Community Resource for Innovation in Polymer Technology (CRIPT): A Scalable Polymer Material Data Structure

Walsh, Dylan J.; Zou, Weizhong; Schneider, Ludwig; Mello, Reid; Deagen, Michael E.; Mysona, Joshua A.; Lin, Tzyy‐Shyang; Pablo, Juan; Jensen, Klavs F.; Audus, Debra J.; Olsen, Bradley D.

doi:10.1021/acscentsci.3c00011

Cited by 33 publications

(45 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Prediction and exploration of physical properties and ne-tuning of chemical reaction behavior in DCvC-based polymers also can be enhanced through emerging articial intelligence options, polymer-specic notations, and databases (e.g., BigSMILES, CRIPT). [245][246][247] Aside from material-based concerns, social and behavioral challenges associated with waste plastics recovery also limit closed-loop recycling of polymer plastics waste. [17][18][19][20][21] Long-lasting solutions toward circularity will require not only multidisciplinary scientic advances but policy reform from industry and government, and elimination of social barriers to recycling.…”

Section: Discussionmentioning

confidence: 99%

“…, BigSMILES, CRIPT). 245–247 Aside from material-based concerns, social and behavioral challenges associated with waste plastics recovery also limit closed-loop recycling of polymer plastics waste. 17–21 Long-lasting solutions toward circularity will require not only multidisciplinary scientific advances but policy reform from industry and government, and elimination of social barriers to recycling.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Circularity in polymers: addressing performance and sustainability challenges using dynamic covalent chemistries

et al. 2023

View full text Add to dashboard Cite

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Circularity in polymers: addressing performance and sustainability challenges using dynamic covalent chemistries

et al. 2023

View full text Add to dashboard Cite

show abstract

“…To obtain high-quality polymer data for generative polymer design, several polymer databases have been constructed including PI1M, 32 PolyInfo, 33 Polymer Genome, 34 and CRIPT. 35 Notably, the PI1M polymer constitutional repeating unit (CRU) dataset 32 is an open-source dataset generated using a recurrent neural network model trained on experimentally synthesized PolyInfo polymers. 33 However, the PI1M database neither guarantees synthetic access nor provides synthetic pathways necessary for polymer synthesis.…”

Section: ■ Introductionmentioning

confidence: 99%

“…On the other hand, Polymer Genome 34 is a polymer dataset that provides synthetic pathways to generated polymers, 36 but the Polymer Genome dataset is proprietary and can only be accessed (but not exhaustively searched) via a web interface. The development of the CRIPT framework 35 is a crucial step toward the development of an open-source dataset but is not yet sufficiently developed to possess enough diverse chemistries to enable generative ML models. Despite recent progress, the limited quantity, synthesizability, and/or accessibility of these data sources limit researchers from developing generative models for polymeric materials.…”

Section: ■ Introductionmentioning

confidence: 99%

Open Macromolecular Genome: Generative Design of Synthetically Accessible Polymers

2023

View full text Add to dashboard Cite

A grand challenge in polymer science lies in the predictive design of new polymeric materials with targeted functionality. However, de novo design of functional polymers is challenging due to the vast chemical space and an incomplete understanding of structure−property relations. Recent advances in deep generative modeling have facilitated the efficient exploration of molecular design space, but data sparsity in polymer science is a major obstacle hindering progress. In this work, we introduce a vast polymer database known as the Open Macromolecular Genome (OMG), which contains synthesizable polymer chemistries compatible with known polymerization reactions and commercially available reactants selected for synthetic feasibility. The OMG is used in concert with a synthetically aware generative model known as Molecule Chef to identify property-optimized constitutional repeating units, constituent reactants, and reaction pathways of polymers, thereby advancing polymer design into the realm of synthetic relevance. As a proof-of-principle demonstration, we show that polymers with targeted octanol−water solubilities are readily generated together with monomer reactant building blocks and associated polymerization reactions. Suggested reactants are further integrated with Reaxys polymerization data to provide hypothetical reaction conditions (e.g., temperature, catalysts, and solvents). Broadly, the OMG is a polymer design approach capable of enabling data-intensive generative models for synthetic polymer design. Overall, this work represents a significant advance, enabling the property targeted design of synthetic polymers subject to practical synthetic constraints.

show abstract

“…or atomistic simulations (e.g., Khazana, , Computational Materials Repository, − etc.) with a couple of exceptions like the High Throughput Experimental Materials (HETEM) Database and Community Resource for Innovation in Polymer Technology (CRIPT) . The materials covered by these databases are mostly inorganic crystals with a few exceptions; for instance, PoLyInfo , is a powerful polymer database formed from a collection of academic papers and can assist polymeric materials design.…”

Section: Introductionmentioning

confidence: 99%

Machine-Learning-Assisted Understanding of Polymer Nanocomposites Composition–Property Relationship: A Case Study of NanoMine Database

Ma,

Finan,

Jany

et al. 2023

Macromolecules

Self Cite

View full text Add to dashboard Cite

The NanoMine database, one of two nodes in the MaterialsMine database, is a new materials data resource that collects annotated data on polymer nanocomposites (PNCs). This work showcases the potential of NanoMine and other materials data resources to assist fundamental materials understanding and therefore rational materials design. This specific case study is built around studying the relationship between the change in the glass transition temperature T g (ΔT g ) and key descriptors of the nanofillers and the polymer matrix in PNCs. We sifted through data from over 2000 experimental samples curated into NanoMine, trained a decision tree classifier to predict the sign of PNC ΔT g , and built a multiple power regression metamodel to predict ΔT g . The successful model used key descriptors including composition, nanoparticle volume fraction, and interfacial surface energy. The results demonstrate the power of using aggregated materials data to gain insight and predictive capability. Further analysis points to the importance of additional analysis of parameters from processing methodologies and continuously adding curated data sets to increase the sample pool size.

show abstract

Community Resource for Innovation in Polymer Technology (CRIPT): A Scalable Polymer Material Data Structure

Cited by 33 publications

References 36 publications

Circularity in polymers: addressing performance and sustainability challenges using dynamic covalent chemistries

Circularity in polymers: addressing performance and sustainability challenges using dynamic covalent chemistries

Open Macromolecular Genome: Generative Design of Synthetically Accessible Polymers

Machine-Learning-Assisted Understanding of Polymer Nanocomposites Composition–Property Relationship: A Case Study of NanoMine Database

Contact Info

Product

Resources

About