jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: David Balcells | Bastian Bjerkem Skjelstad
Source: ChemRxiv (preprint, 2020-08-31)
Field: Materials Science — Materials Chemistry

The tmQM Dataset - Quantum Geometries and Properties of 86k Transition Metal Complexes &lt;div&gt;We report the transition metal quantum mechanics dataset (tmQM), which contains the geometries and properties of a large transition metal-organic compound space. tmQM is comprised of 86,665 mononuclear complexes extracted from the Cambridge Structural Database, including Werner, bioinorganic and organometallic complexes based on a large variety of organic ligands and 30 transition metals (the 3d, 4d and 5d from groups 3 to 12). All complexes are closed-shell, and with a formal charge in the range {+1, 0, -1}e. The tmQM dataset provides the Cartesian coordinates of all metal complexes optimized at the DFTB(GFN2-xTB) level, and their molecular size, stoichiometry, and metal node degree. The quantum properties were computed at the DFT(TPSSh-D3BJ/def2-SVP) level, and include the electronic and dispersion energies, HOMO and LUMO orbital energies, HOMO-LUMO gap, dipole moment, and natural charge of the metal center; DFTB(GFN2-xTB) polarizabilities are also provided. Pairwise representations showed the low correlation between these properties, providing nearly continuous maps with unusual regions of the chemical space; e.g. complexes combining large polarizabilities with wide HOMO-LUMO gaps, and complexes combining low-energy HOMO orbitals with electron-rich metal centers. The&lt;/div&gt;&lt;div&gt;tmQM dataset can be exploited in the data-driven discovery of new metal complexes, including predictive models based on machine learning. These models may have a strong impact on the fields in which transition metal chemistry plays a key role; e.g. catalysis, organic synthesis, and materials science. tmQM is an open dataset that can be downloaded free of charge from https://github.com/bbskjelstad/tmqm&lt;/div&gt; HOMO/LUMO ; Transition metal ; Chemistry ; Molecular orbital ; Computational chemistry ; Chemical physics ; Quantum chemistry ; Crystallography ; Molecule ; Catalysis ; Crystal structure ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; History and advancements in chemistry
Authors: Vitali Nesterov | Mario Wieser | Volker Röth
Source: Open MIND (preprint, 2020-10-08)
Field: Materials Science — Materials Chemistry

3DMolNet: A Generative Network for Molecular Structures With the recent advances in machine learning for quantum chemistry, it is now possible to predict the chemical properties of compounds and to generate novel molecules. Existing generative models mostly use a string- or graph-based representation, but the precise three-dimensional coordinates of the atoms are usually not encoded. First attempts in this direction have been proposed, where autoregressive or GAN-based models generate atom coordinates. Those either lack a latent space in the autoregressive setting, such that a smooth exploration of the compound space is not possible, or cannot generalize to varying chemical compositions. We propose a new approach to efficiently generate molecular structures that are not restricted to a fixed size or composition. Our model is based on the variational autoencoder which learns a translation-, rotation-, and permutation-invariant low-dimensional representation of molecules. Our experiments yield a mean reconstruction error below 0.05 Angstrom, outperforming the current state-of-the-art methods by a factor of four, and which is even lower than the spatial quantization error of most chemical descriptors. The compositional and structural validity of newly generated molecules has been confirmed by quantum chemical methods in a set of experiments. Generative grammar ; Computer science ; Artificial intelligence Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Data Processing Techniques
Authors: AkshatKumar Nigam | Robert Pollice | Mario Krenn | Gabriel dos Passos Gomes | Alán Aspuru‐Guzik
Source: ChemRxiv (preprint, 2020-12-16)
Field: Materials Science — Materials Chemistry

Beyond Generative Models: Superfast Traversal, Optimization, Novelty, Exploration and Discovery (STONED) Algorithm for Molecules using SELFIES Inverse design allows the design of molecules with desirable properties using property optimization. Deep generative models have recently been applied to tackle inverse design, as they possess the ability to optimize molecular properties directly through structure modification using gradients. While the ability to carry out direct property optimizations is promising, the use of generative deep learning models to solve practical problems requires large amounts of data and is very time-consuming. In this work, we propose STONED – a simple and efficient algorithm to perform interpolation and exploration in the chemical space, comparable to deep generative models. STONED bypasses the need for large amounts of data and training times by using string modifications in the SELFIES molecular representation. We achieve comparable performance on typical benchmarks without any training. We demonstrate applications in high-throughput virtual screening for the design of drugs, photovoltaics, and the construction of chemical paths, allowing for both property and structure-based interpolation in the chemical space. We anticipate our results to be a stepping stone for developing more sophisticated inverse design models and benchmarking tools, ultimately helping generative models achieve wide adoption. Chemical space ; Computer science ; Virtual screening ; Generative grammar ; Deep learning ; Machine learning ; Benchmark (surveying) ; Generative model ; Artificial intelligence ; Tree traversal ; Interpolation (computer graphics) ; Algorithm ; Drug discovery ; Theoretical computer science ; Bioinformatics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Multi-Objective Optimization Algorithms
Authors: Nathaniel H. Park | Dmitry Yu. Zubarev | James L. Hedrick | Vivien Kiyek | Christiaan Corbet | Simon Lottier
Source: Macromolecules (article, 2020-11-27)
Field: Materials Science — Materials Chemistry

A Recommender System for Inverse Design of Polycarbonates and Polyesters The convergence of artificial intelligence and machine learning with material science holds significant promise in rapidly accelerating the development timelines of new high-performance polymeric materials. Within this context, we report an inverse design strategy for polycarbonate and polyester discovery based on a recommendation system that proposes polymerization experiments that are likely to produce materials with targeted properties or characteristics. Following the recommendations of the system driven by the historical ring-opening polymerization results, we carried out experiments targeting specific ranges of monomer conversion and dispersity for polyesters or polycarbonates. The results of the experiments were in close agreement with the recommendation targets with few false negatives or positives obtained for each class. Polyester ; Polycarbonate ; Context (archaeology) ; Polymerization ; Dispersity ; Monomer ; Computer science ; Inverse ; Materials science ; Polymer science ; Timeline ; Convergence (economics) ; Polymer chemistry ; Polymer ; Composite material ; Mathematics Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization ; biodegradable polymer synthesis and properties
Authors: Jian Peng | Sangkeun Lee | Andrew Williams | J. Allen Haynes | Dongwon Shin
Source: Calphad (article, 2020-02-01)
Field: Materials Science — Materials Chemistry

Advanced data science toolkit for non-data scientists – A user guide Computer science ; Data science ; Analytics ; Data analysis ; Data mining ; Machine learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Materials Characterization Techniques
Authors: Cody A. Nizinski | Alexa B. Hanson | Branden C. Fullmer | Nicholas J. Mecham | Tolga Tasdizen | Luther W. McDonald
Source: Minerals Engineering (article, 2020-06-07)
Field: Chemistry — Inorganic Chemistry

Effects of process history on the surface morphology of uranium ore concentrates extracted from ore Uranium ; Scanning electron microscope ; Leaching (pedology) ; Materials science ; Calcination ; Impurity ; Uranium ore ; Inductively coupled plasma mass spectrometry ; Sulfuric acid ; Metallurgy ; Particle size ; Uranium oxide ; Nuclear chemistry ; Mineralogy ; Chemistry ; Mass spectrometry ; Geology ; Chromatography ; Catalysis Radioactive element chemistry and processing ; Geochemistry and Geologic Mapping ; Nuclear Materials and Properties
Authors: Emma Lejeune | Bingying Zhao
Source: Journal of the mechanical behavior of biomedical materials/Journal of mechanical behavior of biomedical materials (article, 2020-12-30)
Field: Materials Science — Materials Chemistry

Exploring the potential of transfer learning for metamodels of heterogeneous material deformation Computer science ; Leverage (statistics) ; Transfer of learning ; Fidelity ; MNIST database ; Benchmark (surveying) ; High fidelity ; Computation ; Machine learning ; Artificial intelligence ; Deep learning ; Algorithm ; Engineering Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Advanced Neural Network Applications
Authors: Toru Wada | Ashutosh Thakur | Patchanee Chammingkwan | Minoru Terano | Toshiaki Taniike | Alessandro Piovano | Elena Groppo
Source: Catalysts (article, 2020-09-20)
Field: Chemistry — Inorganic Chemistry

Structural Disorder of Mechanically Activated δ-MgCl2 Studied by Synchrotron X-ray Total Scattering and Vibrational Spectroscopy A combination of synchrotron X-ray total scattering and molecular simulation is a powerful approach for reliable determination of the structure of δ-MgCl2 as an indispensable component of heterogeneous Ziegler–Natta catalysts. Here, the same approach is applied to mechanically activated MgCl2. Four types of mechanically activated MgCl2 samples are prepared using ball-milling in the absence and presence of different donors. The development of structural disorder along the grinding time is compared. It was found that the presence of donors accelerates the formation of δ-MgCl2 in an early stage of grinding, while elongated grinding eventually results in δ-MgCl2 with similar extents of structural disorder in the absence and presence of different donors. The FT-IR investigation consistently verified the morphological similarity between the firmly ground samples. Thus, the structure of δ-MgCl2 is likely governed by mechanical energy when sufficiently ground. Grinding ; Ball mill ; Synchrotron ; Materials science ; Scattering ; Crystallography ; Chemistry ; Composite material ; Optics ; Physics Inorganic Chemistry and Materials ; Inorganic Fluorides and Related Compounds ; Layered Double Hydroxides Synthesis and Applications
Authors: Denis A. Rychkov
Source: Crystals (review, 2020-01-31)
Field: Chemistry — Physical and Theoretical Chemistry

A Short Review of Current Computational Concepts for High-Pressure Phase Transition Studies in Molecular Crystals High-pressure chemistry of organic compounds is a hot topic of modern chemistry. In this work, basic computational concepts for high-pressure phase transition studies in molecular crystals are described, showing their advantages and disadvantages. The interconnection of experimental and computational methods is highlighted, showing the importance of energy calculations in this field. Based on our deep understanding of methods’ limitations, we suggested the most convenient scheme for the computational study of high-pressure crystal structure changes. Finally, challenges and possible ways for progress in high-pressure phase transitions research of organic compounds are briefly discussed. Phase transition ; Current (fluid) ; High pressure ; Work (physics) ; Interconnection ; Gas phase ; Crystal structure prediction ; Field (mathematics) ; Phase (matter) ; Computer science ; Chemical physics ; Statistical physics ; Materials science ; Engineering physics ; Nanotechnology ; Chemistry ; Thermodynamics ; Molecule ; Physical chemistry ; Physics ; Organic chemistry ; Mathematics ; Telecommunications Crystallography and molecular interactions ; Chemical Thermodynamics and Molecular Structure ; High-pressure geophysics and materials
Authors: Chuting Zheng | Yi Li | Jihong Yu
Source: Scientific Data (article, 2020-03-31)
Field: Chemistry — Inorganic Chemistry

Database of open-framework aluminophosphate structures Open-framework aluminophosphates are an important class of inorganic crystalline compounds because of their rich structural chemistry and diverse properties. We have collected 312 open-framework aluminophosphate crystal structures from published literature and established a database for these structures. For each aluminophosphate structure, we have assigned a unique index code and extracted its key chemical and crystallographic information from the original literature and the associated CIF file, such as the name, chemical formula, extra-framework species, Al/P ratio, space group, and unit cell parameters of the compound. More importantly, we have calculated the topological features for each aluminophosphate framework, including local connectivity, framework dimension, coordination sequences, vertex symbols, topology density, and the largest ring. To help experimental chemists identify their products, we have also calculated theoretical XRD peaks for all aluminophosphate structures. This database will provide important insight into understanding the structural chemistry of open-framework aluminophosphate compounds. Crystal structure ; Vertex (graph theory) ; Chemical space ; Computer science ; Dimension (graph theory) ; Crystallography ; Database ; Space (punctuation) ; Chemistry ; Materials science ; Topology (electrical circuits) ; Combinatorics ; Mathematics ; Theoretical computer science ; Graph ; Drug discovery Metal-Organic Frameworks: Synthesis and Applications ; Zeolite Catalysis and Synthesis ; Crystal Structures and Properties
Authors: Félix Therrien | Peter Graf | Vladan Stevanović
Source: The Journal of Chemical Physics (article, 2020-02-20)
Field: Materials Science — Materials Chemistry

Matching crystal structures atom-to-atom Finding an optimal match between two different crystal structures underpins many important materials science problems, including describing solid-solid phase transitions and developing models for interface and grain boundary structures. In this work, we formulate the matching of crystals as an optimization problem where the goal is to find the alignment and the atom-to-atom map that minimize a given cost function such as the Euclidean distance between the atoms. We construct an algorithm that directly solves this problem for large finite portions of the crystals and retrieves the periodicity of the match subsequently. We demonstrate its capacity to describe transformation pathways between known polymorphs and to reproduce experimentally realized structures of semi-coherent interfaces. Additionally, from our findings, we define a rigorous metric for measuring distances between crystal structures that can be used to properly quantify their geometric (Euclidean) closeness. Matching (statistics) ; Metric (unit) ; Euclidean geometry ; Transformation (genetics) ; Boundary (topology) ; Construct (python library) ; Function (biology) ; Phase (matter) ; Crystal (programming language) Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Microstructure and mechanical properties
Authors: Jared O’Leary | Runfang Mao | Evan Pretti | Joel A. Paulson | Jeetain Mittal | Ali Mesbah
Source: Soft Matter (article, 2020-11-24)
Field: Materials Science — Materials Chemistry

Deep learning for characterizing the self-assembly of three-dimensional colloidal systems Creating a systematic framework to characterize the structural states of colloidal self-assembly systems is crucial for unraveling the fundamental understanding of these systems' stochastic and non-linear behavior. The most accurate characterization methods create high-dimensional neighborhood graphs that may not provide useful information about structures unless these are well-defined reference crystalline structures. Dimensionality reduction methods are thus required to translate the neighborhood graphs into a low-dimensional space that can be easily interpreted and used to characterize non-reference structures. We investigate a framework for colloidal system state characterization that employs deep learning methods to reduce the dimensionality of neighborhood graphs. The framework next uses agglomerative hierarchical clustering techniques to partition the low-dimensional space and assign physically meaningful classifications to the resulting partitions. We first demonstrate the proposed colloidal self-assembly state characterization framework on a three-dimensional in silico system of 500 multi-flavored colloids that self-assemble under isothermal conditions. We next investigate the generalizability of the characterization framework by applying the framework to several independent self-assembly trajectories, including a three-dimensional in silico system of 2052 colloidal particles that undergo evaporation-induced self-assembly. Colloid ; Nanotechnology ; Colloidal particle ; Self-assembly ; Deep learning ; Statistical physics ; Materials science ; Computer science ; Artificial intelligence ; Physics ; Engineering ; Chemical engineering Pickering emulsions and particle stabilization ; Material Dynamics and Properties
Authors: Feby Wijaya Pratiwi | Chien-Chung Peng | Si‐Han Wu | Chiung Wen Kuo | Chung‐Yuan Mou | Yi‐Chung Tung | Peilin Chen
Source: Biomedicines (article, 2020-12-24)
Field: Materials Science — Biomaterials

Evaluation of Nanoparticle Penetration in the Tumor Spheroid Using Two-Photon Microscopy Mesoporous silica nanoparticles (MSNs) have emerged as a prominent nanomedicine platform, especially for tumor-related nanocarrier systems. However, there is increasing concern about the ability of nanoparticles (NPs) to penetrate solid tumors, resulting in compromised antitumor efficacy. Because the physicochemical properties of NPs play a significant role in their penetration and accumulation in solid tumors, it is essential to systematically study their relationship in a model system. Here, we report a multihierarchical assessment of the accumulation and penetration of fluorescence-labeled MSNs with nine different physicochemical properties in tumor spheroids using two-photon microscopy. Our results indicated that individual physicochemical parameters separately could not define the MSNs' ability to accumulate in a deeper tumor region; their features are entangled. We observed that the MSNs' stability determined their success in reaching the hypoxia region. Moreover, the change in the MSNs' penetration behavior postprotein crowning was associated with both the original properties of NPs and proteins on their surfaces. Penetration (warfare) ; Nanoparticle ; Nanocarriers ; Nanomedicine ; Mesoporous silica ; Spheroid ; Biophysics ; Nanotechnology ; Fluorescence microscope ; Materials science ; Microscopy ; Chemistry ; Mesoporous material ; Fluorescence ; Optics ; Biology ; Catalysis ; Biochemistry ; Physics Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; Advanced Fluorescence Microscopy Techniques
Authors: Xuezhi Ma | Nathan Youngblood | Xiaoze Liu | Yan Cheng | Preston Cunha | Kaushik Kudtarkar | Xiaomu Wang | Shoufeng Lan
Source: Nanophotonics (article, 2020-11-19)
Field: Materials Science — Materials Chemistry

Engineering photonic environments for two‐dimensional materials Abstract A fascinating photonic platform with a small device scale, fast operating speed, as well as low energy consumption is two‐dimensional (2D) materials, thanks to their in‐plane crystalline structures and out‐of‐plane quantum confinement. The key to further advancement in this research field is the ability to modify the optical properties of the 2D materials. The modifications typically come from the materials themselves, for example, altering their chemical compositions. This article reviews a comparably less explored but promising means, through engineering the photonic surroundings. Rather than modifying materials themselves, this means manipulates the dielectric and metallic environments, both uniform and nanostructured, that directly interact with the materials. For 2D materials that are only one or a few atoms thick, the interaction with the environment can be remarkably efficient. This review summarizes the three degrees of freedom of this interaction: weak coupling, strong coupling, and multifunctionality. In addition, it reviews a relatively timing concept of engineering that directly applied to the 2D materials by patterning. Benefiting from the burgeoning development of nanophotonics, the engineering of photonic environments provides a versatile and creative methodology of reshaping light–matter interaction in 2D materials. Photonics ; Photonic crystal ; Field (mathematics) ; Nanomaterials ; Photonic metamaterial ; Dielectric ; Energy consumption ; Optical materials 2D Materials and Applications ; Strong Light-Matter Interactions ; Electronic and Structural Properties of Oxides
Authors: Daoliang Li | Pan Zhang | Tao Chen | Wei Qin
Source: Remote Sensing (review, 2020-08-11)
Field: Chemistry — Analytical Chemistry

Recent Development and Challenges in Spectroscopy and Machine Vision Technologies for Crop Nitrogen Diagnosis: A Review Recent development of non-destructive optical techniques, such as spectroscopy and machine vision technologies, have laid a good foundation for real-time monitoring and precise management of crop N status. However, their advantages and disadvantages have not been systematically summarized and evaluated. Here, we reviewed the state-of-the-art of non-destructive optical methods for monitoring the N status of crops, and summarized their advantages and disadvantages. We mainly focused on the contribution of spectral and machine vision technology to the accurate diagnosis of crop N status from three aspects: system selection, data processing, and estimation methods. Finally, we discussed the opportunities and challenges of the application of these technologies, followed by recommendations for future work to address the challenges. Computer science ; Emerging technologies ; Agricultural engineering ; Systems engineering ; Artificial intelligence ; Nanotechnology ; Engineering ; Materials science Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing in Agriculture