jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jan Wenzel | Hans Matter | Friedemann Schmidt
Source: Journal of Chemical Information and Modeling (article, 2019-01-07)
Field: Materials Science — Materials Chemistry

Predictive Multitask Deep Neural Network Models for ADME-Tox Properties: Learning from Large Data Sets Successful drug discovery projects require control and optimization of compound properties related to pharmacokinetics, pharmacodynamics, and safety. While volume and chemotype coverage of public and corporate ADME-Tox (absorption, distribution, excretion, metabolism, and toxicity) databases are constantly growing, deep neural nets (DNN) emerged as transformative artificial intelligence technology to analyze those challenging data. Relevant features are automatically identified, while appropriate data can also be combined to multitask networks to evaluate hidden trends among multiple ADME-Tox parameters for implicitly correlated data sets. Here we describe a novel, fully industrialized approach to parametrize and optimize the setup, training, application, and visual interpretation of DNNs to model ADME-Tox data. Investigated properties include microsomal lability in different species, passive permeability in Caco-2/TC7 cells, and logD. Statistical models are developed using up to 50 000 compounds from public or corporate databases. Both the choice of DNN hyperparameters and the type and quantity of molecular descriptors were found to be important for successful DNN modeling. Alternate learning of multiple ADME-Tox properties, resulting in a multitask approach, performs statistically superior on most studied data sets in comparison to DNN single-task models and also provides a scalable method to predict ADME-Tox properties from heterogeneous data. For example, predictive quality using external validation sets was improved from R<sup>2</sup> of 0.6 to 0.7 comparing single-task and multitask DNN networks from human metabolic lability data. Besides statistical evaluation, a new visualization approach is introduced to interpret DNN models termed "response map", which is useful to detect local property gradients based on structure fragmentation and derivatization. This method is successfully applied to visualize fragmental contributions to guide further design in drug discovery programs, as illustrated by CRCX3 antagonists and renin inhibitors, respectively. ADME ; Artificial intelligence ; Artificial neural network ; Machine learning ; Computer science ; Deep learning ; Multi-task learning ; Bioinformatics ; Task (project management) ; Biology ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Tian Wang | Cheng Zhang | Hichem Snoussi | Gang Zhang
Source: Advanced Functional Materials (article, 2019-11-20)
Field: Materials Science — Materials Chemistry

Machine Learning Approaches for Thermoelectric Materials Research Abstract Thermoelectric (TE) materials provide a solid‐state solution in waste heat recovery and refrigeration. During the past few decades, considerable effort has been devoted towards improving the performance of TE materials, which requires the optimization of multiple interrelated properties. A fundamental understanding of the interaction processes between the various energy carriers, such as electrons and phonons, is critical for advances in the development of TE materials. However, this understanding remains challenging primarily due to the inaccessibility of time scales using standard atomistic simulations. Machine learning methods, well known for their data‐analysis capability, have been successfully applied in research on TE materials in recent years. Here, an overview of the machine learning methods used in thermoelectric studies is provided, with the role that each machine learning method plays being systematically discussed. Furthermore, to date, the scale of thermoelectric‐related databases is much smaller than those in other fields, such as e‐commerce, image identification, and speech recognition. To overcome this limitation, possible strategies to utilize small databases in promoting materials science are also discussed. Finally, a brief conclusion and outlook are presented. Thermoelectric effect ; Thermoelectric materials ; Materials science ; Refrigeration ; Identification (biology) ; Computer science ; Nanotechnology ; Machine learning ; Artificial intelligence ; Engineering physics ; Data science ; Mechanical engineering Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Thermal properties of materials
Authors: Andrea Grisafi | Michele Ceriotti
Source: The Journal of Chemical Physics (article, 2019-11-26)
Field: Materials Science — Materials Chemistry

Incorporating long-range physics in atomic-scale machine learning The most successful and popular machine learning models of atomic-scale properties derive their transferability from a locality ansatz. The properties of a large molecule or a bulk material are written as a sum over contributions that depend on the configurations within finite atom-centered environments. The obvious downside of this approach is that it cannot capture nonlocal, nonadditive effects such as those arising due to long-range electrostatics or quantum interference. We propose a solution to this problem by introducing nonlocal representations of the system, which are remapped as feature vectors that are defined locally and are equivariant in O(3). We consider, in particular, one form that has the same asymptotic behavior as the electrostatic potential. We demonstrate that this framework can capture nonlocal, long-range physics by building a model for the electrostatic energy of randomly distributed point-charges, for the unrelaxed binding curves of charged organic molecular dimers, and for the electronic dielectric response of liquid water. By combining a representation of the system that is sensitive to long-range correlations with the transferability of an atom-centered additive model, this method outperforms current state-of-the-art machine-learning schemes and provides a conceptual framework to incorporate nonlocal physics into atomistic machine learning. Statistical physics ; Ansatz ; Range (aeronautics) ; Electrostatics ; Physics ; Locality ; Atom (system on chip) ; Representation (politics) ; Computer science ; Quantum mechanics ; Materials science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Surface Chemistry and Catalysis
Authors: David Armstrong | John M. Berrisford | M.J. Conroy | Aleksandras Gutmanas | Stephen Anyango | Preeti Choudhary | Alice R. Clark | Jose M Dana | Mandar Deshpande | Roisin Dunlop | Paul J. Gane | Romana Gáborová | Deepti Gupta | P Haslam | Jaroslav Koča | Lora Mak | Saqib Mir | Abhik Mukhopadhyay | Nurul Nadzirin | Sreenath Nair | Typhaine Paysan‐Lafosse | Lukáš Pravda | David Sehnal | Osman Salih | Oliver S. Smart | James Tolchard | Mihály Váradi | Radka Svobodová Vařeková | Hossam A. Zaki | Gerard J. Kleywegt | Sameer Velankar
Source: Nucleic Acids Research (article, 2019-10-25)
Field: Materials Science — Materials Chemistry

PDBe: improved findability of macromolecular structure data in the PDB The Protein Data Bank in Europe (PDBe), a founding member of the Worldwide Protein Data Bank (wwPDB), actively participates in the deposition, curation, validation, archiving and dissemination of macromolecular structure data. PDBe supports diverse research communities in their use of macromolecular structures by enriching the PDB data and by providing advanced tools and services for effective data access, visualization and analysis. This paper details the enrichment of data at PDBe, including mapping of RNA structures to Rfam, and identification of molecules that act as cofactors. PDBe has developed an advanced search facility with ∼100 data categories and sequence searches. New features have been included in the LiteMol viewer at PDBe, with updated visualization of carbohydrates and nucleic acids. Small molecules are now mapped more extensively to external databases and their visual representation has been enhanced. These advances help users to more easily find and interpret macromolecular structure data in order to solve scientific problems. Protein Data Bank ; Protein Data Bank (RCSB PDB) ; Visualization ; Identification (biology) ; Biology ; Computer science ; Computational biology ; Data curation ; Data visualization ; Representation (politics) ; Data science ; Information retrieval ; Bioinformatics ; Data mining ; Protein structure ; Biochemistry Enzyme Structure and Function ; Protein Structure and Dynamics ; Advanced Proteomics Techniques and Applications
Authors: Sebastian Matera | William F. Schneider | Andreas Heyden | Aditya Savara
Source: ACS Catalysis (article, 2019-06-13)
Field: Materials Science — Materials Chemistry

Progress in Accurate Chemical Kinetic Modeling, Simulations, and Parameter Estimation for Heterogeneous Catalysis Chemical kinetic modeling in heterogeneous catalysis is advancing in its ability to provide qualitatively or even quantitatively accurate prediction of real-world behavior because of new advances in the physical and chemical representations of catalytic systems, estimation of relevant kinetic parameters, and capabilities in kinetic modeling. This Perspective describes current trends and future areas of advancement in chemical kinetic modeling, simulation, and parameter estimation: ranging from elementary step calculations to multiscale modeling to the role of advanced statistical methods for incorporating uncertainties in predictions. Multiple new or growing methodologies are covered, examples are provided, and forward-looking topics for advancement are noted. Kinetic energy ; Biochemical engineering ; Chemical process ; Estimation theory ; Computer science ; Statistical physics ; Biological system ; Chemistry ; Physics ; Algorithm ; Engineering Catalytic Processes in Materials Science ; Catalysts for Methane Reforming ; Catalysis and Hydrodesulfurization Studies
Authors: Tae Young Jeong | Hakseong Kim | Sang‐Jun Choi | Kenji Watanabe | Takashi Taniguchi | Ki‐Ju Yee | Yong‐Sung Kim | Suyong Jung
Source: Nature Communications (article, 2019-08-23)
Field: Materials Science — Materials Chemistry

Spectroscopic studies of atomic defects and bandgap renormalization in semiconducting monolayer transition metal dichalcogenides Assessing atomic defect states and their ramifications on the electronic properties of two-dimensional van der Waals semiconducting transition metal dichalcogenides (SC-TMDs) is the primary task to expedite multi-disciplinary efforts in the promotion of next-generation electrical and optical device applications utilizing these low-dimensional materials. Here, with electron tunneling and optical spectroscopy measurements with density functional theory, we spectroscopically locate the mid-gap states from chalcogen-atom vacancies in four representative monolayer SC-TMDs-WS<sub>2</sub>, MoS<sub>2</sub>, WSe<sub>2</sub>, and MoSe<sub>2</sub>-, and carefully analyze the similarities and dissimilarities of the atomic defects in four distinctive materials regarding the physical origins of the missing chalcogen atoms and the implications to SC-mTMD properties. In addition, we address both quasiparticle and optical energy gaps of the SC-mTMD films and find out many-body interactions significantly enlarge the quasiparticle energy gaps and excitonic binding energies, when the semiconducting monolayers are encapsulated by non-interacting hexagonal boron nitride layers. Chalcogen ; Monolayer ; Quasiparticle ; Band gap ; van der Waals force ; Materials science ; Density functional theory ; Semiconductor ; Transition metal ; Atom (system on chip) ; Condensed matter physics ; Nanotechnology ; Chemical physics ; Optoelectronics ; Chemistry ; Physics ; Computational chemistry ; Crystallography ; Quantum mechanics ; Molecule 2D Materials and Applications ; MXene and MAX Phase Materials ; Chalcogenide Semiconductor Thin Films
Authors: Wenhong Yang | Timothy Tizhe Fidelis | Wen‐Hua Sun
Source: ACS Omega (review, 2019-12-24)
Field: Materials Science — Materials Chemistry

Machine Learning in Catalysis, From Proposal to Practicing Recently, machine learning (ML) methods have gained popularity and have performed as powerfully predictive tools in various areas of academic and industrious activities. In comparison, their application in catalysis has been underdeveloped. Relying on the rapid development of different algorithms and their implementation, it is the right timing to harvest the potential of ML in catalysis across academy and industry spectra. Herein, we discuss the current applications in the field of homogeneous and heterogeneous catalysis by using various ML approaches. To the best of our knowledge, modern statistical learning techniques will be a strong tool for computational optimization and discovery. This in turn will accurately extract the underlying mechanism in the model that converts readily available data and precatalysts into their promising and useful ones. Popularity ; Homogeneous ; Computer science ; Field (mathematics) ; Machine learning ; Mechanism (biology) ; Biochemical engineering ; Artificial intelligence ; Catalysis ; Data science ; Chemistry ; Engineering ; Organic chemistry ; Mathematics ; Political science Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Authors: Van‐Phuc Dinh | Thuy-Diem-Thuy Huynh | Hung M. Le | Van-Dong Nguyen | Vinh Ai Dao | Nguyễn Quang Hưng | Luu Anh Tuyen | Sunhwa Lee | Junsin Yi | Trinh Duy Nguyen | Le Van Tan
Source: RSC Advances (article, 2019-01-01)
Field: Chemistry — Analytical Chemistry

Insight into the adsorption mechanisms of methylene blue and chromium(<scp>iii</scp>) from aqueous solution onto pomelo fruit peel In this study, the biosorption mechanisms of methylene blue (MB) and Cr(iii) onto pomelo peel collected from our local fruits are investigated by combining experimental analysis with <i>ab initio</i> simulations. Factors that affect the adsorption such as pH, adsorption time, adsorbent dosage and initial adsorbate concentration, are fully considered. Five isotherm models-Langmuir, Freundlich, Sips, Temkin, and Dubinin-Radushkevich-are employed to estimate the capacity of pomelo peel adsorption, whereas four kinetic models-pseudo-first-order, pseudo-second-order, Elovich and intra-diffusion models-are also used to investigate the mechanisms of the uptake of MB and Cr(iii) onto the pomelo fruit peel. The maximum biosorption capacities calculated from the Langmuir models for MB and Cr(iii) at 303 K are, 218.5 mg g<sup>-1</sup> and 11.3 mg g<sup>-1</sup>, respectively. In particular, by combining, for the first time, the experimental FT-IR spectra with those obtained from <i>ab initio</i> calculations, we are able to demonstrate that the primary adsorption mechanisms of the uptake of MB onto pomelo fruit peel are electrostatic attraction and hydrogen-bond formations, whereas the adsorption mechanisms for Cr(iii) are electrostatic attraction and n-d interactions. Methylene blue ; Aqueous solution ; Adsorption ; Chemistry ; Chromium ; Nuclear chemistry ; Chemical engineering ; Organic chemistry ; Catalysis Dye analysis and toxicity ; Electrochemical sensors and biosensors ; Pineapple and bromelain studies
Authors: Maria A. Syzgantseva | Christopher P. Ireland | Fatmah Mish Ebrahim | Berend Smit | Olga A. Syzgantseva
Source: Journal of the American Chemical Society (article, 2019-03-27)
Field: Chemistry — Inorganic Chemistry

Metal Substitution as the Method of Modifying Electronic Structure of Metal–Organic Frameworks Targeted modification of electronic structure is an important step in the optimization of metal-organic frameworks (MOFs) for photovoltaic, sensing, and photocatalytic applications. The key parameters to be controlled include the band gap, the absolute energy position of band edges, the excited state charge separation, and degree of hybridization between metal and ligand sites. Partial metal replacement, or metal doping, within secondary building units is a promising, yet relatively unexplored route to modulate these properties in MOFs. Therefore, in the present study, a general method for selecting metal dopant is worked out in theory and validated by experiment, retaining MIL-125 and UiO-66 as the model systems. Metal mixing enables targeted optimization of key electronic structure parameters. This method is applicable to any MOF architecture and can serve as a roadmap for future synthesis of MOFs with predefined properties. Chemistry ; Substitution (logic) ; Metal ; Metal-organic framework ; Electronic structure ; Combinatorial chemistry ; Computational chemistry ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; X-ray Diffraction in Crystallography ; Metal Extraction and Bioleaching
Authors: David M. Wilkins | Andrea Grisafi | Yang Yang | Ka Un Lao | Robert A. DiStasio | Michele Ceriotti
Source: Proceedings of the National Academy of Sciences (article, 2019-02-07)
Field: Materials Science — Materials Chemistry

Accurate molecular polarizabilities with coupled cluster theory and machine learning The molecular dipole polarizability describes the tendency of a molecule to change its dipole moment in response to an applied electric field. This quantity governs key intra- and intermolecular interactions, such as induction and dispersion; plays a vital role in determining the spectroscopic signatures of molecules; and is an essential ingredient in polarizable force fields. Compared with other ground-state properties, an accurate prediction of the molecular polarizability is considerably more difficult, as this response quantity is quite sensitive to the underlying electronic structure description. In this work, we present highly accurate quantum mechanical calculations of the static dipole polarizability tensors of 7,211 small organic molecules computed using linear response coupled cluster singles and doubles theory (LR-CCSD). Using a symmetry-adapted machine-learning approach, we demonstrate that it is possible to predict the LR-CCSD molecular polarizabilities of these small molecules with an error that is an order of magnitude smaller than that of hybrid density functional theory (DFT) at a negligible computational cost. The resultant model is robust and transferable, yielding molecular polarizabilities for a diverse set of 52 larger molecules (including challenging conjugated systems, carbohydrates, small drugs, amino acids, nucleobases, and hydrocarbon isomers) at an accuracy that exceeds that of hybrid DFT. The atom-centered decomposition implicit in our machine-learning approach offers some insight into the shortcomings of DFT in the prediction of this fundamental quantity of interest. Cluster (spacecraft) ; Computer science ; Computational biology ; Artificial intelligence ; Statistical physics ; Physics ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Longlong Shu | Renhong Liang | Zhenggang Rao | Linfeng Fei | Shanming Ke | Yu Wang
Source: Journal of Advanced Ceramics (review, 2019-05-21)
Field: Materials Science — Materials Chemistry

Flexoelectric materials and their related applications: A focused review Flexoelectricity refers to the mechanical-electro coupling between strain gradient and electric polarization, and conversely, the electro-mechanical coupling between electric field gradient and mechanical stress. This unique effect shows a promising size effect which is usually large as the material dimension is shrunk down. Moreover, it could break the limitation of centrosymmetry, and has been found in numerous kinds of materials which cover insulators, liquid crystals, biological materials, and semiconductors. In this review, we will give a brief report about the recent discoveries in flexoelectricity, focusing on the flexoelectric materials and their applications. The theoretical developments in this field are also addressed. In the end, the perspective of flexoelectricity and some open questions which still remain unsolved are commented upon. Flexoelectricity ; Structural material ; Materials science ; Nanotechnology ; Polarization density ; Electric field ; Polarization (electrochemistry) ; Engineering physics ; Dielectric ; Composite material ; Physics ; Optoelectronics ; Chemistry Nonlocal and gradient elasticity in micro/nano structures ; Advanced Materials and Mechanics ; Composite Material Mechanics
Authors: Л. Б. Бойнович | Alexandre M. Emelyanenko | Kirill A. Emelyanenko | Evgeny Modin
Source: ACS Nano (article, 2019-04-05)
Field: Materials Science — Surfaces, Coatings and Films

<i>Modus Operandi</i> of Protective and Anti-icing Mechanisms Underlying the Design of Longstanding Outdoor Icephobic Coatings Atmospheric icing has become a global concern due to hazardous consequences of ice accretion on air, land, and sea transport and infrastructure. Icephobic surfaces due to their physicochemical properties facilitate a decrease in ice and snow accumulation under outdoor conditions. However, a serious problem of most superhydrophobic surfaces described in the literature is poor operational durability under harsh corrosive and abrasive loads characteristic of atmospheric operation. Here, we elucidate main surface phenomena determining the anti-icing behavior and show experimentally how different mechanisms contribute to long-term durability. For comprehensive exploitation of those mechanisms, we have applied a recently proposed strategy based on fine-tuning of both laser processing and protocols of deposition of the fluorooxysilanes onto the nanotextured surface. Prolonged outdoor tests evidence that a developed strategy for modification of materials on the nanolevel allows overcoming the main drawbacks of icephobic coatings reported so far and results in resistance to destroying atmospheric impacts. Icing ; Durability ; Materials science ; Hazardous waste ; Snow ; Environmental science ; Abrasive ; Deposition (geology) ; Nanotechnology ; Composite material ; Meteorology ; Waste management ; Engineering ; Geology Surface Modification and Superhydrophobicity ; Laser Material Processing Techniques ; Adhesion, Friction, and Surface Interactions
Authors: Kwok Wei Shah | Suxi Wang | Xiang Yun Debbie Soo | Jianwei Xu
Source: Polymers (review, 2019-11-08)
Field: Materials Science — Polymers and Plastics

Viologen-Based Electrochromic Materials: From Small Molecules, Polymers and Composites to Their Applications Organic materials have gained considerable attention for electrochromic (EC) applications owing to improved EC performance and good processability. As a class of well-recognized organic EC materials, viologens have received persistent attention due to the structural versatility and property tunability, and are major active EC components for most of the marketed EC devices. Over the past two decades, extensive efforts have been made to design and synthesize different types of viologen-based materials with enhanced EC properties. This review summarizes chemical structures, preparation and EC properties of various latest viologen-based electrochromes, including small viologen derivatives, main-chain viologen-based polymers, conjugated polymers with viologen side-chains and viologen-based organic/inorganic composites. The performance enhancement mechanisms are concisely discussed. The current marketed viologens-based electrochromic devices (ECDs) are briefly introduced and an outlook on the challenges and future exploration directions for viologen-based materials and their ECDs are also proposed. Viologen ; Electrochromism ; Electrochromic devices ; Materials science ; Polymer ; Nanotechnology ; Composite material ; Chemistry ; Organic chemistry ; Electrode Conducting polymers and applications ; Transition Metal Oxide Nanomaterials ; Polyoxometalates: Synthesis and Applications