jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Cheng Yan | Xiaming Feng | Guoqiang Li
Source: ACS Applied Materials & Interfaces (article, 2021-12-08)
Field: Materials Science — Polymers and Plastics

From Drug Molecules to Thermoset Shape Memory Polymers: A Machine Learning Approach Ultraviolet (UV)-curable thermoset shape memory polymers (TSMPs) with high recovery stress but mild glass transition temperature (<i>T</i><sub>g</sub>) are highly desired for 3D/4D printing lightweight load-bearing structures and devices. However, a bottleneck is that high recovery stress usually means high <i>T</i><sub>g</sub>. For a few TSMPs with high recovery stress, their <i>T</i><sub>g</sub> values are close to the decomposition temperature, and thus, the shape memory effect cannot be triggered safely and effectively. While machine learning (ML) has served as a useful tool to discover new materials and drugs, the grand challenge of using ML to discover new TSMPs persists in the very limited data available. Here, we report an enhanced ML approach by combining the transfer learning-variational autoencoder with a weighted-vector combination method. By learning a large data set with drug molecules in a pretraining process, we were able to effectively map the TSMPs to a hidden space that is much closer to a Gaussian distribution. Through this approach, we created a large compositional space and were able to discover five new types of UV-curable TSMPs with desired properties, one of which was validated by the experiments. Our contribution includes (1) representing the features of TSMPs by drug molecules to overcome the barrier of a limited training data set and (2) developing a ML framework that is able to overcome the barrier of mapping the molar ratio information. It is shown that this approach can effectively learn TSMP features by utilizing the relatedness between the data-scarce (and biased) TSMP target and data-abundant drug source, and the result is much more accurate and more robust than the benchmark set by the support vector machine method using direct label encoding and Morgan encoding. Therefore, it is believed that this framework is a state-of-the-art study in the TSMP field. This study opens new opportunities for discovering not only new TSMPs but also other thermoset polymers. Materials science ; Thermosetting polymer ; Shape-memory polymer ; Polymer ; Molecule ; Nanotechnology ; Drug ; Polymer science ; Composite material ; Organic chemistry ; Pharmacology Polymer composites and self-healing ; Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization
Authors: Yukang Gong | Dongyu Xue | Guohui Chuai | Jing Yu | Qi Liu
Source: Chemical Science (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

DeepReac+: deep active learning for quantitative modeling of organic chemical reactions Various computational methods have been developed for quantitative modeling of organic chemical reactions; however, the lack of universality as well as the requirement of large amounts of experimental data limit their broad applications. Here, we present DeepReac+, an efficient and universal computational framework for prediction of chemical reaction outcomes and identification of optimal reaction conditions based on deep active learning. Under this framework, DeepReac is designed as a graph-neural-network-based model, which directly takes 2D molecular structures as inputs and automatically adapts to different prediction tasks. In addition, carefully-designed active learning strategies are incorporated to substantially reduce the number of necessary experiments for model training. We demonstrate the universality and high efficiency of DeepReac+ by achieving the state-of-the-art results with a minimum of labeled data on three diverse chemical reaction datasets in several scenarios. Collectively, DeepReac+ has great potential and utility in the development of AI-aided chemical synthesis. DeepReac+ is freely accessible at https://github.com/bm2-lab/DeepReac. Universality (dynamical systems) ; Computer science ; Artificial intelligence ; Deep learning ; Artificial neural network ; Machine learning ; Deep neural networks ; Graph ; Chemical reaction ; Theoretical computer science ; Chemistry Machine Learning in Materials Science ; Topic Modeling ; Computational Drug Discovery Methods
Authors: Chee‐Kong Lee | Chengqiang Lu | Yue Yu | Qiming Sun | Chang‐Yu Hsieh | Shengyu Zhang | Qi Liu | Liang Shi
Source: The Journal of Chemical Physics (article, 2021-01-13)
Field: Materials Science — Materials Chemistry

Transfer learning with graph neural networks for optoelectronic properties of conjugated oligomers Despite the remarkable progress of machine learning (ML) techniques in chemistry, modeling the optoelectronic properties of long conjugated oligomers and polymers with ML remains challenging due to the difficulty in obtaining sufficient training data. Here, we use transfer learning to address the data scarcity issue by pre-training graph neural networks using data from short oligomers. With only a few hundred training data, we are able to achieve an average error of about 0.1 eV for the excited-state energy of oligothiophenes against time-dependent density functional theory (TDDFT) calculations. We show that the success of our transfer learning approach relies on the relative locality of low-lying electronic excitations in long conjugated oligomers. Finally, we demonstrate the transferability of our approach by modeling the lowest-lying excited-state energies of poly(3-hexylthiophene) in its single-crystal and solution phases using the transfer learning models trained with the data of gas-phase oligothiophenes. The transfer learning predicted excited-state energy distributions agree quantitatively with TDDFT calculations and capture some important qualitative features observed in experimental absorption spectra. Time-dependent density functional theory ; Excited state ; Transfer of learning ; Conjugated system ; Density functional theory ; Artificial neural network ; Computer science ; Transferability ; Materials science ; Chemical physics ; Artificial intelligence ; Machine learning ; Computational chemistry ; Chemistry ; Polymer ; Physics ; Atomic physics Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Computational Drug Discovery Methods
Authors: Pei‐Song Yang | Huan Zhang | Xin Lai | Kunfeng Wang | Qingyuan Yang | Duli Yu
Source: ACS Omega (article, 2021-06-25)
Field: Chemistry — Inorganic Chemistry

Accelerating the Selection of Covalent Organic Frameworks with Automated Machine Learning Covalent organic frameworks (COFs) have the advantages of high thermal stability and large specific surface and have great application prospects in the fields of gas storage and catalysis. This article mainly focuses on COFs' working capacity of methane (CH<sub>4</sub>). Due to the vast number of possible COF structures, it is time-consuming to use traditional calculation methods to find suitable materials, so it is important to apply appropriate machine learning (ML) algorithms to build accurate prediction models. A major obstacle for the use of ML algorithms is that the performance of an algorithm may be affected by many design decisions. Finding appropriate algorithm and model parameters is quite a challenge for nonprofessionals. In this work, we use automated machine learning (AutoML) to analyze the working capacity of CH<sub>4</sub> based on 403,959 COFs. We explore the relationship between 23 features such as the structure, chemical characteristics, atom types of COFs, and the working capacity. Then, the tree-based pipeline optimization tool (TPOT) in AutoML and the traditional ML methods including multiple linear regression, support vector machine, decision tree, and random forest that manually set model parameters are compared. It is found that the TPOT can not only save complex data preprocessing and model parameter tuning but also show higher performance than traditional ML models. Compared with traditional grand canonical Monte Carlo simulations, it can save a lot of time. AutoML has broken through the limitations of professionals so that researchers in nonprofessional fields can realize automatic parameter configuration for experiments to obtain highly accurate and easy-to-understand results, which is of great significance for material screening. Computer science ; Pipeline (software) ; Machine learning ; Decision tree ; Artificial intelligence ; Set (abstract data type) ; Selection (genetic algorithm) ; Preprocessor ; Stability (learning theory) ; Obstacle ; Data mining Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Machine Learning in Materials Science
Authors: Anjli M. Patel | Sudarshan Vijay | Georg Kastlunger | Jens K. Nørskov | Karen Chan
Source: The Journal of Physical Chemistry Letters (article, 2021-05-26)
Field: Materials Science — Materials Chemistry

Generalizable Trends in Electrochemical Protonation Barriers Predicting activation energies for reaction steps is essential for modeling catalytic processes, but accurate barrier simulations often require considerable computational expense, especially for electrochemical reactions. Given the challenges of barrier computations and the growing promise of electrochemical routes for various processes, generalizable energetic trends in electrochemistry can significantly aid in analyzing reaction networks and building microkinetic models. Herein, we employ density functional theory and machine learning nudged elastic band models to simulate electrochemical protonation of *C, *N, and *O monatomic adsorbates from hydronium on a series of transition metal surfaces. We observe a consistent trend of decreasing protonation reaction energies yet increasing activation barriers from *O to *N to *C. Analysis of bond orders and reaction pathways provides insight into the origin of the observed trends in protonation energetics. We hypothesize that these results are relevant for polyatomic adsorbates, which can simplify analysis of reaction mechanisms and inform catalyst design. Protonation ; Monatomic ion ; Electrochemistry ; Catalysis ; Chemistry ; Chemical physics ; Computational chemistry ; Density functional theory ; Transition state ; Reaction mechanism ; Nanotechnology ; Materials science ; Physical chemistry ; Organic chemistry ; Electrode ; Ion Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; CO2 Reduction Techniques and Catalysts
Authors: Benjamin C. B. Symons | Michael Bane | Paul L. A. Popelier
Source: Journal of Chemical Theory and Computation (article, 2021-10-07)
Field: Materials Science — Materials Chemistry

DL_FFLUX: A Parallel, Quantum Chemical Topology Force Field DL_FFLUX is a force field based on quantum chemical topology that can perform molecular dynamics for flexible molecules endowed with polarizable atomic multipole moments (up to hexadecapole). Using the machine learning method kriging (aka Gaussian process regression), DL_FFLUX has access to atomic properties (energy, charge, dipole moment, etc.) with quantum mechanical accuracy. Newly optimized and parallelized using domain decomposition Message Passing Interface (MPI), DL_FFLUX is now able to deliver this rigorous methodology at scale while still in reasonable time frames. DL_FFLUX is delivered as an add-on to the widely distributed molecular dynamics code DL_POLY 4.08. For the systems studied here (10<sup>3</sup>-10<sup>5</sup> atoms), DL_FFLUX is shown to add minimal computational cost to the standard DL_POLY package. In fact, the optimization of the electrostatics in DL_FFLUX means that, when high-rank multipole moments are enabled, DL_FFLUX is up to 1.25× faster than standard DL_POLY. The parallel DL_FFLUX preserves the quality of the scaling of MPI implementation in standard DL_POLY. For the first time, it is feasible to use the full capability of DL_FFLUX to study systems that are large enough to be of real-world interest. For example, a fully flexible, high-rank polarized (up to and including quadrupole moments) 1 ns simulation of a system of 10 125 atoms (3375 water molecules) takes 30 h (wall time) on 18 cores. Multipole expansion ; Dipole ; Computational science ; Computer science ; Fast multipole method ; Force field (fiction) ; Quantum ; Hyperpolarizability ; Molecular dynamics ; Topology (electrical circuits) ; Polarizability ; Algorithm ; Computational chemistry ; Physics ; Chemistry ; Molecule ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Alexander Lindsay | Roy Stogner | Derek Gaston | Daniel Schwen | Christopher Matthews | Wen Jiang | Larry K. Aagesen | Robert Carlsen | Fande Kong | Andrew E. Slaughter | Cody Permann | Richard Martineau
Source: Nuclear Technology (article, 2021-02-22)
Field: Materials Science — Materials Chemistry

Automatic Differentiation in MetaPhysicL and Its Applications in MOOSE Efficient solution via Newton’s method of nonlinear systems of equations requires an accurate representation of the Jacobian, corresponding to the derivatives of the component residual equations with respect to the degrees of freedom. In practice these systems of equations often arise from spatial discretization of partial differential equations used to model physical phenomena. These equations may involve domain motion or material equations that are complex functions of the systems’ degrees of freedom. Computing the Jacobian by hand in these situations is arduous and prone to error. Finite difference approximations of the Jacobian or its action are prone to truncation error, especially in multiphysics settings. Symbolic differentiation packages may be used, but often result in an excessive number of terms in realistic model scenarios. An alternative to symbolic and numerical differentiation is automatic differentiation (AD), which propagates derivatives with every elementary operation of a computer program, corresponding to continual application of the chain rule. Automatic differentiation offers the guarantee of an exact Jacobian at a relatively small overhead cost. In this work, we outline the adoption of AD in the Multiphysics Object Oriented Simulation Environment (MOOSE) via the MetaPhysicL package. We describe the application of MOOSE’s AD capability to several sets of physics that were previously infeasible to model via hand-coded or Jacobian-free simulation techniques, including arbitrary Lagrangian-Eulerian and level-set simulations of laser melt pools, phase-field simulations with free energies provided through neural networks, and metallic nuclear fuel simulations that require inner Newton loop calculation of nonlinear material properties. Automatic differentiation ; Jacobian matrix and determinant ; Degrees of freedom (physics and chemistry) ; Computer science ; Nonlinear system ; Discretization ; Multiphysics ; Rotation formalisms in three dimensions ; Applied mathematics ; Backward differentiation formula ; Differential equation ; Finite element method ; Mathematics ; Algorithm ; Ordinary differential equation ; Differential algebraic equation ; Mathematical analysis ; Physics ; Computation Nuclear Materials and Properties ; Solidification and crystal growth phenomena ; High-pressure geophysics and materials
Authors: Luqing Li | Menghui Li | Qingqing Cui | Ying Liu | Yuyu Chen | Yujie Wang | Zhengzhu Zhang | Quansheng Chen | Jingming Ning
Source: Food Chemistry (article, 2021-12-29)
Field: Chemistry — Analytical Chemistry

Rapid monitoring of black tea fermentation quality based on a solution-phase sensor array combined with UV-visible spectroscopy Black tea ; Fermentation ; Support vector machine ; Sensor array ; Biological system ; Chemistry ; Computer science ; Analytical Chemistry (journal) ; Chromatography ; Artificial intelligence ; Machine learning ; Food science ; Biology Spectroscopy and Chemometric Analyses ; Tea Polyphenols and Effects ; Advanced Chemical Sensor Technologies
Authors: Kien Nguyen-Cong | Jonathan Willman | Stan Moore | A. B. Belonoshko | Rahulkumar Gayatri | Evan Weinberg | Mitchell Wood | Aidan P. Thompson | Ivan Oleynik
Source:  (article, 2021-10-21)
Field: Materials Science — Materials Chemistry

Billion atom molecular dynamics simulations of carbon at extreme conditions and experimental time and length scales Billion atom molecular dynamics (MD) using quantum-accurate machine-learning Spectral Neighbor Analysis Potential (SNAP) observed long-sought high pressure BC8 phase of carbon at extreme pressure (12 Mbar) and temperature (5,000 K). 24-hour, 4650 node production simulation on OLCF Summit demonstrated an unprecedented scaling and unmatched real-world performance of SNAP MD while sampling 1 nanosecond of physical time. Efficient implementation of SNAP force kernel in LAMMPS using the Kokkos CUDA backend on NVIDIA GPUs combined with excellent strong scaling (better than 97% parallel efficiency) enabled a peak computing rate of 50.0 PFLOPs (24.9% of theoretical peak) for a 20 billion atom MD simulation on the full Summit machine (27,900 GPUs). The peak MD performance of 6.21 Matom-steps/node-s is 22.9 times greater than a previous record for quantum-accurate MD. Near perfect weak scaling of SNAP MD highlights its excellent potential to advance the frontier of quantum-accurate MD to trillion atom simulations on upcoming exascale platforms. Molecular dynamics ; Scaling ; Atom (system on chip) ; Computer science ; Quantum ; Nanosecond ; Node (physics) ; Computational science ; Parallel computing ; Statistical physics ; Physics ; Chemistry ; Computational chemistry ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; High-pressure geophysics and materials ; Ion-surface interactions and analysis
Authors: Mohammad-Reza Mohammadi | Fahimeh Hadavimoghaddam | Saeid Atashrouz | Abdolhossein Hemmati‐Sarapardeh | Ali Abedi | Ahmad Mohaddespour
Source: International Journal of Hydrogen Energy (article, 2021-11-10)
Field: Chemistry — Analytical Chemistry

Application of robust machine learning methods to modeling hydrogen solubility in hydrocarbon fuels Hydrocarbon ; Chemistry ; Hydrocarbon mixtures ; Solubility ; Hydrogen ; Coal ; Fossil fuel ; Organic chemistry Petroleum Processing and Analysis ; Heat transfer and supercritical fluids ; Hydrocarbon exploration and reservoir analysis
Authors: Peiyuan Qu | Jacob W. Cleveland | Eman Ahmed | Fangbei Liu | Sage Dubrawski | Christopher W. Jones | Marcus Weck
Source: Chemical Society Reviews (review, 2021-12-09)
Field: Chemistry — Organic Chemistry

Compartmentalisation of molecular catalysts for nonorthogonal tandem catalysis The development of nonorthogonal tandem catalysis enables the use of a combination of arbitrary catalysts to rapidly synthesize complex products in a substainable, efficient, and timely manner. The key is to compartmentalise the molecular catalysts, thereby overcoming inherent incompatibilities between individual catalysts or reaction conditions. This tutorial review analyses the development of the past two decades in the field of nonorthogonal tandem catalysis with an emphasis on compartmentalisation strategies. We highlight design principles of functional materials for compartmentalisation and suggest future directions in the field of nonorthogonal tandem catalysis. Catalysis ; Tandem ; Chemistry ; Nanotechnology ; Combinatorial chemistry ; Materials science ; Organic chemistry Nanomaterials for catalytic reactions ; Surface Chemistry and Catalysis ; Mesoporous Materials and Catalysis
Authors: Sangyoon Park | Sarinthip Thanakkasaranee | Hojun Shin | Youngsoo Lee | Guman Tak | Jongchul Seo
Source: Polymers (article, 2021-02-27)
Field: Materials Science — Biomaterials

PET/Bio-Based Terpolyester Blends with High Dimensional Thermal Stability To improve the dimensional thermal stability of polyethylene terephthalate (PET), a poly(ethylene glycol 1,4-cyclohexane dimethylene (CHDM) isosorbide (ISB) terephthalate) (PEICT) known as ECOZEN<sup>®</sup>T110 (EZT) was introduced into PET using a melt blending technique. The miscibility, morphology, and thermal properties of the PET/EZT samples were investigated. The introduction of amorphous EZT into semi-crystalline PET increased the glass transition temperature (T<sub>g</sub>) but decreased the crystallinity, which could be related to the transesterification reaction. By adding EZT contents up to 20%, the PET/EZT samples showed a single T<sub>g</sub>, which indicated the miscibility between PET and EZT. However, two T<sub>g</sub> values were observed in the PET/EZT samples with higher EZT contents (30-70%), indicating partial miscibility. This may have been due to the slightly different rheological and thermodynamic parameters that were affected by a higher ratio of bulky (rigid ISB and ductile CHDM) groups in EZT. However, the heat distortion temperature of the PET/EZT samples remarkably increased, which indicated that the dimensional stability was truly enhanced. Although the crystallinity of the PET/EZT samples decreased with increasing EZT content, the tensile strength and Young's modulus decreased slightly. Based on these results, the as-prepared PET/EZT samples with high dimensional stability can be used as a high-temperature polymeric material in various applications. Materials science ; Crystallinity ; Miscibility ; Thermal stability ; Glass transition ; Polyethylene terephthalate ; Cyclohexane ; Chemical engineering ; Ultimate tensile strength ; Composite material ; Polymer ; Organic chemistry ; Chemistry biodegradable polymer synthesis and properties ; Polymer crystallization and properties ; Natural Fiber Reinforced Composites
Authors: Lin Yang | Madeleine P. Gordon | Akanksha K. Menon | Alexandra Bruefach | Kyle Haas | Mary Scott | Ravi Prasher | Jeffrey J. Urban
Source: Science Advances (article, 2021-05-14)
Field: Materials Science — Materials Chemistry

Decoupling electron and phonon transport in single-nanowire hybrid materials for high-performance thermoelectrics Organic-inorganic hybrids have recently emerged as a class of high-performing thermoelectric materials that are lightweight and mechanically flexible. However, the fundamental electrical and thermal transport in these materials has remained elusive due to the heterogeneity of bulk, polycrystalline, thin films reported thus far. Here, we systematically investigate a model hybrid comprising a single core/shell nanowire of Te-PEDOT:PSS. We show that as the nanowire diameter is reduced, the electrical conductivity increases and the thermal conductivity decreases, while the Seebeck coefficient remains nearly constant-this collectively results in a figure of merit, <i>ZT</i>, of 0.54 at 400 K. The origin of the decoupling of charge and heat transport lies in the fact that electrical transport occurs through the organic shell, while thermal transport is driven by the inorganic core. This study establishes design principles for high-performing thermoelectrics that leverage the unique interactions occurring at the interfaces of hybrid nanowires. Nanowire ; Materials science ; Thermoelectric materials ; Thermal conductivity ; Thermoelectric effect ; Seebeck coefficient ; Decoupling (probability) ; Figure of merit ; Phonon ; Nanotechnology ; Condensed matter physics ; Electrical resistivity and conductivity ; Optoelectronics ; Composite material ; Physics ; Thermodynamics Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Thermal Radiation and Cooling Technologies