jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Bingqing Cheng | Mandy Bethkenhagen | Chris J. Pickard | Sébastien Hamel
Source: Nature Physics (article, 2021-09-23)
Field: Materials Science — Materials Chemistry

Phase behaviours of superionic water at planetary conditions Chemical physics ; Physics ; Phase (matter) ; Premelting ; Phase transition ; Energy landscape ; Diffusion ; Range (aeronautics) ; Phase diagram ; Nanopore ; Stacking ; Condensed matter physics ; Thermodynamics ; Nanotechnology ; Materials science Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Spectroscopy and Quantum Chemical Studies
Authors: Guannan Qian | Hai Huang | Fuchen Hou | Weina Wang | Yong Wang | Junhao Lin | Sang‐Jun Lee | Hanfei Yan | Yong S. Chu | P. Pianetta | Xiaojing Huang | Zi‐Feng Ma | Linsen Li | Yijin Liu
Source: Nano Energy (article, 2021-02-25)
Field: Materials Science — Polymers and Plastics

Selective dopant segregation modulates mesoscale reaction kinetics in layered transition metal oxide Materials science ; Dopant ; Mesoscale meteorology ; Kinetics ; Oxide ; Transition metal ; Metal ; Chemical engineering ; Chemical physics ; Nanotechnology ; Catalysis ; Metallurgy ; Doping ; Optoelectronics ; Meteorology ; Organic chemistry Transition Metal Oxide Nanomaterials ; Electronic and Structural Properties of Oxides ; Magnetic and transport properties of perovskites and related materials
Authors: Yunchao Xie | Chi Zhang | Heng Deng | Bujingda Zheng | Jheng‐Wun Su | Kenyon Shutt | Jian Lin
Source: ACS Applied Materials & Interfaces (article, 2021-10-28)
Field: Chemistry — Inorganic Chemistry

Accelerate Synthesis of Metal–Organic Frameworks by a Robotic Platform and Bayesian Optimization Synthesis of materials with desired structures, e.g., metal-organic frameworks (MOFs), involves optimization of highly complex chemical and reaction spaces due to multiple choices of chemical elements and reaction parameters/routes. Traditionally, realizing such an aim requires rapid screening of these nonlinear spaces by experimental conduction with human intuition, which is quite inefficient and may cause errors or bias. In this work, we report a platform that integrates a synthesis robot with the Bayesian optimization (BO) algorithm to accelerate the synthesis of MOFs. This robotic platform consists of a direct laser writing apparatus, precursor injecting and Joule-heating components. It can automate the MOFs synthesis upon fed reaction parameters that are recommended by the BO algorithm. Without any prior knowledge, this integrated platform continuously improves the crystallinity of ZIF-67, a demo MOF employed in this study, as the number of operation iterations increases. This work represents a methodology enabled by a data-driven synthesis robot, which achieves the goal of material synthesis with targeted structures, thus greatly shortening the reaction time and reducing energy consumption. It can be easily generalized to other material systems, thus paving a new route to the autonomous discovery of a variety of materials in a cost-effective way in the future. Bayesian optimization ; Materials science ; Computer science ; Robot ; Intuition ; Nanotechnology ; Nonlinear system ; Process engineering ; Artificial intelligence ; Engineering Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Catalytic Processes in Materials Science
Authors: Elmehdi Moumen | Ayalew H. Assen | Karim Adil | Youssef Belmabkhout
Source: Coordination Chemistry Reviews (article, 2021-05-29)
Field: Chemistry — Inorganic Chemistry

Versatility vs stability. Are the assets of metal–organic frameworks deployable in aqueous acidic and basic media? Robustness (evolution) ; Metal-organic framework ; Chemistry ; Nanotechnology ; Aqueous medium ; Aqueous solution ; Biochemical engineering ; Chemical stability ; Organic chemistry ; Materials science Metal-Organic Frameworks: Synthesis and Applications ; X-ray Diffraction in Crystallography ; Covalent Organic Framework Applications
Authors: Théo P. Gonçalves | Indranil Dutta | Kuo‐Wei Huang
Source: Chemical Communications (article, 2021-01-01)
Field: Chemistry — Inorganic Chemistry

Aromaticity in catalysis: metal ligand cooperation <i>via</i> ligand dearomatization and rearomatization Unlike the conventional model of transition metal catalysis, ligands in metal-ligand cooperative (or bifunctional) catalysis are involved in the substrate activations. Such processes have offered unique mechanistic understandings and led to new concepts for the catalyst design. In particular, unprecedented activities were discovered when the ligand could undergo dearomatization-rearomatization reactions during the catalytic cycle. Aromatization can provide an extra driving force to thermodynamics; consequently, it brings a new perspective to ligand platform design for catalysis. While numerous applications were demonstrated, the influences of changing ligand aromatic properties were often overlooked. In this article, representative ligand systems will be highlighted and a comparison between the Milstein and the Huang pincer systems will be discussed to provide theoretical and conceptual insights. Aromaticity ; Ligand (biochemistry) ; Aromatization ; Chemistry ; Catalysis ; Computational chemistry ; Metal ; Organic chemistry ; Molecule ; Receptor Asymmetric Hydrogenation and Catalysis ; Synthesis and Properties of Aromatic Compounds ; Catalysis for Biomass Conversion
Authors: V. Bhuvaneswari | M. Priyadharshini | Chokkalingam Deepa | D. Balaji | L. Rajeshkumar | M. Ramesh
Source: Materials Today Proceedings (review, 2021-01-01)
Field: Materials Science — Materials Chemistry

Deep learning for material synthesis and manufacturing systems: A review Big data ; Computer science ; Artificial intelligence ; Variety (cybernetics) ; Artificial neural network ; Characterization (materials science) ; Deep learning ; Machine learning ; Process (computing) ; Task (project management) ; Algorithm ; Materials science ; Nanotechnology ; Data mining ; Engineering ; Systems engineering Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Pablo A. Unzueta | Chandler Greenwell | Gregory J. O. Beran
Source: Journal of Chemical Theory and Computation (article, 2021-01-11)
Field: Materials Science — Materials Chemistry

Predicting Density Functional Theory-Quality Nuclear Magnetic Resonance Chemical Shifts via Δ-Machine Learning First-principles prediction of nuclear magnetic resonance chemical shifts plays an increasingly important role in the interpretation of experimental spectra, but the required density functional theory (DFT) calculations can be computationally expensive. Promising machine learning models for predicting chemical shieldings in general organic molecules have been developed previously, though the accuracy of those models remains below that of DFT. The present study demonstrates how much higher accuracy chemical shieldings can be obtained via the Δ-machine learning approach, with the result that the errors introduced by the machine learning model are only one-half to one-third the errors expected for DFT chemical shifts relative to experiment. Specifically, an ensemble of neural networks is trained to correct PBE0/6-31G chemical shieldings up to the target level of PBE0/6-311+G(2d,p). It can predict <sup>1</sup>H, <sup>13</sup>C, <sup>15</sup>N, and <sup>17</sup>O chemical shieldings with root-mean-square errors of 0.11, 0.70, 1.69, and 2.47 ppm, respectively. At the same time, the Δ-machine learning approach is 1-2 orders of magnitude faster than the target large-basis calculations. It is also demonstrated that the machine learning model predicts experimental solution-phase NMR chemical shifts in drug molecules with only modestly worse accuracy than the target DFT model. Finally, the ability to estimate the uncertainty in the predicted shieldings based on variations within the ensemble of neural network models is also assessed. Chemical shift ; Density functional theory ; Machine learning ; Artificial neural network ; Computer science ; Artificial intelligence ; Interpretation (philosophy) ; Algorithm ; Computational chemistry ; Chemistry ; Nuclear magnetic resonance ; Physics Machine Learning in Materials Science ; Molecular spectroscopy and chirality ; Computational Drug Discovery Methods
Authors: Mu He | Lei Zhang
Source: Computational Materials Science (article, 2021-05-12)
Field: Materials Science — Materials Chemistry

Machine learning and symbolic regression investigation on stability of MXene materials MXenes ; Stability (learning theory) ; Support vector machine ; Symbolic regression ; Artificial intelligence ; Machine learning ; Random forest ; Computer science ; Regression ; Logistic regression ; Regression analysis ; Construct (python library) ; Mathematics ; Materials science ; Statistics ; Nanotechnology MXene and MAX Phase Materials ; Advanced Memory and Neural Computing ; 2D Materials and Applications
Authors: Xiangcheng Shi | Xiaoyun Lin | Ran Luo | Shican Wu | Lulu Li | Zhi‐Jian Zhao | Jinlong Gong
Source: JACS Au (review, 2021-11-04)
Field: Materials Science — Materials Chemistry

Dynamics of Heterogeneous Catalytic Processes at Operando Conditions The rational design of high-performance catalysts is hindered by the lack of knowledge of the structures of active sites and the reaction pathways under reaction conditions, which can be ideally addressed by an <i>in situ</i>/<i>operando</i> characterization. Besides the experimental insights, a theoretical investigation that simulates reaction conditions-so-called <i>operando</i> modeling-is necessary for a plausible understanding of a working catalyst system at the atomic scale. However, there is still a huge gap between the current widely used computational model and the concept of <i>operando</i> modeling, which should be achieved through multiscale computational modeling. This Perspective describes various modeling approaches and machine learning techniques that step toward <i>operando</i> modeling, followed by selected experimental examples that present an <i>operando</i> understanding in the thermo- and electrocatalytic processes. At last, the remaining challenges in this area are outlined. Biochemical engineering ; Computer science ; Catalysis ; Characterization (materials science) ; Multiscale modeling ; Nanotechnology ; Perspective (graphical) ; Materials science ; Chemistry ; Artificial intelligence ; Computational chemistry ; Engineering Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalytic Processes in Materials Science
Authors: Stefano Sanvito | Alessandro Lunghi
Source: Trinity's Access to Research Output (TARA) (Trinity College Dublin) (article, 2021-03-19)
Field: Materials Science — Materials Chemistry

The Limit of Spin Lifetime in Solid-State Electronic Spins The development of spin qubits for quantum technologies requires their protection from the main source of finite-temperature decoherence: atomic vibrations. Here we eliminate one of the main barriers to the progress in this field by providing a complete first-principles picture of spin relaxation that includes up to two-phonon processes. Our method is based on machine learning and electronic structure theory and makes the prediction of spin lifetime in realistic systems feasible. We study a prototypical vanadium-based molecular qubit and reveal that the spin lifetime at high temperature is limited by Raman processes due to a small number of THz intramolecular vibrations. These findings effectively change the conventional understanding of spin relaxation in this class of materials and open new avenues for the rational design of long-living spin systems. Quantum decoherence ; Spin engineering ; Spin (aerodynamics) ; Qubit ; Spins ; Condensed matter physics ; Relaxation (psychology) ; Physics ; Quantum mechanics ; Quantum ; Spin polarization Machine Learning in Materials Science ; Molecular Junctions and Nanostructures ; Organic and Molecular Conductors Research
Authors: Qing‐Jie Li | Emine Küçükbenli | Stephen Lam | Boris Khaykovich | Efthimios Kaxiras | Ju Li
Source: Cell Reports Physical Science (article, 2021-03-01)
Field: Materials Science — Materials Chemistry

Development of robust neural-network interatomic potential for molten salt Molten salts are a promising class of ionic liquids for clean energy applications, such as nuclear and solar energy. However, efficient and accurate evaluation of salt properties from a fundamental, microscopic perspective remains a challenge. Here, we apply artificial neural networks to atomistic modeling of molten NaCl to accurately reproduce the properties from ab initio quantum mechanical calculations based on density functional theory (DFT). The obtained neural network interatomic potential (NNIP) effectively captures the effects of both long-range and short-range interactions, which are crucial for modeling ionic liquids. Extensive validations suggest that the NNIP is capable of predicting the structural, thermophysical, and transport properties of molten NaCl as well as properties of crystalline NaCl, demonstrating near-DFT accuracy and 103× higher efficiency in atomistic simulations. This application of NNIP suggests a paradigm shift from empirical/semiempirical/ab initio approaches to an efficient and accurate machine learning scheme in molten salt modeling. Molten salt ; Interatomic potential ; Ab initio ; Ionic liquid ; Density functional theory ; Molecular dynamics ; Ionic bonding ; Range (aeronautics) ; Artificial neural network ; Chemical physics ; Materials science ; Chemistry ; Thermodynamics ; Computational chemistry ; Computer science ; Ion ; Physics ; Machine learning Machine Learning in Materials Science ; Ionic liquids properties and applications ; Molten salt chemistry and electrochemical processes
Authors: Sharon E. Ashbrook | Zachary H. Davis | Russell E. Morris | Cameron M. Rice
Source: Chemical Science (review, 2021-01-01)
Field: Chemistry — Spectroscopy

<sup>17</sup>O NMR spectroscopy of crystalline microporous materials Microporous materials, containing pores and channels of similar dimensions to small molecules have a range of applications in catalysis, gas storage and separation and in drug delivery. Their complex structure, often containing different types and levels of positional, compositional and temporal disorder, makes structural characterisation challenging, with information on both long-range order and the local environment required to understand the structure-property relationships and improve the future design of functional materials. In principle, <sup>17</sup>O NMR spectroscopy should offer an ideal tool, with oxygen atoms lining the pores of many zeolites and phosphate frameworks, playing a vital role in host-guest chemistry and reactivity, and linking the organic and inorganic components of metal-organic frameworks (MOFs). However, routine study is challenging, primarily as a result of the low natural abundance of this isotope (0.037%), exacerbated by the presence of the quadrupolar interaction that broadens the spectral lines and hinders the extraction of information. In this Perspective, we will highlight the current state-of-the-art for <sup>17</sup>O NMR of microporous materials, focusing in particular on cost-effective and atom-efficient approaches to enrichment, the use of enrichment to explore chemical reactivity, the challenge of spectral interpretation and the approaches used to help this and the information that can be obtained from NMR spectra. Finally, we will turn to the remaining challenges, including further improving sensitivity, the high-throughput generation of multiple structural models for computational study and the possibility of <i>in situ</i> and <i>in operando</i> measurements, and give a personal perspective on how these required improvements can be used to help solve important problems in microporous materials chemistry. Microporous material ; Reactivity (psychology) ; Nuclear magnetic resonance spectroscopy ; Chemistry ; Spectroscopy ; Characterization (materials science) ; Molecule ; Nanotechnology ; Materials science ; Chemical physics ; Organic chemistry ; Physics Advanced NMR Techniques and Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Zeolite Catalysis and Synthesis
Authors: Ming Chen
Source: The European Physical Journal B (review, 2021-10-01)
Field: Materials Science — Materials Chemistry

Collective variable-based enhanced sampling and machine learning Sampling (signal processing) ; Computer science ; Variable (mathematics) ; Perspective (graphical) ; Machine learning ; Artificial intelligence ; Quality (philosophy) ; Energy (signal processing) ; Industrial engineering ; Data mining ; Data science ; Engineering ; Mathematics ; Statistics ; Physics Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Protein Structure and Dynamics
Authors: G. Ramkumar | Satyajeet Sahoo | G Anitha | S. Ramesh | P. Nirmala | M. Tamilselvi | Ram Subbiah | S. Rajkumar
Source: Advances in Materials Science and Engineering (article, 2021-01-01)
Field: Materials Science — Polymers and Plastics

An Unconventional Approach for Analyzing the Mechanical Properties of Natural Fiber Composite Using Convolutional Neural Network Over the past few years, natural fiber composites have been a strategy of rapid growth. The computational methods have become a significant tool for many researchers to design and analyze the mechanical properties of these composites. The mechanical properties such as rigidity, effects, bending, and tensile testing are carried out on natural fiber composites. The natural fiber composites were modeled by using some of the computation techniques. The developed convolutional neural network (CNN) is used to accurately predict the mechanical properties of these composites. The ground‐truth information is used for the training process attained from the finite element analyses below the plane stress statement. After completion of the training process, the developed design is authorized using the invisible data through the training. The optimum microstructural model is identified by a developed model embedded with a genetic algorithm (GA) optimizer. The optimizer converges to conformations with highly enhanced properties. The GA optimizer is used to improve the mechanical properties to have the soft elements in the area adjacent to the tip of the crack. Materials science ; Composite number ; Convolutional neural network ; Fiber ; Composite material ; Artificial neural network ; Natural (archaeology) ; Biological system ; Artificial intelligence ; Computer science Natural Fiber Reinforced Composites ; Mechanical Engineering and Vibrations Research ; Textile materials and evaluations
Authors: Mahdi Yonesi | Mario Garcia-Nieto | Gustavo V. Guinea | Fivos Panetsos | José Pérez‐Rigueiro | Daniel González‐Nieto
Source: Pharmaceutics (review, 2021-03-23)
Field: Materials Science — Biomaterials

Silk Fibroin: An Ancient Material for Repairing the Injured Nervous System Silk refers to a family of natural fibers spun by several species of invertebrates such as spiders and silkworms. In particular, silkworm silk, the silk spun by <i>Bombyx mori</i> larvae, has been primarily used in the textile industry and in clinical settings as a main component of sutures for tissue repairing and wound ligation. The biocompatibility, remarkable mechanical performance, controllable degradation, and the possibility of producing silk-based materials in several formats, have laid the basic principles that have triggered and extended the use of this material in regenerative medicine. The field of neural soft tissue engineering is not an exception, as it has taken advantage of the properties of silk to promote neuronal growth and nerve guidance. In addition, silk has notable intrinsic properties and the by-products derived from its degradation show anti-inflammatory and antioxidant properties. Finally, this material can be employed for the controlled release of factors and drugs, as well as for the encapsulation and implantation of exogenous stem and progenitor cells with therapeutic capacity. In this article, we review the state of the art on manufacturing methodologies and properties of fiber-based and non-fiber-based formats, as well as the application of silk-based biomaterials to neuroprotect and regenerate the damaged nervous system. We review previous studies that strategically have used silk to enhance therapeutics dealing with highly prevalent central and peripheral disorders such as stroke, Alzheimer's disease, Parkinson's disease, and peripheral trauma. Finally, we discuss previous research focused on the modification of this biomaterial, through biofunctionalization techniques and/or the creation of novel composite formulations, that aim to transform silk, beyond its natural performance, into more efficient silk-based-polymers towards the clinical arena of neuroprotection and regeneration in nervous system diseases. Fibroin ; SILK ; Biomaterial ; Biocompatibility ; Bombyx mori ; Spider silk ; Regenerative medicine ; Materials science ; Nanotechnology ; Biomedical engineering ; Stem cell ; Medicine ; Biology ; Cell biology ; Pathology ; Composite material Silk-based biomaterials and applications ; Electrospun Nanofibers in Biomedical Applications ; Nerve injury and regeneration
Authors: Oxana Ye. Rodionova | Sergey Kucheryavskiy | Alexey L. Pomerantsev
Source: Chemometrics and Intelligent Laboratory Systems (article, 2021-04-12)
Field: Chemistry — Analytical Chemistry

Efficient tools for principal component analysis of complex data— a tutorial Basic tools for exploration and interpretation of Principal Component Analysis (PCA) results are well-known and thoroughly described in many comprehensive tutorials. However, in the recent decade, several new tools have been developed. Some of them were originally created for solving authentication and classification tasks. In this paper we demonstrate that they can also be useful for the exploratory data analysis. We discuss several important aspects of the PCA exploration of high dimensional datasets, such as estimation of a proper complexity of PCA model, dependence on the data structure, presence of outliers, etc. We introduce new tools for the assessment of the PCA model complexity such as the plots of the degrees of freedom developed for the orthogonal and score distances, as well as the Extreme and Distance plots, which present a new look at the features of the training and test (new) data. These tools are simple and fast in computation. In some cases, they are more efficient than the conventional PCA tools. A simulated example provides an intuitive illustration of their application. Three real-world examples originated from various fields are employed to demonstrate capabilities of the new tools and ways they can be used. The first example considers the reproducibility of a handheld spectrometer using a dataset that is presented for the first time. The other two datasets, which describe the authentication of olives in brine and classification of wines by their geographical origin, are already known and are often used for the illustrative purposes. The paper is written in the form of tutorial; however, we do not touch upon the well-known things, such as the algorithms for the PCA decomposition, or interpretation of scores and loadings. Instead, we pay attention primarily to more advanced topics, such as exploration of data homogeneity, understanding and evaluation of an optimal model complexity. The tutorial is accompanied by links to free software that implements the tools. Principal component analysis ; Computer science ; Component (thermodynamics) ; Programming language ; Data mining ; Artificial intelligence Spectroscopy and Chemometric Analyses ; Fault Detection and Control Systems ; Advanced Statistical Methods and Models