jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zakiya Shireen | Hansani Weeratunge | Adrian Menzel | Andrew W. Phillips | Ronald G. Larson | Kate Smith‐Miles | Elnaz Hajizadeh
Source: arXiv (Cornell University) (preprint, 2022-04-28)
Field: Materials Science — Materials Chemistry

A novel machine learning enabled hybrid optimization framework for efficient and transferable coarse-graining of a model polymer This work presents a novel framework governing the development of an efficient, accurate, and transferable coarse-grained (CG) model of a polyether material. The proposed framework combines the two fundamentally different classical optimization approaches for the development of coarse-grained model parameters; namely bottom-up and top-down approaches. This is achieved through integrating the optimization algorithms into a machine learning (ML) model, trained using molecular dynamics (MD) simulation data. In the bottom-up approach, bonded interactions of the CG model are optimized using deep neural networks (DNN), where atomistic bonded distributions are matched. The atomistic distributions emulate the local chain structure. In the top-down approach, optimization of nonbonded potentials is accomplished by reproducing the temperature-dependent experimental density. We demonstrate that CG model parameters achieved through our machine-learning enabled hybrid optimization framework fulfills the thermodynamic consistency and transferability issues associated with the classical approaches to coarse-graining model polymers. We demonstrate the efficiency, accuracy, and transferability of the developed CG model, using our novel framework through accurate predictions of chain size as well as chain dynamics, including the limiting behavior of the glass transition temperature, diffusion, and stress relaxation spectrum, where none were included in the potential parameterization process. The accuracy of the predicted properties are evaluated in the context of molecular theories and available experimental data. Granularity ; Computer science ; Context (archaeology) ; Consistency (knowledge bases) ; Relaxation (psychology) ; Molecular dynamics ; Work (physics) ; Artificial intelligence ; Statistical physics ; Algorithm ; Machine learning ; Chemistry ; Physics ; Thermodynamics ; Computational chemistry Block Copolymer Self-Assembly ; Polymer crystallization and properties ; Advanced Polymer Synthesis and Characterization
Authors: Zineb Belkacemi
Source: theses.fr (ABES) (preprint, 2022-07-06)
Field: Materials Science — Materials Chemistry

Machine Learning Methods for Molecular Dynamic Simulations With the continually improving computational capacity of computers, machine learning methods have provided novel solutions to problems in a variety of fields. In particular, machine learning has been extensively used in the last decade in the field of computational biochemistry and drug discovery in virtually all stages, such as defining new molecules, determining important sites in targeted proteins, designing adequate forcefields based on experimental results, or improving the efficiency of sampling molecular conformations of a given system. This thesis focuses on the latter task of using machine learning methods for enhanced sampling in molecular dynamics. Molecular Dynamics (MD) simulations have proven to be a very useful complementary tool to experiments. Despite their wide use to capture fast occurring phenomena, there are still many cases where the time scales accessible to MD simulations are far smaller than the time scales needed for the observation of important conformational changes of the system, due to the presence of high energy barriers. Free energy biasing methods have proven to be powerful tools to to accelerate the observation of such changes by modifying the sampling measure. However, most of these methods rely on the prior knowledge of low-dimensional slow degrees of freedom, i.e. collective variables. Alternatively, such low dimensional mappings can be identified using machine learning and dimensionality reduction algorithms. In addition to being used to accelerate sampling, the learned collective variables can also help acquire valuable insight into the studied system, namely by facilitating the visualization of the different states of the system, as well as its free energy landscape. In this work, important notions and definitions of molecular dynamics are first presented before reviewing state of the art machine learning algorithms which were devised or applied in the recent years for automatic collective variable discovery and enhanced sampling. Then, the method developed during this thesis, coined "free energy biasing and machine learning with autoencoders" (FEBILAE), is introduced. This method uses an iterative scheme to alternately generate new simulations and learn collective variables from these simulations using autoencoders. Finally, we present the application of machine learning methods to a real system of interest. Here, autoencoders are used to learn collective variables to perform biased simulations of the heat shock 90 (HSP90) chaperone protein. Computer science ; Artificial intelligence ; Machine learning Machine Learning in Materials Science ; Various Chemistry Research Topics ; Computational Drug Discovery Methods
Authors: Shehrin Sayed | Hannah C. Kleidermacher | Giulianna Hashemi-Asasi | Cheng‐Hsiang Hsu | Sayeef Salahuddin
Source: Research Square (preprint, 2022-06-22)
Field: Materials Science — Materials Chemistry

Unsupervised machine learning-driven search for efficient spin-orbit torque materials Torque ; Spintronics ; Orbit (dynamics) ; Spin (aerodynamics) ; Computer science ; Physics ; Condensed matter physics ; Artificial intelligence ; Quantum mechanics ; Engineering ; Aerospace engineering ; Thermodynamics Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices ; Magnetic properties of thin films
Authors: Entao Yang | James Pressly | Bharath Natarajan | Robert Colby | Karen I. Winey | Robert A. Riggleman
Source: arXiv (Cornell University) (preprint, 2022-04-25)
Field: Materials Science — Materials Chemistry

Understanding Creep Suppression Mechanism in Polymer Nanocomposites through Machine Learning While recent efforts have shown how local structure plays an essential role in the dynamic heterogeneity of homogeneous glass-forming materials, systems containing interfaces such as thin films or composite materials remain poorly understood. It is known that interfaces perturb the molecular packing nearby, however, numerous studies show the dynamics are modified over a much larger range. Here, we examine the dynamics in polymer nanocomposites (PNCs) using a combination of simulations and experiments and quantitatively separate the role of polymer packing from other effects on the dynamics, as a function of distance from the nanoparticle surfaces. After showing good qualitative agreement between the simulations and experiments in glassy structure and creep compliance, we use a recently developed machine learning technique to decompose polymer dynamics in our simulated PNCs into structure-dependent and structure-independent processes. With this decomposition, the free energy barrier for polymer rearrangement can be described as a combination of packing-dependent and packing-independent barriers. We find both barriers are higher near nanoparticles and decrease with applied stress, quantitatively demonstrating that the slow interfacial dynamics is not solely due to polymer packing differences, but also the change of structure-dynamics relationships. Finally, we present how this decomposition can be used to accurately predict strain-time creep curves for PNCs from their static configuration, providing additional insights into the effects of polymer-nanoparticle interfaces on creep suppression in PNCs. Materials science ; Creep ; Polymer ; Molecular dynamics ; Chemical physics ; Nanocomposite ; Polymer nanocomposite ; Nanoparticle ; Dynamics (music) ; Decomposition ; Deformation mechanism ; Deformation (meteorology) ; Nanotechnology ; Composite material ; Computational chemistry ; Microstructure ; Chemistry ; Physics Material Dynamics and Properties ; Polymer Nanocomposites and Properties ; Polymer crystallization and properties
Authors: Artem Mishchenko | Anupam Bhattacharya | Ivan Timokhin | Ratnamala Chatterjee | Qian Yang
Source: Research Square (preprint, 2022-12-16)
Field: Materials Science — Materials Chemistry

Deep learning approach to genome of two-dimensional materials with flat electronic bands Abstract Many-body electron-electron correlations play central role in condensed matter physics; they govern a wide range of phenomena, stretching from superconductivity to magnetism, and are behind numerous technological applications. Two-dimensional (2D) materials with flat electronic bands provide a natural playground to explore this rich interaction-driven physics, thanks to their highly localized electrons. The search for novel 2D flat band materials has since attracted intensive efforts, especially now with the development of open science databases encompassing thousands of materials with computed electronic bands. In this work, we automate the otherwise daunting task of materials search and classification by combining supervised and unsupervised machine learning algorithms. To this end, a convolutional neural network was employed to identify 2D flat band materials, which were then subjected to symmetry-based analysis using a bilayer unsupervised learning algorithm. Such hybrid approach of exploring materials databases allowed us to reveal completely new material classes outside the known flat band paradigms, with high efficiency and accuracy. Our results present a genome of 2D materials hosting flat electronic bands that can be further explored to enrich the physics of electron-electron interactions. Convolutional neural network ; Computer science ; Deep learning ; Electron ; Physics ; Artificial intelligence ; Nanotechnology ; Materials science ; Quantum mechanics Machine Learning in Materials Science ; Expert finding and Q&A systems ; Electronic and Structural Properties of Oxides
Authors: Yaolong Zhang | Junfan Xia | Bin Jiang
Source: The Journal of Chemical Physics (preprint, 2022-02-23)
Field: Materials Science — Materials Chemistry

REANN: A PyTorch-based end-to-end multi-functional deep neural network package for molecular, reactive, and periodic systems In this work, we present a general purpose deep neural network package for representing energies, forces, dipole moments, and polarizabilities of atomistic systems. This so-called recursively embedded atom neural network model takes advantages of both the physically inspired atomic descriptor based neural networks and the message-passing based neural networks. Implemented in the PyTorch framework, the training process is parallelized on both the central processing unit and the graphics processing unit with high efficiency and low memory in which all hyperparameters can be optimized automatically. We demonstrate the state-of-the-art accuracy, high efficiency, scalability, and universality of this package by learning not only energies (with or without forces) but also dipole moment vectors and polarizability tensors in various molecular, reactive, and periodic systems. An interface between a trained model and LAMMPs is provided for large scale molecular dynamics simulations. We hope that this open-source toolbox will allow for future method development and applications of machine learned potential energy surfaces and quantum-chemical properties of molecules, reactions, and materials. Computer science ; Artificial neural network ; Scalability ; Deep learning ; Computational science ; Artificial intelligence ; Dipole ; Parallel computing ; Physics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Topic Modeling
Authors: Xianglin Liu | Jiaxin Zhang | Zongrui Pei
Source: arXiv (Cornell University) (preprint, 2022-09-07)
Field: Materials Science — Materials Chemistry

Machine Learning for High-entropy Alloys: Progress, Challenges and Opportunities High-entropy alloys (HEAs) have attracted extensive interest due to their exceptional mechanical properties and the vast compositional space for new HEAs. However, understanding their novel physical mechanisms and then using these mechanisms to design new HEAs are confronted with their high-dimensional chemical complexity, which presents unique challenges to (i) the theoretical modeling that needs accurate atomic interactions for atomistic simulations and (ii) constructing reliable macro-scale models for high-throughput screening of vast amounts of candidate alloys. Machine learning (ML) sheds light on these problems with its capability to represent extremely complex relations. This review highlights the success and promising future of utilizing ML to overcome these challenges. We first introduce the basics of ML algorithms and application scenarios. We then summarize the state-of-the-art ML models describing atomic interactions and atomistic simulations of thermodynamic and mechanical properties. Special attention is paid to phase predictions, planar-defect calculations, and plastic deformation simulations. Next, we review ML models for macro-scale properties, such as lattice structures, phase formations, and mechanical properties. Examples of machine-learned phase-formation rules and order parameters are used to illustrate the workflow. Finally, we discuss the remaining challenges and present an outlook of research directions, including uncertainty quantification and ML-guided inverse materials design. High entropy alloys ; Workflow ; Computer science ; Uncertainty quantification ; Macro ; Nanotechnology ; Artificial intelligence ; Industrial engineering ; Machine learning ; Phase (matter) ; Materials science ; Engineering ; Physics Machine Learning in Materials Science ; High Entropy Alloys Studies ; Advanced Materials Characterization Techniques
Authors: Suresh Bishnoi | Skyler Badge | Jayadeva | N. M. Anoop Krishnan
Source: arXiv (Cornell University) (preprint, 2022-10-19)
Field: Chemistry — Inorganic Chemistry

Predicting Oxide Glass Properties with Low Complexity Neural Network and Physical and Chemical Descriptors Due to their disordered structure, glasses present a unique challenge in predicting the composition-property relationships. Recently, several attempts have been made to predict the glass properties using machine learning techniques. However, these techniques have the limitations, namely, (i) predictions are limited to the components that are present in the original dataset, and (ii) predictions towards the extreme values of the properties, important regions for new materials discovery, are not very reliable due to the sparse datapoints in this region. To address these challenges, here we present a low complexity neural network (LCNN) that provides improved performance in predicting the properties of oxide glasses. In addition, we combine the LCNN with physical and chemical descriptors that allow the development of universal models that can provide predictions for components beyond the training set. By training on a large dataset (~50000) of glass components, we show the LCNN outperforms state-of-the-art algorithms such as XGBoost. In addition, we interpret the LCNN models using Shapely additive explanations to gain insights into the role played by the descriptors in governing the property. Finally, we demonstrate the universality of the LCNN models by predicting the properties for glasses with new components that were not present in the original training set. Altogether, the present approach provides a promising direction towards accelerated discovery of novel glass compositions. Universality (dynamical systems) ; Computer science ; Artificial neural network ; Property (philosophy) ; Artificial intelligence ; Training set ; Machine learning ; Set (abstract data type) ; Physics Pigment Synthesis and Properties
Authors: Pedro A. Santos-Florez | Shi-Cheng Dai | Yi Yao | Howard Yanxon | Lin Li | Yun-Jiang Wang | Qiang Zhu | Xiao-Xiang Yu
Source: arXiv (Cornell University) (preprint, 2022-07-19)
Field: Materials Science — Materials Chemistry

Short-range order and its impacts on the BCC NbMoTaW multi-principal element alloy by the machine-learning potential We employ a machine-learning force field, trained by a neural network (NN) with bispectrum coefficients as descriptors, to investigate the short-range order (SRO) influences on the BCC NbMoTaW alloy strengthening mechanism. The NN interatomic potential provides a transferable force field with density functional theory accuracy. This novel NN potential is applied to elucidate the SRO effects on the elasticity, vibrational modes, plasticity, and strength in the NbMoTaW multi-principal element alloy (MPEA). The results show the strong attraction among Mo-Ta pairs forming the local ordered B2 structures, which could be tuned via temperature and improved by Nb content. SRO increases the elastic constants and high-frequency phonon modes as well as introduces extra lattice friction of dislocation motion. This approach enables a rapid compositional screening, paves the way for computation-guided materials design of new MPEAs with better performance, and opens avenues for tuning the mechanical properties by processing optimization. Phonon ; Alloy ; Materials science ; Artificial neural network ; Statistical physics ; Dislocation ; Lattice (music) ; Computation ; Condensed matter physics ; Artificial intelligence ; Computer science ; Algorithm ; Physics ; Composite material ; Acoustics Machine Learning in Materials Science ; Metal and Thin Film Mechanics ; Advanced Materials Characterization Techniques
Authors: Shomik Verma | Shivam N. Kajale | Rafael Gómez‐Bombarelli
Source:  (article, 2022-09-19)
Field: Materials Science — Materials Chemistry

Machine learning for accurate and fast bandgap prediction of solid-state materials Semi-Iocal DFT tends to vastly underestimate the bandgap of materials. Here we propose a machine learning calibration workflow to improve the accuracy of cheap DFT calculations. We first compile a dataset of 25k materials with PBE and HSE calculations completed. Using this dataset, we benchmark various machine learning architectures and features to determine which results in the highest accuracy. The best technique is able to improve the accuracy of PBE 10-fold. We then expand the generalizability of the model by utilizing active learning to intelligently sample chemical space. Because HSE data is not available for these new materials, we develop an optimized high-throughput parallelized workflow to calculate HSE bandgaps of lOk additional materials. We therefore develop a cheap, accurate, and generalized ML model for bandgap prediction. Benchmark (surveying) ; Generalizability theory ; Workflow ; Computer science ; Machine learning ; Artificial intelligence ; Calibration ; Throughput ; Band gap ; Algorithm ; Materials science ; Optoelectronics ; Mathematics Machine Learning in Materials Science ; Topic Modeling
Authors: Lixue Cheng | Jiace Sun | Thomas F. Miller
Source: arXiv (Cornell University) (preprint, 2022-04-21)
Field: Materials Science — Materials Chemistry

Accurate Molecular-Orbital-Based Machine Learning Energies via Unsupervised Clustering of Chemical Space We introduce an unsupervised clustering algorithm to improve training efficiency and accuracy in predicting energies using molecular-orbital-based machine learning (MOB-ML). This work determines clusters via the Gaussian mixture model (GMM) in an entirely automatic manner and simplifies an earlier supervised clustering approach [J. Chem. Theory Comput., 15, 6668 (2019)] by eliminating both the necessity for user-specified parameters and the training of an additional classifier. Unsupervised clustering results from GMM have the advantage of accurately reproducing chemically intuitive groupings of frontier molecular orbitals and having improved performance with an increasing number of training examples. The resulting clusters from supervised or unsupervised clustering is further combined with scalable Gaussian process regression (GPR) or linear regression (LR) to learn molecular energies accurately by generating a local regression model in each cluster. Among all four combinations of regressors and clustering methods, GMM combined with scalable exact Gaussian process regression (GMM/GPR) is the most efficient training protocol for MOB-ML. The numerical tests of molecular energy learning on thermalized datasets of drug-like molecules demonstrate the improved accuracy, transferability, and learning efficiency of GMM/GPR over not only other training protocols for MOB-ML, i.e., supervised regression-clustering combined with GPR(RC/GPR) and GPR without clustering. GMM/GPR also provide the best molecular energy predictions compared with the ones from literature on the same benchmark datasets. With a lower scaling, GMM/GPR has a 10.4-fold speedup in wall-clock training time compared with scalable exact GPR with a training size of 6500 QM7b-T molecules. Cluster analysis ; Artificial intelligence ; Computer science ; Pattern recognition (psychology) ; Kriging ; Ground-penetrating radar ; Gaussian process ; Unsupervised learning ; Machine learning ; Mixture model ; Scalability ; Gaussian ; Chemistry ; Computational chemistry ; Radar Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Lingli Kong | Zhengran Ji | Huolin L. Xin
Source: arXiv (Cornell University) (preprint, 2022-09-26)
Field: Materials Science — Materials Chemistry

Electron energy loss spectroscopy database synthesis and automation of core-loss edge recognition by deep-learning neural networks The ionization edges encoded in the electron energy loss spectroscopy (EELS) spectra enable advanced material analysis including composition analyses and elemental quantifications. The development of the parallel EELS instrument and fast, sensitive detectors have greatly improved the acquisition speed of EELS spectra. However, the traditional way of core-loss edge recognition is experience based and human labor dependent, which limits the processing speed. So far, the low signal-noise ratio and the low jump ratio of the core-loss edges on the raw EELS spectra have been challenging for the automation of edge recognition. In this work, a convolutional-bidirectional long short-term memory neural network (CNN-BiLSTM) is proposed to automate the detection and elemental identification of core-loss edges from raw spectra. An EELS spectral database is synthesized by using our forward model to assist in the training and validation of the neural network. To make the synthesized spectra resemble the real spectra, we collected a large library of experimentally acquired EELS core edges. In synthesize the training library, the edges are modeled by fitting the multi-gaussian model to the real edges from experiments, and the noise and instrumental imperfectness are simulated and added. The well-trained CNN-BiLSTM network is tested against both the simulated spectra and real spectra collected from experiments. The high accuracy of the network, 94.9 %, proves that, without complicated preprocessing of the raw spectra, the proposed CNN-BiLSTM network achieves the automation of core-loss edge recognition for EELS spectra with high accuracy. Artificial intelligence ; Computer science ; Artificial neural network ; Convolutional neural network ; Enhanced Data Rates for GSM Evolution ; Spectral line ; Electron energy loss spectroscopy ; Pattern recognition (psychology) ; Automation ; Noise (video) ; Biological system ; Materials science ; Physics ; Nanotechnology ; Engineering Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Electrochemical Analysis and Applications