jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Lauri Himanen | Marc O. J. Jäger | Eiaki V. Morooka | Filippo Federici Canova | Yashasvi S. Ranawat | David Gao | Patrick Rinke | Adam S. Foster
Source: Computer Physics Communications (article, 2019-09-26)
Field: Materials Science — Materials Chemistry

DScribe: Library of descriptors for machine learning in materials science DScribe is a software package for machine learning that provides popular feature transformations (“descriptors”) for atomistic materials simulations. DScribe accelerates the application of machine learning for atomistic property prediction by providing user-friendly, off-the-shelf descriptor implementations. The package currently contains implementations for Coulomb matrix, Ewald sum matrix, sine matrix, Many-body Tensor Representation (MBTR), Atom-centered Symmetry Function (ACSF) and Smooth Overlap of Atomic Positions (SOAP). Usage of the package is illustrated for two different applications: formation energy prediction for solids and ionic charge prediction for atoms in organic molecules. The package is freely available under the open-source Apache License 2.0. Program Title: DScribe Program Files doi: http://dx.doi.org/10.17632/vzrs8n8pk6.1 Licensing provisions: Apache-2.0 Programming language: Python/C/C++ Supplementary material: Supplementary Information as PDF Nature of problem: The application of machine learning for materials science is hindered by the lack of consistent software implementations for feature transformations. These feature transformations, also called descriptors, are a key step in building machine learning models for property prediction in materials science. Solution method: We have developed a library for creating common descriptors used in machine learning applied to materials science. We provide an implementation the following descriptors: Coulomb matrix, Ewald sum matrix, sine matrix, Many-body Tensor Representation (MBTR), Atom-centered Symmetry Functions (ACSF) and Smooth Overlap of Atomic Positions (SOAP). The library has a python interface with computationally intensive routines written in C or C++. The source code, tutorials and documentation are provided online. A continuous integration mechanism is set up to automatically run a series of regression tests and check code coverage when the codebase is updated. Computer science ; Matrix (chemical analysis) ; Computational science ; Matrix multiplication ; Tensor (intrinsic definition) ; Software ; Feature (linguistics) ; Implementation ; Artificial intelligence ; Algorithm ; Physics ; Chemistry ; Mathematics ; Quantum mechanics ; Geometry ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Nathan Brown | Marco Fiscato | Marwin H.S. Segler | Alain C. Vaucher
Source: Journal of Chemical Information and Modeling (article, 2019-03-19)
Field: Materials Science — Materials Chemistry

GuacaMol: Benchmarking Models for de Novo Molecular Design De novo design seeks to generate molecules with required property profiles by virtual design-make-test cycles. With the emergence of deep learning and neural generative models in many application areas, models for molecular design based on neural networks appeared recently and show promising results. However, the new models have not been profiled on consistent tasks, and comparative studies to well-established algorithms have only seldom been performed. To standardize the assessment of both classical and neural models for de novo molecular design, we propose an evaluation framework, GuacaMol, based on a suite of standardized benchmarks. The benchmark tasks encompass measuring the fidelity of the models to reproduce the property distribution of the training sets, the ability to generate novel molecules, the exploration and exploitation of chemical space, and a variety of single and multiobjective optimization tasks. The benchmarking open-source Python code and a leaderboard can be found on https://benevolent.ai/guacamol . Benchmarking ; Python (programming language) ; Suite ; Benchmark (surveying) ; Deep learning ; Generative grammar ; Fidelity Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Mohammad Yaser Masoomi | Ali Morsali | Amarajothi Dhakshinamoorthy | Hermenegildo Garcı́a
Source: Angewandte Chemie International Edition (review, 2019-04-12)
Field: Chemistry — Inorganic Chemistry

Mixed‐Metal MOFs: Unique Opportunities in Metal–Organic Framework (MOF) Functionality and Design Mixed-metal metal-organic frameworks (MM-MOFs) can be considered to be those MOFs having two different metals anywhere in the structure. Herein we summarize the various strategies for the preparation of MM-MOFs and some of their applications in adsorption, gas separation, and catalysis. It is shown that compared to homometallic MOFs, MM-MOFs bring about the opportunity to take advantage of the complexity and the synergism derived from the presence of different metal ions in the structure of MOFs. This is reflected in a superior performance and even stability of MM-MOFs respect to related single-metal MOFs. Emphasis is made on the use of MM-MOFs as catalysts for tandem reactions. Metal-organic framework ; Nanotechnology ; Materials science ; Chemistry ; Adsorption Metal-Organic Frameworks: Synthesis and Applications ; X-ray Diffraction in Crystallography ; Magnetism in coordination complexes
Authors: Frank Noé | Simon Olsson | Jonas Köhler | Hao Wu
Source: Science (article, 2019-09-05)
Field: Materials Science — Materials Chemistry

Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning Computing equilibrium states in condensed-matter many-body systems, such as solvated proteins, is a long-standing challenge. Lacking methods for generating statistically independent equilibrium samples in "one shot," vast computational effort is invested for simulating these systems in small steps, e.g., using molecular dynamics. Combining deep learning and statistical mechanics, we developed Boltzmann generators, which are shown to generate unbiased one-shot equilibrium samples of representative condensed-matter systems and proteins. Boltzmann generators use neural networks to learn a coordinate transformation of the complex configurational equilibrium distribution to a distribution that can be easily sampled. Accurate computation of free-energy differences and discovery of new configurations are demonstrated, providing a statistical mechanics tool that can avoid rare events during sampling without prior knowledge of reaction coordinates. Boltzmann distribution ; Statistical physics ; Statistical mechanics ; Boltzmann machine ; Sampling (signal processing) ; Computer science ; Transformation (genetics) ; Boltzmann constant ; Computation ; Molecular dynamics ; Artificial neural network ; Artificial intelligence ; Physics ; Chemistry ; Algorithm ; Thermodynamics ; Quantum mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Quantum many-body systems
Authors: Sara Abednatanzi | Parviz Gohari Derakhshandeh | Hannes Depauw | François‐Xavier Coudert | Henk Vrielinck | Pascal Van Der Voort | Karen Leus
Source: Chemical Society Reviews (review, 2019-01-01)
Field: Chemistry — Inorganic Chemistry

Mixed-metal metal–organic frameworks Mixed-metal MOFs are metal-organic frameworks that contain at least 2 different metal ions as nodes of their frameworks. They are prepared relatively easily by either a one-pot synthesis with a synthesis mixture containing the different metals, or by a post-synthetic ion-exchange method by soaking a monometallic MOF in a concentrated solution of a different (but compatible) metal-ion. More difficult is the accurate characterization of these materials. Is the formed product a mixture of monometallic MOFs or indeed a MOF with different metallic nodes? Are the metals randomly distributed or do they form domains? What is the oxidation state of the metals? How do the metals mutually influence each other, and impact the material's performance? Advanced characterization techniques are required e.g. X-ray absorption spectroscopy, magnetic resonance and electron microscopy. Computational tools at multiple scales are also often applied. In almost every case, a judicious choice of several techniques is required to unambiguously characterize the mixed-metal MOF. Although still in their infancy, several applications are emerging for mixed-metal MOFs, that improve on conventional monometallic MOFs. In the field of gas sorption and storage, especially the stability and affinity towards the target gases can be largely improved by introducing a second metal ion. In the case of flexible MOFs, the breathing behavior, and in particular the pressure at which the MOF opens, can be tailored. In heterogeneous catalysis, new cascade and tandem reactions become possible, with particular focus on reactions where the two metals in close proximity truly form a mixed-metal transition state. The bimetallic MOF should have a clear benefit over a mixture of the respective monometallic MOFs, and bimetallic enzymes can be a huge source of inspiration in this field. Another very promising application lies in the fields of luminescence and sensing. By tuning the lanthanide metals in mixed-metal lanthanide MOFs and by using the organic linkers as antennae, novel smart materials can be developed, acting as sensors and as thermochromic thermometers. Of course there are also still open challenges, as also mixed-metal MOFs do not escape the typical drawbacks of MOFs, such as low stability in moisture and possible metal leaching in liquids. The ease of synthesis of mixed-metal MOFs is a large bonus. In this critical review, we discuss in detail the synthesis, characterization, computational work and applications of mixed-metal MOFs. Metal-organic framework ; Sorption ; Metal ; Metal ions in aqueous solution ; Luminescence ; Chemistry ; Inorganic chemistry ; Nanotechnology ; Materials science ; Organic chemistry ; Adsorption Metal-Organic Frameworks: Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Gas Sensing Nanomaterials and Sensors
Authors: Raluca Ţărcan | Otto Todor-Boer | Ioan Petrovai | Cosmin Leordean | Simion Aştilean | Ioan Botiz
Source: Journal of Materials Chemistry C (article, 2019-12-23)
Field: Materials Science — Materials Chemistry

Reduced graphene oxide today A summary of the most important technological applications employing reduced graphene oxide. Graphene ; Materials science ; Oxide ; Nanotechnology ; Metallurgy Graphene research and applications ; Graphene and Nanomaterials Applications ; Carbon Nanotubes in Composites
Authors: Andrew F. Zahrt | Jeremy Henle | Brennan T. Rose | Yang Wang | William T. Darrow | Scott E. Denmark
Source: Science (article, 2019-01-18)
Field: Materials Science — Materials Chemistry

Prediction of higher-selectivity catalysts by computer-driven workflow and machine learning Catalyst design in asymmetric reaction development has traditionally been driven by empiricism, wherein experimentalists attempt to qualitatively recognize structural patterns to improve selectivity. Machine learning algorithms and chemoinformatics can potentially accelerate this process by recognizing otherwise inscrutable patterns in large datasets. Herein we report a computationally guided workflow for chiral catalyst selection using chemoinformatics at every stage of development. Robust molecular descriptors that are agnostic to the catalyst scaffold allow for selection of a universal training set on the basis of steric and electronic properties. This set can be used to train machine learning methods to make highly accurate predictive models over a broad range of selectivity space. Using support vector machines and deep feed-forward neural networks, we demonstrate accurate predictive modeling in the chiral phosphoric acid-catalyzed thiol addition to <i>N</i>-acylimines. Cheminformatics ; Workflow ; Computer science ; Machine learning ; Artificial intelligence ; Artificial neural network ; Molecular descriptor ; Set (abstract data type) ; Steric effects ; Quantitative structure–activity relationship ; Random forest ; Process (computing) ; Selection (genetic algorithm) ; Chemistry ; Computational chemistry ; Database ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Surface Chemistry and Catalysis
Authors: Ichiro Hisaki | Xin Chen | Kiyonori Takahashi | Takayoshi Nakamura
Source: Angewandte Chemie International Edition (review, 2019-03-20)
Field: Chemistry — Inorganic Chemistry

Designing Hydrogen‐Bonded Organic Frameworks (HOFs) with Permanent Porosity Designing organic components that can be used to construct porous materials enables the preparation of tailored functionalized materials. Research into porous materials has seen a resurgence in the past decade as a result of finding of self-standing porous molecular crystals (PMCs). Particularly, a number of crystalline systems with permanent porosity that are formed by self-assembly through hydrogen bonding (H-bonding) have been developed. Such systems are called hydrogen-bonded organic frameworks (HOFs). Herein we systematically describe H-bonding patterns (supramolecular synthons) and molecular structures (tectons) that have been used to achieve thermal and chemical durability, a large surface area, and functions, such as selective gas sorption and separation, which can provide design principles for constructing HOFs with permanent porosity. Porosity ; Materials science ; Synthon ; Supramolecular chemistry ; Hydrogen bond ; Sorption ; Chemical engineering ; Nanotechnology ; Durability ; Hydrogen ; Crystal engineering ; Molecule ; Composite material ; Organic chemistry ; Chemistry ; Adsorption Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Supramolecular Chemistry and Complexes
Authors: Grayson Deysher | Christopher E. Shuck | Kanit Hantanasirisakul | Nathan C. Frey | Alexandre C. Foucher | Kathleen Maleski | Asia Sarycheva | Vivek B. Shenoy | Eric A. Stach | Babak Anasori | Yury Gogotsi
Source: ACS Nano (article, 2019-12-05)
Field: Materials Science — Materials Chemistry

Synthesis of Mo<sub>4</sub>VAlC<sub>4</sub> MAX Phase and Two-Dimensional Mo<sub>4</sub>VC<sub>4</sub> MXene with Five Atomic Layers of Transition Metals MXenes are a family of two-dimensional (2D) transition metal carbides, nitrides, and carbonitrides with a general formula of M<sub><i>n</i>+1</sub>X<sub><i>n</i></sub>T<sub><i>x</i></sub>, in which two, three, or four atomic layers of a transition metal (M: Ti, Nb, V, Cr, Mo, Ta, etc.) are interleaved with layers of C and/or N (shown as X), and T<sub><i>x</i></sub> represents surface termination groups such as -OH, ═O, and -F. Here, we report the scalable synthesis and characterization of a MXene with five atomic layers of transition metals (Mo<sub>4</sub>VC<sub>4</sub>T<sub><i>x</i></sub>), by synthesizing its Mo<sub>4</sub>VAlC<sub>4</sub> MAX phase precursor that contains no other MAX phase impurities. These phases display twinning at their central M layers which is not present in any other known MAX phases or MXenes. Transmission electron microscopy and X-ray diffraction were used to examine the structure of both phases. Energy-dispersive X-ray spectroscopy, X-ray photoelectron spectroscopy, Raman spectroscopy, and high-resolution scanning transmission electron microscopy with energy-dispersive X-ray spectroscopy were used to study the composition of these materials. Density functional theory calculations indicate that other five transition metal-layer MAX phases (M'<sub>4</sub>M″AlC<sub>4</sub>) may be possible, where M' and M″ are two different transition metals. The predicted existence of additional Al-containing MAX phases suggests that more M<sub>5</sub>C<sub>4</sub>T<sub><i>x</i></sub> MXenes can be synthesized. Additionally, we characterized the optical, electronic, and thermal properties of Mo<sub>4</sub>VC<sub>4</sub>T<sub><i>x</i></sub>. This study demonstrates the existence of an additional subfamily of M<sub>5</sub>X<sub>4</sub>T<sub><i>x</i></sub> MXenes as well as a twinned structure, allowing for a wider range of 2D structures and compositions for more control over properties, which could lead to many different applications. Transition metal ; Materials science ; Phase transition ; Crystallography ; Condensed matter physics ; Physics ; Chemistry ; Catalysis MXene and MAX Phase Materials ; 2D Materials and Applications ; Advanced Memory and Neural Computing
Authors: Fengwei Xie | Tianlong Zhang | Peter Bryant | Valsala Kurusingal | John M. Colwell | Bronwyn Laycock
Source: Progress in Polymer Science (article, 2019-01-03)
Field: Materials Science — Polymers and Plastics

Degradation and stabilization of polyurethane elastomers Polyurethane ; Elastomer ; Materials science ; Degradation (telecommunications) ; Polyester ; Polymer ; Polybutadiene ; Biocompatibility ; Hydrolysis ; Polycarbonate ; Chemical stability ; Thermal stability ; Biodegradation ; Composite material ; Organic chemistry ; Chemistry ; Copolymer ; Computer science Polymer composites and self-healing ; Marine Biology and Environmental Chemistry ; Silicone and Siloxane Chemistry
Authors: Chunhui Dai | Bin Liu
Source: Energy & Environmental Science (article, 2019-10-01)
Field: Materials Science — Materials Chemistry

Conjugated polymers for visible-light-driven photocatalysis This review summarizes the recent advancements in π-conjugated polymers for various photocatalytic applications under visible light. Photocatalysis ; Conjugated system ; Visible spectrum ; Polymer ; Materials science ; Nanotechnology ; Photochemistry ; Optoelectronics ; Chemistry ; Catalysis ; Organic chemistry ; Composite material Covalent Organic Framework Applications ; Advanced Photocatalysis Techniques ; Luminescence and Fluorescent Materials
Authors: P. N. Navya | Anubhav Kaphle | Sangly P. Srinivas | Suresh K. Bhargava | Vincent M. Rotello | Hemant Kumar Daima
Source: Nano Convergence (review, 2019-07-14)
Field: Materials Science — Biomaterials

Current trends and challenges in cancer management and therapy using designer nanomaterials Nanotechnology has the potential to circumvent several drawbacks of conventional therapeutic formulations. In fact, significant strides have been made towards the application of engineered nanomaterials for the treatment of cancer with high specificity, sensitivity and efficacy. Tailor-made nanomaterials functionalized with specific ligands can target cancer cells in a predictable manner and deliver encapsulated payloads effectively. Moreover, nanomaterials can also be designed for increased drug loading, improved half-life in the body, controlled release, and selective distribution by modifying their composition, size, morphology, and surface chemistry. To date, polymeric nanomaterials, metallic nanoparticles, carbon-based materials, liposomes, and dendrimers have been developed as smart drug delivery systems for cancer treatment, demonstrating enhanced pharmacokinetic and pharmacodynamic profiles over conventional formulations due to their nanoscale size and unique physicochemical characteristics. The data present in the literature suggest that nanotechnology will provide next-generation platforms for cancer management and anticancer therapy. Therefore, in this critical review, we summarize a range of nanomaterials which are currently being employed for anticancer therapies and discuss the fundamental role of their physicochemical properties in cancer management. We further elaborate on the topical progress made to date toward nanomaterial engineering for cancer therapy, including current strategies for drug targeting and release for efficient cancer administration. We also discuss issues of nanotoxicity, which is an often-neglected feature of nanotechnology. Finally, we attempt to summarize the current challenges in nanotherapeutics and provide an outlook on the future of this important field. Nanotechnology ; Nanomaterials ; Nanomedicine ; Cancer therapy ; Drug delivery ; Cancer treatment ; Cancer ; Materials science ; Computer science ; Nanoparticle ; Medicine Nanoparticle-Based Drug Delivery ; Graphene and Nanomaterials Applications ; Nanoplatforms for cancer theranostics
Authors: Justin S. Smith | Benjamin Nebgen | R.I. Zubatyuk | Nicholas Lubbers | Christian Devereux | Kipton Barros | Sergei Tretiak | Olexandr Isayev | Adrián E. Roitberg
Source: Nature Communications (article, 2019-07-01)
Field: Materials Science — Materials Chemistry

Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning Computational modeling of chemical and biological systems at atomic resolution is a crucial tool in the chemist's toolset. The use of computer simulations requires a balance between cost and accuracy: quantum-mechanical methods provide high accuracy but are computationally expensive and scale poorly to large systems, while classical force fields are cheap and scalable, but lack transferability to new systems. Machine learning can be used to achieve the best of both approaches. Here we train a general-purpose neural network potential (ANI-1ccx) that approaches CCSD(T)/CBS accuracy on benchmarks for reaction thermochemistry, isomerization, and drug-like molecular torsions. This is achieved by training a network to DFT data then using transfer learning techniques to retrain on a dataset of gold standard QM calculations (CCSD(T)/CBS) that optimally spans chemical space. The resulting potential is broadly applicable to materials science, biology, and chemistry, and billions of times faster than CCSD(T)/CBS calculations. Computer science ; Scalability ; Thermochemistry ; Coupled cluster ; Artificial neural network ; Transferability ; Computational science ; Chemical space ; Artificial intelligence ; Machine learning ; Drug discovery ; Chemistry ; Physical chemistry ; Molecule Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Dejan Zagorac | Heimo Müller | S. Ruehl | Jelena Zagorac | Silke Rehme
Source: Journal of Applied Crystallography (article, 2019-09-23)
Field: Materials Science — Materials Chemistry

Recent developments in the Inorganic Crystal Structure Database: theoretical crystal structure data and related features The Inorganic Crystal Structure Database (ICSD) is the world's largest database of fully evaluated and published crystal structure data, mostly obtained from experimental results. However, the purely experimental approach is no longer the only route to discover new compounds and structures. In the past few decades, numerous computational methods for simulating and predicting structures of inorganic solids have emerged, creating large numbers of theoretical crystal data. In order to take account of these new developments the scope of the ICSD was extended in 2017 to include theoretical structures which are published in peer-reviewed journals. Each theoretical structure has been carefully evaluated, and the resulting CIF has been extended and standardized. Furthermore, a first classification of theoretical data in the ICSD is presented, including additional categories used for comparison of experimental and theoretical information. Crystal structure ; Scope (computer science) ; Computer science ; Database ; Data structure ; Crystal (programming language) ; Data mining ; Crystallography ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystal Structures and Properties