jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ehsan Moharreri
Source: OpenCommons - UConn (University of Connecticut) (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Understanding Homogeneous and Heterogeneous Transition Metal Oxide Catalytic Systems for Partial Oxidation Reactions Transition metal oxide-based systems as catalysts for oxidative reactions are studied. Utilizing aerobic oxygen as a cost-efficient and benign oxidant is explored for alcohol oxidation reactions. A detailed magnetic study is performed to understand the active phases of manganese oxide catalysts. The role of a well-known homogeneous copper oxide catalyst in an oxidation reaction is explored for making alcohols from aliphatic hydrocarbons. Finally, virtual screening techniques are explored in a preliminary study for materials discovery.\nIn chapter I, aerobic transformation of alcohols is studied by mesoporous manganese oxide and cobalt oxide species. Catalyst optimization, characterization and substrate scope experimentation is performed for self-esterification of alcohols and aldehydes.\nIn chapter II, a comprehensive magnetic characterization of mesoporous manganese oxide species is provided. While conventional methods do not lead to definitive phase determination, magnetic transitions and spin eco NMR techniques prove the mixed phase of meso MnOx materials.\nIn chapter III, a synthetic copper complex is studied. The complex was modeled after the active site of particulate methane monooxygenase (pMMO). A detailed study of the complex structure, reaction medium and by- products showed that an important side reaction interfered with hydrocarbon oxidation. The nitrile solvent decomposes to amide by nucleophilic interactions from hydrogen peroxide.\nIn the final chapter, preliminary computational screening tools are introduced. Extraction of geometrical features of molecular structures and evaluation of total energy with machine learning algorithms are performed and the ground work for application of such work to crystalline materials is proposed. Catalysis ; Homogeneous ; Transition metal ; Oxide ; Chemistry ; Partial oxidation ; Materials science ; Metallurgy ; Thermodynamics ; Organic chemistry ; Physics Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Karthikeyan Saravanan
Source: D-Scholarship@Pitt (University of Pittsburgh) (article, 2019-01-24)
Field: Materials Science — Materials Chemistry

Computational High Throughput Searches for Efficient Catalysts Several screening studies identifying new catalysts for different reactions have been reported over the past decade. Almost all of them employ Kohn-Sham density functional theory (KS-DFT) and thermodynamic descriptors to screen for new catalysts. Though usually considered reliable for descriptor-based analyses, KS-DFT calculations are computationally expensive and intractable for use when screening across the full chemical space of all possible alloy materials. In order to accelerate screening of catalysts, we employ a perturbation theory model, ”Computational Alchemy” to approximate KS-DFT energies at a fraction of the computational cost. In this thesis, we discuss about how computational alchemy and machine learning can be used to reliably screen for efficient catalysts for a wide range of electrochemical processes based on thermodynamic activity descriptors. As a first step, we assess the promise of computational alchemy in predicting binding energies thousands of times faster than DFT. We identify distinct cases where alchemy performs significantly worse, indicating areas where modeling improvements are needed. We find that alchemical estimates yield binding energies within 0.1 eV of DFT values for a wide range of adsorbates. Largest errors (≈ 0.4 eV) were observed when Alchemy predicted BEs of the adsorbates on alloys that were obtained by changing large number of atoms and to a large change in nuclear charge. Using a Machine learning approach, the errors between Alchemy and DFT were corrected. Our results suggest that computational alchemy with machine learning is a very promising tool that warrants further consideration for high-throughput screening of heterogeneous catalysts. Alchemy ; Density functional theory ; Throughput ; Catalysis ; Chemistry ; Computer science ; Yield (engineering) ; Binding energy ; Computational chemistry ; Statistical physics ; Machine learning ; Thermodynamics ; Physics ; Quantum mechanics Machine Learning in Materials Science
Authors: Yukinori Koyama | Yosuke Tsunooka | Kentaro Kutsukake | Toru Ujihara
Source: The Japan Society of Applied Physics (article, 2019-07-10)
Field: Materials Science — Materials Chemistry

Nonparametric Machine Learning of Crystal Growth Simulation for Silicon Carbide Materials science ; Silicon carbide ; Nonparametric statistics ; Crystal (programming language) ; Silicon ; Carbide ; Crystal growth ; Nanotechnology ; Crystallography ; Optoelectronics ; Metallurgy ; Computer science ; Econometrics ; Programming language ; Mathematics Solidification and crystal growth phenomena ; Crystallization and Solubility Studies ; Metallurgical Processes and Thermodynamics
Authors: Hirofumi Hazama | Susumu Sobue | Shin Tajima | Ryoji Asahi
Source: The Japan Society of Applied Physics (article, 2019-01-25)
Field: Materials Science — Materials Chemistry

Phosphorescent material research by combination of high throughput experiment and machine learning Materials science ; Throughput ; Nanotechnology ; Phosphorescence ; Systems engineering ; Engineering physics ; Machine learning ; Biochemical engineering ; Computer science ; Engineering ; Optics ; Telecommunications ; Physics Machine Learning in Materials Science
Authors: Young-Jae Choi | Woo-Il Yang | Seung-Hoon Jhi
Source: APS March Meeting Abstracts (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Phonon Calculations of Phase Change Materials Using Machine-Learning Methods Phonon ; Phase (matter) ; Materials science ; Computer science ; Artificial intelligence ; Condensed matter physics ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Thermography and Photoacoustic Techniques ; Thermal properties of materials
Authors: Alexander S. Gzyl
Source: University of Alberta Library (article, 2019-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A Study of the Utility of a Machine-Learning Approach Applied to the Prediction of Site Occupancy and New Members of the Half-Heusler Family Predicting the formation and structures of non-molecular inorganic compounds has long been a fundamental goal in solid state chemistry. In this thesis, machine learning approaches have been applied to confront this challenge, focusing in particular on the large family of half-Heusler compounds because they exhibit many useful materials properties but have not always been structurally well characterized. Two specific problems have been tackled: assigning the correct site distributions in existing half-Heusler compounds, and predicting the formation of new half-Heusler compounds. The site preference within the structures of half-Heusler compounds have been evaluated through a machine-learning approach. A support-vector machine algorithm was applied to develop a model which was trained on 179 experimentally reported structures and 23 descriptors based solely on the chemical composition. The model gave excellent performance with sensitivity of 93%, specificity of 96% and accuracy of 95%. As an illustration of data sanitization, two compounds (GdPtSb, HoPdBi) flagged by the model to have potentially incorrect site assignments were resynthesized and structurally characterized. The predictions of the correct site assignments from the machine-learning model were confirmed by single-crystal and powder X-ray diffraction analysis. These site assignments also correspond to the lowest total energy configurations as revealed from first-principles calculations. A machine-learning ensemble was used to predict new half-Heusler compounds. Compositions were selected for synthesis if they were also adopted by a full-Heusler compound counterpart. The model gave excellent performance with sensitivity of 90.0%, Specificity of 98.0%, and accuracy of 97.7%. Perturbations in site occupancy (e.g., vacancies, disorder) led to changes in crystal symmetry. Synthetic minority oversampling (SMOTE) and ensemble methods have been combined and applied for the first time to a materials science problem, and the performance of this approach has been evaluated Occupancy ; Artificial intelligence ; Machine learning ; Actuarial science ; Computer science ; Business ; Engineering ; Architectural engineering Heusler alloys: electronic and magnetic properties
Authors: Chenru Duan | Jon Paul Janet | Fang Liu | Aditya Nandy | Heather J. Kulik
Source: ChemRxiv (article, 2019-01-23)
Field: Materials Science — Materials Chemistry

Learning from Failure: Predicting Electronic Structure Calculation Outcomes with Machine Learning Models High-throughput computational screening for chemical discovery mandates the automated and unsupervised simulation of thousands of new molecules and materials. In challenging materials spaces, such as open shell transition metal chemistry, characterization requires time-consuming first-principles simulation that often necessitates human intervention. These calculations can frequently lead to a null result, e.g., the calculation does not converge or the molecule does not stay intact during a geometry optimization. To overcome this challenge toward realizing fully automated chemical discovery in transition metal chemistry, we have developed the first machine learning models that predict the likelihood of successful simulation outcomes. We train support vector machine and artificial neural network classifiers to predict simulation outcomes (i.e., geometry optimization result and degree of deviation) for a chosen electronic structure method based on chemical composition. For these static models, we achieve an area under the curve of at least 0.95, minimizing computational time spent on non- productive simulations and therefore enabling efficient chemical space exploration. We introduce a metric of model uncertainty based on the distribution of points in the latent space to systematically improve model prediction confidence. In a complementary approach, we train a convolutional neural network classification model on simulation output electronic and geometric structure time series data. This dynamic model generalizes more readily than the static classifier by becoming more predictive as input simulation length increases. Finally, we describe approaches for using these models to enable autonomous job control in transition metal complex discovery. Computer science ; Chemical space ; Artificial neural network ; Machine learning ; Artificial intelligence ; Convolutional neural network ; Metric (unit) ; Classifier (UML) ; Algorithm ; Drug discovery ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; History and advancements in chemistry
Authors: 知輝 保田 | Tomoki Yasuda | 真一 大川原 | Shinichi Ookawara | 史郎 吉川 | Shiro Yoshikawa | 秀行 松本 | Hideyuki Matsumoto
Source: Institutional Repositories DataBase (IRDB) (article, 2019-09-01)
Field: Materials Science — Materials Chemistry

Machine learning-based framework of descriptor selection for porous material design Selection (genetic algorithm) ; Material selection ; Porous medium ; Noise (video) Material Selection and Properties ; Machine Learning in Materials Science ; Cellular and Composite Structures
Authors: Elizabeth J. Kautz | Alexander Hagen | Jesse Johns | Douglas E. Burkes
Source: Computational Materials Science (preprint, 2019-02-01)
Field: Materials Science — Materials Chemistry

A machine learning approach to thermal conductivity modeling: A case study on irradiated uranium-molybdenum nuclear fuels Thermal conductivity ; Nuclear fuel ; Nuclear engineering ; Materials science ; Irradiation ; Artificial neural network ; Uranium ; Artificial intelligence ; Test data ; Computer science ; Machine learning ; Process engineering ; Engineering ; Nuclear physics ; Composite material ; Metallurgy ; Physics Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Machine Learning in Materials Science
Authors: 耕太 鈴木 | Kota Suzuki | 恒星 大浦 | Kosei Ohura | 雅章 平山 | Masaaki Hirayama | 功 田中 | Isao Tanaka | 了次 菅野 | Ryoji KANNO
Source: Institutional Repositories DataBase (IRDB) (article, 2019-12-01)
Field: Materials Science — Materials Chemistry

Novel Material Search Method of Lithium Ion Conductors Using Machine Learning Electrical conductor ; Lithium (medication) ; Ion ; Key (lock) Machine Learning in Materials Science ; Advanced Battery Technologies Research ; Advanced Battery Materials and Technologies
Authors: Doosun Hong | Jaehoon Oh | Jung-Woo Choi | Soonho Kwon | Hyuck-Mo Lee
Source:  (article, 2019-06-27)
Field: Materials Science — Materials Chemistry

Unraveling the relation between the electronic structure of materials and chemisorption by using machine learning Relation (database) ; Chemisorption ; Computer science ; Chemistry ; Data mining ; Adsorption ; Physical chemistry Machine Learning in Materials Science
Authors: Karteek K. Bejagam | Yaxin An | Samrendra Singh | Sanket A. Deshmukh
Source: Bulletin of the American Physical Society (article, 2019-03-08)
Field: Materials Science — Materials Chemistry

Machine-Learning Provides New Insights into the Coil-to-Globule Transitions of Thermosensitive Polymers Electromagnetic coil ; Polymer ; Materials science ; Chemical physics ; Polymer science ; Chemistry ; Composite material ; Engineering ; Electrical engineering Machine Learning in Materials Science
Authors: pHakima Mokranep
Source: Journal of Astrophysics & Aerospace Technology (article, 2019-02-01)
Field: Chemistry — Spectroscopy

Molecular astrophysics: Formation and interaction of molecules and new approach: Deep Learning (AI) in astronomy Astronomy ; Astrophysics ; Physics ; Astrobiology Molecular Spectroscopy and Structure ; SAS software applications and methods ; Astronomy and Astrophysical Research
Authors: Wentao Li and Zhigang Sun
Source: The Journal of Atomic and Molecular Sciences (article, 2019-01-30)
Field: Materials Science — Materials Chemistry

An Potential Energy Surface for the $O+O_2$ Reaction Using Neural Network Approach A new potential energy surface (PES) of the $O_3$ system was reported, based on the PESs reported by Dawes et al. and Schinke et al. The PES of $O_3$ reported by Dawes et al. was fitted using least square method based upon accurate high level ab initio points, and has been applied to many dynamics calculations. However, it is computationally slow and need lots of time to obtain the energy points from the PES. At the same time, the threshold of the ab initio points is low as 2.6 eV, relative to the minimum of the PES, which limits its application. On the contrary, the PES reported by Schinke et al. numerically is fast and extends to high energy. In this work, we first calculated the energy points from these two PESs. Then we fitted these energy points using PIP-NN approach. In this way, a revised version of the PES of $O_3$ was constructed and we used it to perform quantum reactive scattering dynamics calculation. Energy (signal processing) ; Physics ; Quantum mechanics Machine Learning in Materials Science
Authors: Stephen Wu
Source: Institutional Repositories DataBase (IRDB) (article, 2019-06-05)
Field: Materials Science — Materials Chemistry

Design of high thermal conductivity polymers based on a Bayesian machine learning approach Bayesian probability ; Thermal conductivity ; Bayesian optimization ; Bayesian inference ; Support vector machine ; Pattern recognition (psychology) ; Thermal Thermal properties of materials ; Machine Learning in Materials Science ; Polymer crystallization and properties
Authors: Kohei Shimamura | Fuyuki Shimojo | Shigenori Tanaka
Source: The Brain & Neural Networks (article, 2019-12-05)
Field: Materials Science — Materials Chemistry

Recent Progress and Current Issues in Development of Artificial Neural Network Interatomic Potential for Molecular Dynamics Simulation 分子動力学（MD）法は，原子毎に立てられたNewtonの運動方程式を逐次的に解くことで系全体の原子ダイナミクスを追跡できる計算機シミュレーション手法であり，ミクロな現象の解明に役立つことから材料分野や生物分野では標準的な手法として認識されている．近年，人工ニューラルネットワーク（ANN）の万能近似性を活用して，従来のMD法が抱えていた精度と計算コストの難点を克服するANN原子間相互作用ポテンシャル（ANN potential）の開発が活発に行われ新局面を迎えている．本稿では，ANN potentialの基本的なアルゴリズムについて，応用例を交えながら，現在直面している回帰学習のデータ不均衡問題などの解決すべき課題について述べる． Current (fluid) ; Artificial neural network ; Computer science ; Molecular dynamics ; Artificial intelligence ; Chemistry ; Engineering ; Computational chemistry ; Electrical engineering Machine Learning in Materials Science
Authors: Charles B. Musgrave
Source: ECS Meeting Abstracts (article, 2019-09-01)
Field: Materials Science — Materials Chemistry

(Invited) Combined Ab Initio and Machine Learning Approaches to Discover Materials for Hydrogen Generation In this talk we will present our results using ab initio and machine learned models to discover candidate perovskite STWS materials. This includes predicting the stability of these materials at the operating temperature, whether these materials form as perovskites and in what specific phase, the energy of oxygen vacancies and other defects that mediate the redox process, and finally the viability of these metal oxides as STWS redox materials. The Gibbs energy, G, determines the equilibrium conditions of chemical reactions and materials stability. Unfortunately, G has been tabulated for only a small fraction of known inorganic compounds, thus impeding an analysis of whether materials are stable and synthesizable at operating conditions. We used a statistical learning approach to identify a simple and accurate descriptor to predict G(T) for stoichiometric inorganic compounds with ~40 meV/atom resolution, and minimal computational cost, for temperatures between 300 K and 1800 K. We then applied this descriptor to screen various compositions for their stability at STWS temperatures. Candidate materials that form as perovskites were then analyzed using a machine learned one-dimensional tolerance factor, τ , that we developed that correctly predicts 92% of compounds as perovskite or non-perovskite to predict which of the predicted stable compounds form perovskites. Finally, we applied DFT on the remaining viable candidates to predict the thermodynamics of STWS reactions. This requires predicting the correct perovskite phase, symmetry breaking distortions and modeling the correct defect, spin and charge state of the defect mediating the oxidation and reduction processes at the operating conditions. Perovskite (structure) ; Ab initio ; Stoichiometry ; Materials science ; Gibbs free energy ; Hydrogen ; Phase (matter) ; Redox ; Stability (learning theory) ; Computer science ; Thermodynamics ; Chemistry ; Machine learning ; Physical chemistry ; Physics ; Crystallography Machine Learning in Materials Science
Authors: Shoichi Ishida | Kei Terayama | Ryosuke Kojima | Kiyosei Takasu | Yasushi Okuno
Source: ChemRxiv (preprint, 2019-07-01)
Field: Materials Science — Materials Chemistry

Prediction and Interpretable Visualization of Synthetic Reactions Using Graph Convolutional Networks Recently, many research groups have been addressing data-driven approaches for reaction prediction and retrosynthetic analysis. Although the performances of the data-driven approach have progressed due to recent advances of machine learning and deep learning techniques, problems such as improving capability of reaction prediction and the black-box problem of neural networks still persist for practical use by chemists. To expand data-driven approaches to chemists, we focused on two challenges: improvement of reaction prediction and interpretability of the prediction. In this paper, we propose an interpretable prediction framework using Graph Convolutional Networks (GCN) for reaction prediction and Integrated Gradients (IGs) for visualization of contributions to the prediction to address these challenges. As a result, our model showed better performances than the approach using Extended-Connectivity Fingerprint (ECFP). Furthermore, IGs based visualization of the GCN prediction successfully highlighted reaction-related atoms. Interpretability ; Computer science ; Visualization ; Graph ; Machine learning ; Artificial intelligence ; Convolutional neural network ; Deep learning ; Graph drawing ; Data mining ; Theoretical computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: 耕太 鈴木 | Kota Suzuki
Source: Institutional Repositories DataBase (IRDB) (article, 2019-02-01)
Field: Materials Science — Materials Chemistry

Development of material search methods in lithium ion conducting oxides using machine learning Lithium (medication) ; Ion ; Development (topology) ; Process (computing) Machine Learning in Materials Science ; Advancements in Battery Materials ; Advanced Battery Technologies Research
Authors: Lindsay Bassman | Aravind Krishnamoorthy | Pankaj Rajak | Fuyuki Shimojo | Rajiv K. Kalia | Aiichiro Nakano | Priya Vashishta
Source: Bulletin of the American Physical Society (article, 2019-03-07)
Field: Materials Science — Materials Chemistry

Quantum-simulation-informed machine learning of dynamic properties of two-dimensional and layered materials Computer science ; Quantum ; Materials science ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: Jon Paul Janet | Fang Liu | Aditya Nandy | Chenru Duan | Tzuhsiung Yang | Sean Lin | Heather J. Kulik
Source: DSpace@MIT (Massachusetts Institute of Technology) (article, 2019-03-01)
Field: Chemistry — Physical and Theoretical Chemistry

DesDesigning in the Face of Uncertainty: Exploiting Electronic Structure and Machine Learning Models for Discovery in Inorganic Chemistry Recent transformative advances in computing power and algorithms have made computational chemistry central to the discovery and design of new molecules and materials. First-principles simulations are increasingly accurate and applicable to large systems with the speed needed for high-throughput computational screening. Despite these strides, the combinatorial challenges associated with the vastness of chemical space mean that more than just fast and accurate computational tools are needed for accelerated chemical discovery. In transition-metal chemistry and catalysis, unique challenges arise. The variable spin, oxidation state, and coordination environments favored by elements with well-localized d or f electrons provide great opportunity for tailoring properties in catalytic or functional (e.g., magnetic) materials but also add layers of uncertainty to any design strategy. We outline five key mandates for realizing computationally driven accelerated discovery in inorganic chemistry: (i) fully automated simulation of new compounds, (ii) knowledge of prediction sensitivity or accuracy, (iii) faster-than-fast property prediction methods, (iv) maps for rapid chemical space traversal, and (v) a means to reveal design rules on the kilocompound scale. Through case studies in open-shell transition-metal chemistry, we describe how advances in methodology and software in each of these areas bring about new chemical insights. We conclude with our outlook on the next steps in this process toward realizing fully autonomous discovery in inorganic chemistry using computational chemistry. Theory of computation ; Face (sociological concept) ; Computer science ; Artificial intelligence ; Machine learning ; Chemistry ; Biochemical engineering ; Nanotechnology ; Data science ; Materials science ; Engineering ; Algorithm ; Sociology History and advancements in chemistry ; Machine Learning in Materials Science