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

In the field of polymer informatics, utilizing machine learning (ML) techniques to evaluate the glass transition temperature Tg and other properties of polymers has attracted extensive attention. This data-centric approach is much more efficient and practical than the laborious experimental measurements when encountered a daunting number of polymer structures. Various ML models are demonstrated to perform well for Tg prediction. Nevertheless, they are trained on different data sets, using different structure representations, and based on different feature engineering methods. Thus, the critical question arises on selecting a proper ML model to better handle the Tg prediction with generalization ability. To provide a fair comparison of different ML techniques and examine the key factors that affect the model performance, we carry out a systematic benchmark study by compiling 79 different ML models and training them on a large and diverse data set. The three major components in setting up an ML model are structure representations, feature representations, and ML algorithms. In terms of polymer structure representation, we consider the polymer monomer, repeat unit, and oligomer with longer chain structure. Based on that feature, representation is calculated, including Morgan fingerprinting with or without substructure frequency, RDKit descriptors, molecular embedding, molecular graph, etc. Afterward, the obtained feature input is trained using different ML algorithms, such as deep neural networks, convolutional neural networks, random forest, support vector machine, LASSO regression, and Gaussian process regression. We evaluate the performance of these ML models using a holdout test set and an extra unlabeled data set from high-throughput molecular dynamics simulation. The ML model’s generalization ability on an unlabeled data set is especially focused, and the model’s sensitivity to topology and the molecular weight of polymers is also taken into consideration. This benchmark study provides not only a guideline for the Tg prediction task but also a useful reference for other polymer informatics tasks

Interlaced, Nanostructured Interface with Graphene Buffer Layer Reduces Thermal Boundary Resistance in Nano/Microelectronic Systems

Improving heat transfer in hybrid nano/microelectronic systems is a challenge, mainly due to the high thermal boundary resistance (TBR) across the interface. Herein, we focus on gallium nitride (GaN)/diamond interfaceas a model system with various high power, high temperature, and optoelectronic applicationsand perform extensive reverse nonequilibrium molecular dynamics simulations, decoding the interplay between the pillar length, size, shape, hierarchy, density, arrangement, system size, and the interfacial heat transfer mechanisms to substantially reduce TBR in GaN-on-diamond devices. We found that changing the conventional planar interface to nanoengineered, interlaced architecture with optimal geometry results in >80% reduction in TBR. Moreover, introduction of conformal graphene buffer layer further reduces the TBR by ∼33%. Our findings demonstrate that the enhanced generation of intermediate frequency phonons activates the dominant group velocities, resulting in reduced TBR. This work has important implications on experimental studies, opening up a new space for engineering hybrid nano/microelectronics

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

In the field of polymer informatics, utilizing machine learning (ML) techniques to evaluate the glass transition temperature Tg and other properties of polymers has attracted extensive attention. This data-centric approach is much more efficient and practical than the laborious experimental measurements when encountered a daunting number of polymer structures. Various ML models are demonstrated to perform well for Tg prediction. Nevertheless, they are trained on different data sets, using different structure representations, and based on different feature engineering methods. Thus, the critical question arises on selecting a proper ML model to better handle the Tg prediction with generalization ability. To provide a fair comparison of different ML techniques and examine the key factors that affect the model performance, we carry out a systematic benchmark study by compiling 79 different ML models and training them on a large and diverse data set. The three major components in setting up an ML model are structure representations, feature representations, and ML algorithms. In terms of polymer structure representation, we consider the polymer monomer, repeat unit, and oligomer with longer chain structure. Based on that feature, representation is calculated, including Morgan fingerprinting with or without substructure frequency, RDKit descriptors, molecular embedding, molecular graph, etc. Afterward, the obtained feature input is trained using different ML algorithms, such as deep neural networks, convolutional neural networks, random forest, support vector machine, LASSO regression, and Gaussian process regression. We evaluate the performance of these ML models using a holdout test set and an extra unlabeled data set from high-throughput molecular dynamics simulation. The ML model’s generalization ability on an unlabeled data set is especially focused, and the model’s sensitivity to topology and the molecular weight of polymers is also taken into consideration. This benchmark study provides not only a guideline for the Tg prediction task but also a useful reference for other polymer informatics tasks

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

In the field of polymer informatics, utilizing machine learning (ML) techniques to evaluate the glass transition temperature Tg and other properties of polymers has attracted extensive attention. This data-centric approach is much more efficient and practical than the laborious experimental measurements when encountered a daunting number of polymer structures. Various ML models are demonstrated to perform well for Tg prediction. Nevertheless, they are trained on different data sets, using different structure representations, and based on different feature engineering methods. Thus, the critical question arises on selecting a proper ML model to better handle the Tg prediction with generalization ability. To provide a fair comparison of different ML techniques and examine the key factors that affect the model performance, we carry out a systematic benchmark study by compiling 79 different ML models and training them on a large and diverse data set. The three major components in setting up an ML model are structure representations, feature representations, and ML algorithms. In terms of polymer structure representation, we consider the polymer monomer, repeat unit, and oligomer with longer chain structure. Based on that feature, representation is calculated, including Morgan fingerprinting with or without substructure frequency, RDKit descriptors, molecular embedding, molecular graph, etc. Afterward, the obtained feature input is trained using different ML algorithms, such as deep neural networks, convolutional neural networks, random forest, support vector machine, LASSO regression, and Gaussian process regression. We evaluate the performance of these ML models using a holdout test set and an extra unlabeled data set from high-throughput molecular dynamics simulation. The ML model’s generalization ability on an unlabeled data set is especially focused, and the model’s sensitivity to topology and the molecular weight of polymers is also taken into consideration. This benchmark study provides not only a guideline for the Tg prediction task but also a useful reference for other polymer informatics tasks

Synthesis of Multiresponsive and Dynamic Chitosan-Based Hydrogels for Controlled Release of Bioactive Molecules

An inexpensive, facile, and environmentally benign method has been developed for the preparation of multiresponsive, dynamic, and self-healing chitosan-based hydrogels. A dibenzaldehyde-terminated telechelic poly(ethylene glycol) (PEG) was synthesized and was allowed to form Schiff base linkages between the aldehyde groups and the amino groups in chitosan. Upon mixing the telechelic PEG with chitosan at 20 °C, hydrogels with solid content of 4–8% by mass were generated rapidly in <60 s. Because of the dynamic equilibrium between the Schiff base linkage and the aldehyde and amine reactants, the hydrogels were found to be self-healable and sensitive to many biochemical-stimuli, such as pH, amino acids, and vitamin B6 derivatives. In addition, chitosan could be digested by enzymes such as papain, leading to the decomposition of the hydrogels. Encapsulation and controlled release of small molecules such as rhodamine B and proteins such as lysozyme have been successfully carried out, demonstrating the potential biomedical applications of these chitosan-based dynamic hydrogels

Synthesis of Multiresponsive and Dynamic Chitosan-Based Hydrogels for Controlled Release of Bioactive Molecules

An inexpensive, facile, and environmentally benign method has been developed for the preparation of multiresponsive, dynamic, and self-healing chitosan-based hydrogels. A dibenzaldehyde-terminated telechelic poly(ethylene glycol) (PEG) was synthesized and was allowed to form Schiff base linkages between the aldehyde groups and the amino groups in chitosan. Upon mixing the telechelic PEG with chitosan at 20 °C, hydrogels with solid content of 4–8% by mass were generated rapidly in <60 s. Because of the dynamic equilibrium between the Schiff base linkage and the aldehyde and amine reactants, the hydrogels were found to be self-healable and sensitive to many biochemical-stimuli, such as pH, amino acids, and vitamin B6 derivatives. In addition, chitosan could be digested by enzymes such as papain, leading to the decomposition of the hydrogels. Encapsulation and controlled release of small molecules such as rhodamine B and proteins such as lysozyme have been successfully carried out, demonstrating the potential biomedical applications of these chitosan-based dynamic hydrogels

α-Aldehyde Terminally Functional Methacrylic Polymers from Living Radical Polymerization: Application in Protein Conjugation “Pegylation”

Application of proteins and peptides as human therapeutics is expanding rapidly as drug discovery becomes more prevalent. Conjugation of polymers to proteins can circumvent many problems and pegylation of proteins is now emerging as acceptable practice. This paper describes the synthesis of α-aldehyde-terminated poly(methoxyPEG)methacrylates from Cu(I) mediated living radical polymerization (Mn = 11 000, 22 000 and 32 000; PDi < 1.15), and their efficient conjugation to lysozyme, as a model protein. This offers an attractive and flexible alternative to linear poly(ethylene glycol) opening up the possibility of using the full power of living radical polymerization

Unstructured Self-Assembled Molecular Lamella Induces Ultrafast Thermal Transfer through a Cathode/Separator Interphase in Lithium-Ion Batteries

Thermal issues associated with lithium-ion batteries (LIBs) can dramatically affect their life cycle and overall performance. However, the effective heat transfer is deeply restrained by the high thermal resistance across the cathode (lithium cobalt oxide, LCO)–separator (polyethylene, PE) interface. This work presents a new approach to tailoring the interfacial thermal resistance, namely, unstructured self-assembled lamella (USAL). Compared to the popular self-assembled monolayers, although the USAL gives a redundant interface and amorphous molecule patterns, it can also provide many benefits, including easy assembly, more thermal bridges, and ready pressurization. Three small organic molecules (SOMs) were assembled into an LCO–PE interface, providing unique functional groups, −NH2, −SH, and −CH3, to illustrate its energy conversion efficiency. Through molecular dynamics simulations, our results show that the USAL can facilitate interfacial heat transfer remarkably. A 3-aminopropyl trimethoxysilane (APTMS)-coated LCO–PE system with 11.4 Å thickness demonstrates the maximum enhancement of thermal conductance, about 320% of the pristine system. Such enhancement is attributed to the developed double heat passages by strong non-bonded interactions across LCO–SOM and PE–SOM interfaces, a tuned temperature field, and high compatibility between SOMs and PE. Importantly, due to SOMs’ amorphous morphology, the pressure can be imposed and further enhance the interfacial heat transfer. Results show the improved thermal conductance rises the most for the APTMS-coated LCO–PE system with 11.4 Å thickness at 10 GPa, almost 685% higher than that of the pristine system. The high efficiency of heat transfer comes as a result of the enhanced binding strength across the LCO–SOM and SOM–PE interface, the reduced phonon scattering in PE and SOMs, and the high LCO stiffness. These investigations are expected to provide a new perspective for modulating the heat transfer across the interphase of LIBs and achieve more effective thermal management for the multi-material system

Combining Enzymatic Monomer Transformation with Photoinduced Electron Transfer − Reversible Addition–Fragmentation Chain Transfer for the Synthesis of Complex Multiblock Copolymers

A novel and facile method, involving enzymatic monomer synthesis and a photocontrolled polymerization technique, has been successfully employed for the preparation of high-order multiblock copolymers. New acrylate monomers were synthesized via enzymatic transacylation between an activated monomer, i.e., 2,2,2-trifluoroethyl acrylate (TFEA), and various functional alcohols. These synthesized monomers were successfully polymerized without further purification via photoinduced electron transfer–reversible addition–fragmentation chain transfer (PET-RAFT) polymerization under low energy blue LED light (4.8 W) in the presence of an iridium-based photoredox catalyst (<i>fac</i>-[Ir­(ppy)₃]). In this condition, PET-RAFT allows us to achieve high monomer conversion (∼100%) with excellent integrity of the end group (>80%). Different multiblock (co)­polymers, including poly­(hexyl acrylate) pentablock homopolymer, poly­(methyl acylate-<i>b</i>-ethyl acrylate-<i>b</i>-<i>n</i>-propyl acrylate-<i>b-n</i>-butyl acrylate-<i>b</i>-<i>n</i>-pentyl acrylate) pentablock copolymer, and poly­(3-oxobutyl acrylate-<i>b</i>-methyl acrylate-<i>b</i>-3-(trimethylsilyl)­prop-2-yn-1-yl acrylate) triblock copolymer containing functional groups were rapidly prepared via sequential addition of monomers without purification steps

Additional file 4 of Complete chloroplast genome structural characterization of two Phalaenopsis (Orchidaceae) species and comparative analysis with their alliance

Supplementary Material