jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: special to C EN Fernando Gomollón-Bel
Source: C&EN Global Enterprise (article, 2022-05-23)
Field: Materials Science — Materials Chemistry

Machine learning spots metals in single-atom catalysts An algorithm could accelerate the way chemists identify and analyze single-atom catalysts ( J. Am. Chem. Soc. 2022, DOI: 10.1021/jacs.1c12466 ). Using machine learning techniques, the method scans microscopy images and successfully detects metallic atoms. Researchers expect this technique to reduce human biases in image selection and characterization, as well as improve their understanding of heterogeneous catalysts . The standard method of identifying individual metal atom catalysts on a substrate involves a human expert analyzing high-resolution images taken with scanning transmission electron microscopy (STEM). “Microscopy is the most conclusive technique to confirm the presence of metal atoms supported on catalysts, because you directly see them,” says lead author Sharon J. Mitchell, a chemist at the Swiss Federal Institute of Technology (ETH), Zurich . The method is prone to human biases and has long processing times, however. To better predict a catalyst’s behavior, Mitchell and her team created hundreds of STEM Spots ; Catalysis ; Atom (system on chip) ; Materials science ; Metallurgy ; Chemistry ; Computer science ; Physical chemistry ; Embedded system ; Organic chemistry Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Materials Characterization Techniques
Authors: Atsuto Seko
Source: arXiv (Cornell University) (preprint, 2022-09-28)
Field: Materials Science — Materials Chemistry

Systematic development of polynomial machine learning potentials for metallic and alloy systems Machine learning potentials (MLPs) developed from extensive datasets constructed from density functional theory (DFT) calculations have become increasingly appealing for many researchers. This paper presents a framework of polynomial-based MLPs, called polynomial MLPs. The systematic development of accurate and computationally efficient polynomial MLPs for many elemental and binary alloy systems and their predictive powers for various properties are also demonstrated. Consequently, many polynomial MLPs are available in a repository website. The repository will help many scientists perform accurate and efficient large-scale atomistic simulations and crystal structure searches. Polynomial ; Computer science ; Binary number ; Development (topology) ; Theoretical computer science ; Artificial intelligence ; Machine learning ; Mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Nasir Mehmood | Rashid Ahmad | Aqsa Gul | Anwar Zaman | G. Murtaza | Jamil Ahmad | F. Y. Khattak
Source: SPIN (article, 2022-03-01)
Field: Materials Science — Materials Chemistry

Predicting Lattice Constants of Half-Heusler Alloys Through Deep Neural Network Models Using Symbols of Elements and/or Ionic Radii In this paper, we have developed models based on Deep-Learning Neural Network (DNN) for accurately predicting lattice constants of half-Heusler alloys. A commercial software WIEN2k employing the Density Functional Theory (DFT) is first used to generate data of the lattice constants of 377 half-Heusler alloys for the training, testing and validation of the models. These models use elemental symbols or/and ionic radii as input parameters. The model that uses only symbols of the constituent element and the model that uses symbols in combination with the radii of the ions predict lattice constants of half-Heusler alloys with an average error of less than 1% to the data obtained from the WIEN2k calculations. The average error stays below 2% in the prediction by the model that uses radii of the ions alone. These results show a great promise for these models to be extended for the prediction of structural and elastic properties of not only new half-Heusler alloys but also other new materials such as full-Heusler, spinels, perovskites, etc. with greater convenience, saving hours of computation time. WIEN2k ; Lattice constant ; Ionic radius ; Materials science ; Artificial neural network ; Ion ; Density functional theory ; Lattice (music) ; Computation ; Thermodynamics ; Algorithm ; Computer science ; Computational chemistry ; Physics ; Artificial intelligence ; Chemistry ; Quantum mechanics ; Local-density approximation Machine Learning in Materials Science ; Heusler alloys: electronic and magnetic properties ; X-ray Diffraction in Crystallography
Authors: Yang Liu | Jun Li
Source: ChemRxiv (preprint, 2022-04-04)
Field: Materials Science — Materials Chemistry

Permutation-Invariant-Polynomial Neural-Network-based Δ-Machine Learn-ing Approach: A Case for the HO2 Self-reaction and its Dynamics Study The potential energy surface (PES) plays a central role in chemistry. As the size of the reaction system increases, it would be more and more difficult to develop its globally accurate full-dimensional PES. One unavoidable difficulty is that it is too expensive to calculate electronic energies of ample configurations for complicated reactions. Δ-machine learning is a highly cost-effective method as only a small number of high-level ab initio energies are required to improve a potential energy surface (PES) fit to a large number of low-level points. Here, we propose a permutation-invariant-polynomial neural-network (PIP-NN)-based Δ-machine learning approach to construct full-dimensional accurate PESs for complicated reactions. The approach is applied to the HO2 + HO2 → H2O2 + O2 reaction, a key process in combustion and atmosphere. The full-dimensional triplet state PES is constructed with a large number of density functional theory (DFT) points, which cover all dynamically relevant regions. Only 14% of the DFT dataset are used to successfully bring the DFT PES to the UCCSD(T)-F12a/AVTZ quality. On this PES of high quality, quasi-classical trajectory (QCT) calculations are performed to study the dynamics of the title reaction. A surprising mode-speciﬁc dynamics is observed, in which exciting a spectator mode leads to significant enhancement of the reactivity at low collision energy. This special mechanism can be attributed to increased attraction potential caused by the excited spectator mode. Such mode specificity may be quite prevalent in free radical reactions involving HO2, which is common in combustion and atmosphere. Potential energy surface ; Invariant (physics) ; Invariant polynomial ; Ab initio ; Artificial neural network ; Density functional theory ; Computer science ; Polynomial ; Chemistry ; Computational chemistry ; Artificial intelligence ; Mathematics ; Quantum mechanics ; Physics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Spectroscopy and Quantum Chemical Studies
Authors: Yang Liu | Jun Li
Source: ChemRxiv (preprint, 2022-04-25)
Field: Materials Science — Materials Chemistry

Permutation-Invariant-Polynomial Neural-Network-based Δ-Machine Learn-ing Approach: A Case for the HO2 Self-reaction and its Dynamics Study The potential energy surface (PES) plays a central role in chemistry. As the size of the reaction system increases, it would be more and more difficult to develop its globally accurate full-dimensional PES. One unavoidable difficulty is that it is too expensive to calculate electronic energies of ample configurations for complicated reactions. Δ-machine learning is a highly cost-effective method as only a small number of high-level ab initio energies are required to improve a potential energy surface (PES) fit to a large number of low-level points. Here, we propose a permutation-invariant-polynomial neural-network (PIP-NN)-based Δ-machine learning approach to construct full-dimensional accurate PESs for complicated reactions. The approach is applied to the HO2 + HO2 → H2O2 + O2 reaction, a key process in combustion and atmosphere. The full-dimensional triplet state PES is constructed with a large number of density functional theory (DFT) points, which cover all dynamically relevant regions. Only 14% of the DFT dataset are used to successfully bring the DFT PES to the UCCSD(T)-F12a/AVTZ quality. On this PES of high quality, quasi-classical trajectory (QCT) calculations are performed to study the dynamics of the title reaction. A surprising mode-speciﬁc dynamics is observed, in which exciting a spectator mode leads to significant enhancement of the reactivity at low collision energy. This special mechanism can be attributed to increased attraction potential caused by the excited spectator mode. Such mode specificity may be quite prevalent in free radical reactions involving HO2, which is common in combustion and atmosphere. Potential energy surface ; Invariant (physics) ; Invariant polynomial ; Ab initio ; Artificial neural network ; Density functional theory ; Computer science ; Excited state ; Polynomial ; Chemistry ; Computational chemistry ; Artificial intelligence ; Quantum mechanics ; Mathematics ; Physics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Spectroscopy and Quantum Chemical Studies
Authors: Rongzhi Dong | Yong Zhao | Yuqi Song | Nihang Fu | Sadman Sadeed Omee | Sourin Dey | Qinyang Li | Lai Wei | Jianjun Hu
Source: arXiv (Cornell University) (preprint, 2022-03-27)
Field: Materials Science — Materials Chemistry

DeepXRD, a Deep Learning Model for Predicting of XRD spectrum from Materials Composition One of the long-standing problems in materials science is how to predict a material's structure and then its properties given only its composition. Experimental characterization of crystal structures has been widely used for structure determination, which is however too expensive for high-throughput screening. At the same time, directly predicting crystal structures from compositions remains a challenging unsolved problem. Herein we propose a deep learning algorithm for predicting the XRD spectrum given only the composition of a material, which can then be used to infer key structural features for downstream structural analysis such as crystal system or space group classification or crystal lattice parameter determination or materials property predictions. Benchmark studies on two datasets show that our DeepXRD algorithm can achieve good performance for XRD prediction as evaluated over our test sets. It can thus be used in high-throughput screening in the huge materials composition space for new materials discovery. Crystal structure ; Benchmark (surveying) ; Characterization (materials science) ; Computer science ; Composition (language) ; Crystal (programming language) ; Materials science ; Crystal structure prediction ; Key (lock) ; Algorithm ; Artificial intelligence ; Biological system ; Nanotechnology ; Chemistry ; Crystallography ; Geology Machine Learning in Materials Science ; Geochemistry and Geologic Mapping ; X-ray Diffraction in Crystallography
Authors: Anupam Bhattacharya | Ivan Timokhin | Ratnamala Chatterjee | Qian Yang | Artem Mishchenko
Source: arXiv (Cornell University) (preprint, 2022-07-19)
Field: Materials Science — Materials Chemistry

Machine learning approach to genome of two-dimensional materials with flat electronic bands Many-body physics of electron-electron correlations plays a central role in condensed mater physics, it governs a wide range of phenomena, stretching from superconductivity to magnetism, and is behind numerous technological applications. To explore this rich interaction-driven physics, two-dimensional (2D) materials with flat electronic bands provide a natural playground thanks to their highly localised electrons. Currently, thousands of 2D materials with computed electronic bands are available in open science databases, awaiting such exploration. Here we used a new machine learning algorithm combining both supervised and unsupervised machine intelligence to automate the otherwise daunting task of materials search and classification, to build a genome of 2D materials hosting flat electronic bands. To this end, a feedforward artificial neural network was employed to identify 2D flat band materials, which were then classified by a bilayer unsupervised learning algorithm. Such a hybrid approach of exploring materials databases allowed us to reveal completely new material classes outside the known flat band paradigms, offering new systems for in-depth study on their electronic interactions. Artificial neural network ; Artificial intelligence ; Computer science ; Magnetism ; Unsupervised learning ; Electron ; Physics ; Condensed matter physics Machine Learning in Materials Science ; Expert finding and Q&A systems ; Topic Modeling
Authors: arkus Eisenbach | Mariia Karabin | Massimiliano Lupo Pasini | Junqi Yin
Source: arXiv (Cornell University) (preprint, 2022-07-20)
Field: Materials Science — Materials Chemistry

Machine Learning for First Principles Calculations of Material Properties for Ferromagnetic Materials The investigation of finite temperature properties using Monte-Carlo (MC) methods requires a large number of evaluations of the system's Hamiltonian to sample the phase space needed to obtain physical observables as function of temperature. DFT calculations can provide accurate evaluations of the energies, but they are too computationally expensive for routine simulations. To circumvent this problem, machine-learning (ML) based surrogate models have been developed and implemented on high-performance computing (HPC) architectures. In this paper, we describe two ML methods (linear mixing model and HydraGNN) as surrogates for first principles density functional theory (DFT) calculations with classical MC simulations. These two surrogate models are used to learn the dependence of target physical properties from complex compositions and interactions of their constituents. We present the predictive performance of these two surrogate models with respect to their complexity while avoiding the danger of overfitting the model. An important aspect of our approach is the periodic retraining with newly generated first principles data based on the progressive exploration of the system's phase space by the MC simulation. The numerical results show that HydraGNN model attains superior predictive performance compared to the linear mixing model for magnetic alloy materials. Overfitting ; Computer science ; Surrogate model ; Monte Carlo method ; Hamiltonian (control theory) ; Statistical physics ; Phase space ; Physical system ; Algorithm ; Mathematical optimization ; Artificial intelligence ; Applied mathematics ; Machine learning ; Mathematics ; Physics ; Artificial neural network ; Thermodynamics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Ferroelectric and Negative Capacitance Devices
Authors: Animesh K. Ghose | Mikhail Segal | Fanchen Meng | Liang Zhu | Mark S. Hybertsen | Xiaohui Qu | Eli Stavitski | Shinjae Yoo | Deyu Lu | Matthew R. Carbone
Source: arXiv (Cornell University) (preprint, 2022-10-01)
Field: Materials Science — Materials Chemistry

Uncertainty-aware predictions of molecular X-ray absorption spectra using neural network ensembles As machine learning (ML) methods continue to be applied to a broad scope of problems in the physical sciences, uncertainty quantification is becoming correspondingly more important for their robust application. Uncertainty aware machine learning methods have been used in select applications, but largely for scalar properties. In this work, we showcase an exemplary study in which neural network ensembles are used to predict the X-ray absorption spectra of small molecules, as well as their point-wise uncertainty, from local atomic environments. The performance of the resulting surrogate clearly demonstrates quantitative correlation between errors relative to ground truth and the predicted uncertainty estimates. Significantly, the model provides an upper bound on the expected error. Specifically, an important quality of this uncertainty-aware model is that it can indicate when the model is predicting on out-of-sample data. This allows for its integration with large scale sampling of structures together with active learning or other techniques for structure refinement. Additionally, our models can be generalized to larger molecules than those used for training, and also successfully track uncertainty due to random distortions in test molecules. While we demonstrate this workflow on a specific example, ensemble learning is completely general. We believe it could have significant impact on ML-enabled forward modeling of a broad array of molecular and materials properties. Artificial neural network ; Computer science ; Ground truth ; Machine learning ; Uncertainty quantification ; Artificial intelligence ; Workflow ; Sampling (signal processing) ; Scope (computer science) ; Data mining ; Algorithm Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Miguel A. Caro
Source: arXiv (Cornell University) (preprint, 2022-09-15)
Field: Materials Science — Materials Chemistry

Machine learning based modeling of disordered elemental semiconductors: understanding the atomic structure of a-Si and a-C Disordered elemental semiconductors, most notably a-C and a-Si, are ubiquitous in a myriad of different applications. These exploit their unique mechanical and electronic properties. In the past couple of decades, density functional theory (DFT) and other quantum mechanics-based computational simulation techniques have been successful at delivering a detailed understanding of the atomic and electronic structure of crystalline semiconductors. Unfortunately, the complex structure of disordered semiconductors sets the time and length scales required for DFT simulation of these materials out of reach. In recent years, machine learning (ML) approaches to atomistic modeling have been developed that provide an accurate approximation of the DFT potential energy surface for a small fraction of the computational time. These ML approaches have now reached maturity and are starting to deliver the first conclusive insights into some of the missing details surrounding the intricate atomic structure of disordered semiconductors. In this Topical Review we give a brief introduction to ML atomistic modeling and its application to amorphous semiconductors. We then take a look at how ML simulations have been used to improve our current understanding of the atomic structure of a-C and a-Si. Semiconductor ; Density functional theory ; Electronic structure ; Materials science ; Nanotechnology ; Statistical physics ; Chemical physics ; Computer science ; Engineering physics ; Condensed matter physics ; Physics ; Computational chemistry ; Chemistry ; Optoelectronics Machine Learning in Materials Science ; Semiconductor materials and devices ; Advancements in Semiconductor Devices and Circuit Design
Authors: Fan, Zhao | Bengang Xing | Cao, Penghui
Source: arXiv (Cornell University) (preprint, 2022-03-12)
Field: Materials Science — Materials Chemistry

Convolutional neural networks enable high-fidelity prediction of path-dependent diffusion barrier spectra in multi-principal element alloys The emergent multi-principal element alloys (MPEAs) provide a vast compositional space to search for novel materials for technological advances. However, how to efficiently identify optimal compositions from such a large design space for targeted properties is a grand challenge in material science. Here we developed a convolutional neural network (CNN) model that can accurately and efficiently predict path-dependent vacancy migration energy barriers, which are critical to diffusion behaviors and many high-temperature properties, of MPEAs at any compositions and with different chemical short-range orders within a given alloy system. The success of the CNN model makes it promising for developing a database of diffusion barriers for different MPEA systems, which would accelerate alloy screening for the discovery of new compositions with desirable properties. Besides, the length scale of local configurations relevant to migration energy barriers is uncovered, and the implications of this success to other aspects of materials science are discussed. Convolutional neural network ; Diffusion ; Computer science ; Path (computing) ; Principal (computer security) ; High fidelity ; Fidelity ; Space (punctuation) ; Scale (ratio) ; Materials science ; Computational science ; Artificial intelligence ; Physics Machine Learning in Materials Science ; High Entropy Alloys Studies ; Electrocatalysts for Energy Conversion
Authors: Ian Rouse | Vladimir Lobaskin
Source: arXiv (Cornell University) (preprint, 2022-11-15)
Field: Materials Science — Materials Chemistry

Machine-learning based prediction of small molecule -- surface interaction potentials Predicting the adsorption affinity of a small molecule to a target surface is of importance to a range of fields, from catalysis to drug delivery and human safety, but a complex task to perform computationally when taking into account the effects of the surrounding medium. We present a flexible machine-learning approach to predict potentials of mean force (PMFs) and adsorption energies for chemical -- surface pairs from the separate interaction potentials of each partner with a set of probe atoms. We use a pre-existing library of PMFs obtained via atomistic molecular dynamics for a variety of inorganic materials and molecules to train the model. We find good agreement between original and predicted PMFs in both training and validation groups, confirming the predictive power of this approach, and demonstrate the flexibility of the model by producing PMFs for molecules and surfaces outside the training set. Flexibility (engineering) ; Molecule ; Adsorption ; Set (abstract data type) ; Task (project management) ; Surface (topology) ; Molecular dynamics ; Predictive power ; Range (aeronautics) ; Computer science ; Chemical physics ; Biological system ; Chemistry ; Materials science ; Nanotechnology ; Computational chemistry ; Physics ; Engineering ; Physical chemistry ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Surface Chemistry and Catalysis
Authors: Uwem Ekwere Inyang | Minister Ezekiel Obunikut
Source: Zenodo (CERN European Organization for Nuclear Research) (review, 2022-04-27)
Field: Chemistry — Analytical Chemistry

Artificial Neural Network and Their Applications in Food Materials: A Review This paper is a review of artificial neural network technique for the prediction of drying parameters of food materials. The meaning of ANN, the importance, areas that ANN could be applied, future prospects and summary of previous researchers work using ANN for the prediction of drying parameters were considered. These drying parameters are not limited to the following: thickness, temperature, velocity, moisture content, drying rate that are used in the prediction. Thus, ANNs hold a great deal of promise for modeling complex tasks in process control and simulation and in applications for food safety, preservation and quality control. This method eliminates the need for manual calculations and the ANN representing more tools for prediction drying parameters of food materials. This technique is preferred for large data set for robust, accuracy and less time consuming benefits. The method/leaning algorithm mostly used was Levenberg-Marquardt back propagation and the coefficient of determination (R<sup>2</sup>) was above 0.9 and the moisture content was one of the key output parameter that was determined. Artificial neural network ; Computer science ; Artificial intelligence Spectroscopy and Chemometric Analyses