Perspectives on development of biomedical polymer materials in artificial intelligence age

Xie, Shijin

doi:10.1177/08853282231151822

Cited by 10 publications

(6 citation statements)

References 135 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…AI is an advanced technology supporting material design [99][100][101][102][103][104][105][106][107][108][109][110][111][112][113][114], material production sustainability [115][116][117][118][119][120][121], and material data processing, usable to generate new polymeric materials [122][123][124][125][126][127][128][129][130][131][132][133][134][135][136]. In Table 3 are listed some selected works about AI application in the material fabrication process.…”

Section: (Ket Iii) Artificial Intelligencementioning

confidence: 99%

Intelligent Materials and Nanomaterials Improving Physical Properties and Control Oriented on Electronic Implementations

Massaro

2023

Electronics

View full text Add to dashboard Cite

The review highlights possible research topics matching the experimental physics of matter with advances in electronics to improve the intelligent design and control of innovative smart materials. Specifically, following the European research guidelines of Key Enabling Technologies (KETs), I propose different topics suitable for project proposals and research, including advances in nanomaterials, nanocomposite materials, nanotechnology, and artificial intelligence (AI), with a focus on electronics implementation. The paper provides a new research framework addressing the study of AI driving electronic systems and design procedures to determine the physical properties of versatile materials and to control dynamically the material’s “self-reaction” when applying external stimuli. The proposed research framework allows one to ideate new circuital solutions to be integrated in intelligent embedded systems formed of materials, algorithms and circuits. The challenge of the review is to bring together different research concepts and topics regarding innovative materials to provide a research direction for possible AI applications. The discussed research topics are classified as Technology Readiness Levels (TRL) 1 and 2.

show abstract

Section: (Ket Iii) Artificial Intelligencementioning

confidence: 99%

Intelligent Materials and Nanomaterials Improving Physical Properties and Control Oriented on Electronic Implementations

Massaro

2023

Electronics

View full text Add to dashboard Cite

show abstract

“…Machine learning (ML) is contributing to many areas of chemistry and materials research, as diverse as solar cells, photoresist, high-entropy alloys, drug design , and formulation discovery, and biomedical polymers . Many introductory texts − and review articles − provide tutorials and explications of applications of ML to chemistry and materials (and scientific discovery more generally).…”

Section: Introductionmentioning

confidence: 99%

In Pursuit of the Exceptional: Research Directions for Machine Learning in Chemical and Materials Science

Schrier,

Norquist,

Buonassisi

et al. 2023

J. Am. Chem. Soc.

View full text Add to dashboard Cite

Exceptional molecules and materials with one or more extraordinary properties are both technologically valuable and fundamentally interesting, because they often involve new physical phenomena or new compositions that defy expectations. Historically, exceptionality has been achieved through serendipity, but recently, machine learning (ML) and automated experimentation have been widely proposed to accelerate target identification and synthesis planning. In this Perspective, we argue that the data-driven methods commonly used today are well-suited for optimization but not for the realization of new exceptional materials or molecules. Finding such outliers should be possible using ML, but only by shifting away from using traditional ML approaches that tweak the composition, crystal structure, or reaction pathway. We highlight case studies of high-T c oxide superconductors and superhard materials to demonstrate the challenges of ML-guided discovery and discuss the limitations of automation for this task. We then provide six recommendations for the development of ML methods capable of exceptional materials discovery: (i) Avoid the tyranny of the middle and focus on extrema; (ii) When data are limited, qualitative predictions that provide direction are more valuable than interpolative accuracy; (iii) Sample what can be made and how to make it and defer optimization; (iv) Create room (and look) for the unexpected while pursuing your goal; (v) Try to fill-in-the-blanks of input and output space; (vi) Do not confuse human understanding with model interpretability. We conclude with a description of how these recommendations can be integrated into automated discovery workflows, which should enable the discovery of exceptional molecules and materials.

show abstract

“…Machine learning (ML) is contributing to many areas of chemistry and materials research, as diverse as solar cells, 1 photoresist, 2 high-entropy alloys, 3 drug design 4,5 and formulation 6 discovery, and biomedical polymers. 7 Many introductory texts [8][9][10] and review articles [11][12][13][14][15] provide tutorials and explications of applications of ML to chemistry and materials (and scientific discovery more generally 16 ). However, these applications have been demonstrated mainly in the context of incremental improvement and optimization.…”

Section: Introductionmentioning

confidence: 99%

In Pursuit of the Exceptional: Research Directions for Machine Learning in Chemical and Materials Science

Schrier,

Norquist,

Buonassisi

et al. 2023

Preprint

View full text Add to dashboard Cite

Exceptional molecules and materials with one or more extraordinary properties are both technologically valuable and fundamentally interesting because they often involve new physical phenomena or new compositions that defy expectations. Historically, exceptionality has been achieved through serendipity, but recently, machine learning (ML) and automated experimentation have been widely proposed to accelerate target identification and synthesis planning. In this Perspective, we argue that the data-driven methods commonly used today are well-suited for optimization but not for realizing new exceptional materials or molecules. Finding such outliers should be possible using ML, but only by shifting away from using traditional ML approaches that tweak the composition, crystal structure, or reaction pathway. We highlight case studies of high-Tc oxide superconductors and superhard materials to demonstrate the challenges of ML-guided discovery and discuss the limitations of automation for this task. We then provide six recommendations for the development of ML methods capable of exceptional materials discovery: (i) Avoid the tyranny of the middle and focus on extrema; (ii) When data is limited, qualitative predictions that provide direction are more valuable than interpolative accuracy; (iii) Sample what can be made and how to make it, and defer optimization; (iv) Create room (and look) for the unexpected while pursuing your goal; (v) Try to fill-in-the-blanks of input and output space; (vi) Do not confuse human understanding with model interpretability. We conclude with a description of how these recommendations can be integrated into automated discovery workflows that should enable the discovery of exceptional molecules and materials.

show abstract

Perspectives on development of biomedical polymer materials in artificial intelligence age

Cited by 10 publications

References 135 publications

Intelligent Materials and Nanomaterials Improving Physical Properties and Control Oriented on Electronic Implementations

Intelligent Materials and Nanomaterials Improving Physical Properties and Control Oriented on Electronic Implementations

In Pursuit of the Exceptional: Research Directions for Machine Learning in Chemical and Materials Science

In Pursuit of the Exceptional: Research Directions for Machine Learning in Chemical and Materials Science

Contact Info

Product

Resources

About