jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Leif D. Jacobson | James Stevenson | Farhad Ramezanghorbani | Delaram Ghoreishi | Karl Leswing | Ed Harder | Robert Abel
Source: ChemRxiv (preprint, 2022-02-21)
Field: Materials Science — Materials Chemistry

Transferable Neural Network Potential Energy Surfaces for Closed-Shell Organic Molecules: Extension to Ions Transferable high dimensional neural network potentials (HDNNP) have shown great promise as an avenue to increase the accuracy and domain of applicability of existing atomistic force fields for organic systems relevant to life science. We have previously reported such a potential (Schrödinger-ANI) that has broad coverage of druglike molecules. We extend that work here to cover ionic and zwitterionic druglike molecules expected to be relevant to drug discovery research activities. We report a novel HDNNP architechture, which we call QRNN, that predicts atomic charges and uses these charges as descriptors in an energy model which delivers conformational energies within chemical accuracy when measured against the reference theory it is trained to. Further, we find that delta learning based on a semi-empirical level of theory approximately halves the errors. We test the models on torsion energy profiles, relative conformational energies, geometric parameters and relative tautomer errors. Molecule ; Artificial neural network ; Potential energy ; Ion ; Torsion (gastropod) ; Extension (predicate logic) ; Ionic bonding ; Organic molecules ; Work (physics) ; Computer science ; Chemistry ; Physics ; Statistical physics ; Chemical physics ; Artificial intelligence ; Atomic physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: V.A. Yartys | Min Zhu
Source: Journal of Alloys and Compounds (article, 2022-08-24)
Field: Materials Science — Materials Chemistry

Recent advances in hydrogen storage materials Hydrogen storage ; Intermetallic ; Hydride ; Gravimetric analysis ; Enthalpy ; Materials science ; Hydrogen ; Energy storage ; Thermal stability ; Thermodynamics ; Inorganic chemistry ; Chemistry ; Metallurgy ; Metal ; Alloy ; Organic chemistry Hydrogen Storage and Materials ; Magnesium Alloys: Properties and Applications ; Ammonia Synthesis and Nitrogen Reduction
Authors: Irvin Séchepée | Paul Paulain | Yuka Nagasaki | Riku Tanaka | Hiroaki Matsumoto | Vincent Velay
Source: MATERIALS TRANSACTIONS (article, 2022-12-24)
Field: Materials Science — Materials Chemistry

Excellent Balance of Ultimate Tensile Strength and Ductility in a Ti–6Al–2Sn–4Zr–2Mo–Si Alloy Having Duplex α + α′ Microstructure: Effect of Microstructural Factors from Experimental Study and Machine Learning This research focuses on the systematic study of a Ti–6Al–2Sn–4Zr–2Mo–Si titanium alloy and the characterization of α + β (equiaxed and bimodal) and α + α′ (duplex) microstructures. It provides more insights on the outstanding advantages of the duplex (α + α′) microstructure, especially on its exceptional work hardening and strength-ductility balance. The heat treatment conditions required to form equiaxed, bimodal and duplex microstructures and their effects on the grain size and the phase proportion are discussed. It shows how the microstructural parameters can be controlled thanks to the heat treatment temperatures, the holding times and possible aging processes. The influence of such microstructural factors on the tensile properties of each alloy is investigated, especially on strength (proof stress, ultimate tensile strength), ductility (plastic elongation) and work hardening properties. The duplex (α + α′) microstructure is compared with the equiaxed and bimodal microstructures and its advantages are displayed, highlighting the better strength-ductility balance and superior work hardening properties of the duplex microstructure. Indeed, the deformed microstructure of the duplex (α + α′) microstructure reveals more homogeneous strain partitioning than that of the bimodal (α + β) microstructure. Thus, this work proved the potential of an optimized duplex (α + α′) microstructure for the enhanced tensile properties at room temperature. Finally, a machine learning model using gradient boosting regression trees is used to quantify the importance of the microstructural factors (type of microstructure, grain size and phase ratio) on the mechanical properties. Microstructure ; Materials science ; Equiaxed crystals ; Ultimate tensile strength ; Titanium alloy ; Alloy ; Work hardening ; Metallurgy ; Ductility (Earth science) ; Grain size ; Composite material ; Strain hardening exponent ; Creep Titanium Alloys Microstructure and Properties ; Hydrogen embrittlement and corrosion behaviors in metals ; Welding Techniques and Residual Stresses
Authors: Akira Takahashi | Yu Kumagai | Hirotaka Aoki | Ryo Tamura | Fumiyasu Oba
Source: Science and Technology of Advanced Materials Methods (article, 2022-02-09)
Field: Materials Science — Materials Chemistry

Adaptive sampling methods via machine learning for materials screening High-throughput virtual screening by using a combination of first-principles calculations and Bayesian optimization (BO) has attracted much attention as a method for efficient material exploration. The purpose of the virtual screening is often to search for the materials whose properties meet a certain target criterion, while the conventional BO aims to find the global extremum. Some recent works use the conventional BO by converting target properties for such motivation. On the other hand, an adaptive sampling method, where the acquisition function is based on the probability that a data point achieves a target property within a specific range, is suggested previously [Kishio et al., Chemom. Intell. Lab. Syst. 127, 70 (2013)]. In this paper, we demonstrate that such adaptive sampling is effective for the exploration of the materials whose properties meet target criteria. We conducted simulations of material exploration using an in-house database constructed by first-principles calculations and compared the performance of the adaptive sampling and conventional BO approaches. Furthermore, we evaluate and discuss the performance of acquisition functions extended to multi-objective problems for material exploration, considering multiple-target properties simultaneously. Bayesian optimization ; Computer science ; Sampling (signal processing) ; Adaptive sampling ; Property (philosophy) ; Range (aeronautics) ; Function (biology) ; Machine learning ; Bayesian probability ; Point (geometry) ; Artificial intelligence ; Data mining ; Mathematics ; Monte Carlo method ; Engineering ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallization and Solubility Studies
Authors: Paul L. Houston | Apurba Nandi | Joel M. Bowman
Source: The Journal of Physical Chemistry A (article, 2022-08-12)
Field: Materials Science — Materials Chemistry

A Machine Learning Approach for Rate Constants. III. Application to the Cl(<sup>2</sup>P) + CH<sub>4</sub> → CH<sub>3</sub> + HCl Reaction The temperature dependence of the thermal rate constant for the reaction Cl(<sup>3</sup>P) + CH<sub>4</sub> → HCl + CH<sub>3</sub> is calculated using a Gaussian Process machine learning (ML) approach to train on and predict thermal rate constants over a large temperature range. Following procedures developed in two previous reports, we use a training data set of approximately 40 reaction/potential surface combinations, each of which is used to calculate the corresponding database of rate constant at approximately eight temperatures. For the current application, we train on the entire data set and then predict the temperature dependence of the title reaction employing a "split" data set for correction at low and high temperatures to capture both tunneling and recrossing. The results are an improvement on recent RPMD calculations compared to accurate quantum ones, using the same high-level ab initio potential energy surface. Both tunneling at low temperatures and significant recrossing at high temperatures are observed to influence the rate constants. The recrossing effects, which are not described by TST and even sophisticated tunneling corrections, do appear in experiment at temperatures above around 600 K. The ML results describe these effects and in fact merge at 600 K with RPMD results (which can describe recrossing), and both are close to experiment at the highest experimental temperatures. These results are in accord with a recent high-level experiment-theory study of this reaction. Reaction rate constant ; Quantum tunnelling ; Ab initio ; Merge (version control) ; Chemistry ; Potential energy surface ; Thermal ; Reaction rate ; Thermodynamics ; Physics ; Quantum mechanics ; Computer science ; Kinetics Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Quantum, superfluid, helium dynamics
Authors: Xudong Du | Chengan Zhou | Hongbai Bai | Xingxing Liu
Source: Optical Materials Express (article, 2022-09-15)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Inverse design paradigm for fast and accurate prediction of a functional metasurface via deep convolutional neural networks Data-driven deep learning frameworks have significantly advanced the development of modern machine learning, and after achieving great success in the field of image, speech, and video recognition and processing, they have also begun to permeate other disciplines such as physics, chemistry, and the discovery of new drugs and new materials. Our work proposes a deep learning-based model consisting of two parts: a forward simulation network that contains a transposed convolutional network, up and down sampling blocks and dense layers can rapidly predict optical responses from metasurface structures, and an inverse design network that contains convolutional neural networks and dense layers can automatically construct metasurface based on the input optical responses. Our model assists in discovering the complex and non-intuitive relationship between the moth-eye metasurface and optical responses, and designs a metasurface with excellent optical properties (ultra-broadband anti-reflection or nonlinear function of reflectivity), while avoiding traditional time-consuming case-by-case numerical simulations in the metasurface design. This work provides a fast, practical, and robust method to study complex light-matter interactions and to accelerate the demand-based design of nanophotonic devices, opening a new avenue for the development of real nanophotonic applications. Nanophotonics ; Computer science ; Deep learning ; Convolutional neural network ; Broadband ; Inverse problem ; Artificial intelligence ; Artificial neural network ; Field (mathematics) ; Materials science ; Telecommunications ; Optoelectronics Metamaterials and Metasurfaces Applications ; Animal Vocal Communication and Behavior ; Photonic Crystals and Applications
Authors: L. A. Savintseva | Alexander Avdoshin | Stanislav K. Ignatov
Source: Russian Journal of Physical Chemistry B (article, 2022-06-01)
Field: Materials Science — Polymers and Plastics

Charge Transport in Biomimetic Models of Organic Neuromorphous Materials The process is modelled of charge transport in materials based on organic semiconductors and bioorganic molecules: dicyanvinyl oligothiophenes, porphine and quinone derivatives, which are considered as the base for creating molecular artificial neural networks (ANNs). The energy and electronic characteristics of these molecules are estimated by quantum chemical methods, and their parameters in the OPLS-AA force field are determined. The charge dynamics was modeled using the kinetic Monte Carlo method in systems of 1200 molecules balanced by the molecular dynamics method with an estimate of the tunneling constants according to the Marcus theory. Based on a comparison of the charge mobility in the simulated systems, possible ways of modifying the studied molecules to create ANNs are discussed. Materials science ; Charge (physics) ; Chemical engineering ; Chemistry ; Chemical physics ; Nanotechnology ; Physics ; Engineering Conducting polymers and applications ; Organic Electronics and Photovoltaics ; Synthesis and Properties of Aromatic Compounds
Authors: Kenta Fukada | Michiko Seyama
Source: Scientific Reports (article, 2022-01-18)
Field: Materials Science — Materials Chemistry

Designing a multilayer film via machine learning of scientific literature Scientists who design chemical substances often use materials informatics (MI), a data-driven approach with either computer simulation or artificial intelligence (AI). MI is a valuable technique, but applying it to layered structures is difficult. Most of the proposed computer-aided material search techniques use atomic or molecular simulations, which are limited to small areas. Some AI approaches have planned layered structures, but they require a physical theory or abundant experimental results. There is no universal design tool for multilayer films in MI. Here, we show a multilayer film can be designed through machine learning (ML) of experimental procedures extracted from chemical-coating articles. We converted material names according to International Union of Pure and Applied Chemistry rules and stored them in databases for each fabrication step without any physicochemical theory. Compared with experimental results which depend on authors, experimental protocol is superiority at almost unified and less data loss. Connecting scientific knowledge through ML enables us to predict untrained film structures. This suggests that AI imitates research activity, which is normally inspired by other scientific achievements and can thus be used as a general design technique. Computer science ; Protocol (science) ; Experimental data ; Artificial intelligence ; Machine learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Topic Modeling
Authors: Tsuneo Kashiwagi | Kiwamu Sue | Yoshihiro Takebayashi | Takumi Ono
Source: Chemical Engineering Science (article, 2022-08-10)
Field: Materials Science — Electronic, Optical and Magnetic Materials

High-throughput synthesis of silver nanoplates and optimization of optical properties by machine learning Absorption (acoustics) ; Wavelength ; Throughput ; Materials science ; Particle (ecology) ; Absorption spectroscopy ; Particle size ; Nanotechnology ; Chemical engineering ; Analytical Chemistry (journal) ; Optics ; Optoelectronics ; Chemistry ; Computer science ; Chromatography ; Composite material ; Physics ; Telecommunications Gold and Silver Nanoparticles Synthesis and Applications ; Laser-Ablation Synthesis of Nanoparticles ; Quantum Dots Synthesis And Properties
Authors: Abhiroop Bhattacharya | Jaime Benavides | Luis Felipe Gerlein | Sylvain G. Cloutier
Source: Scientific Reports (article, 2022-12-19)
Field: Chemistry — Analytical Chemistry

Deep-learning framework for fully-automated recognition of TiO2 polymorphs based on Raman spectroscopy Emerging machine learning techniques can be applied to Raman spectroscopy measurements for the identification of minerals. In this project, we describe a deep learning-based solution for automatic identification of complex polymorph structures from their Raman signatures. We propose a new framework using Convolutional Neural Networks and Long Short-Term Memory networks for compound identification. We train and evaluate our model using the publicly-available RRUFF spectral database. For model validation purposes, we synthesized and identified different TiO<sub>2</sub> polymorphs to evaluate the performance and accuracy of the proposed framework. TiO<sub>2</sub> is a ubiquitous material playing a crucial role in many industrial applications. Its unique properties are currently used advantageously in several research and industrial fields including energy storage, surface modifications, optical elements, electrical insulation to microelectronic devices such as logic gates and memristors. The results show that our model correctly identifies pure Anatase and Rutile with a high degree of confidence. Moreover, it can also identify defect-rich Anatase and modified Rutile based on their modified Raman Spectra. The model can also correctly identify the key component, Anatase, from the P25 Degussa TiO<sub>2</sub>. Based on the initial results, we firmly believe that implementing this model for automatically detecting complex polymorph structures will significantly increase the throughput, while dramatically reducing costs. Anatase ; Raman spectroscopy ; Computer science ; Rutile ; Artificial intelligence ; Convolutional neural network ; Identification (biology) ; Deep learning ; Microelectronics ; Throughput ; Materials science ; Machine learning ; Nanotechnology ; Chemistry ; Physics ; Chemical engineering ; Engineering ; Telecommunications ; Optics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Electrochemical Analysis and Applications
Authors: Maximilian J. Werny | Kirsten B. Siebers | Nic Friederichs | Coen Hendriksen | Florian Meirer | Bert M. Weckhuysen
Source: Journal of the American Chemical Society (article, 2022-11-08)
Field: Materials Science — Materials Chemistry

Advancing the Compositional Analysis of Olefin Polymerization Catalysts with High-Throughput Fluorescence Microscopy To optimize the performance of supported olefin polymerization catalysts, novel methodologies are required to evaluate the composition, structure, and morphology of both pristine and prepolymerized samples in a resource-efficient, high-throughput manner. Here, we report on a unique combination of laboratory-based confocal fluorescence microscopy and advanced image processing that allowed us to quantitatively assess support fragmentation in a large number of autofluorescent metallocene-based catalyst particles. Using this approach, significant inter- and intraparticle heterogeneities were detected and quantified in a representative number of prepolymerized catalyst particles (2D: ≥135, 3D: 40). The heterogeneity that was observed over several stages of slurry-phase ethylene polymerization (10 bar) is primarily attributed to the catalyst particles' diverse support structures and to the inhomogeneities in the metallocene distribution. From a mechanistic point of view, the 2D and 3D analyses revealed extensive contributions from a layer-by-layer fragmentation mechanism in synergy with a less pronounced sectioning mechanism. A significant number of catalyst particles were also found to display limited support fragmentation at the onset of the reaction (i.e., at lower polymer yields). This delay in activity or "dormancy" is believed to contribute to a broadening of the particle size distribution during the early stages of polymerization. 2D and 3D catalyst screening via confocal fluorescence microscopy represents an accessible and fast approach to characterize the structure of heterogeneous catalysts and assess the distribution of their fluorescent components and reaction products. The automation of both image segmentation and postprocessing with machine learning can yield a powerful diagnostic tool for future research as well as quality control on industrial catalysts. Polymerization ; Chemistry ; Catalysis ; Polymer ; Chemical engineering ; Fluorescence microscope ; Heterogeneous catalysis ; Fluorescence ; Nanotechnology ; Materials science ; Organic chemistry ; Optics Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization ; Luminescence and Fluorescent Materials
Authors: Taewon Jin | Yousung Jung
Source: Bulletin of the Korean Chemical Society (article, 2022-02-16)
Field: Materials Science — Materials Chemistry

Recent progress in computational discovery of Heusler alloys Abstract Heusler alloy is an intermetallic alloy with a wide range of compositional space. Due to this compositional tunability, novel Heusler alloys have been studied for a variety of applications including permanent magnets, thermoelectric, and catalysis. Recently, computational approaches have been actively used to aid the fast discovery of novel Heusler alloys by identifying stable compositions with desired properties. In this mini‐review, we briefly overview several computational approaches used for Heusler alloys, namely, density functional theory (DFT) calculations, high‐throughput virtual screening (HTVS), and machine learning (ML) to further accelerate these screening processes. Future directions for the computational screening of Heusler alloy in other application fields are discussed. Intermetallic ; Materials science ; Alloy ; Density functional theory ; Virtual screening ; Nanotechnology ; Thermoelectric effect ; Metallurgy ; Chemistry ; Computational chemistry ; Molecular dynamics ; Physics ; Thermodynamics Machine Learning in Materials Science ; Heusler alloys: electronic and magnetic properties ; Titanium Alloys Microstructure and Properties
Authors: Victor Fung | Shuyi Jia | Jiaxin Zhang | Sirui Bi | Junqi Yin | Panchapakesan Ganesh
Source: Machine Learning Science and Technology (article, 2022-11-10)
Field: Materials Science — Materials Chemistry

Atomic structure generation from reconstructing structural fingerprints Abstract Data-driven machine learning methods have the potential to dramatically accelerate the rate of materials design over conventional human-guided approaches. These methods would help identify or, in the case of generative models, even create novel crystal structures of materials with a set of specified functional properties to then be synthesized or isolated in the laboratory. For crystal structure generation, a key bottleneck lies in developing suitable atomic structure fingerprints or representations for the machine learning model, analogous to the graph-based or SMILES representations used in molecular generation. However, finding data-efficient representations that are invariant to translations, rotations, and permutations, while remaining invertible to the Cartesian atomic coordinates remains an ongoing challenge. Here, we propose an alternative approach to this problem by taking existing non-invertible representations with the desired invariances and developing an algorithm to reconstruct the atomic coordinates through gradient-based optimization using automatic differentiation. This can then be coupled to a generative machine learning model which generates new materials within the representation space, rather than in the data-inefficient Cartesian space. In this work, we implement this end-to-end structure generation approach using atom-centered symmetry functions as the representation and conditional variational autoencoders as the generative model. We are able to successfully generate novel and valid atomic structures of sub-nanometer Pt nanoparticles as a proof of concept. Furthermore, this method can be readily extended to any suitable structural representation, thereby providing a powerful, generalizable framework towards structure-based generation. Computer science ; Cartesian coordinate system ; Invertible matrix ; Generative model ; Theoretical computer science ; Representation (politics) ; Algorithm ; Invariant (physics) ; Generative grammar ; Artificial intelligence ; Mathematics ; Geometry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallography and molecular interactions