligence and software for various applications of computational sustainability ML Method , These methods are also used to advance material discovery , vice versa .

Scientists from Cornell University and the California Institute of technology published 《Computational sustainability meets materials science》 A review article entitled . The synergy between ecology and materials science applications is described . It emphasizes the interpretability of the model and the combination of previous scientific knowledge to better adjust the model 、 Produce scientific solutions , And make up for the lack of training data .

 The rise of machine learning : From material design to biomedicine 、 Quantum computing .... Then to industrial applications

Thesis link :https://www.nature.com/articles/s41578-021-00348-2

Machine learning and text mining

A promising application of machine learning is text mining , Extract information from articles and other documents , And integrate it into structured data sets . Researchers at the Polytechnic University of Catalonia published 《Time to kick-start text mining for biomaterials》 Comments entitled , This feat is particularly important for complex and difficult to organize data , For example, biomaterial data . Applying text and data mining tools to biomaterials first needs to solve several specific field challenges . These include the high heterogeneity of data and the multidisciplinary and rapidly developing language used in biomaterial publications .

 The rise of machine learning : From material design to biomedicine 、 Quantum computing .... Then to industrial applications

Thesis link :https://www.nature.com/articles/s41578-020-0215-z

Machine learning and quantum technology

Another important trend is to promote the ability to experiment 、 An autonomous system that measures the results and makes decisions in the next iteration . This method is particularly advantageous for experiments that require cumbersome manual optimization : One example is the optimization of superconducting qubits , As Oxford University Natalia Ares stay 《Machine learning as an enabler of qubit scalability》 As discussed in , In this field , Machine learning may be the key factor to realize the scalability of related qubits .

 The rise of machine learning : From material design to biomedicine 、 Quantum computing .... Then to industrial applications

Thesis link :https://www.nature.com/articles/s41578-021-00321-z

Machine learning and industrial applications

Like other fields of Science , From principle verification demonstration to practical application , Synergies with industrial partners will be crucial . Toyota Institute Muratahan Aykol And colleagues at 《Machine learning for continuous innovation in battery technologies》 I think , This is especially true for the application of machine learning in battery optimization , Under real conditions, the data obtained from the test is essential . The researchers discussed data-driven 、 How can machine learning based methods help battery researchers meet the needs of battery continuous innovation .

 The rise of machine learning : From material design to biomedicine 、 Quantum computing .... Then to industrial applications

Thesis link :https://www.nature.com/articles/s41578-020-0216-y

To explore the views of an industrial researcher ,《Nature Reviews Materials》 The magazine interviewed then Google Research The engineer Patrick Riley, He said ,「 As the field matures , An important trend is to turn to machine learning as an independent component system , But as a good integrated gear .」

 The rise of machine learning : From material design to biomedicine 、 Quantum computing .... Then to industrial applications

Thesis link :https://www.nature.com/articles/s41578-021-00349-1

All the promises of machine learning may not be realized . Machine learning is a powerful tool , But let machine learning algorithms match the enthusiasm and creativity of researchers who contribute to material science , It will take a long time . This may be a good thing .

Reference link :https://www.nature.com/articles/s41578-021-00351-7

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