jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Nicolae C. Iovanac | Robert MacKnight | Brett M. Savoie
Source: The Journal of Physical Chemistry A (article, 2022-01-05)
Field: Materials Science — Materials Chemistry

Actively Searching: Inverse Design of Novel Molecules with Simultaneously Optimized Properties Combining quantum chemistry characterizations with generative machine learning models has the potential to accelerate molecular discovery. In this paradigm, quantum chemistry acts as a relatively cost-effective oracle for evaluating the properties of particular molecules, while generative models provide a means of sampling chemical space based on learned structure-function relationships. For practical applications, multiple potentially orthogonal properties must be optimized in tandem during a discovery workflow. This carries additional difficulties associated with the specificity of the targets and the ability for the model to reconcile all properties simultaneously. Here, we demonstrate an active learning approach to improve the performance of multi-target generative chemical models. We first demonstrate the effectiveness of a set of baseline models trained on single property prediction tasks in generating novel compounds (i.e., not present in the training data) with various property targets, including both interpolative and extrapolative generation scenarios. For property ranges where accurate targeting proves difficult, the novel compounds suggested by the model are characterized using quantum chemistry and the new molecules closest to expressing the desired properties are fed back into the generative model for additional training. This gradually improves the generative models' understanding of targeted areas of chemical space and shifts the distribution of the generated compounds toward the targeted values. We then demonstrate the effectiveness of this active learning approach in generating compounds with multiple chemical constraints, including vertical ionization potential, electron affinity, and dipole moment targets, and validate the results at the ωB97X-D3/def2-TZVP level. This method requires no modifications to extant generative approaches, but rather utilizes their inherent generative and predictive aspects for self-refinement, and can be applied to situations where any number of properties with varying degrees of correlation must be optimized simultaneously. Chemical space ; Computer science ; Generative model ; Generative grammar ; Property (philosophy) ; Machine learning ; Artificial intelligence ; Workflow ; Chemistry ; Drug discovery Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Qi Li | Hong Fan | Yu Bai | Ying Li | Muhammad Ikram | Yongkai Wang | Yiping Huo | Zhongyue Zhang
Source: New Journal of Physics (article, 2022-05-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Deep learning for circular dichroism of nanohole arrays Abstract Chiral metasurfaces with nanohole structures have a strong circular dichroism (CD) response and are easy to prepare. Therefore, they are widely used in many fields, such as biological monitoring and analytical chemistry. In this work, a deep learning (DL) framework based on the convolutional neural network (CNN) is proposed to predict the CD response of chiral metasurfaces. A dataset containing many data values is used to predict CD values, which are found to be highly consistent with those obtained from COMSOL Multiphysics simulation. Results show that the proposed CNN-based DL model is about a thousand of times faster than conventional finite element methods. It can accurately map chiral metasurfaces and predict their optical response with negligible loss functions. The insights gained from this research may be helpful in the study of complex optical chirality and the design of highly sensitive sensing systems in DL networks. Multiphysics ; Circular dichroism ; Physics ; Convolutional neural network ; Chirality (physics) ; Finite element method ; Biological system ; Nanotechnology ; Optics ; Artificial intelligence ; Crystallography ; Computer science ; Chiral symmetry ; Quantum mechanics ; Chemistry ; Materials science Metamaterials and Metasurfaces Applications ; Animal Vocal Communication and Behavior ; Plasmonic and Surface Plasmon Research
Authors: Xiaona Huang | Kun Luo | Yidi Shen | Yanan Yue | Qi An
Source: Energy and AI (article, 2022-10-14)
Field: Materials Science — Materials Chemistry

Grain boundaries induce significant decrease in lattice thermal conductivity of CdTe Semiconductors are promising in photoelectric and thermoelectric devices, for which the thermal transport properties are of particular interest. However, they have not been fully understood, especially when crystalline imperfections are present. Here, using cadmium telluride (CdTe) as an example, we illustrate how grain boundaries (GBs) affect the thermal transport properties of semiconductors. We develop a machine-learning force field from density functional theory calculations for predicting the lattice thermal conductivity (LTC) via equilibrium molecular dynamics simulations. The LTC of crystalline CdTe decreases with the relationship of κL∼1/T in the simulation temperature range of 300 – 900 K, in which the isotropic LTC decreases from 3.34 to 0.23 W/(m⋅K) due to the enhanced anharmonicity. More important, after introducing GBs, the LTC is suppressed in all directions, especially in the direction normal to the GB planes. More severe LTC suppression occurs in CdTe with Σ9 GB than that with Σ3 GB at 300 K, decreasing by 92.8% and 61.4% along the direction normal to the GB planes compared to the isotropic LTC of the crystalline CdTe, respectively. The decreased LTC is consistent with the weaker bonding near GB planes and lower shear modulus of the defective material. The analyses of the phonon dispersion curves, vibrational density of states, and phonon participation ratio indicate that the decreased LTC mainly arises from phonon scattering at GBs. Overall, our work highlights that GBs can greatly influence the LTC of semiconductors, thus providing a promising approach for thermal property design. Condensed matter physics ; Grain boundary ; Phonon ; Materials science ; Cadmium telluride photovoltaics ; Thermal conductivity ; Thermoelectric effect ; Semiconductor ; Phonon scattering ; Isotropy ; Optoelectronics ; Thermodynamics ; Composite material ; Optics ; Physics Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Thermal properties of materials
Authors: Shreyas Honrao | Othmane Benafan | John W. Lawson
Source: Shape Memory and Superelasticity (article, 2022-12-08)
Field: Materials Science — Materials Chemistry

Data-Driven Study of Shape Memory Behavior of Multi-Component Ni–Ti Alloys in Large Compositional and Processing Space Shape-memory alloy ; Ternary operation ; Nickel titanium ; Materials science ; Component (thermodynamics) ; Hysteresis ; Transformation (genetics) ; Ductility (Earth science) ; Computer science ; Metallurgy ; Thermodynamics ; Creep Shape Memory Alloy Transformations ; Machine Learning in Materials Science ; Titanium Alloys Microstructure and Properties
Authors: Veniero Lenzi | Anna Paula Safenraider Crema | Sergey Pyrlin | L. Marques
Source: Polymers (review, 2022-04-19)
Field: Materials Science — Polymers and Plastics

Current State and Perspectives of Simulation and Modeling of Aliphatic Isocyanates and Polyisocyanates Aliphatic isocyanates and polyisocyanates are central molecules in the fabrication of polyurethanes, coatings, and adhesives and, due to their excellent mechanical and stability properties, are continuously investigated in advanced applications; however, despite the growing interest in isocyanate-based systems, atomistic simulations on them have been limited by the lack of accurate parametrizations for these molecular species. In this review, we will first provide an overview of current research on isocyanate systems to highlight their most promising applications, especially in fields far from their typical usage, and to justify the need for further modeling works. Next, we will discuss the state of their modeling, from first-principle studies to atomistic molecular dynamics simulations and coarse-grained approaches, highlighting the recent advances in atomistic modeling. Finally, the most promising lines of research in the modeling of isocyanates are discussed in light of the possibilities opened by novel approaches, such as machine learning. Isocyanate ; Molecular dynamics ; Nanotechnology ; Modeling and simulation ; Computer science ; Biochemical engineering ; Materials science ; Systems engineering ; Engineering ; Computational chemistry ; Chemistry ; Polyurethane ; Simulation Polymer composites and self-healing ; Corrosion Behavior and Inhibition ; Fuel Cells and Related Materials
Authors: Fu-Heng Zhai | Yufei Guan | Yuanyuan Li | Shuai Chen | Ronghuan He
Source: ACS Applied Nano Materials (article, 2022-07-05)
Field: Materials Science — Materials Chemistry

Predicting the Fluorescence Properties of Hairpin-DNA-Templated Silver Nanoclusters via Deep Learning DNA-templated silver nanoclusters (AgNCs) are widely used as fluorescent probes in various fields owing to their structural stability and fluorescence tunability. The selection of suitable DNA sequences and synthesis conditions with a predictable method is therefore needed, which benefits the preparation of DNA-templated AgNCs with the desired properties and further extension of their application in other fields. Here, we develop and propose a deep learning protocol based on recurrent neural network (RNN) algorithms to predict the fluorescence properties of hairpin-DNA-templated AgNCs (H-AgNCs). Using the stem sequence of hairpin DNA and experimental parameters as input features, the RNN model can identify the number of fluorescence emission peak of H-AgNCs and the fluorescence color of single-peaked H-AgNCs (spH-AgNCs) with the training accuracies of 81.4% and 83.0%, respectively. For each color category, the DNA base distributions of the hairpin stem on the testing set are consistent with those of the training set, indicating the good generalization ability of the constructed model. In addition, a prediction accuracy of 56% is experimentally verified using 20 testing samples, which is 2.3 times higher than that of the random probability, indicating the potential uses of the model for experimental guidance. Nanoclusters ; Fluorescence ; Generalization ; DNA ; Biological system ; Materials science ; DNA sequencing ; Stability (learning theory) ; Computer science ; Nanotechnology ; Chemistry ; Machine learning ; Mathematics ; Physics ; Biology ; Optics Nanocluster Synthesis and Applications ; Gold and Silver Nanoparticles Synthesis and Applications ; Advanced biosensing and bioanalysis techniques
Authors: Alexandre F. Santos | Roberto Aguado | Marcos L. Corazza | Quim Tarrés | José Luis Sanchez-Salvador | Ángeles Blanco | Carlos Negro | Marc Delgado‐Aguilar
Source: Cellulose (article, 2022-05-24)
Field: Materials Science — Biomaterials

Artificial neural network for aspect ratio prediction of lignocellulosic micro/nanofibers Abstract In this work a wide sample analysis, under similar conditions, has been carried out and a calibration strategy based on a careful selection of input variables combined with sensitivity analysis has enabled us to build accurate neural network models, with high correlation (R &gt; 0.99), for the prediction of the aspect ratio of micro/nanofiber products. The model is based on cellulose content, applied energy, fiber length and diameter of the pre-treated pulps. The number of samples used to generate the neural network model was relatively low, consisting of just 15 samples coming from pine pulps that had undergone thermomechanical, kraft and bleached kraft treatments to produce a significant range of aspect ratio. However, the ANN model, involving 4 inputs and 4 hidden neurons and calibrated on the basis of pine dataset, was accurate and robust enough to predict the aspect ratio of micro/nanofiber materials obtained from other cellulose sources including very different softwood and hardwood species such as Spruce, Eucalyptus and Aspen (R = 0.84). The neural network model was able to capture the nonlinearities involved in the data providing insight about the profile of the aspect ratio achieved with further homogenization during the fibrillation process. Nanofiber ; Kraft paper ; Cellulose ; Materials science ; Hardwood ; Artificial neural network ; Biological system ; Kraft process ; Pulp and paper industry ; Aspect ratio (aeronautics) ; Composite material ; Process engineering ; Computer science ; Chemical engineering ; Machine learning ; Botany ; Engineering Advanced Cellulose Research Studies ; Natural Fiber Reinforced Composites ; Nanocomposite Films for Food Packaging
Authors: Gerald Koinig | Nikolai Kuhn | Chiara Barretta | Karl Friedrich | Daniel Vollprecht
Source: Polymers (article, 2022-09-20)
Field: Chemistry — Analytical Chemistry

Evaluation of Improvements in the Separation of Monolayer and Multilayer Films via Measurements in Transflection and Application of Machine Learning Approaches Small plastic packaging films make up a quarter of all packaging waste generated annually in Austria. As many plastic packaging films are multilayered to give barrier properties and strength, this fraction is considered hardly recyclable and recovered thermally. Besides, they can not be separated from recyclable monolayer films using near-infrared spectroscopy in material recovery facilities. In this paper, an experimental sensor-based sorting setup is used to demonstrate the effect of adapting a near-infrared sorting rig to enable measurement in transflection. This adaptation effectively circumvents problems caused by low material thickness and improves the sorting success when separating monolayer and multilayer film materials. Additionally, machine learning approaches are discussed to separate monolayer and multilayer materials without requiring the near-infrared sorter to explicitly learn the material fingerprint of each possible combination of layered materials. Last, a fast Fourier transform is shown to reduce destructive interference overlaying the spectral information. Through this, it is possible to automatically find the Fourier component at which to place the filter to regain the most spectral information possible. Monolayer ; Materials science ; Sorting ; Infrared ; Fourier transform infrared spectroscopy ; Fourier transform ; Fingerprint (computing) ; Filter (signal processing) ; Computer science ; Interference (communication) ; Composite material ; Optoelectronics ; Process engineering ; Optics ; Nanotechnology ; Artificial intelligence ; Channel (broadcasting) ; Computer vision ; Engineering ; Algorithm Spectroscopy and Chemometric Analyses ; Industrial Vision Systems and Defect Detection ; Water Quality Monitoring and Analysis
Authors: Bohayra Mortazavi | Xiaoying Zhuang | Timon Rabczuk | Alexander V. Shapeev
Source: Physica E Low-dimensional Systems and Nanostructures (article, 2022-03-01)
Field: Materials Science — Materials Chemistry

Outstanding thermal conductivity and mechanical properties in the direct gap semiconducting penta-NiN2 monolayer confirmed by first-principles Monolayer ; Nanosheet ; Materials science ; Band gap ; Semiconductor ; Thermal stability ; Direct and indirect band gaps ; Bulk modulus ; Electronic band structure ; Lattice (music) ; Electronic structure ; Thermal conductivity ; Nanotechnology ; Condensed matter physics ; Crystallography ; Chemistry ; Composite material ; Optoelectronics ; Physics MXene and MAX Phase Materials ; Machine Learning in Materials Science ; 2D Materials and Applications
Authors: Piotr Cysewski | Tomasz Jeliński | Maciej Przybyłek | Wiktor Nowak | Michał Olczak
Source: Pharmaceutics (article, 2022-12-16)
Field: Materials Science — Materials Chemistry

Solubility Characteristics of Acetaminophen and Phenacetin in Binary Mixtures of Aqueous Organic Solvents: Experimental and Deep Machine Learning Screening of Green Dissolution Media The solubility of active pharmaceutical ingredients is a mandatory physicochemical characteristic in pharmaceutical practice. However, the number of potential solvents and their mixtures prevents direct measurements of all possible combinations for finding environmentally friendly, operational and cost-effective solubilizers. That is why support from theoretical screening seems to be valuable. Here, a collection of acetaminophen and phenacetin solubility data in neat and binary solvent mixtures was used for the development of a nonlinear deep machine learning model using new intuitive molecular descriptors derived from COSMO-RS computations. The literature dataset was augmented with results of new measurements in aqueous binary mixtures of 4-formylmorpholine, DMSO and DMF. The solubility values back-computed with the developed ensemble of neural networks are in perfect agreement with the experimental data, which enables the extensive screening of many combinations of solvents not studied experimentally within the applicability domain of the trained model. The final predictions were presented not only in the form of the set of optimal hyperparameters but also in a more intuitive way by the set of parameters of the Jouyban-Acree equation often used in the co-solvency domain. This new and effective approach is easily extendible to other systems, enabling the fast and reliable selection of candidates for new solvents and directing the experimental solubility screening of active pharmaceutical ingredients. Phenacetin ; Dissolution ; Solubility ; Acetaminophen ; Aqueous medium ; Aqueous solution ; Chemistry ; Organic chemistry ; Materials science ; Chromatography Crystallization and Solubility Studies ; Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods
Authors: Hamada A. A. Noreldeen | Shao‐Bin He | Kai‐Yuan Huang | Chen-Ting Zhu | Qing-Lin Zhou | Hua‐Ping Peng | Hao‐Hua Deng | Wei Chen
Source: Analytical and Bioanalytical Chemistry (article, 2022-10-25)
Field: Materials Science — Materials Chemistry

Machine learning–based sensor array: full and reduced fluorescence data for versatile analyte detection based on gold nanocluster as a single probe Analyte ; Dimensionality reduction ; Curse of dimensionality ; Linear discriminant analysis ; Chemistry ; Metal ions in aqueous solution ; Fluorescence ; Analytical Chemistry (journal) ; Artificial intelligence ; Biological system ; Computer science ; Pattern recognition (psychology) ; Ion ; Chromatography ; Physics ; Optics Nanocluster Synthesis and Applications ; Advanced biosensing and bioanalysis techniques ; Molecular Sensors and Ion Detection
Authors: Onurcan Kaya | Luigi Colombo | Aleandro Antidormi | Mario Lanza | Stephan Roche
Source: Nanoscale Horizons (article, 2022-12-20)
Field: Materials Science — Materials Chemistry

Revealing the improved stability of amorphous boron-nitride upon carbon doping We report on a large improvement of the thermal stability and mechanical properties of amorphous boron-nitride upon carbon doping. By generating versatile force fields using first-principles and machine learning simulations, we investigate the structural properties of amorphous boron-nitride with varying contents of carbon (from a few percent to 40 at%). We found that for 20 at% of carbon, the sp<sup>3</sup>/sp<sup>2</sup> ratio reaches a maximum with a negligible graphitisation effect, resulting in an improvement of the thermal stability by up to 20% while the bulk Young's modulus increases by about 30%. These results provide a guide to experimentalists and engineers to further tailor the growth conditions of BN-based compounds as non-conductive diffusion barriers and ultralow dielectric coefficient materials for a number of applications including interconnect technology. Boron nitride ; Materials science ; Doping ; Amorphous carbon ; Thermal stability ; Amorphous solid ; Nitride ; Carbon fibers ; Boron ; Carbon nitride ; Chemical engineering ; Stability (learning theory) ; Nanotechnology ; Optoelectronics ; Composite material ; Chemistry ; Crystallography ; Composite number ; Organic chemistry ; Computer science ; Catalysis ; Engineering Diamond and Carbon-based Materials Research ; Metal and Thin Film Mechanics ; Semiconductor materials and devices
Authors: Jingbai Li | Steven A. Lopez
Source: Chemistry - A European Journal (article, 2022-04-26)
Field: Materials Science — Materials Chemistry

Excited‐State Distortions Promote the Photochemical 4π‐Electrocyclizations of Fluorobenzenes via Machine Learning Accelerated Photodynamics Simulations Benzene fluorination increases chemoselectivities for Dewar-benzenes via 4π-disrotatory electrocyclization. However, the origin of the chemo- and regioselectivities of fluorobenzenes remains unexplained because of the experimental limitations in resolving the excited-state structures on ultrafast timescales. The computational cost of multiconfigurational nonadiabatic molecular dynamics simulations is also currently cost-prohibitive. We now provide high-fidelity structural information and reaction outcome predictions with machine-learning-accelerated photodynamics simulations of a series of fluorobenzenes, C<sub>6</sub> F<sub>6-n</sub> H<sub>n</sub> , n=0-3, to study their S<sub>1</sub> →S<sub>0</sub> decay in 4 ns. We trained neural networks with XMS-CASPT2(6,7)/aug-cc-pVDZ calculations, which reproduced the S<sub>1</sub> absorption features with mean absolute errors of 0.04 eV (<2 nm). The predicted nonradiative decay constants for C<sub>6</sub> F<sub>4</sub> H<sub>2</sub> , C<sub>6</sub> F<sub>6</sub> , C<sub>6</sub> F<sub>3</sub> H<sub>3</sub> , and C<sub>6</sub> F<sub>5</sub> H are 116, 60, 28, and 12 ps, respectively, in broad qualitative agreement with the experiments. Our calculations show that a pseudo Jahn-Teller distortion of fluorinated benzenes leads to an S<sub>1</sub> local-minimum region that extends the excited-state lifetimes of fluorobenzenes. The pseudo Jahn-Teller distortions reduce when fluorination decreases. Our analysis of the S<sub>1</sub> dynamics shows that the pseudo-Jahn-Teller distortions promote an excited-state cis-trans isomerization of a π<sub>C-C</sub> bond. We characterized the surface hopping points from our NAMD simulations and identified instantaneous nuclear momentum as a factor that promotes the electrocyclizations. Surface hopping ; Excited state ; Isomerization ; Chemistry ; Photoexcitation ; Molecular dynamics ; Photochemistry ; Computational chemistry ; Atomic physics ; Molecular physics ; Physics Machine Learning in Materials Science ; Radical Photochemical Reactions ; Fluorine in Organic Chemistry