jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xiaonan Wang | Rajanikanth Jayaseelan | DrJobie Gerken | DrGuoyu Lin
Source:  (preprint, 2021-09-10)
Field: Materials Science — Materials Chemistry

Artificial Intelligence in the Material Coefficient Evaluation Workflow Machine Learning (ML) and Digital Twins (DT) are at the heart of today’s different industries, ranging from advanced manufacturing to biomedical systems to resilient ecosystems, civil infrastructures, smart cities, and healthcare. They have become indispensable for solving complex problems in science, engineering, and technology development. The purpose of the MMLDT-CSET 2021 conference is to facilitate the transition of ML and DT from fundamental research to mainstream fields and technologies through advanced data science, mechanistic methods, and computational technologies. This 3-day conference features technical tracks of emerging ML-DT fields and applications, special public lectures, short courses, and demonstrations. The conference will be held in a hybrid format, featuring both on-site and virtual sessions. Workflow ; Computer science ; Artificial intelligence ; Database Machine Learning in Materials Science ; Digital Transformation in Industry
Authors: Irene Estévez | Fabricia de Matos Oliveira | Marcio Luiz de Oliveira | Rosa Maria Plácido Pereira | Nataliia Romanyshyn | Stéphane Clain | Georgi Smirnov | L. Rebouta | M. I. Vasilevskiy
Source:  (article, 2021-10-27)
Field: Chemistry — Inorganic Chemistry

ARTIFICIAL INTELLIGENCE ASSISTED POLARIMETRY FOR MATERIALS’ DISCRIMINATION AND CHARACTERIZATION Polarimetry ; Characterization (materials science) ; Computer science ; Artificial intelligence ; Materials science ; Optics ; Nanotechnology ; Physics Pigment Synthesis and Properties
Authors: Pierre Ladevèze | David Néron | Paul-William Gerbaud
Source: HAL (Le Centre pour la Communication Scientifique Directe) (preprint, 2021-01-01)
Field: Materials Science — Materials Chemistry

A Data-Driven Computation for complex materials with memory and application International audience Computer science ; Computation ; Parallel computing ; Programming language Machine Learning in Materials Science ; Parallel Computing and Optimization Techniques ; Neural Networks and Applications
Authors: Yue Yu | Huaiqian You | Stewart Silling | Marta D’Elia
Source:  (preprint, 2021-09-28)
Field: Materials Science — Materials Chemistry

Data-driven nonlocal models from molecular dynamics Machine Learning (ML) and Digital Twins (DT) are at the heart of today’s different industries, ranging from advanced manufacturing to biomedical systems to resilient ecosystems, civil infrastructures, smart cities, and healthcare. They have become indispensable for solving complex problems in science, engineering, and technology development. The purpose of the MMLDT-CSET 2021 conference is to facilitate the transition of ML and DT from fundamental research to mainstream fields and technologies through advanced data science, mechanistic methods, and computational technologies. This 3-day conference features technical tracks of emerging ML-DT fields and applications, special public lectures, short courses, and demonstrations. The conference will be held in a hybrid format, featuring both on-site and virtual sessions. Dynamics (music) ; Statistical physics ; Molecular dynamics ; Computer science ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Advanced Thermodynamics and Statistical Mechanics ; Spectroscopy and Quantum Chemical Studies
Authors: Yue Yu | Huaiqian You | Stewart Silling | Marta D’Elia
Source:  (preprint, 2021-09-28)
Field: Materials Science — Materials Chemistry

Data-driven nonlocal models from molecular dynamics Machine Learning (ML) and Digital Twins (DT) are at the heart of today’s different industries, ranging from advanced manufacturing to biomedical systems to resilient ecosystems, civil infrastructures, smart cities, and healthcare. They have become indispensable for solving complex problems in science, engineering, and technology development. The purpose of the MMLDT-CSET 2021 conference is to facilitate the transition of ML and DT from fundamental research to mainstream fields and technologies through advanced data science, mechanistic methods, and computational technologies. This 3-day conference features technical tracks of emerging ML-DT fields and applications, special public lectures, short courses, and demonstrations. The conference will be held in a hybrid format, featuring both on-site and virtual sessions. Dynamics (music) ; Statistical physics ; Molecular dynamics ; Computer science ; Data science ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Advanced Thermodynamics and Statistical Mechanics ; Spectroscopy and Quantum Chemical Studies
Authors: Ruru Ma | Thomas Linker | Liqiu Yang | Ankit Mishra | Deepak Kamal | Yifei Wang | Ken‐ichi Nomura | Fuyuki Shimojo | Aiichiro Nakano | Rajiv K. Kalia | Priya Vashishta
Source: Bulletin of the American Physical Society (article, 2021-03-16)
Field: Materials Science — Materials Chemistry

Design of novel polymer-metal interfaces using first principles-informed artificial intelligence techniques Materials science ; Polymer ; Computer science ; Nanotechnology ; Composite material Machine Learning in Materials Science ; Advanced Sensor and Energy Harvesting Materials ; Additive Manufacturing and 3D Printing Technologies
Authors: 亜希子 池田
Source: Institutional Repositories DataBase (IRDB) (article, 2021-10-01)
Field: Materials Science — Materials Chemistry

The pursuit for functional and soft 2D material structures : having curiosity making new things and new ways to use them. The blackened sample bottle "eureka" moment A large step toward the development of a catalyst for hydrogen evolution reaction Soft 2D materials are fascinating! A new perspective on material development using artificial intelligence Perspective (graphical) ; Curiosity ; Sample (material) ; Moment (physics) ; Key (lock) Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Chemical and Physical Properties of Materials
Authors: Ramachandra Canumalla
Source:  (article, 2021-05-26)
Field: Materials Science — Materials Chemistry

Conventional high-temperature titanium alloys: A data-driven analysis and future directions Materials science ; Titanium ; Metallurgy ; Computer science ; Environmental science Titanium Alloys Microstructure and Properties ; Intermetallics and Advanced Alloy Properties ; Additive Manufacturing Materials and Processes
Authors: Faisal Asad | Charbel Farhat | Philip Avery
Source:  (preprint, 2021-09-17)
Field: Materials Science — Materials Chemistry

A Mechanics-Informed, Data-Driven Approach to Material Modeling and Application to Multiscale Problems Machine Learning (ML) and Digital Twins (DT) are at the heart of today’s different industries, ranging from advanced manufacturing to biomedical systems to resilient ecosystems, civil infrastructures, smart cities, and healthcare. They have become indispensable for solving complex problems in science, engineering, and technology development. The purpose of the MMLDT-CSET 2021 conference is to facilitate the transition of ML and DT from fundamental research to mainstream fields and technologies through advanced data science, mechanistic methods, and computational technologies. This 3-day conference features technical tracks of emerging ML-DT fields and applications, special public lectures, short courses, and demonstrations. The conference will be held in a hybrid format, featuring both on-site and virtual sessions. Computer science ; Management science ; Statistical physics ; Physics ; Engineering Machine Learning in Materials Science ; Injection Molding Process and Properties ; Composite Material Mechanics
Authors: Toshiki Tokuhira | Yuki Tanaka | Y. Nakanishi | Kazuki Mita | Hiroshi Godai | Shiho Mukaida | Koichi Iwakabe | Kenji Nagata
Source: Seikei-Kakou (article, 2021-09-20)
Field: Materials Science — Materials Chemistry

Automatic Analysis of Synchrotron Radiated X-ray Scattering Data of Crystalline Polymers Using Artificial Intelligence for Science Materials science ; Synchrotron ; Polymer ; Synchrotron radiation ; Scattering ; X-ray ; Polymer science ; Optics ; Composite material ; Physics Machine Learning in Materials Science
Authors: Ramachandra Canumalla
Source:  (article, 2021-05-24)
Field: Materials Science — Materials Chemistry

A data-driven analysis of Ti-based alloys for aircraft landing gear beams and future directions Landing gear ; Aircraft industry ; Computer science ; Automotive engineering ; Aeronautics ; Environmental science ; Aerospace engineering ; Engineering Titanium Alloys Microstructure and Properties ; Metallurgy and Material Forming ; Aluminum Alloy Microstructure Properties
Authors: Emma Lejeune
Source:  (preprint, 2021-09-10)
Field: Materials Science — Materials Chemistry

Benchmark Datasets, Data Driven Modeling, and Predicting Full Field Quantities of Interest for Soft Heterogeneous Materials Machine Learning (ML) and Digital Twins (DT) are at the heart of today’s different industries, ranging from advanced manufacturing to biomedical systems to resilient ecosystems, civil infrastructures, smart cities, and healthcare. They have become indispensable for solving complex problems in science, engineering, and technology development. The purpose of the MMLDT-CSET 2021 conference is to facilitate the transition of ML and DT from fundamental research to mainstream fields and technologies through advanced data science, mechanistic methods, and computational technologies. This 3-day conference features technical tracks of emerging ML-DT fields and applications, special public lectures, short courses, and demonstrations. The conference will be held in a hybrid format, featuring both on-site and virtual sessions. Benchmark (surveying) ; Field (mathematics) ; Computer science ; Data mining ; Mathematics ; Cartography ; Geography Machine Learning in Materials Science
Authors: May Ali Alsaffar | Jamal M. Ali | Mohamed A. Abdel Ghany | Bamidele Victor Ayodele
Source: IOP Conference Series Earth and Environmental Science (article, 2021-06-01)
Field: Materials Science — Polymers and Plastics

Attaining material sustainability by incorporating nanoparticles additives to improve the mechanical properties of polypropylene composites: data driven modelling Abstract Polypropylene is commonly employed in several industrial applications such as packaging, cable insulation and automotive. Research interest has focused on how to improve its mechanical properties to reduce the effect of low impact toughness of polypropylene. One of the sustainable ways to achieve this is by incorporating graphene nanoplatelets to form a composite. This study investigates the application of a hybrid support vector machine (SVM) and artificial neural networks (ANN) model to predict the effect of incorporating graphene on the mechanical properties of polyproline composites. The effect of parameters such as maleic anhydride grafted polypropylene (MAPP), Talc, and exfoliated graphene nanoplatelets on the tensile strength and modulus of the polypropylene composites was modelled by using ANN. Testing various topologies was accomplished. An optimized ANN structure of 3-7-2 indicating 3 input-layer, 7 hidden layer, and 2 output-layer was tested. Both the SVM and the ANN predict well the mechanical properties of polyproline composites. However, the ANN with R 2 of 0.999 offers the best predictions. Polypropylene ; Materials science ; Composite material ; Toughness ; Ultimate tensile strength ; Composite number ; Maleic anhydride ; Polymer ; Copolymer Natural Fiber Reinforced Composites ; Polymer Nanocomposites and Properties ; Polymer crystallization and properties
Authors: Debabrata Palai | Debabrata Paloi | 俊太 千頭 | Shunta Chikami | 翔一 前田 | Shoichi Maeda | 智広 林 | Tomohiro Hayashi
Source: Institutional Repositories DataBase (IRDB) (article, 2021-12-01)
Field: Materials Science — Surfaces, Coatings and Films

Data-driven prediction of protein adsorption on model organic surfaces: from self-assembled monolayers to polymer surfaces Polymer ; Monolayer ; Adsorption ; Polymer adsorption ; Protein adsorption Polymer Surface Interaction Studies ; Diatoms and Algae Research ; Advanced Biosensing Techniques and Applications
Authors: Glen M. Hocky | Andrew Dickson White
Source: arXiv (Cornell University) (preprint, 2021-08-30)
Field: Materials Science — Materials Chemistry

Natural Language Processing Models That Automate Programming Will Transform Chemistry Research and Teaching Natural language processing models have emerged that can generate usable software and automate a number of programming tasks with high fidelity. These tools have yet to have an impact on the chemistry community. Yet, our initial testing demonstrates that this form of Artificial Intelligence is poised to transform chemistry and chemical engineering research. Here, we review developments that brought us to this point, examine applications in chemistry, and give our perspective on how this may fundamentally alter research and teaching. USable ; Computer science ; Perspective (graphical) ; Natural language ; Software engineering ; Chemistry ; Artificial intelligence ; Multimedia Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Shidang Xu | Jiali Li | Pengfei Cai | Xiaoli Liu | Bin Liu | Xiaonan Wang
Source: ChemRxiv (preprint, 2021-06-11)
Field: Materials Science — Materials Chemistry

A Self-Improving Photosensitizer Discovery System via Bayesian Optimization and Quantum Chemical Calculation Artificial intelligence (AI) based self-learning or self-improving material discovery system is the holy grail of next-generation material discovery and materials science. Herein, we demonstrate how to combine accurate prediction of material performance via quantum chemical calculations and Bayesian optimization-based active learning to realize a self-improving discovery system for high-performance photosensitizers (PS). Through self-improving cycles, such a system can improve the model prediction accuracy (best mean average error of 0.09 eV for singlet-triplet spitting) and high-performance PS search ability, realizing the efficient discovery of PS. From a molecular space with more than 7 million molecules, 5950 potential high-performance PSs were discovered. Bayesian optimization ; Photosensitizer ; Holy Grail ; Bayesian probability ; Computer science ; Singlet state ; Quantum chemical ; Chemical space ; Nanotechnology ; Artificial intelligence ; Materials science ; Molecule ; Drug discovery ; Physics ; Chemistry ; Excited state ; Quantum mechanics Machine Learning in Materials Science
Authors: Kazuhito Hashimoto
Source: Science and Technology of Advanced Materials Methods (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Launching <i>STAM Methods</i>: imaginative new publication platform for highlighting data-driven methods to accelerate research on innovative materials Science and Technology of Materials Science (STAM) celebrated its 20th anniversary in 2019 [1]. Thanks to the continuous efforts of former Editors-in-Chief, editorial board members, and members of ... Editorial board ; Engineering ethics ; Data science ; Computer science ; Engineering ; Library science Machine Learning in Materials Science ; Thin-Film Transistor Technologies ; Ion-surface interactions and analysis
Authors: Fabio Le Piane | Matteo Baldoni | Mauro Gáspari | Francesco Mercuri
Source: Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) (article, 2021-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Molecular And Materials Basic Ontology: development and first steps Advanced materials and their applications have become a key field of research, and it looks like this trend is not going to change soon. For that reason, the need for systematic and efficient methods for organizing knowledge in the field and conduct computational or experimental investigations is stronger than ever. In this work, we present a basic implementation of MAMBO - an ontology for molecular materials and their applications in real-life scenarios. The development of MAMBO has been guided by the needs of the research community involved in the development of novel materials with functional properties, with particular attention to the nanoscale. MAMBO aims at extending the current work in the field, while retaining a modular nature in order to allow straightforward extension of concepts and relations to neighboring domains. Our work is expected to enable the systematic integration of computational and experimental data in specific domains of interest (nanomaterials, molecular materials, organic an polymeric materials, supramolecular and bio-organic systems, etc.). Moreover, MAMBO is developed with a strong focus on the applications of data-driven frameworks for the design of novel materials with tailored characteristics Ontology ; Computer science ; Data science ; Epistemology ; Philosophy History and advancements in chemistry
Authors: Gao Li | Zhaoxian Qin | Sachil Sharma | Yongwu Peng
Source: Research Square (preprint, 2021-11-03)
Field: Materials Science — Materials Chemistry

Self-Assembly of Silver Clusters into One- and Two-Dimensional Structures as Artificial Intelligent Sensors of Alcohol Abstract Reggeization and response to external stimulus is an important part of artificial intelligence, which would significantly improve the quality of life in the future. The development of new materials for the design of sensitive and responsive sensors has become a crucial component. Here, two silver cluster-based polymers (Ag-CBPs), including one-dimensional (1D) {Ag22(L1)8(CF3CO2)14(CH3OH)2}n chain and two-dimensional (2D) {[Ag12(L2)2(CO2CF3)14(H2O)4(AgCO2CF3)4](HNEt3)2}n film, are designed and used to simulate the human nose - an elegant sensor to smells, to distinguish organic solvents. We study the relationship between the atomic structures of Ag-CBPs determined by X-ray diffraction and electrical properties in the presence of organic solvents (e.g. methanol, ethanol). The ligands, cations and the ligated solvent molecules not only play an important role in the self-assembly process of Ag-CBP materials, but also determine their physiochemical properties. An application of cluster-based polymers is demonstrated in the artificial intelligent sensors. Alcohol ; Nanotechnology ; Self-assembly ; Materials science ; Chemistry ; Organic chemistry Advanced Nanomaterials in Catalysis
Authors: C. Guguta | Danny Stam | Arianna Lanza | Gustavo Santiso-Quinones | Gunther Steinfeld | Doriana Ungur | Coca Iordache | Mihaela Pop
Source: Acta Crystallographica Section A Foundations and Advances (article, 2021-08-14)
Field: Materials Science — Materials Chemistry

The future of co-crystallisation: a new workflow based on AI predictions, the Crystal16 platform and electron diffraction Febuxostat (FB) is a poorly water-soluble BCS class II drug that is used for the treatment of the inflammatory disease arthritis urica (gout).FB has a rich solid form landscape, including many polymorphs, solvates, salts and a few co-crystals [1,2].With the aim of improving the aqueous solubility of FB we expanded the search for novel salts and co-crystals by applying modeling techniques followed by directed crystallization experiments.Novel salt and co-crystal forms of FB were obtained in a controlled manner using the Crystal16 platform [3].Making use of the integrated transmission technology together with 16 parallel reactors at a volume of 1 mL, the Crystal16 easily allows the scientist to assess salt or co-crystal formation.The salt/co-crystal formation was evidenced by powder X-ray diffraction and differential scanning calorimetry.Aqueous powder dissolution was carried out to determine if solubility improvement is achieved.Within the scope of this workflow, the nanocrystalline powders were not ideal for crystal structure elucidation from powder/single crystal X-ray diffraction but suited for electron diffraction experiments [4 -5 ].By using a dedicated electron diffractometer [6], the crystalline structures of these materials were easily accessible.Here we report on the successful crystallization and characterization of pharmaceutical relevant co-crystals using a new workflow: AI (artificial intelligence) predictions [7], the Crystal16 platform and an electron diffractometer. Crystallization ; Diffractometer ; Powder diffraction ; Materials science ; Crystallography ; Solubility ; Crystal (programming language) ; Dissolution ; Aqueous solution ; Chemical engineering ; Chemistry ; Crystal structure ; Computer science ; Organic chemistry Crystallization and Solubility Studies ; X-ray Diffraction in Crystallography ; Crystallography and molecular interactions
Authors: Xueping Zhang | Jiachen Yang | Hao Qu | Zhi Gen Yu | Dilip Krishna Nandakumar | Yaoxin Zhang | Swee Ching Tan
Source: Advanced Science (article, 2021-03-01)
Field: Chemistry — Spectroscopy

Humidity Management Systems: Machine‐Learning‐Assisted Autonomous Humidity Management System Based on Solar‐Regenerated Super Hygroscopic Complex (Adv. Sci. 6/2021) In article number 2003939, a super hygroscopic material‐driven humidity control system assisted by artificial intelligence with seamless autonomous operation was developed by Swee Ching Tan and co‐workers for concurrent freshwater production and low‐energy dehumidification. [Image: see text] Humidity ; Materials science ; Environmental science ; Computer science ; Process engineering ; Meteorology ; Engineering ; Physics Aerogels and thermal insulation
Authors: Tathagata Biswas | Arunima K. Singh
Source: npj Computational Materials (preprint, 2021-11-19)
Field: Materials Science — Materials Chemistry

Excitonic effects in absorption spectra of carbon dioxide reduction photocatalysts Abstract The formation and disassociation of excitons play a crucial role in any photovoltaic or photocatalytic application. However, excitonic effects are seldom considered in materials discovery studies due to the monumental computational cost associated with the examination of these properties. Here, we study the excitonic properties of nearly 50 photocatalysts using state-of-the-art Bethe–Salpeter formalism. These ~50 materials were recently recognized as promising photocatalysts for CO 2 reduction through a data-driven screening of 68,860 materials. Here, we propose three screening criteria based on the optical properties of these materials, taking excitonic effects into account, to further down select six materials. Furthermore, we study the correlation between the exciton binding energies obtained from the Bethe–Salpeter formalism and those obtained from the computationally much less-expensive Wannier–Mott model for these chemically diverse ~50 materials. This work presents a paradigm towards the inclusion of excitonic effects in future materials discovery for solar-energy harvesting applications. Exciton ; Formalism (music) ; Binding energy ; Photocatalysis ; Optical spectra ; Materials science ; Photovoltaic system ; Spectral line ; Physics ; Nanotechnology ; Chemical physics ; Chemistry ; Condensed matter physics ; Quantum mechanics Machine Learning in Materials Science ; Quantum Dots Synthesis And Properties ; Electronic and Structural Properties of Oxides