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Organic molecules and polymers have a broad range of applications in biomedical, chemical, and materials science fields. Traditional design approaches for organic molecules and polymers are mainly experimentally-driven, guided by experience, intuition, and conceptual insights. Though they have been successfully applied to discover many important materials, these methods are facing significant challenges due to the tremendous demand of new materials and vast design space of organic molecules and polymers. Accelerated and inverse materials design is an ideal solution to these challenges. With advancements in high-throughput computation, artificial intelligence (especially machining learning, ML), and the growth of materials databases, ML-assisted materials design is emerging as a promising tool to flourish breakthroughs in many areas of materials science and engineering. To date, using ML-assisted approaches, the quantitative structure property/activity relation for material property prediction can be established more accurately and efficiently. In addition, materials design can be revolutionized and accelerated much faster than ever, through ML-enabled molecular generation and inverse molecular design. In this perspective, we review the recent progresses in ML-guided design of organic molecules and polymers, highlight several successful examples, and examine future opportunities in biomedical, chemical, and materials science fields. We further discuss the relevant challenges to solve in order to fully realize the potential of ML-assisted materials design for organic molecules and polymers. In particular, this study summarizes publicly available materials databases, feature representations for organic molecules, open-source tools for feature generation, methods for molecular generation, and ML models for prediction of material properties, which serve as a tutorial for researchers who have little experience with ML before and want to apply ML for various applications. Last but not least, it draws insights into the current limitations of ML-guided design of organic molecules and polymers. We anticipate that ML-assisted materials design for organic molecules and polymers will be the driving force in the near future, to meet the tremendous demand of new materials with tailored properties in different fields.
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Opportunities for materials science: From molecules to neural networks
This article addresses why biomaterials are a growing part of materials science. We consider two areas at two different scales. At the nanometer scale, enzymes are heterogeneous nanoparticles of extraordinary deformability; this property allows us to view biomolecules informed by concepts of materials science and nonlinear physics. A degree of universality in the mechanical behavior of the molecules appears in the ubiquitous softening transitions; some results obtained dynamically by nanorheology, and others obtained in equilibrium experiments through the method of the DNA springs are summarized. These soft molecules represent an opportunity for studies of dissipation at the atomic scale. At the mesoscopic scale, composite functional materials with biological components hold promise for applications such as low power, chemically driven, biodegradable devices. A concrete example, and a program for the future, is the artificial axon. It is a synthetic structure that supports action potentials based on the same physical mechanism as the voltage spikes in nerve cells. A network of such axons, which is yet to come, would constitute an artificial brain. Beyond device applications, the focus here is on the basic science, namely, a constructivist approach to cybernetics, algorithmic mathematics, and the brain.
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- Award ID(s):
- 1809381
- PAR ID:
- 10104684
- Date Published:
- Journal Name:
- MRS Bulletin
- Volume:
- 44
- Issue:
- 2
- ISSN:
- 0883-7694
- Page Range / eLocation ID:
- 124 to 129
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1557/mrs.2019.23
