jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ryo Kobayashi
Source: The Journal of Open Source Software (article, 2021-01-20)
Field: Materials Science — Materials Chemistry

nap: A molecular dynamics package with parameter-optimization programs for classical and machine-learning potentials Kobayashi, R., (2021). nap: A molecular dynamics package with parameter-optimization programs for classical and machine-learning potentials. Journal of Open Source Software, 6(57), 2768, https://doi.org/10.21105/joss.02768 Nap ; Software package ; Computer science ; Software ; Dynamics (music) ; Molecular dynamics ; Artificial intelligence ; Open source software ; Statistical physics ; Machine learning ; Physics ; Psychology ; Programming language ; Chemistry ; Computational chemistry ; Neuroscience Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Meng Zhan | Junsheng Liu | Deli Wang | Xiuyun Chen | Lizhen Zhang | Sheliang Wang
Source: Materials (article, 2021-11-02)
Field: Materials Science — Materials Chemistry

Optimized Neural Network Prediction Model of Shape Memory Alloy and Its Application for Structural Vibration Control The traditional mathematical model of shape memory alloy (SMA) is complicated and difficult to program in numerical analysis. The artificial neural network is a nonlinear modeling method which does not depend on the mathematical model and avoids the inevitable error in the traditional modeling method. In this paper, an optimized neural network prediction model of shape memory alloy and its application for structural vibration control are discussed. The superelastic properties of austenitic SMA wires were tested by experiments. The material property test data were taken as the training samples of the BP neural network, and a prediction model optimized by the genetic algorithm was established. By using the improved genetic algorithm, the position and quantity of the SMA wires were optimized in a three-storey spatial structure, and the dynamic response analysis of the optimal arrangement was carried out. The results show that, compared with the unoptimized neural network prediction model of SMA, the optimized prediction model is in better agreement with the test curve and has higher stability, it can well reflect the effect of loading rate on the superelastic properties of SMA, and is a high precision rate-dependent dynamic prediction model. Moreover, the BP network constitutive model is simple to use and convenient for dynamic simulation analysis of an SMA passive control structure. The controlled structure with optimized SMA wires can inhibit the structural seismic responses more effectively. However, it is not the case that the more SMA wires, the better the shock absorption effect. When SMA wires exceed a certain number, the vibration reduction effect gradually decreases. Therefore, the seismic effect can be reduced economically and effectively only when the number and location of SMA wires are properly configured. When four SMA wires are arranged, the acceptable shock absorption effect is obtained, and the sum of the structural storey drift can be reduced by 44.51%. SMA* ; Shape-memory alloy ; Artificial neural network ; Vibration ; Nonlinear system ; Genetic algorithm ; Computer science ; Test data ; Smart material ; Position (finance) ; Structural engineering ; Control theory (sociology) ; Materials science ; Biological system ; Algorithm ; Artificial intelligence ; Engineering ; Acoustics ; Machine learning ; Control (management) ; Composite material Shape Memory Alloy Transformations
Authors: Rajeev Kumar | Zening Liu | Bradley S. Lokitz | Jihua Chen | Jan‐Michael Y. Carrillo | Jacek Jakowski | C. Patrick Collier | Scott T. Retterer | Rigoberto C. Advíncula
Source: MRS Communications (article, 2021-07-12)
Field: Materials Science — Biomaterials

Harnessing autocatalytic reactions in polymerization and depolymerization Abstract Autocatalysis and its relevance to various polymeric systems are discussed by taking inspiration from biology. A number of research directions related to synthesis, characterization, and multi-scale modeling are discussed in order to harness autocatalytic reactions in a useful manner for different applications ranging from chemical upcycling of polymers (depolymerization and reconstruction after depolymerization), self-generating micelles and vesicles, and polymer membranes. Overall, a concerted effort involving in situ experiments, multi-scale modeling, and machine learning algorithms is proposed to understand the mechanisms of physical and chemical autocatalysis. It is argued that a control of the autocatalytic behavior in polymeric systems can revolutionize areas such as kinetic control of the self-assembly of polymeric materials, synthesis of self-healing and self-immolative polymers, as next generation of materials for a sustainable circular economy. Graphic Abstract Autocatalysis ; Depolymerization ; Materials science ; Polymer ; Nanotechnology ; Characterization (materials science) ; Polymerization ; Micelle ; Nanoreactor ; Polymer science ; Polymer chemistry ; Catalysis ; Organic chemistry ; Chemistry ; Nanoparticle ; Composite material Supramolecular Self-Assembly in Materials ; Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization
Authors: Jong‐Kwon Ha | Kicheol Kim | Seung Kyu Min
Source: Journal of Chemical Theory and Computation (article, 2021-01-20)
Field: Materials Science — Materials Chemistry

Machine Learning-Assisted Excited State Molecular Dynamics with the State-Interaction State-Averaged Spin-Restricted Ensemble-Referenced Kohn–Sham Approach We present a machine learning-assisted excited state molecular dynamics (ML-ESMD) based on the ensemble density functional theory framework. Since we represent a diabatic Hamiltonian in terms of generalized valence bond ansatz within the state-interaction state-averaged spin-restricted ensemble-referenced Kohn-Sham (SI-SA-REKS) method, we can avoid singularities near conical intersections, which are crucial in excited state molecular dynamics simulations. We train the diabatic Hamiltonian elements and their analytical gradients with the SchNet architecture to construct machine learning models, while the phase freedom of off-diagonal elements of the Hamiltonian is cured by introducing the phase-less loss function. Our machine learning models show reasonable accuracy with mean absolute errors of ∼0.1 kcal/mol and ∼0.5 kcal/mol/Å for the diabatic Hamiltonian elements and their gradients, respectively, for penta-2,4-dieniminium cation. Moreover, by exploiting the diabatic representation, our models can predict correct conical intersection structures and their topologies. In addition, our ML-ESMD simulations give almost identical result with a direct dynamics at the same level of theory. Diabatic ; Conical intersection ; Ansatz ; Kohn–Sham equations ; Hamiltonian (control theory) ; Excited state ; Molecular dynamics ; Density functional theory ; Surface hopping ; Hamiltonian mechanics ; Statistical physics ; Physics ; Quantum mechanics ; Phase space ; Mathematics Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Advanced Chemical Physics Studies
Authors: Thomas Martynec | Christos Karapanagiotis | Sabine H. L. Klapp | Stefan Kowarik
Source: Communications Materials (article, 2021-09-02)
Field: Materials Science — Materials Chemistry

Machine learning predictions of surface migration barriers in nucleation and non-equilibrium growth Abstract Machine learning is playing an increasing role in the discovery of new materials and may also facilitate the search for optimum growth conditions for crystals and thin films. Here, we perform kinetic Monte-Carlo simulations of sub-monolayer growth. We consider a generic homoepitaxial growth scenario that covers a wide range of conditions with different diffusion barriers (0.4–0.55 eV) and lateral binding energies (0.1–0.4 eV). These simulations are used as a training data set for a convolutional neural network that can predict diffusion barriers and binding energies. Specifically, a single Monte-Carlo image of the morphology is sufficient to determine the energy barriers with an accuracy of approximately 10 meV and the neural network is tolerant to images with noise and lower than atomic-scale resolution. We believe this new machine learning method will be useful for fundamental studies of growth kinetics and growth optimization through better knowledge of microscopic parameters. Nucleation ; Kinetic Monte Carlo ; Monte Carlo method ; Convolutional neural network ; Diffusion ; Artificial neural network ; Materials science ; Range (aeronautics) ; Statistical physics ; Noise (video) ; Computer science ; Monolayer ; Binding energy ; Artificial intelligence ; Machine learning ; Chemical physics ; Algorithm ; Nanotechnology ; Image (mathematics) ; Chemistry ; Physics ; Thermodynamics ; Mathematics ; Atomic physics Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Electron and X-Ray Spectroscopy Techniques
Authors: Mingyuan Xu | Tong Zhu | John Z. H. Zhang
Source: Journal of Chemical Information and Modeling (article, 2021-11-09)
Field: Materials Science — Materials Chemistry

Automated Construction of Neural Network Potential Energy Surface: The Enhanced Self-Organizing Incremental Neural Network Deep Potential Method In recent years, the use of deep learning (neural network) potential energy surface (NNPES) in molecular dynamics simulation has experienced explosive growth as it can be as accurate as quantum chemistry methods while being as efficient as classical mechanic methods. However, the development of NNPES is highly nontrivial. In particular, it has been troubling to construct a dataset that is as small as possible yet can cover the target chemical space. In this work, an ESOINN-DP method is developed, which has the enhanced self-organizing incremental neural network (ESOINN) and a newly proposed error indicator at its core. With ESOINN-DP, one can construct the NNPES with little human intervention, and this method ensures that the constructed reference dataset covers the target chemical space with minimum redundancy. The performance of the ESOINN-DP method has been well validated by developing neural network potential energy surfaces for water clusters, tripeptides, and by de-redundancy of a sub-dataset of the ANI-1 database. We believe that the ESOINN-DP method provides a novel idea for the construction of NNPES and, especially, the reference datasets, and it can be used for molecular dynamics (MD) simulations of various gas-phase and condensed-phase chemical systems. Artificial neural network ; Computer science ; Redundancy (engineering) ; Artificial intelligence ; Chemical space ; Construct (python library) ; Potential energy surface ; Deep learning ; Machine learning ; Data mining ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Qingfeng Zeng | Yong Gao | Kang Guan | Jiantao Liu | Zhiqiang Feng
Source: Journal of Advanced Ceramics (article, 2021-04-26)
Field: Materials Science — Materials Chemistry

Machine learning and a computational fluid dynamic approach to estimate phase composition of chemical vapor deposition boron carbide Abstract Chemical vapor deposition is an important method for the preparation of boron carbide. Knowledge of the correlation between the phase composition of the deposit and the deposition conditions (temperature, inlet gas composition, total pressure, reactor configuration, and total flow rate) has not been completely determined. In this work, a novel approach to identify the kinetic mechanisms for the deposit composition is presented. Machine leaning (ML) and computational fluid dynamic (CFD) techniques are utilized to identify core factors that influence the deposit composition. It has been shown that ML, combined with CFD, can reduce the prediction error from about 25% to 7%, compared with the ML approach alone. The sensitivity coefficient study shows that BHCl 2 and BCl 3 produce the most boron atoms, while C 2 H 4 and CH 4 are the main sources of carbon atoms. The new approach can accurately predict the deposited boron–carbon ratio and provide a new design solution for other multi-element systems. Boron ; Boron carbide ; Chemical vapor deposition ; Deposition (geology) ; Carbon fibers ; Materials science ; Computational fluid dynamics ; Gas composition ; Carbide ; Chemical composition ; Phase (matter) ; Work (physics) ; Composition (language) ; Analytical Chemistry (journal) ; Thermodynamics ; Metallurgy ; Chemistry ; Mechanical engineering ; Nanotechnology ; Composite material ; Environmental chemistry ; Composite number ; Organic chemistry ; Engineering Thermal properties of materials ; Semiconductor materials and devices ; Advancements in Semiconductor Devices and Circuit Design
Authors: Sergey Magedov | Christopher Koh | Walter Malone | Nicholas Lubbers | Benjamin Nebgen
Source: Journal of Applied Physics (article, 2021-02-08)
Field: Materials Science — Materials Chemistry

Bond order predictions using deep neural networks Machine learning is an extremely powerful tool for the modern theoretical chemist since it provides a method for bypassing costly algorithms for solving the Schrödinger equation. Already, it has proven able to infer molecular and atomic properties such as charges, enthalpies, dipoles, excited state energies, and others. Most of these machine learning algorithms proceed by inferring properties of individual atoms, even breaking down total molecular energy into individual atomic contributions. In this paper, we introduce a modified version of the Hierarchically Interacting Particle Neural Network (HIP-NN) capable of making predictions on the bonds between atoms rather than on the atoms themselves. We train the modified HIP-NN to infer bond orders for a large number of small organic molecules as computed via the Natural Bond Orbital package. We demonstrate that the trained model is extensible to molecules much larger than those in the training set by studying its performance on the COMP6 dataset. This method has applications in cheminformatics and force field parameterization and opens a promising future for machine learning models to predict other quantities that are defined between atoms such as density matrix elements, Hamiltonian parameters, and molecular reactivities. Artificial neural network ; Hamiltonian (control theory) ; Bond order ; Computer science ; Dipole ; Statistical physics ; Excited state ; Artificial intelligence ; Machine learning ; Molecule ; Algorithm ; Bond length ; Physics ; Quantum mechanics ; Mathematics ; Mathematical optimization Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Yong Yi | Liming Wang | Fanghui Yin
Source: Applied Physics Letters (article, 2021-05-31)
Field: Materials Science — Materials Chemistry

Interval support vector regression enables high-throughput machine learning predictions for dielectric constant of polymer dielectrics Accurate and rapid prediction of dielectric constant (ε) for polymer-based dielectrics at various frequencies remains challenging. We construct a dataset of dielectrics with an easily attainable numerical representation scheme. We propose an interval support vector regression with a particle swarm optimization to accelerate the ε prediction, discovery, and design of polymer dielectrics at various frequencies (spanning from 100 Hz to 1015 Hz). The key features affecting dielectric constant could be identified, and this is highly valuable to target the discovering of polymer dielectrics as high-throughput screening and tailor the desirable property. Dielectric ; Particle swarm optimization ; Throughput ; Computer science ; Interval (graph theory) ; Materials science ; Support vector machine ; High-κ dielectric ; Constant (computer programming) ; Artificial intelligence ; Biological system ; Electronic engineering ; Machine learning ; Mathematics ; Optoelectronics ; Engineering Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Neural Networks and Applications
Authors: Bowen Zheng | Grace X. Gu
Source: ACS Applied Nano Materials (article, 2021-02-23)
Field: Materials Science — Materials Chemistry

Prediction of Graphene Oxide Functionalization Using Gradient Boosting: Implications for Material Chemical Composition Identification Graphene oxides have exhibited alluring potential for state-of-the-art applications such as biomedical devices and functional nanocomposites. The types and concentrations of oxygen-containing functional groups are fingerprints of graphene oxides, dictating the properties and usage of the nanomaterial. Compared to pure graphene, the properties of graphene oxides are more challenging to model from a theoretical perspective, mainly because of the profound but implicit influences of the functional groups within. Machine learning is a potent method to uncover the hidden structure–property relations and to accelerate material discovery. Here, we develop a machine learning-based strategy to determine the functionalization properties of monolayer graphene oxides, labeled by the oxygen-to-carbon ratio and relative concentrations of functional groups. Trained by mechanical responses upon uniaxial tension computed by reactive molecular dynamics simulations, our proposed gradient boosting machine learning model can accurately identify the chemical composition of graphene oxides in the reserved data set. The difference in prediction accuracies between oxygen coverage and functional group composition is rationalized by graphene oxide molecular mechanisms. The proposed data-driven strategy can contribute to the predictive modeling of functionalized two-dimensional materials of a broad variety. Graphene ; Oxide ; Materials science ; Nanomaterials ; Surface modification ; Nanotechnology ; Density functional theory ; Nanocomposite ; Computer science ; Machine learning ; Chemistry ; Computational chemistry Graphene research and applications ; Machine Learning in Materials Science ; Graphene and Nanomaterials Applications
Authors: Timur Gimadiev | Ramil Nugmanov | Dinar Batyrshin | Timur Madzhidov | Satoshi Maeda | Pavel Sidorov | Alexandre Varnek
Source: Journal of Chemical Information and Modeling (article, 2021-01-27)
Field: Materials Science — Materials Chemistry

Combined Graph/Relational Database Management System for Calculated Chemical Reaction Pathway Data Presently, quantum chemical calculations are widely used to generate extensive data sets for machine learning applications; however, generally, these sets only include information on equilibrium structures and some close conformers. Exploration of potential energy surfaces provides important information on ground and transition states, but analysis of such data is complicated due to the number of possible reaction pathways. Here, we present RePathDB, a database system for managing 3D structural data for both ground and transition states resulting from quantum chemical calculations. Our tool allows one to store, assemble, and analyze reaction pathway data. It combines relational database CGR DB for handling compounds and reactions as molecular graphs with a graph database architecture for pathway analysis by graph algorithms. Original condensed graph of reaction technology is used to store any chemical reaction as a single graph. Computer science ; Graph database ; Graph ; Quantum chemical ; Transition state ; Relational database ; Theoretical computer science ; Chemical database ; Data mining ; Chemistry ; Database ; Molecule ; Catalysis ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Yifu Zhu | Ting Xue | Yingying Sheng | Jingkun Xu | Xiaoyu Zhu | Weiqiang Li | Xinyu Lu | Liangmei Rao | Yangping Wen
Source: Microchemical Journal (article, 2021-07-29)
Field: Materials Science — Polymers and Plastics

Ionic liquid-assisted ultrasonic exfoliation of phosphorene nanocomposite with single walled carbon nanohorn as nanozyme sensor for derivative voltammetric smart analysis of 5-hydroxytryptamine Exfoliation joint ; Ionic liquid ; Materials science ; Phosphorene ; Derivative (finance) ; Ultrasonic sensor ; Electrochemistry ; Chemical engineering ; Nanocomposite ; Graphene ; Phase (matter) ; Electrode ; Nanotechnology ; Chemistry ; Organic chemistry ; Catalysis ; Physical chemistry Conducting polymers and applications ; 2D Materials and Applications ; Electrochemical sensors and biosensors
Authors: Matthew J. Gaunt | Jacob M. Janey | Danielle M. Schultz | Tim Cernak
Source: Chem (article, 2021-09-01)
Field: Materials Science — Materials Chemistry

Myths of high-throughput experimentation and automation in chemistry Throughput ; Automation ; Laboratory automation ; Mythology ; Engineering ; Biochemical engineering ; Nanotechnology ; Chemistry ; Computer science ; Data science ; Materials science ; Telecommunications ; Mechanical engineering ; History ; Classics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Scientific Computing and Data Management
Authors: Dündar E. Yılmaz | W. Hunter Woodward | Adri C. T. van Duin
Source: Journal of Chemical Theory and Computation (article, 2021-10-13)
Field: Materials Science — Materials Chemistry

Machine Learning-Assisted Hybrid ReaxFF Simulations We have developed a machine learning (ML)-assisted Hybrid ReaxFF simulation method ("Hybrid/Reax"), which alternates reactive and non-reactive molecular dynamics simulations with the assistance of ML models to simulate phenomena that require longer time scales and/or larger systems than are typically accessible to ReaxFF. Hybrid/Reax uses a specialized tracking tool during the reactive simulations to further accelerate chemical reactions. Non-reactive simulations are used to equilibrate the system after the reactive simulation stage. ML models are used between reactive and non-reactive stages to predict non-reactive force field parameters of the system based on the updated bond topology. Hybrid/Reax simulation cycles can be continued until the desired chemical reactions are observed. As a case study, this method was used to study the cross-linking of a polyethylene (PE) matrix analogue (decane) with the cross-linking agent dicumyl peroxide (DCP). We were able to run relatively long simulations [>20 million molecular dynamics (MD) steps] on a small test system (4660 atoms) to simulate cross-linking reactions of PE in the presence of DCP. Starting with 80 PE molecules, more than half of them cross-linked by the end of the Hybrid/Reax cycles on a single Xeon processor in under 48 h. This simulation would take approximately 1 month if run with pure ReaxFF MD on the same machine. ReaxFF ; Molecular dynamics ; Force field (fiction) ; Computer science ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Protein Structure and Dynamics