jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Pilsun Yoo | Debsindhu Bhowmik | Kshitij Mehta | Pei Zhang | Frank Liu | Massimiliano Lupo Pasini | Stephan Irle
Source: Scientific Reports (article, 2023-11-16)
Field: Materials Science — Materials Chemistry

Deep learning workflow for the inverse design of molecules with specific optoelectronic properties The inverse design of novel molecules with a desirable optoelectronic property requires consideration of the vast chemical spaces associated with varying chemical composition and molecular size. First principles-based property predictions have become increasingly helpful for assisting the selection of promising candidate chemical species for subsequent experimental validation. However, a brute-force computational screening of the entire chemical space is decidedly impossible. To alleviate the computational burden and accelerate rational molecular design, we here present an iterative deep learning workflow that combines (i) the density-functional tight-binding method for dynamic generation of property training data, (ii) a graph convolutional neural network surrogate model for rapid and reliable predictions of chemical and physical properties, and (iii) a masked language model. As proof of principle, we employ our workflow in the iterative generation of novel molecules with a target energy gap between the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO). Chemical space ; Workflow ; Computer science ; Property (philosophy) ; HOMO/LUMO ; Convolutional neural network ; Graph ; Inverse ; Theoretical computer science ; Deep learning ; Artificial intelligence ; Molecule ; Chemistry ; Database ; Mathematics ; Drug discovery Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Soumyashree S. Panda | Sushil Kumar | Devdutt Tripathi | Ravi S. Hegde
Source: Journal of Nanophotonics (article, 2023-07-31)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Deep learning aids simultaneous structure–material design discovery: a case study on designing phase change material metasurfaces The capabilities of modern precision nanofabrication and the wide choice of materials [plasmonic metals, high-index dielectrics, phase change materials (PCM), and 2D materials] make the inverse design of nanophotonic structures such as metasurfaces increasingly difficult. Deep learning is becoming increasingly relevant for nanophotonics inverse design. Although deep learning design methodologies are becoming increasingly sophisticated, the problem of the simultaneous inverse design of structure and material has not received much attention. In this contribution, we propose a deep learning-based inverse design methodology for simultaneous material choice and device geometry optimization. To demonstrate the utility of the proposed method, we consider the topical problem of active metasurface design using PCMs. We consider a set of four commonly used PCMs in both fully amorphous and crystalline material phases for the material choice and an arbitrarily specifiable polygonal meta-atom shape for the geometry part, which leads to a vast structure/material design space. We find that a suitably designed deep neural network can achieve good optical spectrum prediction capability in an ample design space. Furthermore, we show that this forward model has a sufficiently high predictive ability to be used in a surrogate-optimization setup resulting in the inverse design of active metasurfaces of switchable functionality. Nanophotonics ; Material Design ; Computer science ; Inverse ; Deep learning ; Inverse problem ; Plasmon ; Materials science ; Artificial intelligence ; Nanotechnology ; Optoelectronics ; Geometry ; Mathematics Metamaterials and Metasurfaces Applications ; Photonic Crystals and Applications ; Animal Vocal Communication and Behavior
Authors: Bita Yarahmadi | Seyed Majid Hashemianzadeh | Seyed Mohammad-Reza Milani Hosseini
Source: Heliyon (article, 2023-07-01)
Field: Chemistry — Analytical Chemistry

A new approach to prediction riboflavin absorbance using imprinted polymer and ensemble machine learning algorithms The molecularly imprinted polymer (MIP) is useful for measuring the amount of riboflavin (vitamin B2), in various samples using UV/Vis instruments. The practical optimization of the MIP synthesis conditions has a number of drawbacks, like the need to spend money, the need to spend time, the use of the compounds that cause contamination, needing laboratory equipment and tools. Using machine learning (ML) to predict the amount of riboflavin absorbance is a creative solution to overcome the problems and shortcomings of optimizing polymer synthesis conditions. In fact, by using the model without needing real work in the laboratory, the optimum laboratory conditions are determined, and as a result the maximized absorption of the riboflavin is obtained. In this paper, MIP was synthesized for selective extraction of the riboflavin, and UV/Vis spectrophotometry was used to quantitatively measure riboflavin absorbance. Various factors affect the performance of the polymer. The effect of six important factors, including the molar ratio of the template, the molar ratio of monomer, the molar ratio of cross-linker, loading time, stirring rate, and pH, were investigated. Then, using ensemble ML algorithms, like gradient boosting (GB), extra trees (ET), random forest (RF), and Ada boost (Ada) algorithms, an accurate model was created to predict the riboflavin absorption. Also, the mutual information feature selection method was used to determine the important features. The results of using feature selection method was shown that variables such as the molar ratio of the template, the molar ratio of the monomer, and the molar ratio of the cross-linker had a high effect on riboflavin absorbance. The GB and Ada boost algorithms performed better than ET and RF algorithms. After tuning the n-estimator hyper parameter (n-estimator = 300), the GB algorithm was shown an excellent performance in predicting the absorbance of riboflavin and the maximum R<sup>2</sup>-scoring of the model was obtained at 0.965995, the minimum of the mean absolute error (MAE), and mean square error (MSE) of the model respectively were obtained -0.003711 and -0.000078. Therefore, by using the proposed model, it is possible to predict riboflavin absorbance theoretically, and with high accuracy by changing the inputs of model, and using the model instead of working in the lab saves time, money, chemical compounds, and lab ware. Absorbance ; Riboflavin ; Algorithm ; Computer science ; Polymer ; Molecularly imprinted polymer ; Feature selection ; Monomer ; Chemistry ; Machine learning ; Materials science ; Chromatography ; Selectivity ; Organic chemistry ; Catalysis ; Biochemistry Analytical chemistry methods development ; Electrochemical sensors and biosensors ; Advanced Chemical Sensor Technologies
Authors: Zhehao Sun | Hang Yin | Zongyou Yin
Source: Matter (article, 2023-08-01)
Field: Materials Science — Materials Chemistry

Leveraging machine learning in the innovation of functional materials Computer science ; Field (mathematics) ; Artificial intelligence ; Machine learning ; Nanotechnology ; Data science ; Materials science ; Mathematics Machine Learning in Materials Science ; Nanowire Synthesis and Applications ; 2D Materials and Applications
Authors: Naoki Yoshihara | Yuya Tahara | Masaru Noda
Source: Asia-Pacific Journal of Chemical Engineering (article, 2023-04-09)
Field: Materials Science — Materials Chemistry

Machine learning method for determining chemical vapor deposition conditions for large‐area graphene growth Abstract The synthesis of wafer‐scale single‐crystal graphene sheets has become essential to realize future electronic device applications. To synthesize large‐area boundary‐free graphene, it is effective to use chemical vapor deposition (CVD) on copper (Cu) surfaces that possess a thin oxide layer. In this study, we constructed machine learning (ML) modeling to design experimental CVD conditions for the formation of large‐area graphene. The constructed ML model predicted the graphene domain size from the experimental CVD growth conditions and the spectral information of the Cu surface. Furthermore, we demonstrated the formation of large‐area graphene domain on the Cu surface using the CVD conditions determined by the constructed ML model, which provided faster graphene growth compared to previously reported strategies. Graphene ; Chemical vapor deposition ; Materials science ; Oxide ; Wafer ; Nanotechnology ; Deposition (geology) ; Chemical engineering ; Metallurgy Graphene research and applications ; Semiconductor materials and devices ; Advancements in Semiconductor Devices and Circuit Design
Authors: Chenglin Han | Hongxing Zhao | Tianzhi Yang | Xueqing Liu | Mingchi Yu | Gongdong Wang
Source: Polymer Testing (article, 2023-09-22)
Field: Materials Science — Materials Chemistry

Optimizing interlaminar toughening of carbon-based filler/polymer nanocomposites by machine learning Currently, most designs for interlayer toughening of carbon-based filler/polymer nanocomposites are highly dependent on experimental iterative trial and error, and there is no rational design framework. This work uses machine learning to build a fast and accurate predictive model and assess the extent to which key features affect performance, giving researchers ideas for designing new materials and greatly improving efficiency. A training database is built by first collecting the features of the domain that affect the interlaminar performance. A stacking model fusion of the three machine learning models was then performed to construct a highly accurate fast prediction model. Besides, the importance of key features is evaluated during model training using the Random Forest Algorithm (RFA). Finally, by predicting the performance of materials and analyzing the importance of characteristics to guide material preparation, the development cycle is shortened and costs are reduced. Materials science ; Toughening ; Nanocomposite ; Filler (materials) ; Stacking ; Random forest ; Key (lock) ; Work (physics) ; Polymer ; Computer science ; Machine learning ; Composite material ; Mechanical engineering Machine Learning in Materials Science ; Epoxy Resin Curing Processes ; Corrosion Behavior and Inhibition
Authors: Sophie Baker | Joshua Pagotto | Timothy T. Duignan | Alister J. Page
Source: The Journal of Physical Chemistry Letters (article, 2023-10-16)
Field: Materials Science — Materials Chemistry

High-Throughput Aqueous Electrolyte Structure Prediction Using IonSolvR and Equivariant Graph Neural Network Potentials Neural network potentials have recently emerged as an efficient and accurate tool for accelerating <i>ab initio</i> molecular dynamics (AIMD) in order to simulate complex condensed phases such as electrolyte solutions. Their principal limitation, however, is their requirement for sufficiently large and accurate training sets, which are often composed of Kohn-Sham density functional theory (DFT) calculations. Here we examine the feasibility of using existing density functional tight-binding (DFTB) molecular dynamics trajectory data available in the IonSolvR database in order to accelerate the training of E(3)-equivariant graph neural network potentials. We show that the solvation structure of Na<sup>+</sup> and Cl<sup>-</sup> in aqueous NaCl solutions can be accurately reproduced with remarkably small amounts of data (i.e., 100 MD frames). We further show that these predictions can be systematically improved further via an embarrassingly parallel resampling approach. Solvation ; Artificial neural network ; Computer science ; Density functional theory ; Equivariant map ; Molecular graph ; Molecular dynamics ; Aqueous solution ; Electrolyte ; Ab initio ; Graph theory ; Graph ; Computational chemistry ; Biological system ; Chemistry ; Algorithm ; Theoretical computer science ; Mathematics ; Molecule ; Artificial intelligence ; Physical chemistry Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Advanced Chemical Physics Studies
Authors: Gang Seob Jung | Hunjoo Myung | Stephan Irle
Source: Machine Learning Science and Technology (article, 2023-04-14)
Field: Materials Science — Materials Chemistry

Artificial neural network potentials for mechanics and fracture dynamics of two-dimensional crystals <sup>**</sup> Abstract Understanding the mechanics and failure of materials at the nanoscale is critical for their engineering and applications. The accurate atomistic modeling of brittle failure with crack propagation in covalent crystals requires a quantum mechanics-based description of individual bond-breaking events. Artificial neural network potentials (NNPs) have emerged to overcome the traditional, physics-based modeling tradeoff between accuracy and accessible time and length scales. Previous studies have shown successful applications of NNPs for describing the structure and dynamics of molecular systems and amorphous or liquid phases of materials. However, their application to deformation and failure processes in materials is still uncommon. In this study, we discuss the apparent limitations of NNPs for the description of deformation and fracture under loadings and propose a way to generate and select training data for their employment in simulations of deformation and fracture simulations of crystals. We applied the proposed approach to 2D crystalline graphene, utilizing the density-functional tight-binding method for more efficient and extensive data generation in place of density functional theory. Then, we explored how the data selection affects the accuracy of the developed artificial NNPs. It revealed that NNP’s reliability should not only be measured based on the total energy and atomic force comparisons for reference structures but also utilize comparisons for physical properties, e.g. stress–strain curves and geometric deformation. In sharp contrast to popular reactive bond order potentials, our optimized NNP predicts straight crack propagation in graphene along both armchair and zigzag (ZZ) lattice directions, as well as higher fracture toughness of ZZ edge direction. Our study provides significant insight into crack propagation mechanisms on atomic scales and highlights strategies for NNP developments of broader materials. Fracture mechanics ; Zigzag ; Brittleness ; Materials science ; Molecular dynamics ; Multiscale modeling ; Graphene ; Deformation (meteorology) ; Statistical physics ; Density functional theory ; Amorphous solid ; Fracture (geology) ; Strain energy density function ; Mechanics ; Nanotechnology ; Structural engineering ; Physics ; Geometry ; Computational chemistry ; Composite material ; Mathematics ; Finite element method ; Chemistry ; Engineering ; Quantum mechanics ; Crystallography Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Graphene research and applications
Authors: Evgeniia E. Ondar | Mikhail V. Polynski | Valentine P. Ananikov
Source: ChemPhysChem (article, 2023-02-21)
Field: Chemistry — Spectroscopy

Predicting <sup>195</sup>Pt NMR Chemical Shifts in Water‐Soluble Inorganic/Organometallic Complexes with a Fast and Simple Protocol Combining Semiempirical Modeling and Machine Learning Water-soluble Pt complexes are the key components in medicinal chemistry and catalysis. The well-known cisplatin family of anticancer drugs and industrial hydrosylilation catalysts are two leading examples. On the molecular level, the activity mechanisms of such complexes mostly involve changes in the Pt coordination sphere. Using <sup>195</sup> Pt NMR spectroscopy for operando monitoring would be a valuable tool for uncovering the activity mechanisms; however, reliable approaches for the rapid correlation of Pt complex structure with <sup>195</sup> Pt chemical shifts are very challenging and not available for everyday research practice. While NMR shielding is a response property, molecular 3D structure determines NMR spectra, as widely known, which allows us to build up 3D structure to <sup>195</sup> Pt chemical shift correlations. Accordingly, we present a new workflow for the determination of lowest-energy configurational/conformational isomers based on the GFN2-xTB semiempirical method and prediction of corresponding chemical shifts with a Machine Learning (ML) model tuned for Pt complexes. The workflow was designed for the prediction of <sup>195</sup> Pt chemical shifts of water-soluble Pt(II) and Pt(IV) anionic, neutral, and cationic complexes with halide, NO<sub>2</sub> <sup>-</sup> , (di)amino, and (di)carboxylate ligands with chemical shift values ranging from -6293 to 7090 ppm. The model offered an accuracy (normalized root-mean-square deviation/RMSD) of 1.08 %/145.02 ppm on the held-out test set. Chemistry ; Chemical shift ; Cationic polymerization ; Nuclear magnetic resonance spectroscopy ; Catalysis ; Computational chemistry ; Proton NMR ; Halide ; Molecular dynamics ; Physical chemistry ; Stereochemistry ; Inorganic chemistry ; Organic chemistry Advanced NMR Techniques and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: A. Thorn | Daviti Gochitashvili | Saba Kharabadze | Aleksey N. Kolmogorov
Source: Physical Chemistry Chemical Physics (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Machine learning search for stable binary Sn alloys with Na, Ca, Cu, Pd, and Ag We present our findings of a large-scale screening for new synthesizable materials in five M-Sn binaries, M = Na, Ca, Cu, Pd, and Ag. The focus on these systems was motivated by the known richness of M-Sn properties with potential applications in energy storage, electronics packaging, and superconductivity. For the systematic exploration of the large configuration space, we relied on our recently developed MAISE-NET framework that constructs accurate neural network interatomic potentials and utilizes them to accelerate <i>ab initio</i> global structure searches. The scan of over two million candidate phases at a fraction of the typical <i>ab initio</i> calculation cost has uncovered 29 possible intermetallics thermodynamically stable at different temperatures and pressures (1 bar and 20 GPa). Notable predictions of ambient-pressure materials include a simple hP6-NaSn<sub>2</sub> phase, fcc-based Pd-rich alloys, tI36-PdSn<sub>2</sub> with a new prototype, and several high-temperature Sn-rich ground states in the Na-Sn, Cu-Sn, and Ag-Sn systems. Our modeling work also involved <i>ab initio</i> (re)examination of previously observed M-Sn compounds that helped explain the entropy-driven stabilization of known Cu-Sn phases. The study demonstrates the benefits of guiding structure searches with machine learning potentials and significantly expands the number of predicted thermodynamically stable crystalline intermetallics achieved with this strategy so far. Binary number ; Materials science ; Crystallography ; Chemistry ; Mathematics ; Arithmetic Machine Learning in Materials Science ; Nuclear Materials and Properties ; Thermodynamic and Structural Properties of Metals and Alloys
Authors: Murphy M. Peksen
Source: Hydrogen (article, 2023-07-25)
Field: Materials Science — Materials Chemistry

Material and Performance Optimisation for Syngas Preparation Using Artificial Intelligence (AI)-Based Machine Learning (ML) Working towards a more sustainable future with zero emissions, the International Future Laboratory for Hydrogen Economy at the Technical University of Munich (TUM) exhibits concerted efforts across various hydrogen technologies. The current research focuses on pre-reforming processes for high-quality reversible solid oxide cell feedstock preparation. An AI-based machine learning model has been developed, trained, and deployed to predict and optimise the controlled utilisation of methane gas. Using a blend of design of experiments and a validated 3D computational fluid dynamics model, pre-reforming process data have been generated for various syngas mixtures. The results of this study indicate that it is possible to achieve a targeted methane utilisation rate of 20% while decreasing the amount of catalyst material by 11%. Furthermore, it was found that precise process parameters could be determined efficiently and with minimal resource consumption in order to achieve higher methane fuel utilisation rates of 25% and 30%. The machine learning model has been effectively employed to analyse and optimise the fuel outlet conditions of the pre-reforming process, contributing to a better understanding of high-quality syngas preparation and furthering sustainable research efforts for a safe reversible solid oxide cell (r-SOC) process. Syngas ; Methane ; Process engineering ; Raw material ; Steam reforming ; Process (computing) ; Fuel cells ; Environmental science ; Hydrogen production ; Computer science ; Hydrogen ; Chemical engineering ; Engineering ; Chemistry Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Advancements in Solid Oxide Fuel Cells
Authors: Souvik Manna | Surya Sekhar Manna | Sandeep Das | Biswarup Pathak
Source: Electrochimica Acta (article, 2023-09-04)
Field: Materials Science — Materials Chemistry

Metal-solvent interaction contribution on voltage for metal ion battery: An interpretable machine learning approach Voltage ; Battery (electricity) ; Solvent ; Electrochemistry ; Anode ; Metal ; Interaction energy ; Metal ions in aqueous solution ; Chemistry ; Computer science ; Electrode ; Materials science ; Nanotechnology ; Molecule ; Electrical engineering ; Organic chemistry ; Engineering ; Physics ; Thermodynamics ; Power (physics) ; Physical chemistry Machine Learning in Materials Science ; Advancements in Battery Materials ; Advanced Battery Materials and Technologies
Authors: Oyawale Adetunji Moses | Mukhtar Lawan Adam | Zijian Chen | Collins Izuchukwu Ezeh | Hao Huang | Zhuo Wang | Zixuan Wang | Boyuan Wang | Wentao Li | Chensu Wang | Zongyou Yin | Yang Lü | Xue‐Feng Yu | Haitao Zhao
Source: Artificial Intelligence Chemistry (article, 2023-11-18)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine learning and robot-assisted synthesis of diverse gold nanorods via seedless approach The challenge of data-driven synthesis of advanced nanomaterials can be minimized by using machine learning algorithms to optimize synthesis parameters and expedite the innovation process. In this study, a high-throughput robotic platform was employed to synthesize over 1356 gold nanorods with varying aspect ratios via a seedless approach. The developed models guided us in synthesizing gold nanorods with customized morphology, resulting in highly repeatable morphological yield with quantifiable structure-modulating precursor adjustments. The study provides insight into the dynamic relationships between key structure-modulating precursors and the structural morphology of gold nanorods based on the expected aspect ratio. The high-throughput robotic platform-fabricated gold nanorods demonstrated precise aspect ratio control when spectrophotometrically investigated and further validated with the transmission electron microscopy characterization. These findings demonstrate the potential of high-throughput robot-assisted synthesis and machine learning in the synthesis optimization of gold nanorods and aided in the development of models that can aid in the synthesis of as-desired gold nanorods. Nanorod ; Nanotechnology ; Throughput ; Materials science ; Nanomaterials ; Yield (engineering) ; Computer science Gold and Silver Nanoparticles Synthesis and Applications ; Machine Learning in Materials Science ; Advanced Nanomaterials in Catalysis