jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Qian Chen | Yunhai Xiong | Xiang Chen
Source: arXiv (Cornell University) (preprint, 2020-12-01)
Field: Materials Science — Materials Chemistry

Directed Graph Attention Neural Network Utilizing 3D Coordinates for Molecular Property Prediction The prosperity of computer vision (CV) and natural language procession (NLP) in recent years has spurred the development of deep learning in many other domains. The advancement in machine learning provides us with an alternative option besides the computationally expensive density functional theories (DFT). Kernel method and graph neural networks have been widely studied as two mainstream methods for property prediction. The promising graph neural networks have achieved comparable accuracy to the DFT method for specific objects in the recent study. However, most of the graph neural networks with high precision so far require fully connected graphs with pairwise distance distribution as edge information. In this work, we shed light on the Directed Graph Attention Neural Network (DGANN), which only takes chemical bonds as edges and operates on bonds and atoms of molecules. DGANN distinguishes from previous models with those features: (1) It learns the local chemical environment encoding by graph attention mechanism on chemical bonds. Every initial edge message only flows into every message passing trajectory once. (2) The transformer blocks aggregate the global molecular representation from the local atomic encoding. (3) The position vectors and coordinates are used as inputs instead of distances. Our model has matched or outperformed most baseline graph neural networks on QM9 datasets even without thorough hyper-parameters searching. Moreover, this work suggests that models directly utilizing 3D coordinates can still reach high accuracies for molecule representation even without rotational and translational invariance incorporated. Computer science ; Artificial neural network ; Pairwise comparison ; Theoretical computer science ; Artificial intelligence ; Molecular graph ; Graph ; Algorithm Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Yuma Iwasaki | Ryohto Sawada | Eiji Saitoh | Masahiko Ishida
Source: Research Square (preprint, 2020-08-14)
Field: Materials Science — Materials Chemistry

Identification of stable alloy with giant magnetization via machine learning based autonomous materials search Abstract Discovery of new magnets with high magnetization has always been important in human history because it has given birth to powerful motors and memory devices. Currently, the binary alloy Fe 3 Co 1 exhibits the largest magnetization of any stable alloys explained by the Slater-Pauling rule. A multi-element alloy system has been expected to include an alloy with magnetization beyond that of Fe 3 Co 1 , but it has been difficult to identify appropriate elements and compositions because of combinatorial explosion in the multi-element system. In this work, we found a new alloy with giant magnetization beyond that of Fe 3 Co 1 via an autonomous materials search system combining machine learning and ab-initio calculation. After autonomous/automated exploration in the large material space of multi-element alloy for six weeks, the system unexpectedly indicated that Ir and/or Pt impurities would enhance the magnetization of FeCo alloys. To confirm this experimentally, we comprehensively synthesized Fe x Co y Ir 1-x-y and Fe x Co y Pt 1-x-y alloys and found that some of them have magnetization beyond that of Fe 3 Co 1 . Magnetization ; Alloy ; Materials science ; Condensed matter physics ; Metallurgy ; Physics ; Magnetic field Machine Learning in Materials Science ; Magnetic Properties and Applications ; Microstructure and Mechanical Properties of Steels
Authors: Punyaslok Pattnaik | Shampa Raghunathan | Tarun Kalluri | Prabhakar Bhimalapuram | C. V. Jawahar | U. Deva Priyakumar
Source: ChemRxiv (preprint, 2020-05-08)
Field: Materials Science — Materials Chemistry

Machine Learning for Accurate Force Calculations in Molecular Dynamics Simulations The computationally expensive nature of ab initio molecular dynamics simulations severely limits its ability to simulate large system sizes and long time scales, both of which are necessary to imitate experimental conditions. In this work, we explore an approach to make use of the data obtained using the quantum mechanical density functional theory (DFT) on small systems and use deep learning to subsequently simulate large systems by taking liquid argon as a test case. A suitable vector representation was chosen to represent the surrounding environment of each Ar atom, and a DNetFF machine learning model where, the neural network was trained to predict the difference in resultant forces obtained by DFT and classical force fields was introduced. Molecular dynamics simulations were then performed using forces from the neural network for various system sizes and time scales depending on the properties we calculated. A comparison of properties obtained from the classical force field and the neural network model was presented alongside available experimental data to validate the proposed method. Computer science ; Force field (fiction) ; Molecular dynamics ; Artificial neural network ; Representation (politics) ; Statistical physics ; Field (mathematics) ; Work (physics) ; Artificial intelligence ; Physics ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Quantum, superfluid, helium dynamics ; Advanced Physical and Chemical Molecular Interactions
Authors: YU Dong-fang | Jinwei Wang
Source: Journal of Information Hiding and Privacy Protection (article, 2020-01-01)
Field: Chemistry — Analytical Chemistry

A Survey on Machine Learning in Chemical Spectral Analysis Chemical spectral analysis is contemporarily undergoing a revolution and drawing much attention of scientists owing to machine learning algorithms, in particular convolutional networks. Hence, this paper outlines the major machine learning and especially deep learning methods contributed to interpret chemical images, and overviews the current application, development and breakthrough in different spectral characterization. Brief categorization of reviewed literatures is provided for studies per application apparatus: X-Ray spectra, UV-Vis-IR spectra, Micro-scope, Raman spectra, Photoluminescence spectrum. End with the overview of existing circumstances in this research area, we provide unique insight and promising directions for the chemical imaging field to fully couple machine learning subsequently. Computer science ; Spectral analysis ; Artificial intelligence ; Physics ; Astronomy Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: Jize Zhang | Bhavya Kailkhura | T. Yong-Jin Han
Source: arXiv (Cornell University) (preprint, 2020-12-02)
Field: Materials Science — Materials Chemistry

Leveraging Uncertainty from Deep Learning for Trustworthy Materials Discovery Workflows In this paper, we leverage predictive uncertainty of deep neural networks to answer challenging questions material scientists usually encounter in machine learning based materials applications workflows. First, we show that by leveraging predictive uncertainty, a user can determine the required training data set size necessary to achieve a certain classification accuracy. Next, we propose uncertainty guided decision referral to detect and refrain from making decisions on confusing samples. Finally, we show that predictive uncertainty can also be used to detect out-of-distribution test samples. We find that this scheme is accurate enough to detect a wide range of real-world shifts in data, e.g., changes in the image acquisition conditions or changes in the synthesis conditions. Using microstructure information from scanning electron microscope (SEM) images as an example use case, we show that leveraging uncertainty-aware deep learning can significantly improve the performance and dependability of classification models. Trustworthiness ; Workflow ; Computer science ; Deep learning ; Data science ; Artificial intelligence ; Computer security ; Database Machine Learning in Materials Science ; Scientific Computing and Data Management
Authors: Jun Zhang | Yao-Kun Lei | Yi Yang | Yi Qin Gao
Source: ChemRxiv (preprint, 2020-05-04)
Field: Materials Science — Materials Chemistry

Deep Learning for Multi-Scale Molecular Modeling Molecular simulations are widely applied in the study of chemical and bio-physical systems. However, theaccessible timescales of atomistic simulations are limited, and extracting equilibrium properties of systemscontaining rare events remains challenging. Two distinct strategies are usually adopted in this regard: eithersticking to the atomistic level and performing enhanced sampling, or trading details for speed by leveragingcoarse-grained models. Although both strategies are promising, either of them, if adopted individually,exhibits severe limitations. In this paper we propose a machine-learning approach to ally both strategies sothat simulations on different scales can benefit mutually from their cross-talks: Accurate coarse-grained (CG)models can be inferred from the fine-grained (FG) simulations through deep generative learning; In turn, FGsimulations can be boosted by the guidance of CG models via deep reinforcement learning. Our methoddefines a variational and adaptive training objective which allows end-to-end training of parametricmolecular models using deep neural networks. Through multiple experiments, we show that our method isefficient and flexible, and performs well on challenging chemical and bio-molecular systems. Computer science ; Artificial intelligence ; Deep learning ; Reinforcement learning ; Generative grammar ; Machine learning ; Deep neural networks ; Artificial neural network ; Scale (ratio) ; Parametric statistics ; Mathematics ; Physics Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Topic Modeling
Authors: А. Н. Филанович | А. А. Повзнер
Source: 2020 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT) (article, 2020-05-01)
Field: Materials Science — Materials Chemistry

Machine Learning Methods for Predicting the Lattice Characteristics of Materials Data on 5244 crystalline compounds from the open AFLOWlib repository are used to build machine learning models, which enable to predict important features of phonon spectrum of a material (Debye temperature and Gruneisen parameter) required for simulation of its lattice properties. We build two types of descriptors: the first one contains data solely on the chemical composition of a compound and the second one incorporates information on the elemental properties of atoms that make up the compound and additionally contains several features regarding its crystal structure. The regression models are built using four popular approaches - gradient boosting (GB), random forests (RF), artificial neural networks (ANN) and support vector machines (SVM). Prior the regression a search for the best values of hyperparameters has been performed for each of the model supplemented with a 5-fold cross validation. We compare prediction accuracy of models based on different methods as well as trained on each of the descriptors. Support vector machine ; Random forest ; Gradient boosting ; Boosting (machine learning) ; Artificial intelligence ; Hyperparameter ; Artificial neural network ; Computer science ; Machine learning ; Regression ; Lattice (music) ; Data mining ; Mathematics ; Physics ; Statistics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Jin Soo Lim | Jonathan Vandermause | M. A. Van Spronsen | Albert Musaelian | C. R. O'Connor | Tobias Egle | Yu Xie | Lixin Sun | Nicola Molinari | Jacob Florian | Kaining Duanmu | R. J. Madix | Philippe Sautet | C. M. Friend | Boris Kozinsky
Source: ChemRxiv (preprint, 2020-02-06)
Field: Materials Science — Materials Chemistry

Evolution of Metastable Structures in Bimetallic Catalysts from Microscopy and Machine-Learning Molecular Dynamics Restructuring of interface plays a crucial role in materials science and heterogeneous catalysis. Bimetallic systems, in particular, often adopt very different composition and morphology at surfaces compared to the bulk. For the first time, we reveal a detailed atomistic picture of the long-timescale restructuring of Pd deposited on Ag, using microscopy, spectroscopy, and novel simulation methods. Encapsulation of Pd by Ag always precedes layer-by-layer dissolution of Pd, resulting in significant Ag migration out of the surface and extensive vacancy pits. These metastable structures are of vital catalytic importance, as Ag-encapsulated Pd remains much more accessible to reactants than bulk-dissolved Pd. The underlying mechanisms are uncovered by performing fast and large-scale machine-learning molecular dynamics, followed by our newly developed method for complete characterization of atomic surface restructuring events. Our approach is broadly applicable to other multimetallic systems of interest and enables the previously impractical mechanistic investigation of restructuring dynamics. Bimetallic strip ; Metastability ; Restructuring ; Molecular dynamics ; Catalysis ; Materials science ; Dissolution ; Chemical physics ; Characterization (materials science) ; Nanoscopic scale ; Atomic force microscopy ; Nanotechnology ; Chemical engineering ; Chemistry ; Physical chemistry ; Computational chemistry ; Business Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; nanoparticles nucleation surface interactions
Authors: Jun Zhang | Yao-Kun Lei | Yi Yang | Yi Qin Gao
Source: ChemRxiv (preprint, 2020-06-29)
Field: Materials Science — Materials Chemistry

Deep Learning for Variational Multi-Scale Molecular Modeling Molecular simulations are widely applied in the study of chemical and bio-physical systems. However, theaccessible timescales of atomistic simulations are limited, and extracting equilibrium properties of systemscontaining rare events remains challenging. Two distinct strategies are usually adopted in this regard: eithersticking to the atomistic level and performing enhanced sampling, or trading details for speed by leveragingcoarse-grained models. Although both strategies are promising, either of them, if adopted individually,exhibits severe limitations. In this paper we propose a machine-learning approach to ally both strategies sothat simulations on different scales can benefit mutually from their cross-talks: Accurate coarse-grained (CG)models can be inferred from the fine-grained (FG) simulations through deep generative learning; In turn, FGsimulations can be boosted by the guidance of CG models via deep reinforcement learning. Our methoddefines a variational and adaptive training objective which allows end-to-end training of parametricmolecular models using deep neural networks. Through multiple experiments, we show that our method isefficient and flexible, and performs well on challenging chemical and bio-molecular systems. Computer science ; Artificial intelligence ; Deep learning ; Reinforcement learning ; Generative grammar ; Deep neural networks ; Artificial neural network ; Machine learning ; Scale (ratio) ; Parametric statistics ; Mathematics ; Physics Machine Learning in Materials Science ; Topic Modeling ; Block Copolymer Self-Assembly
Authors: Jin Soo Lim | Jonathan Vandermause | M. A. Van Spronsen | Albert Musaelian | C. R. O'Connor | Tobias Egle | Yu Xie | Lixin Sun | Nicola Molinari | Jacob Florian | Kaining Duanmu | R. J. Madix | Philippe Sautet | C. M. Friend | Boris Kozinsky
Source: ChemRxiv (preprint, 2020-02-13)
Field: Materials Science — Materials Chemistry

Evolution of Metastable Structures in Bimetallic Catalysts from Microscopy and Machine-Learning Molecular Dynamics Restructuring of interface plays a crucial role in materials science and heterogeneous catalysis. Bimetallic systems, in particular, often adopt very different composition and morphology at surfaces compared to the bulk. For the first time, we reveal a detailed atomistic picture of the long-timescale restructuring of Pd deposited on Ag, using microscopy, spectroscopy, and novel simulation methods. Encapsulation of Pd by Ag always precedes layer-by-layer dissolution of Pd, resulting in significant Ag migration out of the surface and extensive vacancy pits. These metastable structures are of vital catalytic importance, as Ag-encapsulated Pd remains much more accessible to reactants than bulk-dissolved Pd. The underlying mechanisms are uncovered by performing fast and large-scale machine-learning molecular dynamics, followed by our newly developed method for complete characterization of atomic surface restructuring events. Our approach is broadly applicable to other multimetallic systems of interest and enables the previously impractical mechanistic investigation of restructuring dynamics. Bimetallic strip ; Metastability ; Restructuring ; Catalysis ; Molecular dynamics ; Nanoscopic scale ; Dissolution ; Materials science ; Characterization (materials science) ; Chemical physics ; Atomic force microscopy ; Nanotechnology ; Chemical engineering ; Chemistry ; Physical chemistry ; Computational chemistry Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Ayana Ghosh
Source: OpenCommons - UConn (University of Connecticut) (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Predicting Materials Behavior with Atomistic Simulations and Machine Learning Data driven approaches based on machine learning (ML) algorithms are very popular in the domain of physical sciences for the determination of yet unknown structure- properties-performance relationships for a wide range of different material families. This dissertation focuses on studying a number of such cases where various ML algorithms and statistical techniques, coupled with appropriate materials data obtained from experiments and atomistic simulations, are employed to build comprehensive ML-based frameworks capable of predicting complex materials behavior. The materials spaces investigated encompass isolated organic molecules, polymer crystals, inorganic multiferroics and actinides, while the target system characteristics or functionalities include molecular crystallization propensity, ferroelectricity and magnetism, which are in turn connected to the structural and electronic properties of the considered materials. In order to gain electronic-level understanding (Human Learning) of functionalities, such as ferroelectricity and magnetism, we have examined four different systems using density functional theory (DFT) approaches. These studies provided sufficient introductory knowledge for construction of targeted, data-driven ML-based frameworks — described in this dissertation — for further evaluation of the materials properties of interest, as well as for prediction of novel materials with similar or advanced characteristics. Artificial intelligence ; Computer science ; Statistical physics ; Physics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; X-ray Diffraction in Crystallography
Authors: Asif Chowdhury
Source: Scholar Commons (University of South Carolina) (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

A Machine Learning Based Approach to Accelerate Catalyst Discovery Computational catalysis, in contrast to experimental catalysis, uses approximations such as density functional theory (DFT) to compute properties of reaction intermediates. But DFT calculations for a large number of surface species on variety of active site models are resource intensive. In this work, we are building a machine learning based predictive framework for adsorption energies of intermediate species, which can reduce the computational overhead significantly. Our work includes the study and development of appropriate machine learning models and effective fingerprints or descriptors to predict energies accurately for different scenarios. Furthermore, Bayesian inverse problem, that integrates experimental catalysis with its computational counterpart, uses Markov chain Monte Carlo (MCMC) methods to refine the uncertainties on the quantities-of-interest such as turnover frequency. However, large number of forward simulations required by MCMC can become a bottleneck, especially in computational catalysis, where the evaluation of likelihood functions involves finding the solution to microkinetic models. A novel and faster MCMC method is proposed to reduce the number of expensive target evaluations and to shorten the burn-in period by emulating the target along with using a better informed proposal distribution. Computer science ; Artificial intelligence Machine Learning in Materials Science
Authors: Lagnajit Pattanaik | John Ingraham | Colin A. Grambow | William H. Green
Source: ChemRxiv (preprint, 2020-05-17)
Field: Materials Science — Materials Chemistry

Generating Transition States of Isomerization Reactions with Deep Learning Lack of quality data and difficulty generating these data hinder quantitative understanding of reaction kinetics. Specifically, conventional methods to generate transition state structures are deficient in speed, accuracy, or scope. We describe a novel method to generate three-dimensional transition state structures for isomerization reactions using reactant and product geometries. Our approach relies on a graph neural network to predict the transition state distance matrix and a least squares optimization to reconstruct the coordinates based on which entries of the distance matrix the model perceives to be important. We feed the structures generated by our algorithm through a rigorous quantum mechanics workflow to ensure the predicted transition state corresponds to the ground truth reactant and product. In both generating viable geometries and predicting accurate transition states, our method achieves excellent results. We envision workflows like this, which combine neural networks and quantum chemistry calculations, will become the preferred methods for computing chemical reactions. Isomerization ; Transition state ; Stochastic matrix ; Artificial neural network ; Computer science ; Workflow ; Scope (computer science) ; Graph ; Quantum ; Product (mathematics) ; Algorithm ; Chemistry ; Statistical physics ; Theoretical computer science ; Artificial intelligence ; Mathematics ; Machine learning ; Physics ; Catalysis ; Quantum mechanics Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods