Hybrid substitution workflows should accelerate the uptake of chemical recyclates in polymer formulations

Article Chemistry, Applied

In silico formulation prediction of drug/cyclodextrin/polymer ternary complexes by machine learning and molecular modeling techniques

Junjun Li et al.

Summary: A prediction model for ternary cyclodextrin formulations was successfully developed using machine learning and molecular modeling, which will significantly accelerate the formulation screening process.

CARBOHYDRATE POLYMERS (2022)

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Article Chemistry, Physical

Using Machine Learning to Greatly Accelerate Path Integral Ab Initio Molecular Dynamics

Chenghan Li et al.

Summary: Ab initio molecular dynamics (AIMD) is a popular approach for modeling complicated systems. However, its computational cost limits its application for large-scale simulations. This paper presents a computational approach that combines machine learning with path integral contraction schemes, achieving significant acceleration while maintaining accuracy.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2022)

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Review Polymer Science

Machine learning for polymeric materials: an introduction

Morgan M. Cencer et al.

Summary: Polymers are versatile materials and researchers are using data science and polymer informatics to design new materials and understand their structure-property relationships. Despite many useful tools and databases available, they are not widely utilized.

POLYMER INTERNATIONAL (2022)

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Article Multidisciplinary Sciences

Polyply; a python suite for facilitating simulations of macromolecules and nanomaterials

Fabian Grunewald et al.

Summary: Molecular dynamics simulations are crucial for material design and biomacromolecule studies. However, generating input files and starting coordinates is a major bottleneck, especially for complex systems. To address this issue, we introduce the polyply software suite, which includes a multi-scale graph matching algorithm and a generic multi-scale random walk protocol. We evaluate the quality and performance of this approach through practical applications.

NATURE COMMUNICATIONS (2022)

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Article Polymer Science

Data-Driven Modelling of Polyethylene Recycling under High-Temperature Extrusion

Fanny Casteran et al.

Summary: This article investigates two main problems: the controlled thermo-mechanical degradation of polyethylene using the extrusion process for recycling applications, and the data-based modelling of reactive extrusion processes. The study utilizes experiments, numerical methods, and machine learning techniques to address these issues and successfully predicts process outputs and material characteristics.

POLYMERS (2022)

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Article Chemistry, Physical

Reactive Molecular Dynamics Simulations of the Depolymerization of Polyethylene Using Graphene-Oxide-Supported Platinum Nanoparticles

Wengang Zhang et al.

Summary: Plastic materials, although beneficial to society, present challenges in terms of slow degradation and recycling. Chemical recycling, utilizing reactive molecular dynamics simulations, offers promising pathways for converting plastic materials into valuable products. This study focuses on the catalytic depolymerization of polyethylene using platinum nanoparticles and highlights the reduction in barrier for depolymerization. The results demonstrate the potential of using reactive MD methods to design recycling approaches for polymers.

JOURNAL OF PHYSICAL CHEMISTRY A (2022)

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Article Biotechnology & Applied Microbiology

Quantum computing for chemical and biomolecular product design

Martin P. Andersson et al.

Summary: Chemical process design has long benefited from computer-aided methods, and now quantum computing resources are opening up new possibilities for chemical and biomolecular product design. Hybrid algorithms are becoming more prevalent, allowing for the solution of a wider range of problems in chemical product design as quantum computing resources continue to grow.

CURRENT OPINION IN CHEMICAL ENGINEERING (2022)

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Article Materials Science, Multidisciplinary

Modeling glass transition temperatures of epoxy systems: a machine learning study

Sven Meier et al.

Summary: This study utilizes machine learning models to predict the glass transition temperature of thermosets, and validates the accuracy and reliability of the model through experimental data. The results contribute to improving sustainability and accelerating the discovery of new materials.

JOURNAL OF MATERIALS SCIENCE (2022)

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Article Chemistry, Multidisciplinary

Predicting the Properties of High-Performance Epoxy Resin by Machine Learning Using Molecular Dynamics Simulations

Joohee Choi et al.

Summary: This study uses molecular dynamics simulations and machine learning to predict the adhesive properties of epoxy resin, overcoming the limitations of the trial-and-error process and providing new insights for optimizing epoxy resin formulations.

NANOMATERIALS (2022)

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Article Nanoscience & Nanotechnology

From Drug Molecules to Thermoset Shape Memory Polymers: A Machine Learning Approach

Cheng Yan et al.

Summary: This study successfully discovered five new types of UV-curable thermoset shape memory polymers using machine learning techniques. By combining transfer learning-variational autoencoder and a weighted-vector combination, researchers overcame the challenge of limited data, opening new opportunities in the field of TSMP. Through representation of TSMP features with drug molecules and development of a ML framework, the approach showed more accurate and robust results compared to traditional methods.

ACS APPLIED MATERIALS & INTERFACES (2021)

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Article Pharmacology & Pharmacy

An integrated computational methodology with data-driven machine learning, molecular modeling and PBPK modeling to accelerate solid dispersion formulation design

Hanlu Gao et al.

Summary: Computational tools, including machine learning, molecular dynamic simulation and physiologically based pharmacokinetic modeling, have been integrated to accelerate the development of solid dispersion (SD) formulations. Prediction models for dissolution behavior and human pharmacokinetic profile have been constructed and validated, showing good performance in predicting the important information for formulation development. The use of integrated computational methodology is expected to significantly facilitate pharmaceutical formulation development compared to traditional trial-and-error approaches in the laboratory.

EUROPEAN JOURNAL OF PHARMACEUTICS AND BIOPHARMACEUTICS (2021)

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Article Engineering, Environmental

Material Flows of Polyurethane in the United States

Chao Liang et al.

Summary: The study highlights the challenges in recycling polyurethanes but also suggests chemical recycling methods and biobased pathways as potential solutions to reduce fossil fuel consumption and exposure to toxic precursors in polyurethane production.

ENVIRONMENTAL SCIENCE & TECHNOLOGY (2021)

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Article Chemistry, Medicinal

Benchmarking Machine Learning Models for Polymer Informatics: An Example of Glass Transition Temperature

Lei Tao et al.

Summary: The utilization of machine learning techniques in polymer informatics for evaluating properties such as glass transition temperature (T-g) has gained significant attention. A benchmark study was conducted to compare different ML models and explore key factors affecting the prediction of T-g, including structure representations, feature representations, and ML algorithms. This study not only provides guidance for Tg prediction, but also serves as a valuable reference for other polymer informatics tasks.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2021)

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Review Chemistry, Multidisciplinary

Machine Learning of Coarse-Grained Models for Organic Molecules and Polymers: Progress, Opportunities, and Challenges

Huilin Ye et al.

Summary: This article discusses the application of machine learning in developing efficient and accurate coarse-grained models. It introduces two classes of coarse-grained methods and explores important principles in constructing these models and improving them through machine learning techniques.

ACS OMEGA (2021)

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Article Chemistry, Physical

Deep Learning Coordinate-Free Quantum Chemistry

Matthew K. Matlock et al.

Summary: This study demonstrates that accurate quantum chemical computations can be done without geometric information using deep learning on graph encodings in a coordinate-free domain. The choice of graph-encoding architecture significantly affects the performance of these methods, with local variable model Wave accurately calculating quantum chemical properties.

JOURNAL OF PHYSICAL CHEMISTRY A (2021)

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Article Chemistry, Physical

Extending the Time Scales of Nonadiabatic Molecular Dynamics via Machine Learning in the Time Domain

Alexey Akimov

Summary: A novel methodology for direct modeling of long-time scale nonadiabatic dynamics in extended nanoscale and solid-state systems is developed using machine learning models trained directly in the time domain. The importance of using periodic and aperiodic functions to transform time into effective input modes for artificial neural networks is highlighted, along with the significance of conducting extended simulations to gain new insights into excited states photophysics.

JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2021)

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Review Environmental Sciences

Current progress in waste tire rubber devulcanization

Ricky Saputra et al.

Summary: The rubber industry has evolved to meet global demands, with focus on the recovery and regeneration of waste tire rubber. Various devulcanization methods have been explored to selectively cleave vulcanizate's crosslinks while retaining polymeric networks, offering new sustainability pathways and economic opportunities.

CHEMOSPHERE (2021)

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Article Polymer Science

Machine learning assisted discovery of new thermoset shape memory polymers based on a small training dataset

Cheng Yan et al.

Summary: This study proposes methodologies to address the difficulties in applying ML in the field of thermoset shape memory polymers (TSMPs) and develops a new ML framework for predicting the recovery stresses of TSMPs. By using these methods, 14 mostly unknown TSMPs with greater recovery stress than the known ones were identified and one of them was validated by molecular dynamics simulation.

POLYMER (2021)

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Article Chemistry, Multidisciplinary

The Martini Model in Materials Science

Riccardo Alessandri et al.

Summary: The Martini model, originally designed for biomolecular simulations, has been increasingly utilized in the field of soft materials science. Its building block principle can be applied beyond biomolecular systems. The future development of the Martini model in materials science, particularly with the release of Martini 3, holds great potential.

ADVANCED MATERIALS (2021)

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Article Green & Sustainable Science & Technology

Multistage Chemical Recycling of Polyurethanes and Dicarbamates: A Glycolysis-Hydrolysis Demonstration

Pegah Zahedifar et al.

Summary: This paper reviews and experimentally studies the full recycling of polyurethanes. Combined glycolysis and hydrolysis led to an amine production yield of about 30% for model systems, demonstrating the high potential for further research through future optimization of reaction conditions and catalysis.

SUSTAINABILITY (2021)

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Article Green & Sustainable Science & Technology

Extending Multilevel Statistical Entropy Analysis towards Plastic Recyclability Prediction

Philippe Nimmegeers et al.

Summary: The paper aims to improve the multilevel SEA method to assess the recyclability of plastics, highlighting the potential of the developed extended method. By calculating the relative decomposition energies, the method can estimate the recyclability of plastics.

SUSTAINABILITY (2021)

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Article Chemistry, Physical

Q-Force: Quantum Mechanically Augmented Molecular Force Fields

Selim Sami et al.

Summary: The Q-Force protocol enhances transferable force fields with molecule-specific parameters derived from quantum mechanical calculations, improving the accuracy of molecular dynamics simulations. Its molecular fragmentation procedure allows treatment of large molecules with low computational cost, making it widely applicable for atomistic molecular dynamics simulations.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2021)

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Article Chemistry, Physical

First-Principles-Based Machine-Learning Molecular Dynamics for Crystalline Polymers with van der Waals Interactions

Sung Jun Hong et al.

Summary: This study demonstrates the effective development of ML-FFs for the polymer PTFE, showing excellent consistency with density functional theory calculations for longer chain structures. When integrated with molecular dynamics simulations, the ML-FF successfully describes various physical properties of a PTFE bundle.

JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2021)

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Editorial Material Multidisciplinary Sciences

A binding global agreement to address the life cycle of plastics To eliminate plastic pollution, a holistic approach is needed

Nils Simon et al.

SCIENCE (2021)

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Article Chemistry, Physical

Reaction Mechanism of MHETase, a PET Degrading Enzyme

Alexandre V. Pinto et al.

Summary: This study analyzed the reaction mechanism of MHETase and found that it catalyzes the conversion of MHET in two steps, with a rate-limiting step activation barrier of 19.35 kcal/mol. The results supported the hypothesis that a transient tetrahedral intermediate mediates the reaction mechanism.

ACS CATALYSIS (2021)

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Article Polymer Science

Deep Learning of Binary Solution Phase Behavior of Polystyrene

Jeffrey G. Ethier et al.

Summary: This study demonstrates the effectiveness of machine learning in predicting the cloud point temperatures of polymer solutions, providing a tool for predicting liquid-liquid equilibrium of polymers.

ACS MACRO LETTERS (2021)

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Review Chemistry, Multidisciplinary

Computational and data driven molecular material design assisted by low scaling quantum mechanics calculations and machine learning

Wei Li et al.

Summary: Electronic structure methods based on quantum mechanics are widely used in predicting molecular properties, but high computational costs hinder calculations for large systems. Low scaling QM methods and machine learning techniques have been adopted to reduce costs and assist molecular material design in recent years.

CHEMICAL SCIENCE (2021)

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Article Computer Science, Artificial Intelligence

Machine learning discovery of high-temperature polymers

Lei Tao et al.

Summary: By formulating a machine learning model, accurate predictions of the glass transition temperature for polymers with different molecular structures can be made, leading to the discovery of numerous promising candidates for high-temperature polymers.

PATTERNS (2021)

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Article Computer Science, Interdisciplinary Applications