jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Bilal Chehaibou | Eva Izquierdo | Audrey Chu | Claire Abadie | Mariarosa Cavallo | Adrien Khalili | Tung Huu Dang | Charlie Gréboval | Xiang Xu | Sandrine Ithurria | Grégory Vincent | Bruno Gallas | Gabriel Mugny | Arthur Arnaud | Emmanuel Lhuillier | Christophe Delerue
Source: Nanoscale (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

The complex optical index of PbS nanocrystal thin films and their use for short wave infrared sensor design As nanocrystals (NCs) gain maturity, they become central building blocks for optoelectronics in devices such as solar cells and, more recently, infrared focal plane arrays. Now that the proof of concept of these devices has been established, their optimization requires a deeper understanding of their electronic and optical features to engineer their optoelectronic properties accurately. Though PbS NCs have been extensively investigated, the complex optical index of PbS NC thin films remains mostly unknown. Some previous works have unveiled the optical index for this type of material optimized for solar cells (excitonic peak at 940 nm), but longer wavelengths remain scarce and surface chemistry effects, which are known to be of central importance for layer doping, are simply unexplored. Here, we conduct a systematic investigation of the complex optical index of PbS NC thin films using broadband spectrally resolved ellipsometry. The obtained results are then compared with simulations combining tight-binding (TB) modeling at the NC level and the Bruggeman model to expand the results to the film scale. While TB calculation gives the NC optical indices, we extract the key NC film parameters such as the NC volume fraction and ligand indices by fitting the Bruggeman formula to ellipsometry measurements. We also bring evidence that this joint modeling method can be conducted without the need for ellipsometry data while preserving the main feature of the experimental results. Finally, the unveiled optical indices are used to model the absorption of short-wave infrared diode stacks based on PbS NCs and are relevant for state-of-the-art devices. Our electromagnetic modeling shows that the absorption within the contact is now a major limitation of the current device operated at the telecom wavelength. Ellipsometry ; Materials science ; Thin film ; Infrared ; Optoelectronics ; Absorption (acoustics) ; Nanocrystal ; Doping ; Optics ; Nanotechnology ; Physics Quantum Dots Synthesis And Properties ; Chalcogenide Semiconductor Thin Films ; Nanocluster Synthesis and Applications
Authors: Nikolaj Rønne | Mads-Peter Verner Christiansen | Andreas Møller Slavensky | Zeyuan Tang | Florian Brix | Mikkel Elkjær Pedersen | Malthe Kjær Bisbo | Bjørk Hammer
Source: The Journal of Chemical Physics (article, 2022-10-14)
Field: Materials Science — Materials Chemistry

Atomistic structure search using local surrogate model We describe a local surrogate model for use in conjunction with global structure search methods. The model follows the Gaussian approximation potential formalism and is based on the smooth overlap of atomic positions descriptor with sparsification in terms of a reduced number of local environments using mini-batch k-means. The model is implemented in the Atomistic Global Optimization X framework and used as a partial replacement of the local relaxations in basin hopping structure search. The approach is shown to be robust for a wide range of atomistic systems, including molecules, nanoparticles, surface supported clusters, and surface thin films. The benefits in a structure search context of a local surrogate model are demonstrated. This includes the ability to benefit from transfer learning from smaller systems as well as the possibility to perform concurrent multi-stoichiometry searches. Formalism (music) ; Statistical physics ; Local search (optimization) ; Gaussian ; Computer science ; Surrogate model ; Local structure ; Mathematical optimization ; Biological system ; Algorithm ; Mathematics ; Physics ; Chemical physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: José Garrido Torres | Sii Hong Lau | Pranay Anchuri | Jason M. Stevens | José E. Tábora | Jun Li | Alina Borovika | Ryan P. Adams | Abigail G. Doyle
Source: ChemRxiv (preprint, 2022-08-16)
Field: Materials Science — Materials Chemistry

A Multi-Objective Active Learning Platform and Web App for Reaction Optimization We report the development of an open-source Experimental Design via Bayesian Optimization platform for multi-objective reaction optimization. Using high-throughput experimentation (HTE) and virtual screening datasets containing high-dimensional continuous and discrete variables, we optimized the performance of the platform by fine-tuning the algorithm components such as reaction encodings, surrogate model parameters and initialization techniques. Having established the framework, we applied the optimizer to real-word test scenarios for the simultaneous optimization of reaction yield and enantioselectivity in a Ni/photoredox-catalyzed enantioselective cross-electrophile coupling of styrene oxide with two different aryl iodide substrates. Starting with no previous experimental data, the Bayesian optimizer identified reaction conditions that surpassed the previously human-driven optimization campaigns within 15 and 24 experiments, for each substrate, among 1,728 possible configurations available in each optimization. To make the platform more accessible to non-experts, we developed a Graphical User Interface (GUI) that can be accessed online through a web-based application and incorporated features such as conditions modification on-the-fly and data visualization. This web-application does not require software installation, removing any programming barrier to use the platform, which enables chemists to integrate Bayesian optimization routines into their everyday laboratory practices. Bayesian optimization ; Computer science ; Initialization ; Machine learning Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Lokasani Bhanuprakash | Soney Varghese | Sitesh Kumar Singh
Source: Journal of Nanomaterials (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Glass Fibre Reinforced Epoxy Composites Modified with Graphene Nanofillers: Electrical Characterization Composites with improved electrical properties created new pathways in electrical engineering industries. In this work, electrical studies conducted on continuous unidirectional E‐glass fibre‐reinforced epoxy composites modified with three different graphite oxide fillers are discussed. The three different fillers are (i) graphite oxide (GO), (ii) exfoliated graphite oxide (EGO), and (iii) reduced exfoliated graphite oxide (rEGO). Incorporation of GO fillers exhibited significant improvement in the dielectric characteristics of the composites, where it showed 42% enhancement in breakdown strength values. Dielectric constant measurements of GO‐filled composites have also demonstrated considerable enhancement in the values where the fillers promoted interfacial and dipolar polarization phenomena in the material. On the other hand, in the case of EGO and rEGO fillers, conducting nature induced from graphitic structure had significantly reduced the dielectric properties of their composites. Materials science ; Epoxy ; Composite material ; Graphene ; Characterization (materials science) ; Glass fiber ; Composite epoxy material ; Nanotechnology High voltage insulation and dielectric phenomena ; Dielectric materials and actuators ; Electromagnetic wave absorption materials
Authors: Refat Atef Ghunem | E.A. Cherney | M. Farzaneh | Gelareh Momen | Hazlee Azil Illias | Gustavo Arturo Mier Malagon | Vidyadhar Peesapati | Fanghui Yin
Source: IEEE Transactions on Dielectrics and Electrical Insulation (review, 2022-06-27)
Field: Materials Science — Surfaces, Coatings and Films

Development and Application of Superhydrophobic Outdoor Insulation: A Review The aim of this article is to present a review of the development and application of superhydrophobic insulation for outdoor high-voltage applications. The methods of preparing insulation exhibiting superhydrophobic properties to mitigate the pollution problem under high voltages in the outdoors are explored. In addition, the existing evaluation techniques at the material development stage are reviewed and potential new modifications are discussed. The self-cleaning and anti-icing properties, the electrical performance, and prospects of superhydrophobic insulators are also addressed. Although superhydrophobic insulators may show promising behavior in small-scale laboratory conditions that do not use electrical stress, the effect of surface micro–nano structure responsible for the superhydrophobicity on the performance under the actual outdoor and weathering conditions under voltage remains to be fully understood and verified. Therefore, there is a need to verify the feasibility of superhydrophobic insulators for outdoor high-voltage applications with a solid evaluation protocol, while using material screening methods in the laboratory, and with reference to field experience. Materials science ; Voltage ; High voltage ; Nanotechnology ; Engineering ; Electrical engineering Surface Modification and Superhydrophobicity ; High voltage insulation and dielectric phenomena ; Aerosol Filtration and Electrostatic Precipitation
Authors: Irene Soffritti | Maria D'Accolti | Carolina Cason | Luca Lanzoni | Matteo Bisi | Antonella Volta | Giuseppina Campisciano | Sante Mazzacane | Francesca Bini | Eleonora Mazziga | Paola Toscani | Elisabetta Caselli | Manola Comar
Source: Infection and Drug Resistance (article, 2022-03-01)
Field: Chemistry — Organic Chemistry

Introduction of Probiotic-Based Sanitation in the Emergency Ward of a Children’s Hospital During the COVID-19 Pandemic PCHS usage was associated with a stable 80% decrease in surface pathogens compared to levels detected for chemical disinfection (<i>P</i> < 0.01), accompanied by an up to 2 log decrease in resistance genes (<i>Pc</i> < 0.01). The effects were reversed when reintroducing chemical disinfection, which counteracted the action of the PCHS. SARS-CoV-2 was not detectable in both the pre-PCHS and PCHS periods. As the control of microbial contamination is a major issue, especially during pandemic emergencies, collected data suggest that PCHS may be successfully used to control virus spread without simultaneous worsening of the AMR concern. Pandemic ; Sanitation ; Contamination ; Emergency response ; Infection control ; Coronavirus disease 2019 (COVID-19) Antimicrobial agents and applications ; Viral gastroenteritis research and epidemiology ; Child Nutrition and Water Access
Authors: Yunqi Li | Ying Jiang | Liquan Wang | Jianfeng Li
Source: Chinese Journal of Polymer Science (article, 2022-10-20)
Field: Materials Science — Materials Chemistry

Data and Machine Learning in Polymer Science Perspective (graphical) ; Identification (biology) ; Convergence (economics) ; Computer science ; Property (philosophy) ; Focus (optics) ; Key (lock) ; Artificial intelligence ; Polymer ; Machine learning ; Interpretation (philosophy) ; Boundary (topology) ; Data science ; Materials science ; Mathematics ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Guoxu Zhang | Yajie Song | Wei Zhao | Hanwen An | Jiajun Wang
Source: Cell Reports Physical Science (article, 2022-08-26)
Field: Materials Science — Materials Chemistry

Machine learning-facilitated multiscale imaging for energy materials The relationship between the structure of a material and its properties, the so-called structure-property correlations, is at the center of materials science. The microstructure of a material is an essential feature for the optimization of physicochemical properties with improved performance. However, it remains a challenge to recognize and extract all relevant information from microscopic images. Machine learning (ML) has entered the field of multiscale characterization and visualization in energy materials. The aim of this review is to provide concise tutorials on multiscale imaging techniques and ML methods, showing how they can be incorporated for solving physical and chemical problems. With a particular focus on image segmentation, we discuss noteworthy applications of ML in X-ray and electron microscopy imaging for rechargeable batteries, solar cells, and fuel cells and discuss how ML can facilitate identifying microstructures, enhancing image quality, and tracking dynamic processes occurring both inside and at the materials interfaces. Characterization (materials science) ; Computer science ; Visualization ; Segmentation ; Focus (optics) ; Property (philosophy) ; Artificial intelligence ; Microstructure ; Tracking (education) ; Feature (linguistics) ; Field (mathematics) ; Materials science ; Computer vision ; Nanotechnology ; Physics ; Optics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: Youngchun Kwon | Dongseon Lee | Jin Woo Kim | Youn-Suk Choi | Sun Kim
Source: ACS Omega (article, 2022-12-02)
Field: Materials Science — Materials Chemistry

Exploring Optimal Reaction Conditions Guided by Graph Neural Networks and Bayesian Optimization The optimization of organic reaction conditions to obtain the target product in high yield is crucial to avoid expensive and time-consuming chemical experiments. Advancements in artificial intelligence have enabled various data-driven approaches to predict suitable chemical reaction conditions. However, for many novel syntheses, the process to determine good reaction conditions is inevitable. Bayesian optimization (BO), an iterative optimization algorithm, demonstrates exceptional performance to identify reagents compared to synthesis experts. However, BO requires several initial randomly selected experimental results (yields) to train a surrogate model (approximately 10 experimental trials). Parts of this process, such as the cold-start problem in recommender systems, are inefficient. Here, we present an efficient optimization algorithm to determine suitable conditions based on BO that is guided by a graph neural network (GNN) trained on a million organic synthesis experiment data. The proposed method determined 8.0 and 8.7% faster high-yield reaction conditions than state-of-the-art algorithms and 50 human experts, respectively. In 22 additional optimization tests, the proposed method needed 4.7 trials on average to find conditions higher than the yield of the conditions recommended by five synthesis experts. The proposed method is considered in a situation of having a reaction dataset for training GNN. Bayesian optimization ; Computer science ; Artificial neural network ; Process (computing) ; Graph ; Yield (engineering) ; Artificial intelligence ; Machine learning ; Bayesian network ; Process optimization ; Mathematical optimization ; Mathematics ; Engineering ; Theoretical computer science ; Materials science Machine Learning in Materials Science ; Machine Learning and Data Classification ; Computational Drug Discovery Methods
Authors: Thomas Linker | Ken‐ichi Nomura | Anikeya Aditya | Shogo Fukshima | Rajiv K. Kalia | Aravind Krishnamoorthy | Aiichiro Nakano | Pankaj Rajak | Kohei Shimmura | Fuyuki Shimojo | Priya Vashishta
Source: Science Advances (article, 2022-03-23)
Field: Materials Science — Materials Chemistry

Exploring far-from-equilibrium ultrafast polarization control in ferroelectric oxides with excited-state neural network quantum molecular dynamics Ferroelectric materials exhibit a rich range of complex polar topologies, but their study under far-from-equilibrium optical excitation has been largely unexplored because of the difficulty in modeling the multiple spatiotemporal scales involved quantum-mechanically. To study optical excitation at spatiotemporal scales where these topologies emerge, we have performed multiscale excited-state neural network quantum molecular dynamics simulations that integrate quantum-mechanical description of electronic excitation and billion-atom machine learning molecular dynamics to describe ultrafast polarization control in an archetypal ferroelectric oxide, lead titanate. Far-from-equilibrium quantum simulations reveal a marked photo-induced change in the electronic energy landscape and resulting cross-over from ferroelectric to octahedral tilting topological dynamics within picoseconds. The coupling and frustration of these dynamics, in turn, create topological defects in the form of polar strings. The demonstrated nexus of multiscale quantum simulation and machine learning will boost not only the emerging field of ferroelectric topotronics but also broader optoelectronic applications. Excited state ; Ferroelectricity ; Quantum ; Ultrashort pulse ; Excitation ; Materials science ; Polarization (electrochemistry) ; Topology (electrical circuits) ; Physics ; Condensed matter physics ; Chemical physics ; Optoelectronics ; Chemistry ; Quantum mechanics Electronic and Structural Properties of Oxides ; Machine Learning in Materials Science ; Physics of Superconductivity and Magnetism
Authors: Maya Salem | Michael Cowan | Giannis Mpourmpakis
Source: ACS Omega (article, 2022-01-28)
Field: Materials Science — Materials Chemistry

Predicting Segregation Energy in Single Atom Alloys Using Physics and Machine Learning Single atom alloys (SAAs) show great promise as catalysts for a wide variety of reactions due to their tunable properties, which can enhance the catalytic activity and selectivity. To design SAAs, it is imperative for the heterometal dopant to be stable on the surface as an active catalytic site. One main approach to probe SAA stability is to calculate surface segregation energy. Density functional theory (DFT) can be applied to investigate the surface segregation energy in SAAs. However, DFT is computationally expensive and time-consuming; hence, there is a need for accelerated frameworks to screen metal segregation for new SAA catalysts across combinations of metal hosts and dopants. To this end, we developed a model that predicts surface segregation energy using machine learning for a series of SAA periodic slabs. The model leverages elemental descriptors and features inspired by the previously developed bond-centric model. The initial model accurately captures surface segregation energy across a diverse series of FCC-based SAAs with various surface facets and metal-host pairs. Following our machine learning methodology, we expanded our analysis to develop a new model for SAAs formed from FCC hosts with FCC, BCC, and HCP dopants. Our final, five-feature model utilizes second-order polynomial kernel ridge regression. The model is able to predict segregation energies with a high degree of accuracy, which is due to its physically motivated features. We then expanded our data set to test the accuracy of the five features used. We find that the retrained model can accurately capture <i>E</i> <sub>seg</sub> trends across different metal hosts and facets, confirming the significance of the features used in our final model. Finally, we apply our pretrained model to a series of Ir- and Pd-based SAA cuboctahedron nanoparticles (NPs), ranging in size and FCC dopants. Remarkably, our model (trained on periodic slabs) accurately predicts the DFT segregation energies of the SAA NPs. The results provide further evidence supporting the use of our model as a general tool for the rapid prediction of SAA segregation energies. By creating a framework to predict the metal segregation from bulk surfaces to NPs, we can accelerate the SAA catalyst design while simultaneously unraveling key physicochemical properties driving thermodynamic stabilization of SAAs. Dopant ; Atom (system on chip) ; Materials science ; Surface energy ; Density functional theory ; Surface (topology) ; Hybrid functional ; Kernel (algebra) ; Catalysis ; Artificial intelligence ; Machine learning ; Computer science ; Computational chemistry ; Chemistry ; Mathematics ; Doping ; Geometry ; Optoelectronics ; Composite material Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalytic Processes in Materials Science
Authors: Srimanta Mitra | Aquil Ahmad | Sajib Biswas | A. K. Das
Source: Computational Materials Science (article, 2022-10-07)
Field: Materials Science — Materials Chemistry

A machine learning approach to predict the structural and magnetic properties of Heusler alloy families Overfitting ; Materials science ; Lattice constant ; Outlier ; Condensed matter physics ; Alloy ; Random forest ; Magnetic moment ; Artificial intelligence ; Computer science ; Metallurgy ; Physics ; Quantum mechanics ; Artificial neural network Machine Learning in Materials Science ; Heusler alloys: electronic and magnetic properties ; Microstructure and Mechanical Properties of Steels
Authors: Russlan Jaafreh | Kang Yoo Seong | Jung-Gu Kim | Kotiba Hamad
Source: Materials Letters (article, 2022-04-20)
Field: Materials Science — Materials Chemistry

A deep learning perspective into the figure-of-merit of thermoelectric materials Figure of merit ; Materials science ; Perspective (graphical) ; Thermoelectric effect ; Range (aeronautics) ; Work (physics) ; Engineering physics ; Thermodynamics ; Artificial intelligence ; Computer science ; Optoelectronics ; Composite material ; Physics Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Thermal properties of materials
Authors: Torben L. Villadsen | N. F. W. Ligterink | Mie Andersen
Source: Astronomy and Astrophysics (article, 2022-07-19)
Field: Chemistry — Spectroscopy

Predicting binding energies of astrochemically relevant molecules via machine learning Context . The behaviour of molecules in space is to a large extent governed by where they freeze out or sublimate. The molecular binding energy is therefore an important parameter for many astrochemical studies. This parameter is usually determined with time-consuming experiments, computationally expensive quantum chemical calculations, or the inexpensive yet relatively inaccurate linear addition method. Aims . In this work, we propose a new method for predicting binding energies (BEs) based on machine learning that is accurate, yet computationally inexpensive. Methods . We created a machine-learning (ML) model based on Gaussian process regression (GPR) and trained it on a database of BEs of molecules collected from laboratory experiments presented in the literature. The molecules in the database are categorised by their features, such as mono- or multilayer coverage, binding surface, functional groups, valence electrons, and H-bond acceptors and donors. Results . We assessed the performance of the model with five-fold and leave-one-molecule-out cross validation. Predictions are generally accurate, with differences between predicted binding energies and values from the literature of less than ±20%. We used the validated model to predict the binding energies of 21 molecules that were recently detected in the interstellar medium, but for which binding energy values are unknown. We used a simplified model to visualise where the snow lines of these molecules would be located in a protoplanetary disk. Conclusions . This work demonstrates that ML can be employed to accurately and rapidly predict BEs of molecules. Machine learning complements current laboratory experiments and quantum chemical computational studies. The predicted BEs will find use in the modelling of astrochemical and planet-forming environments. Physics ; Astrophysics ; Molecule ; Binding energy ; Atomic physics ; Quantum mechanics Molecular Spectroscopy and Structure ; Advanced Chemical Physics Studies ; Mass Spectrometry Techniques and Applications