jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Raymond Monroe
Source:  (article, 2020-12-02)
Field: Materials Science — Materials Chemistry

Developing Data-Driven Tools to Enable Formulating Non-Standard Steel Casting Alloy Composition and For Determining the Design Properties of Common Cast Steel Alloy Grades Alloy ; Metallurgy ; Casting ; Composition (language) ; Materials science Metal Alloys Wear and Properties ; Metallurgy and Material Forming ; Aluminum Alloy Microstructure Properties
Authors: Stelios Karozis | M.E. Kainourgiakis
Source:  (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Introduction to a Data-driven Analysis Tool of Molecular Dynamics Self-Assembled Lipid Bilayer Trajectories (short paper). Lipid bilayer ; Dynamics (music) ; Bilayer ; Computer science ; Nanotechnology ; Chemistry ; Physics ; Materials science ; Membrane Machine Learning in Materials Science ; Protein Structure and Dynamics ; Genetics, Bioinformatics, and Biomedical Research
Authors: Brooks H. Pate | Channing West | Reilly E. Sonstrom | Haley N. Scolati | Kevin Mayer | Martin Holdren
Source: Proceedings of the 2020 International Symposium on Molecular Spectroscopy (article, 2020-06-23)
Field: Chemistry — Physical and Theoretical Chemistry

CHEMICAL ANALYSIS CHALLENGES IN PHARMACEUTICAL CHEMISTRY AND UNDERGRADUATE PHYSICAL CHEMISTRY LABORATORY PROJECTS USING ROTATIONAL SPECTROSCOPY The rotational spectroscopy research group at the University of Virginia (UVa) is also tasked with administering the year-long undergraduate physical chemistry laboratory course. The course includes an introduction to quantum chemistry, experiments in molecular spectroscopy (NMR, rotational, vibrational, and electronic) and, in the spring, a capstone miniature research project. Over the past few years, molecular rotational spectroscopy using the research instruments at UVa has been extensively incorporated into the course. The spectroscopy measurements have been motivated by challenging chemical analysis problems in pharmaceutical chemistry and attempt to connect to current research topics in physical and analytical chemistry. Two laboratory projects from the past year will be described. In the fall semester, the lab considered the problem of correctly identifying diastereomers in molecules with multiple chiral centers. The project requires combining quantum chemistry calculations with spectral analysis. The project introduces the students into the broader theme of how quantum chemistry and artificial intelligence approaches are changing the way that spectra are analyzed. In the diastereomer analysis problem, a large focus is on the revolution in structure assignment by NMR spectroscopy that has occurred as that field has coupled quantum chemistry modeling of 1H and 13C NMR spectra with Bayesian statistical analysis. A spring miniature research project focuses on the recent interest in creating selectively deuterated active pharmaceutical ingredients that have higher efficacy and safety than their normal isotope versions. Since the ruling two years ago that deuterated versions of APIs would be awarded new patent protection, the pharmaceutical industry has started to focus on the unmet needs in chemistry required to produce high-purity, selectively deuterated drugs. One unmet need is the analysis of isotopologue and isotopomer impurities in deuterated reagents and this is a problem where rotational spectroscopy has unique chemical analysis capabilities. Students are tasked with evaluating the potential of existing methods for performing isotopic impurity testing and then perform an analysis using rotational spectroscopy on a commercial deuterated reagent for pharmaceutical chemistry. Chemistry ; Spectroscopy ; Analytical Chemistry (journal) ; Engineering physics ; Nanotechnology ; Organic chemistry ; Engineering ; Materials science ; Physics Various Chemistry Research Topics
Authors: M. M. Montemore | Chukwudi F. Nwaokorie | Gbolade O. Kayode
Source: ChemRxiv (preprint, 2020-04-06)
Field: Materials Science — Materials Chemistry

General Screening of Surface Alloys for Catalysis Intensive research in catalysis has resulted in design parameters for many important catalytic reactions; however, designing new catalysts remains difficult, partly due to the time and expense needed to screen a large number of potential catalytic surfaces. Here, we create a general, efficient model that can be used to screen surface alloys for many reactions without any quantum-based calculations. This model allows the prediction of the adsorption energies of a variety of species (explicitly shown for C, N, O, OH, H, S, K, F) on metal alloy surfaces that include combinations of nearly all of the d-block metals. We find that a few simple structural features, chosen using data-driven techniques and physical understanding, can be used to predict electronic structure properties. These electronic structure properties are then used to predict adsorption energies, which are in turn used to predict catalytic performance. This framework is interpretable and gives insight into how underlying structural features affect adsorption and catalytic performance. We apply the model to screen more than 107 unique surface sites on approximately 106 unique surfaces for 7 important reactions. We identify novel surfaces with high predicted catalytic performance, and demonstrate challenges and opportunities in catalyst development using surface alloys. This work shows the utility of a general, reusable model that can be applied in new contexts without requiring new data to be generated. Catalysis ; Adsorption ; Variety (cybernetics) ; Surface (topology) ; Alloy ; Metal ; Materials science ; Chemical physics ; Work (physics) ; Electronic structure ; Heterogeneous catalysis ; Nanotechnology ; Computer science ; Biochemical engineering ; Chemistry ; Computational chemistry ; Thermodynamics ; Physical chemistry ; Physics ; Mathematics ; Metallurgy ; Artificial intelligence ; Organic chemistry ; Engineering Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalytic Processes in Materials Science
Authors: M. M. Montemore | Chukwudi F. Nwaokorie | Gbolade O. Kayode
Source: ChemRxiv (preprint, 2020-01-26)
Field: Materials Science — Materials Chemistry

General Screening of Surface Alloys for Catalysis Intensive research in catalysis has resulted in design parameters for many important catalytic reactions; however, designing new catalysts remains difficult, partly due to the time and expense needed to screen a large number of potential catalytic surfaces. Here, we create a general, efficient model that can be used to screen surface alloys for many reactions without any quantum-based calculations. This model allows the prediction of the adsorption energies of a variety of species (explicitly shown for C, N, O, OH, H, S, K, F) on metal alloy surfaces that include combinations of nearly all of the d-block metals. We find that a few simple structural features, chosen using data-driven techniques and physical understanding, can be used to predict electronic structure properties. These electronic structure properties are then used to predict adsorption energies, which are in turn used to predict catalytic performance. This framework is interpretable and gives insight into how underlying structural features affect adsorption and catalytic performance. We apply the model to screen more than 107 unique surface sites on approximately 106 unique surfaces for 7 important reactions. We identify novel surfaces with high predicted catalytic performance, and demonstrate challenges and opportunities in catalyst development using surface alloys. This work shows the utility of a general, reusable model that can be applied in new contexts without requiring new data to be generated. Catalysis ; Adsorption ; Variety (cybernetics) ; Surface (topology) ; Alloy ; Metal ; Chemical physics ; Materials science ; Electronic structure ; Work (physics) ; Nanotechnology ; Computer science ; Biochemical engineering ; Chemistry ; Computational chemistry ; Thermodynamics ; Physical chemistry ; Physics ; Mathematics ; Organic chemistry ; Metallurgy ; Artificial intelligence Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalytic Processes in Materials Science
Authors: Rick Mullin﻿
Source: C&EN Global Enterprise (article, 2020-02-10)
Field: Materials Science — Materials Chemistry

AGC taps Citrine for AI in materials discovery AGC Glass Europe will collaborate with Citrine Informatics, a California–based specialist in artificial intelligence for materials discovery and development. AGC will provide experimental data to build AI models using Citrine’s iterative testing platform. The goal is glass with better optical and mechanical properties for markets including automotive and electronics. Citrine has been collaborating with Lanxess to improve glass fibers for plastics reinforcement and with BASF on environmental catalysts. — Computer science ; Materials science Machine Learning in Materials Science
Authors: U. Shokirov | Boriyhon Mukhitdinov | Sadritdin Turabdjanov | R. Choriev | X. Kadirov
Source: Chemical Technology, Control and Management (article, 2020-12-16)
Field: Chemistry — Inorganic Chemistry

Modeling of the synthesis of pyrroles in chemical production This work has studied the reaction of catalytic conversion of butyn-2-diol-1,4 and ammonia in the presence of complex mixed polyfunctional catalysts. It was found that in order to take into account uncertainties of various nature, it was proposed to solve the problem of ensuring the safety of chemical production at a qualitatively new level using new information technologies based on the creation of intelligent decision support systems for managing the safety of chemical technological processes and systems at all stages of occurrence. These systems make it possible to formulate recommendations for various decision-makers on the management of the safety of chemical production, both on the basis of operational observation data and using methods and models based on artificial intelligence embedded in expert systems, including the knowledge of specialists and experience in operating chemically dangerous objects. Production (economics) ; Chemistry ; Business ; Computer science ; Economics ; Microeconomics Vanadium and Halogenation Chemistry ; Chemical and Physical Properties in Aqueous Solutions ; Free Radicals and Antioxidants
Authors: Chuan-Yu Fang | Chih Chen
Source:  (article, 2020-10-21)
Field: Materials Science — Materials Chemistry

Research of mechanical properties of Copper foils with low temperature annealing With the development of the fifth-generation (5G) mobile communication network and artificial intelligence (AI), IC applications are moving towards diversification and ultimate performance. Demand drives the growth of technology, and the growth of technology is leading the advance of semiconductor industry. The semiconductor industry challenges the physical limits and aims to follow Moore’s Law by reducing the size of the chip continuously, but the front-end process has reached the limit. Relying on the back-end packaging techniques to continue Moore’s Law, packaging has progressed from 2D to 2.5D, and now it has progressed to 3D packaging. In 3D packaging, copper still plays an important role. Copper is the main material of TSV, and it is used in copper to copper direct bonding [1], which shows the importance of copper to the development of technology, so we are committed to studying the property of copper. Annealing (glass) ; Copper ; Materials science ; Metallurgy ; Temperature measurement ; Composite material ; Thermodynamics ; Physics Microstructure and mechanical properties ; Aluminum Alloys Composites Properties ; Metal Forming Simulation Techniques
Authors: Shinya Hosokawa
Source: physica status solidi (b) (article, 2020-11-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

The Structure of Non‐Crystalline Materials and Chalcogenide Functional Materials The structure of non-crystalline materials is one of long-standing issues to be solved in the areas of solid state physics, materials sciences and physical chemistry. Recent developments of intense synchrotron X-ray and neutron facilities enable to improve the quality of experimental structural data. In particular, element-specific techniques and new data analysis methods have recently been introduced for the determination of the structure of non-crystalline materials. Data-driven sciences also provide promising tools for handling experimental data. Moreover, to support or to confirm the experimental results, ab initio molecular dynamics calculations are indispensable. Recently, non-periodic sites in crystals, such as impurities and clusters, have become important subjects in materials science. Although the materials themselves are crystals, positions of impurity sites cannot be easily determined by usual diffraction methods, and thus, this field is scientifically similar to the field of non-crystalline materials. To present and discuss new findings and ideas concerning the above scientific subjects, we have organized “The 14th International Conference on the Structure of Non-Crystalline Materials” between 3 and 8 November 2019 in Kobe, Japan, and the subsequent satellite meeting of “Pan Pacific International Symposium on Chalcogenide Functional Materials” on the 10th and 11th of November 2019 at Kumamoto University, Japan. At the same time, we planned to publish this special issue in physica status solidi (b) concerning the conference topics. Authors of the special issue are both attendees and non-attendees of the above two meetings. As a result, we were able to collect and publish about 35 articles. We are very happy if readers of this special issue will find new scientific insights and help for their future studies. Kumamoto, Japan October 2020 Chalcogenide ; Publication ; Nanotechnology ; Materials science ; Engineering physics ; Chemistry ; Physics ; Political science ; Metallurgy Crystal Structures and Properties ; Solid-state spectroscopy and crystallography ; X-ray Diffraction in Crystallography
Authors: Brian DeCost | Jason Hattrick‐Simpers | Zachary Trautt | A. Gilad Kusne | Elı́as Campo | Martin A. Green
Source: arXiv (Cornell University) (preprint, 2020-03-18)
Field: Materials Science — Materials Chemistry

Scientific AI in materials science: a path to a sustainable and scalable\n paradigm Recently there has been an ever-increasing trend in the use of machine\nlearning (ML) and artificial intelligence (AI) methods by the materials\nscience, condensed matter physics, and chemistry communities. This perspective\narticle identifies key scientific, technical, and social opportunities that the\nmaterials community must prioritize to consistently develop and leverage\nScientific AI to provide a credible path towards the advancement of current\nmaterials-limited technologies. Here we highlight the intersections of these\nopportunities with a series of proposed paths forward. The opportunities are\nroughly sorted from scientific/technical (e.g., development of robust,\nphysically meaningful multiscale material representations) to social (e.g.,\npromoting an AI-ready workforce). The proposed paths forward range from\ndeveloping new infrastructure and capabilities to deploying them in industry\nand academia. We provide a brief introduction to AI in materials science and\nengineering, followed by detailed discussions of each of the opportunities and\npaths forward.\n Leverage (statistics) ; Scalability ; Workforce ; Computer science ; Data science ; Key (lock) ; Path (computing) ; Management science ; Engineering ethics ; Knowledge management ; Artificial intelligence ; Engineering ; Political science Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Ferroelectric and Negative Capacitance Devices
Authors: Samuel M. Blau | Hetal Patel | Evan Walter Clark Spotte‐Smith | Xiaowei Xie | Shyam Dwaraknath | Kristin Persson
Source: ChemRxiv (preprint, 2020-10-01)
Field: Materials Science — Materials Chemistry

A Chemically Consistent Graph Architecture for Massive Reaction Networks Applied to Solid-Electrolyte Interphase Formation Modeling reactivity with chemical reaction networks could yield fundamental mechanistic understanding that would expedite the development of processes and technologies for energy storage, medicine, catalysis, and more. Thus far, reaction networks have been limited in size by chemically inconsistent graph representations of multi-reactant reactions (e.g. A + B reacts to C) that cannot enforce stoichiometric constraints, precluding the use of optimized shortest-path algorithms. Here, we report a chemically consistent graph architecture that overcomes these limitations using a novel multi-reactant representation and iterative cost-solving procedure. Our approach enables the identification of all low-cost pathways to desired products in massive reaction networks containing reactions of any stoichiometry, allowing for the investigation of vastly more complex systems than previously possible. Leveraging our architecture, we construct the first ever electrochemical reaction network from first-principles thermodynamic calculations to describe the formation of the Li-ion solid electrolyte interphase (SEI), which is critical for passivation of the negative electrode. Using this network comprised of nearly 6,000 species and 4.5 million reactions, we interrogate the formation of a key SEI component, lithium ethylene dicarbonate. We automatically identify previously proposed mechanisms as well as multiple novel pathways containing counter-intuitive reactions that have not, to our knowledge, been reported in the literature. We envision that our framework and data-driven methodology will facilitate efforts to engineer the composition-related properties of the SEI - or of any complex chemical process - through selective control of reactivity. Computer science ; Electrochemistry ; Stoichiometry ; Chemistry ; Combinatorial chemistry ; Distributed computing ; Electrode ; Organic chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Fuel Cells and Related Materials
Authors: Lance Li
Source:  (article, 2020-04-01)
Field: Materials Science — Materials Chemistry

Research Toward Monolithic Three-Dimensional ICs Internet of things and artificial intelligence demand further performance improvements in integrated circuit systems. One ongoing effort is to continue the transistor scaling with either new device architectures or adopting new materials with superior gate controllability [1]. Another attractive approach is to construct threedimensional integrated circuits (3D ICs) with monolithic integration; for example, adding sensor functionalities, or constructing upper-layer logic circuits or memory devices on CMOS Si wafers [2]. The research on materials and processes compatible with the backend-of-line (BOEL) fabrication temperature (<400 °C), is urgently needed. In this presentation, I like to discuss on few potential components useful for achieving monolithic 3D ICs including 2D layered materials such as transition metal dichalcogenide based semiconductors, hexagonal boron nitride (hBN) insulators [3], and 1D semiconducting carbon nanotubes. The proof-of-concept monolithic integration of carbon nanotube transistors on our 28 nm CMOS technology wafers has also been demonstrated [4]. Materials science ; Integrated circuit ; Transistor ; Wafer ; CMOS ; Electronic circuit ; Nanotechnology ; Optoelectronics ; Computer science ; Electronic engineering ; Electrical engineering ; Engineering Graphene research and applications ; 2D Materials and Applications ; Advanced Memory and Neural Computing
Authors: Jiahao Yan | Han Wei | Han Xie | Xiaokun Gu | Hua Bao
Source: arXiv (Cornell University) (preprint, 2020-04-18)
Field: Materials Science — Materials Chemistry

Seeking for low thermal conductivity atomic configurations in $\rm{Si_{0.5}Ge_{0.5}}$ alloys with Bayesian Optimization The emergence of data-driven science has opened up new avenues for understanding the thermophysical properties of materials. For decades, alloys are known to possess very low thermal conductivity, but the extreme thermal conductivity can be achieved by alloying has never been identified. In this work, we combine the Bayesian optimization with a high throughput thermal conductivity calculation to search for the lowest thermal conductivity atomic configuration of $\rm{Si_{0.5}Ge_{0.5}}$ alloy. It is found layered structures are most beneficial for reducing the thermal conductivity among all atomic configurations, which is attributed to the strong branch-folding effect. Furthermore, the roles of interface roughness and layer thicknesses in producing the lowest thermal conductivity are investigated. Through another comprehensive search using Bayesian optimization, the layered structure with smooth interfaces and optimized layer thickness arrangement is identified as the optimal structure with the lowest thermal conductivity. Thermal conductivity ; Materials science ; Conductivity ; Alloy ; Bayesian optimization ; Work (physics) ; Layer (electronics) ; Thermal ; Thermodynamics ; Nanotechnology ; Composite material ; Computer science ; Chemistry ; Physics ; Machine learning ; Physical chemistry Machine Learning in Materials Science ; Thermal properties of materials ; Block Copolymer Self-Assembly
Authors: Guanjian Cheng | Xin-Gao Gong | Wan‐Jian Yin
Source: arXiv (Cornell University) (preprint, 2020-11-22)
Field: Materials Science — Materials Chemistry

Crystal structure prediction via combining graph network and Bayesian optimization We developed a density functional theory-free approach for crystal structure prediction via combing graph network (GN) and Bayesian optimization (BO). GN is adopted to establish the correlation model between crystal structure and formation enthalpies. BO is to accelerate searching crystal structure with optimal formation enthalpy. The approach of combining GN and BO for crystal Structure Searching (GN-BOSS), in principle, can predict crystal structure at given chemical compositions without additional constraints on cell shapes and lattice symmetries. The applicability and efficiency of GN-BOSS approach is then verified via solving the classical Ph-vV challenge. It can correctly predict the crystal structures of 24 binary compounds from scratch with averaged computational cost ~ 30 minutes each by only one CPU core. GN-BOSS approach may open a new avenue to data-driven crystal structural prediction without using the expensive DFT calculations. Boss ; Crystal structure ; Crystal structure prediction ; Crystal (programming language) ; Computer science ; Density functional theory ; Bayesian optimization ; Algorithm ; Materials science ; Theoretical computer science ; Artificial intelligence ; Chemistry ; Computational chemistry ; Crystallography Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions