An interpretable 3D multi-hierarchical representation-based deep neural network for environmental, health and safety properties prediction of organic solvents

Zhang, Jun; Wang, Qin; Lei, Yang; Shen, Weifeng

doi:10.1039/d3gc04801b

Cited by 1 publication

(1 citation statement)

References 39 publications

(57 reference statements)

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“…This strategy, alongside other MCTS-based studies elsewhere, , has leveraged controlled search spaces to uncover new organic materials. Moreover, ML methods have proved applicable in learning, predicting, and optimization of environmental, health, and safety properties in prospective environmental sustainability studies. − In our earlier work, we demonstrated a computational framework for application-specific metal–organic frameworks − that was based on generative models and demonstrated adaptability across different materials, such as ionic liquids. These techniques are pivotal in unlocking the vast potential of materials design and finding better applications tailored to a particular target.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Framework for the Design of High-Performing and Sustainable Polyamides

Muthoka,

Park,

Yoo

et al. 2024

ACS Sustainable Chem. Eng.

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Developing high-performance materials with tailored properties is a key challenge that requires innovative molecular design methods. This study presents a machine learning-based approach, integrating Monte Carlo tree search and a recurrent neural network for the molecular design of polyamide systems, and we evaluate their properties with molecular dynamics simulations. By benchmarking against the performance of polyimides, we guide our algorithm to design sustainable polyamides with superior properties. A key consideration in our design approach is the incorporation of biodegradability as a critical design factor, in line with the growing demand for sustainable materials. We use an objective function targeting an optimal profile of low octanol−water partition coefficient (log P), high thermal stability (TS ef ), and substantial thermal conductivity (k). With 20 independent runs, which resulted in an evaluation of 133,655 monomers for design compatibility and generation of 5255 polyamide systems, our approach successfully designed promising 157 polyamide monomers, satisfying the criteria of log P < 0, TS ef ≥ 2, and k ≥ 0.5 W/m K. These selections were validated by additional molecular dynamics (MD) calculations, revealing our objective function leads to sustainable and high-performance materials. Furthermore, we performed a detailed analysis of the structure−property relationship. Our algorithm successfully demonstrates the effectiveness of this integrated computational approach in guiding the design of novel polyamides.

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Section: Introductionmentioning

confidence: 99%

Comprehensive Framework for the Design of High-Performing and Sustainable Polyamides

Muthoka,

Park,

Yoo

et al. 2024

ACS Sustainable Chem. Eng.

View full text Add to dashboard Cite

show abstract

An interpretable 3D multi-hierarchical representation-based deep neural network for environmental, health and safety properties prediction of organic solvents

Abstract: A 3D multi-hierarchical representation-based deep neural network (3D-MrDNN) architecture for prediction of the environmental, health and safety properties of organic solvents.

Cited by 1 publication

References 39 publications

Comprehensive Framework for the Design of High-Performing and Sustainable Polyamides

Comprehensive Framework for the Design of High-Performing and Sustainable Polyamides

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