jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zahid Hassan | Yannick Matt | Salma Begum | Manuel Tsotsalas | Stefan Bräse
Source: Advanced Functional Materials (article, 2020-01-09)
Field: Chemistry — Inorganic Chemistry

Assembly of Molecular Building Blocks into Integrated Complex Functional Molecular Systems: Structuring Matter Made to Order Abstract Function‐inspired design of molecular building blocks for their assembly into complex systems has been an objective in engineering nanostructures and materials modulation at nanoscale. This article summarizes recent research and inspiring progress in the design/synthesis of various custom‐made chiral, switchable, and highly responsive molecular building blocks for the construction of diverse covalent/noncovalent assemblies with tailored topologies, properties, and functions. Illustrating the judicious selection of building blocks, orthogonal functionalities, and innate physical/chemical properties that bring diversity and complex functions once reticulated into materials, special focus is given to their assembly into porous crystalline networks such as metal/covalent–organic frameworks (MOFs/COFs), surface‐mounted frameworks (SURMOFs), metal–organic cages/rings (MOCs), cross‐linked polymer gels, porous organic polymers (POPs), and related architectures that find diverse applications in life science and various other functional materials. Smart and stimuli‐responsive or dynamic building blocks, once embedded into materials, can be remotely modulated by external stimuli (light, electrons, chemicals, or mechanical forces) for controlling the structure and properties, thus being applicable for dynamic photochemical and mechanochemical control in constructing new forms of matter made to order. Then, an overview of current challenges, limitations, as well as future research directions and opportunities in this field, are discussed. Nanotechnology ; Materials science ; Structuring ; Biomimetics ; Smart material ; Covalent bond ; Computer science ; Physics Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Luminescence and Fluorescent Materials
Authors: Kejun Liu | Lihuan Wang | Renhao Dong⧫
Source: Journal of Materials Chemistry C (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Two-dimensional conjugated polymer films <i>via</i> liquid-interface-assisted synthesis toward organic electronic devices Development of conjugated polymers in two dimensions <italic>via</italic> liquid-interface-assisted synthesis as active layers for use in electronic devices. Conjugated system ; Materials science ; Polymer ; Interface (matter) ; Nanotechnology ; Chemical engineering ; Optoelectronics ; Composite material Covalent Organic Framework Applications ; Luminescence and Fluorescent Materials ; Advanced Photocatalysis Techniques
Authors: Adrien Soupart | Fabienne Alary | Jean‐Louis Heully | Paul I. P. Elliott | Isabelle M. Dixon
Source: Inorganic Chemistry (article, 2020-08-18)
Field: Materials Science — Materials Chemistry

Theoretical Study of the Full Photosolvolysis Mechanism of [Ru(bpy)<sub>3</sub>]<sup>2+</sup>: Providing a General Mechanistic Roadmap for the Photochemistry of [Ru(N^N)<sub>3</sub>]<sup>2+</sup>-Type Complexes toward Both Cis and Trans Photoproducts A complete mechanistic picture for the photochemical release of bipyridine (bpy) from the archetypal complex [Ru(bpy)<sub>3</sub>]<sup>2+</sup> is presented for the first time following the description of the ground and lowest triplet potential energy surfaces, as well as their key crossing points, involved in successive elementary steps along pathways toward <i>cis</i>- and <i>trans</i>-[Ru(bpy)<sub>2</sub>(NCMe)<sub>2</sub>]<sup>2+</sup>. This work accounts for two main pathways that are identified involving (a) two successive photochemical reactions for photodechelation, followed by the photorelease of a monodentate bpy ligand, and (b) a novel one-photon mechanism in which the initial photoexcitation is followed by dechelation, solvent coordination, and bpy release processes, all of which occur sequentially within the triplet excited-state manifold before the final relaxation to the singlet state and formation of the final photoproducts. For the reaction between photoexcited [Ru(bpy)<sub>3</sub>]<sup>2+</sup> and acetonitrile, which is taken as a model reaction, pathways toward cis and trans photoproducts are uphill processes, in line with the comparative inertness of the complex in this solvent. Factors involving the nature of the departing ligand and retained "spectator" ligands are considered, and their role in the selection of mechanistic pathways involving overall two sequential photon absorptions versus one photon absorption for the formation of both cis or trans photoproducts is discussed in relation to notable examples from the literature. This study ultimately provides a generalized roadmap of accessible photoproductive pathways for light-induced reactivity mechanisms of photolabile [Ru(N^N)(N^N')(N^N″)]<sup>2+</sup>-type complexes. Chemistry ; Mechanism (biology) ; Ruthenium ; Reaction mechanism ; Photochemistry ; Catalysis ; Organic chemistry Porphyrin and Phthalocyanine Chemistry ; Metal-Catalyzed Oxygenation Mechanisms ; Metal complexes synthesis and properties
Authors: Michael Nguyen‐Truong | Yan Vivian Li | Zhijie Wang
Source: Bioengineering (review, 2020-10-03)
Field: Materials Science — Biomaterials

Mechanical Considerations of Electrospun Scaffolds for Myocardial Tissue and Regenerative Engineering Biomaterials to facilitate the restoration of cardiac tissue is of emerging importance. While there are many aspects to consider in the design of biomaterials, mechanical properties can be of particular importance in this dynamically remodeling tissue. This review focuses on one specific processing method, electrospinning, that is employed to generate materials with a fibrous microstructure that can be combined with material properties to achieve the desired mechanical behavior. Current methods used to fabricate mechanically relevant micro-/nanofibrous scaffolds, in vivo studies using these scaffolds as therapeutics, and common techniques to characterize the mechanical properties of the scaffolds are covered. We also discuss the discrepancies in the reported elastic modulus for physiological and pathological myocardium in the literature, as well as the emerging area of in vitro mechanobiology studies to investigate the mechanical regulation in cardiac tissue engineering. Lastly, future perspectives and recommendations are offered in order to enhance the understanding of cardiac mechanobiology and foster therapeutic development in myocardial regenerative medicine. Mechanobiology ; Tissue engineering ; Regenerative medicine ; Biomedical engineering ; Electrospinning ; Materials science ; Biomaterial ; Nanotechnology ; Biofabrication ; Scaffold ; Medicine ; Stem cell ; Anatomy ; Cell biology Electrospun Nanofibers in Biomedical Applications ; Tissue Engineering and Regenerative Medicine ; Cardiac Structural Anomalies and Repair
Authors: Sunny Gupta | Alex Kutana | Boris I. Yakobson
Source: Nature Communications (article, 2020-06-12)
Field: Materials Science — Materials Chemistry

Heterobilayers of 2D materials as a platform for excitonic superfluidity Excitonic condensate has been long-sought within bulk indirect-gap semiconductors, quantum wells, and 2D material layers, all tried as carrying media. Here, we propose intrinsically stable 2D semiconductor heterostructures with doubly-indirect overlapping bands as optimal platforms for excitonic condensation. After screening hundreds of 2D materials, we identify candidates where spontaneous excitonic condensation mediated by purely electronic interaction should occur, and hetero-pairs Sb<sub>2</sub>Te<sub>2</sub>Se/BiTeCl, Hf<sub>2</sub>N<sub>2</sub>I<sub>2</sub>/Zr<sub>2</sub>N<sub>2</sub>Cl<sub>2</sub>, and LiAlTe<sub>2</sub>/BiTeI emerge promising. Unlike monolayers, where excitonic condensation is hampered by Peierls instability, or other bilayers, where doping by applied voltage is required, rendering them essentially non-equilibrium systems, the chemically-specific heterostructures predicted here are lattice-matched, show no detrimental electronic instability, and display broken type-III gap, thus offering optimal carrier density without any gate voltages, in true-equilibrium. Predicted materials can be used to access different parts of electron-hole phase diagram, including BEC-BCS crossover, enabling tantalizing applications in superfluid transport, Josephson-like tunneling, and dissipationless charge counterflow. Superfluidity ; Heterojunction ; Semiconductor ; Exciton ; Condensation ; Doping ; Quantum dot ; Rendering (computer graphics) 2D Materials and Applications ; Electronic and Structural Properties of Oxides ; Organic and Molecular Conductors Research
Authors: Yashaswi Pathak | Karandeep Singh Juneja | Girish Varma | Masahiro Ehara | U. Deva Priyakumar
Source: Physical Chemistry Chemical Physics (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Deep learning enabled inorganic material generator Recent years have witnessed utilization of modern machine learning approaches for predicting the properties of materials using available datasets. However, to identify potential candidates for material discovery, one has to systematically scan through a large chemical space and subsequently calculate the properties of all such samples. On the other hand, generative methods are capable of efficiently sampling the chemical space and can generate molecules/materials with desired properties. In this study, we report a deep learning based inorganic material generator (DING) framework consisting of a generator module and a predictor module. The generator module is developed based on conditional variational autoencoders (CVAEs) and the predictor module consists of three deep neural networks trained for predicting the enthalpy of formation, volume per atom and energy per atom chosen to demonstrate the proposed method. The predictor and generator modules have been developed using a one-hot key representation of the material composition. A series of tests were done to examine the robustness of the predictor models, to demonstrate the continuity of the latent material space, and its ability to generate materials exhibiting target property values. The DING architecture proposed in this paper can be extended to other properties based on which the chemical space can be efficiently explored for interesting materials/molecules. Chemical space ; Robustness (evolution) ; Generator (circuit theory) ; Computer science ; Deep learning ; Artificial intelligence ; Artificial neural network ; Representation (politics) ; Biological system ; Machine learning ; Chemistry ; Power (physics) ; Physics ; Thermodynamics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Maxwell Dylla | Alexander Dunn | Shashwat Anand | Anubhav Jain | G. Jeffrey Snyder
Source: Research (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning Chemical Guidelines for Engineering Electronic Structures in Half-Heusler Thermoelectric Materials Half-Heusler materials are strong candidates for thermoelectric applications due to their high weighted mobilities and power factors, which is known to be correlated to valley degeneracy in the electronic band structure. However, there are over 50 known semiconducting half-Heusler phases, and it is not clear how the chemical composition affects the electronic structure. While all the n-type electronic structures have their conduction band minimum at either the Γ- or <i>X</i>-point, there is more diversity in the p-type electronic structures, and the valence band maximum can be at either the Γ-, <i>L</i>-, or <i>W</i>-point. Here, we use high throughput computation and machine learning to compare the valence bands of known half-Heusler compounds and discover new chemical guidelines for promoting the highly degenerate <i>W</i>-point to the valence band maximum. We do this by constructing an "orbital phase diagram" to cluster the variety of electronic structures expressed by these phases into groups, based on the atomic orbitals that contribute most to their valence bands. Then, with the aid of machine learning, we develop new chemical rules that predict the location of the valence band maximum in each of the phases. These rules can be used to engineer band structures with band convergence and high valley degeneracy. Thermoelectric effect ; Materials science ; Engineering physics ; Thermoelectric materials ; Nanotechnology ; Engineering ; Physics ; Thermodynamics Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Heusler alloys: electronic and magnetic properties
Authors: Lena Simine | Thomas C. Allen | Peter J. Rossky
Source: Proceedings of the National Academy of Sciences (article, 2020-06-08)
Field: Materials Science — Materials Chemistry

Predicting optical spectra for optoelectronic polymers using coarse-grained models and recurrent neural networks Coarse-grained modeling of conjugated polymers has become an increasingly popular route to investigate the physics of organic optoelectronic materials. While ultraviolet (UV)-vis spectroscopy remains one of the key experimental methods for the interrogation of these materials, a rigorous bridge between simulated coarse-grained structures and spectroscopy has not been established. Here, we address this challenge by developing a method that can predict spectra of conjugated polymers directly from coarse-grained representations while avoiding repetitive procedures such as <i>ad hoc</i> back-mapping from coarse-grained to atomistic representations followed by spectral computation using quantum chemistry. Our approach is based on a generative deep-learning model: the long-short-term memory recurrent neural network (LSTM-RNN). The latter is suggested by the apparent similarity between natural languages and the mathematical structure of perturbative expansions of, in our case, excited-state energies perturbed by conformational fluctuations. We also use this model to explore the level of sensitivity of spectra to the coarse-grained representation back-mapping protocol. Our approach presents a tool uniquely suited for improving postsimulation analysis protocols, as well as, potentially, for including spectral data as input in the refinement of coarse-grained potentials. Computer science ; Representation (politics) ; Artificial neural network ; Spectroscopy ; Similarity (geometry) ; Spectral line ; Biological system ; Artificial intelligence ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Organic Electronics and Photovoltaics
Authors: Juhwan Noh | Geun Ho Gu | Sungwon Kim | Yousung Jung
Source: Journal of Chemical Information and Modeling (article, 2020-03-25)
Field: Materials Science — Materials Chemistry

Uncertainty-Quantified Hybrid Machine Learning/Density Functional Theory High Throughput Screening Method for Crystals Computational high throughput screening (HTS) has emerged as a significant tool in material science to accelerate the discovery of new materials with target properties in recent years. However, despite many successful cases in which HTS led to the novel discovery, currently, the major bottleneck in HTS is a large computational cost of density functional theory (DFT) calculations that scale cubically with system size, limiting the chemical space that can be explored. The present work aims at addressing this computational burden of HTS by presenting a machine learning (ML) framework that can efficiently explore the chemical space. Our model is built upon an existing crystal graph convolutional neural network (CGCNN) to obtain formation energy of a crystal structure but is modified to allow uncertainty quantification for each prediction using the hyperbolic tangent activation function and dropout algorithm (CGCNN-HD). The uncertainty quantification is particularly important since typical usage of CGCNN (due to the lack of gradient implementation) does not involve structural relaxation which could cause substantial prediction errors. The proposed method is benchmarked against an existing application that identified promising photoanode material among the >7,000 hypothetical Mg-Mn-O ternary compounds using all DFT-HTS. In our approach, we perform the approximate HTS using CGCNN-HD and refine the results using full DFT for those selected (denoted as ML/DFT-HTS). The proposed hybrid model reduces the required DFT calculations by a factor of >50 compared to the previous DFT-HTS in making the same discovery of Mg<sub>2</sub>MnO<sub>4</sub>, experimentally validated new photoanode material. Further analysis demonstrates that the addition of HD components with uncertainty measures in the CGCNN-HD model increased the <i>discoverability</i> of promising materials relative to all DFT-HTS from 30% (CGCNN) to 68% (CGCNN-HD). The present ML/DFT-HTS with uncertainty quantification can thus be a fast alternative to DFT-HTS for efficient exploration of the vast chemical space. Density functional theory ; Computer science ; Bottleneck ; Chemical space ; Uncertainty quantification ; Throughput ; Convolutional neural network ; Computational science ; Machine learning ; Algorithm ; Materials science ; Artificial intelligence ; Drug discovery ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Ling Tang | Zhenqing Yang | Tongqi Wen | Kai‐Ming Ho | Matt Kramer | Cai‐Zhuang Wang
Source: Physical Chemistry Chemical Physics (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Development of interatomic potential for Al–Tb alloys using a deep neural network learning method The developed deep neural network (DNN) potential can describe the structural properties of the Al<sub>90</sub>Tb<sub>10</sub> liquid and the formation energies of Al–Tb crystals with the accuracy of <italic>ab initio</italic> calculations. Interatomic potential ; Artificial neural network ; Deep learning ; Materials science ; Artificial intelligence ; Nanotechnology ; Computer science ; Chemistry ; Molecular dynamics ; Computational chemistry Machine Learning in Materials Science ; Thermodynamic and Structural Properties of Metals and Alloys ; X-ray Diffraction in Crystallography
Authors: Xiaofang Qi | Wencheng Ma | Yifan Dang | Wenjia Su | Lijun Liu
Source: Journal of Crystal Growth (article, 2020-08-06)
Field: Materials Science — Materials Chemistry

Optimization of the melt/crystal interface shape and oxygen concentration during the Czochralski silicon crystal growth process using an artificial neural network and a genetic algorithm Artificial neural network ; Crystal (programming language) ; Materials science ; Crystal growth ; Genetic algorithm ; Limiting oxygen concentration ; Biological system ; Silicon ; Algorithm ; Oxygen ; Computer science ; Chemistry ; Artificial intelligence ; Crystallography ; Machine learning ; Optoelectronics Solidification and crystal growth phenomena ; Thin-Film Transistor Technologies ; Silicon and Solar Cell Technologies
Authors: Huziel E. Sauceda | Michael Gastegger | Stefan Chmiela | Klaus‐Robert Müller | Alexandre Tkatchenko
Source: The Journal of Chemical Physics (article, 2020-09-24)
Field: Materials Science — Materials Chemistry

Molecular force fields with gradient-domain machine learning (GDML): Comparison and synergies with classical force fields Modern machine learning force fields (ML-FF) are able to yield energy and force predictions at the accuracy of high-level ab initio methods, but at a much lower computational cost. On the other hand, classical molecular mechanics force fields (MM-FF) employ fixed functional forms and tend to be less accurate, but considerably faster and transferable between molecules of the same class. In this work, we investigate how both approaches can complement each other. We contrast the ability of ML-FF for reconstructing dynamic and thermodynamic observables to MM-FFs in order to gain a qualitative understanding of the differences between the two approaches. This analysis enables us to modify the generalized AMBER force field by reparametrizing short-range and bonded interactions with more expressive terms to make them more accurate, without sacrificing the key properties that make MM-FFs so successful. Force field (fiction) ; Observable ; Complement (music) ; Computer science ; Domain (mathematical analysis) ; Work (physics) ; Field (mathematics) ; Range (aeronautics) ; Ab initio ; Statistical physics ; Class (philosophy) ; Artificial intelligence ; Classical mechanics ; Physics ; Mathematics ; Materials science ; Chemistry ; Mathematical analysis ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Eun Hyun Cho | Xuepeng Deng | Changlong Zou | Li‐Chiang Lin
Source: The Journal of Physical Chemistry C (article, 2020-12-07)
Field: Chemistry — Inorganic Chemistry

Machine Learning-Aided Computational Study of Metal–Organic Frameworks for Sour Gas Sweetening Nanoporous materials, such as metal–organic frameworks (MOFs), have shown great potential as adsorbents for separations in a wide variety of energy- or environment-related applications. One promising application is sour gas sweetening; a raw natural gas contains small amounts of H2S that can be detrimental to the efficient utilization of the energy source. However, the large database of nanoporous materials has made the discovery of optimum materials significantly demanding. While molecular simulations can play a complementary role in facilitating the materials search, their brute-force utilization still requires a vast amount of computational resources. In this study, we incorporate a machine learning algorithm with structural and chemistry descriptors as inputs for efficient screening. Specifically, the random forest regressor, which can also be useful for elucidating structure–property relationships, is employed. For reliable predictions with machine learning, the choices of features play considerably important roles. In addition to commonly adopted geometrical and chemical features, we propose and incorporate a set of newly designed features for training the model. These new features represent preferential binding sites of open-metal sites and dense framework atoms on the pore surface. Our analysis shows that the inclusion of the newly designed features greatly improves the machine learning performance. Our work can pave the way for the future design of nanoporous materials for sour gas sweetening. These newly designed features can also be used for the development of machine learning models for other applications, especially those involving molecules with strong dipole and/or quadruple moments, such as carbon capture. Nanoporous ; Sour gas ; Computer science ; Machine learning ; Artificial intelligence ; Nanotechnology ; Natural gas ; Materials science ; Chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography