jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Konstantin Gubaev | Evgeny V. Podryabinkin | Alexander V. Shapeev
Source: The Journal of Chemical Physics (article, 2018-04-19)
Field: Materials Science — Materials Chemistry

Machine learning of molecular properties: Locality and active learning In recent years, the machine learning techniques have shown great potent1ial in various problems from a multitude of disciplines, including materials design and drug discovery. The high computational speed on the one hand and the accuracy comparable to that of density functional theory on another hand make machine learning algorithms efficient for high-throughput screening through chemical and configurational space. However, the machine learning algorithms available in the literature require large training datasets to reach the chemical accuracy and also show large errors for the so-called outliers-the out-of-sample molecules, not well-represented in the training set. In the present paper, we propose a new machine learning algorithm for predicting molecular properties that addresses these two issues: it is based on a local model of interatomic interactions providing high accuracy when trained on relatively small training sets and an active learning algorithm of optimally choosing the training set that significantly reduces the errors for the outliers. We compare our model to the other state-of-the-art algorithms from the literature on the widely used benchmark tests. Active learning (machine learning) ; Computational learning theory ; Locality ; Benchmark (surveying) ; Instance-based learning ; Online machine learning ; Training set ; Set (abstract data type) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning and Algorithms
Authors: Fabian Grünewald | Giulia Rossi | Alex H. de Vries | ‪Siewert J. Marrink | Luca Monticelli
Source: The Journal of Physical Chemistry B (article, 2018-07-02)
Field: Materials Science — Polymers and Plastics

Transferable MARTINI Model of Poly(ethylene Oxide) Motivated by the deficiencies of the previous MARTINI models of poly(ethylene oxide) (PEO), we present a new model featuring a high degree of transferability. The model is parametrized on (a) a set of 8 free energies of transfer of dimethoxyethane (PEO dimer) from water to solvents of varying polarity; (b) the radius of gyration in water at high dilution; and (c) matching angle and dihedral distributions from atomistic simulations. We demonstrate that our model behaves well in five different areas of application: (1) it produces accurate densities and phase behavior or small PEO oligomers and water mixtures; (2) it yields chain dimensions in good agreement with the experiment in three different solvents (water, diglyme, and benzene) over a broad range of molecular weights (∼1.2 kg/mol to 21 kg/mol); (3) it reproduces qualitatively the structural features of lipid bilayers containing PEGylated lipids in the brush and mushroom regime; (4) it is able to reproduce the phase behavior of several PEO-based nonionic surfactants in water; and (5) it can be combined with the existing MARTINI PS to model PS-PEO block copolymers. Overall, the new PEO model outperforms previous models and features a high degree of transferability. Ethylene oxide ; Poly ethylene ; Oxide ; Chemistry ; Materials science ; Ethylene ; Composite material ; Organic chemistry ; Polymer ; Copolymer Polymer crystallization and properties ; Surfactants and Colloidal Systems ; Advanced Polymer Synthesis and Characterization
Authors: Peter Eriksson | Alexey Tal | Andréas Skallberg | Caroline Brommesson | Zhangjun Hu | Robert Boyd | Weine Olovsson | Neal Fairley | Igor A. Abrikosov | Xuanjun Zhang | Kajsa Uvdal
Source: Scientific Reports (article, 2018-04-27)
Field: Materials Science — Materials Chemistry

Cerium oxide nanoparticles with antioxidant capabilities and gadolinium integration for MRI contrast enhancement The chelating gadolinium-complex is routinely used as magnetic resonance imaging (MRI) -contrast enhancer. However, several safety issues have recently been reported by FDA and PRAC. There is an urgent need for the next generation of safer MRI-contrast enhancers, with improved local contrast and targeting capabilities. Cerium oxide nanoparticles (CeNPs) are designed with fractions of up to 50% gadolinium to utilize the superior MRI-contrast properties of gadolinium. CeNPs are well-tolerated in vivo and have redox properties making them suitable for biomedical applications, for example scavenging purposes on the tissue- and cellular level and during tumor treatment to reduce in vivo inflammatory processes. Our near edge X-ray absorption fine structure (NEXAFS) studies show that implementation of gadolinium changes the initial co-existence of oxidation states Ce<sup>3+</sup> and Ce<sup>4+</sup> of cerium, thereby affecting the scavenging properties of the nanoparticles. Based on ab initio electronic structure calculations, we describe the most prominent spectral features for the respective oxidation states. The as-prepared gadolinium-implemented CeNPs are 3-5 nm in size, have r<sub>1</sub>-relaxivities between 7-13 mM<sup>-1</sup> s<sup>-1</sup> and show clear antioxidative properties, all of which means they are promising theranostic agents for use in future biomedical applications. Gadolinium ; Cerium ; Nanoparticle ; In vivo ; Chelation ; Cerium oxide ; MRI contrast agent ; Chemistry ; Materials science ; Magnetic resonance imaging ; XANES ; Oxide ; Nanotechnology ; Inorganic chemistry ; Medicine ; Organic chemistry ; Radiology ; Spectroscopy Lanthanide and Transition Metal Complexes ; Nanocluster Synthesis and Applications ; Advanced Nanomaterials in Catalysis
Authors: A. Pia P. Kröger | Jos M. J. Paulusse
Source: Journal of Controlled Release (review, 2018-08-08)
Field: Chemistry — Organic Chemistry

Single-chain polymer nanoparticles in controlled drug delivery and targeted imaging As a relatively new class of materials, single-chain polymer nanoparticles (SCNPs) just entered the field of (biomedical) applications, with recent advances in polymer science enabling the formation of bio-inspired nanosized architectures. Exclusive intramolecular collapse of individual polymer chains results in individual nanoparticles. With sizes an order of magnitude smaller than conventional polymer nanoparticles, SCNPs are in the size regime of many proteins and viruses (1-20 nm). Multifaceted syntheses and design strategies give access to a wide set of highly modular SCNP materials. This review describes how SCNPs have been rendered water-soluble and highlights ongoing research efforts towards biocompatible SCNPs with tunable properties for controlled drug delivery, targeted imaging and protein mimicry. Nanoparticle ; Nanotechnology ; Polymer ; Drug delivery ; Single chain ; Targeted drug delivery ; Biocompatible material ; Modular design ; Materials science ; Chemistry ; Computer science ; Organic chemistry ; Biomedical engineering ; Engineering Advanced Polymer Synthesis and Characterization ; Nanoparticle-Based Drug Delivery ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Hua Tong | Hajime Tanaka
Source: Physical Review X (article, 2018-03-14)
Field: Materials Science — Materials Chemistry

Revealing Hidden Structural Order Controlling Both Fast and Slow Glassy Dynamics in Supercooled Liquids The dynamics of a supercooled liquid near the glass transition is characterized by two-step relaxation, fast and slow relaxations. Because of the apparently disordered nature of glassy structures, there have been long debates over whether the origin of drastic slowing-down of the relaxation accompanied by heterogeneous dynamics is thermodynamic or dynamic. Furthermore, it has been elusive whether there is any deep connection between fast and slow modes. To settle these issues, here we introduce a set of new structural order parameters characterizing sterically favored structures with high local packing capability, and then access structure-dynamics correlation by a novel nonlocal approach. We find that the particle mobility is under control of the static order parameter field. The fast process is controlled by the instantaneous order parameter field locally, resulting in short-time particle-scale dynamics. Then the mobility field progressively develops with time t, following the initial order parameter field from disorder to more ordered regions. As is well known, the heterogeneity in the mobility field (dynamic heterogeneity) is maximized with a characteristic length 4 , when t reaches the relaxation time . We discover that this mobility pattern can be predicted solely by a spatial coarse graining of the initial order parameter field at t 0 over a length without any dynamical information. Furthermore, we find a relation 4 , indicating that the static length grows coherently with the dynamic one 4 upon cooling. This further suggests an intrinsic link between and : the growth of the static length is the origin of dynamical slowing-down. These we confirm for the first time in binary glass formers both in two and three spatial dimensions. Thus, a static structure has two intrinsic characteristic lengths, particle size and , which control dynamics in local and nonlocal manners, resulting in the emergence of the two key relaxation modes, fast and slow processes, respectively. Because the two processes share a common structural origin, we can even predict a dynamic propensity pattern at long timescale from the fast pattern. The presence of such intrinsic structure-dynamics correlation strongly indicates a thermodynamic nature of glass transition. Supercooling ; Statistical physics ; Relaxation (psychology) ; Dynamical heterogeneity ; Field (mathematics) ; Dynamics (music) ; Order (exchange) ; Length scale ; Granularity ; Physics ; Molecular dynamics ; Chemical physics ; Condensed matter physics ; Glass transition ; Thermodynamics ; Computer science ; Mathematics ; Mechanics ; Quantum mechanics Material Dynamics and Properties ; Liquid Crystal Research Advancements ; Theoretical and Computational Physics
Authors: Lin Shen | Weitao Yang
Source: Journal of Chemical Theory and Computation (article, 2018-02-13)
Field: Materials Science — Materials Chemistry

Molecular Dynamics Simulations with Quantum Mechanics/Molecular Mechanics and Adaptive Neural Networks Direct molecular dynamics (MD) simulation with ab initio quantum mechanical and molecular mechanical (QM/MM) methods is very powerful for studying the mechanism of chemical reactions in a complex environment but also very time-consuming. The computational cost of QM/MM calculations during MD simulations can be reduced significantly using semiempirical QM/MM methods with lower accuracy. To achieve higher accuracy at the ab initio QM/MM level, a correction on the existing semiempirical QM/MM model is an attractive idea. Recently, we reported a neural network (NN) method as QM/MM-NN to predict the potential energy difference between semiempirical and ab initio QM/MM approaches. The high-level results can be obtained using neural network based on semiempirical QM/MM MD simulations, but the lack of direct MD samplings at the ab initio QM/MM level is still a deficiency that limits the applications of QM/MM-NN. In the present paper, we developed a dynamic scheme of QM/MM-NN for direct MD simulations on the NN-predicted potential energy surface to approximate ab initio QM/MM MD. Since some configurations excluded from the database for NN training were encountered during simulations, which may cause some difficulties on MD samplings, an adaptive procedure inspired by the selection scheme reported by Behler [ Behler Int. J. Quantum Chem. 2015 , 115 , 1032 ; Behler Angew. Chem., Int. Ed. 2017 , 56 , 12828 ] was employed with some adaptions to update NN and carry out MD iteratively. We further applied the adaptive QM/MM-NN MD method to the free energy calculation and transition path optimization on chemical reactions in water. The results at the ab initio QM/MM level can be well reproduced using this method after 2-4 iteration cycles. The saving in computational cost is about 2 orders of magnitude. It demonstrates that the QM/MM-NN with direct MD simulations has great potentials not only for the calculation of thermodynamic properties but also for the characterization of reaction dynamics, which provides a useful tool to study chemical or biochemical systems in solution or enzymes. Molecular mechanics ; Molecular dynamics ; Computer science ; Artificial neural network ; Statistical physics ; Physics ; Classical mechanics ; Quantum mechanics ; Artificial intelligence Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: David J. Wales
Source: Annual Review of Physical Chemistry (article, 2018-04-20)
Field: Materials Science — Materials Chemistry

Exploring Energy Landscapes Recent advances in the potential energy landscapes approach are highlighted, including both theoretical and computational contributions. Treating the high dimensionality of molecular and condensed matter systems of contemporary interest is important for understanding how emergent properties are encoded in the landscape and for calculating these properties while faithfully representing barriers between different morphologies. The pathways characterized in full dimensionality, which are used to construct kinetic transition networks, may prove useful in guiding such calculations. The energy landscape perspective has also produced new procedures for structure prediction and analysis of thermodynamic properties. Basin-hopping global optimization, with alternative acceptance criteria and generalizations to multiple metric spaces, has been used to treat systems ranging from biomolecules to nanoalloy clusters and condensed matter. This review also illustrates how all this methodology, developed in the context of chemical physics, can be transferred to landscapes defined by cost functions associated with machine learning. Curse of dimensionality ; Energy landscape ; Context (archaeology) ; Metric (unit) ; Computer science ; Construct (python library) ; Fitness landscape ; Perspective (graphical) ; Statistical physics ; Data science ; Theoretical computer science ; Management science ; Artificial intelligence ; Physics ; Engineering ; Geography Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Phase Equilibria and Thermodynamics
Authors: Ayala Lampel | Rein V. Ulijn | Tell Tuttle
Source: Chemical Society Reviews (review, 2018-01-01)
Field: Materials Science — Biomaterials

Guiding principles for peptide nanotechnology through directed discovery Life's diverse molecular functions are largely based on only a small number of highly conserved building blocks - the twenty canonical amino acids. These building blocks are chemically simple, but when they are organized in three-dimensional structures of tremendous complexity, new properties emerge. This review explores recent efforts in the directed discovery of functional nanoscale systems and materials based on these same amino acids, but that are not guided by copying or editing biological systems. The review summarises insights obtained using three complementary approaches of searching the sequence space to explore sequence-structure relationships for assembly, reactivity and complexation, namely: (i) strategic editing of short peptide sequences; (ii) computational approaches to predicting and comparing assembly behaviours; (iii) dynamic peptide libraries that explore the free energy landscape. These approaches give rise to guiding principles on controlling order/disorder, complexation and reactivity by peptide sequence design. Sequence space ; Sequence (biology) ; Copying ; Peptide ; Computer science ; Nanotechnology ; Reactivity (psychology) ; Computational biology ; Chemical space ; Combinatorial chemistry ; Chemistry ; Biology ; Drug discovery ; Materials science ; Biochemistry ; Mathematics Supramolecular Self-Assembly in Materials ; Chemical Synthesis and Analysis ; Antimicrobial Peptides and Activities
Authors: Pasquale Sacco | Franco Furlani | Gaia de Marzo | Eleonora Marsich | Sergio Paoletti | Ivan Donati
Source: Gels (review, 2018-08-09)
Field: Materials Science — Biomaterials

Concepts for Developing Physical Gels of Chitosan and of Chitosan Derivatives Chitosan macro- and micro/nano-gels have gained increasing attention in recent years, especially in the biomedical field, given the well-documented low toxicity, degradability, and non-immunogenicity of this unique biopolymer. In this review we aim at recapitulating the recent gelling concepts for developing chitosan-based physical gels. Specifically, we describe how nowadays it is relatively simple to prepare networks endowed with different sizes and shapes simply by exploiting physical interactions, namely (i) hydrophobic effects and hydrogen bonds-mostly governed by chitosan chemical composition-and (ii) electrostatic interactions, mainly ensured by physical/chemical chitosan features, such as the degree of acetylation and molecular weight, and external parameters, such as pH and ionic strength. Particular emphasis is dedicated to potential applications of this set of materials, especially in tissue engineering and drug delivery sectors. Lastly, we report on chitosan derivatives and their ability to form gels. Additionally, we discuss the recent findings on a lactose-modified chitosan named Chitlac, which has proved to form attractive gels both at the macro- and at the nano-scale. Chitosan ; Chemistry ; Chemical engineering ; Materials science ; Nanotechnology ; Engineering ; Organic chemistry Nanocomposite Films for Food Packaging ; biodegradable polymer synthesis and properties ; Hydrogels: synthesis, properties, applications
Authors: Kyungdoc Kim | Seokho Kang | Jiho Yoo | Youngchun Kwon | Young Min Nam | Dongseon Lee | Inkoo Kim | Youn-Suk Choi | Yongsik Jung | Sangmo Kim | Won‐Joon Son | Jhun-Mo Son | Hyo Sug Lee | Sunghan Kim | Jaikwang Shin | Sungwoo Hwang
Source: npj Computational Materials (article, 2018-11-26)
Field: Materials Science — Materials Chemistry

Deep-learning-based inverse design model for intelligent discovery of organic molecules Abstract The discovery of high-performance functional materials is crucial for overcoming technical issues in modern industries. Extensive efforts have been devoted toward accelerating and facilitating this process, not only experimentally but also from the viewpoint of materials design. Recently, machine learning has attracted considerable attention, as it can provide rational guidelines for efficient material exploration without time-consuming iterations or prior human knowledge. In this regard, here we develop an inverse design model based on a deep encoder-decoder architecture for targeted molecular design. Inspired by neural machine language translation, the deep neural network encoder extracts hidden features between molecular structures and their material properties, while the recurrent neural network decoder reconstructs the extracted features into new molecular structures having the target properties. In material design tasks, the proposed fully data-driven methodology successfully learned design rules from the given databases and generated promising light-absorbing molecules and host materials for a phosphorescent organic light-emitting diode by creating new ligands and combinatorial rules. Computer science ; Deep learning ; Artificial intelligence ; Artificial neural network ; Process (computing) ; Rational design ; Architecture ; Machine translation ; Computer architecture ; Machine learning ; Nanotechnology ; Materials science ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Conducting polymers and applications
Authors: Linfeng Zhang | Jiequn Han | Handong Wang | Wissam A. Saidi | Roberto Car | E Weinan
Source: arXiv (Cornell University) (preprint, 2018-05-23)
Field: Materials Science — Materials Chemistry

End-to-end Symmetry Preserving Inter-atomic Potential Energy Model for Finite and Extended Systems Machine learning models are changing the paradigm of molecular modeling, which is a fundamental tool for material science, chemistry, and computational biology. Of particular interest is the inter-atomic potential energy surface (PES). Here we develop Deep Potential - Smooth Edition (DeepPot-SE), an end-to-end machine learning-based PES model, which is able to efficiently represent the PES for a wide variety of systems with the accuracy of ab initio quantum mechanics models. By construction, DeepPot-SE is extensive and continuously differentiable, scales linearly with system size, and preserves all the natural symmetries of the system. Further, we show that DeepPot-SE describes finite and extended systems including organic molecules, metals, semiconductors, and insulators with high fidelity. End-to-end principle ; Symmetry (geometry) ; Physics ; Energy (signal processing) ; Computer science ; Mathematics ; Geometry ; Quantum mechanics ; Artificial intelligence Machine Learning in Materials Science ; Nuclear Physics and Applications ; X-ray Diffraction in Crystallography
Authors: Wei Li | Kevin G. Field | Dane Morgan
Source: npj Computational Materials (article, 2018-07-12)
Field: Materials Science — Materials Chemistry

Automated defect analysis in electron microscopic images Abstract Electron microscopy and defect analysis are a cornerstone of materials science, as they offer detailed insights on the microstructure and performance of a wide range of materials and material systems. Building a robust and flexible platform for automated defect recognition and classification in electron microscopy will result in the completion of analysis orders of magnitude faster after images are recorded, or even online during image acquisition. Automated analysis has the potential to be significantly more efficient, accurate, and repeatable than human analysis, and it can scale with the increasingly important methods of automated data generation. Herein, an automated recognition tool is developed based on a computer vison–based approach; it sequentially applies a cascade object detector, convolutional neural network, and local image analysis methods. We demonstrate that the automated tool performs as well as or better than manual human detection in terms of recall and precision and achieves quantitative image/defect analysis metrics close to the human average. The proposed approach works for images of varying contrast, brightness, and magnification. These promising results suggest that this and similar approaches are worth exploring for detecting multiple defect types and have the potential to locate, classify, and measure quantitative features for a range of defect types, materials, and electron microscopic techniques. Computer science ; Artificial intelligence ; Convolutional neural network ; Pattern recognition (psychology) ; Computer vision ; Brightness ; Optics ; Physics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Image Processing Techniques and Applications