jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Akinori Saeki
Source: ECS Meeting Abstracts (article, 2023-08-28)
Field: Materials Science — Materials Chemistry

(Invited) Machine Learning and Fast Experimental Screening-Assisted Development of Organic Solar Cell Non-fullerene, a small molecular electron acceptor, has substantially improved the power conversion efficiency of organic photovoltaics (OPVs).[1] However, the large structural freedom of π-conjugated polymers and molecules makes it difficult to be explored with limited resources. Machine learning, which is based on the rapidly growing artificial intelligence technology, is a high-throughput method to accelerate the speed of material design and process optimization; however, it suffers from limitations in terms of prediction accuracy, interpretability, data collection, and available data (particularly, experimental data). This recognition motivates the present review, which focuses on utilizing the experimental dataset for ML to efficiently aid OPV research. The author discusses the trends in ML-OPV publications, the NFA category, and the effects of data size and explanatory variables (fingerprints or Mordred descriptors) on the prediction accuracy and explainability, which broadens the scope of ML and would be useful for the development of next-generation solar cell materials.[2] Despite the advance of ML, the predictive accuracy of ML currently remains insufficient for the design of OPV semiconductors that exhibit a complex connectivity between chemical structure and PCE. In this study, we examined the impact of data selection and the introduction of artificially generated failure data on ML predictions of NFA solar cells. The authors demonstrated that an ML model empowered by artificially generated failure data (~0% PCE by insoluble polymers based on an inappropriate choice of solubilizing side alkyl chains) led to improved predictions.[3] This approach was validated through the synthesis and characterization of twelve polymers (benzothiadiazole, thienothiophene, or tetrazine coupled with benzodithiophene; benzobisthiazole coupled with dioxo-benzodithiophene). Our work offers a facile approach to mitigate the difficulties of the ML-driven development of OPV materials that is also readily applicable to other material science fields. Reference [1] Kranthiraja, A. Saeki, Adv. Funct. Mater. 31 (2021) 2011168 [2] Miyake, A. Saeki, J. Phys. Chem. Lett. 12 (2021) 12391. [3] Miyake, K. Kranthiraja, F. Ishiwari, A. Saeki, Chem. Mater. 34 (2022) 6912. Interpretability ; Organic solar cell ; Computer science ; Artificial intelligence ; Deep learning ; Data collection ; Photovoltaic system ; Machine learning ; Big data ; Biochemical engineering ; Nanotechnology ; Materials science ; Data mining ; Engineering ; Electrical engineering Machine Learning in Materials Science
Authors: Yuho Shimano | Alex Kutana | Ryoji Asahi
Source: Research Square (preprint, 2023-09-29)
Field: Materials Science — Materials Chemistry

Machine learning and atomistic origin of high dielectric permittivity in oxides Abstract Discovering new stable materials with large dielectric permittivity is important for future energy storage and electronics applications. Theoretical and computational approaches help design new materials by elucidating microscopic mechanisms and establishing structure-property relations. Ab initio methods can be used to reliably predict the dielectric response, but for fast materials screening, machine learning (ML) approaches, which can directly infer properties from the structural information, are needed. Here, random forest and graph convolutional neural network models are trained and tested to predict the dielectric constant from the structural information. We create a database of the dielectric properties of oxides and design, train, and test the two ML models. Both approaches show similar performance and can successfully predict response based on the structure. The analysis of the feature importance allows identification of local geometric features leading to the high dielectric permittivity of the crystal. Dimensionality reduction and clustering further confirms the relevance of descriptors and compositional features for obtaining high dielectric permittivity. Dielectric ; Permittivity ; Cluster analysis ; Materials science ; Artificial intelligence ; Computer science ; Curse of dimensionality ; Convolutional neural network ; Dimensionality reduction ; Machine learning ; Optoelectronics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Multiferroics and related materials
Authors: Wei Fang | Yu Cheng Zhu | Yihan Cheng | Yi-Ping Hao | Jeremy O. Richardson
Source: arXiv (Cornell University) (preprint, 2023-04-28)
Field: Materials Science — Materials Chemistry

Robust Gaussian Process Regression method for efficient reaction pathway optimization: application to surface processes Simulation of surface processes is a key part of computational chemistry that offers atomic-scale insights into mechanisms of heterogeneous catalysis, diffusion dynamics, as well as quantum tunneling phenomena. The most common theoretical approaches involve optimization of reaction pathways, including semiclassical tunneling pathways (called instantons). However, the computational effort can be demanding, especially for instanton optimizations with ab initio electronic structure. Recently, machine learning has been applied to accelerate reaction-pathway optimization, showing great potential for a wide range of applications. However, previous methods suffer from practical issues such as unfavorable scaling with respect to the size of the descriptor, and were mostly designed for reactions in the gas phase. We propose an improved framework based on Gaussian process regression for general transformed coordinates, which can alleviate the size problem. The descriptor combines internal and Cartesian coordinates, which improves the performance for modeling surface processes. We demonstrate with eleven instanton optimizations in three example systems that the new approach makes ab initio instanton optimization significantly cheaper, such that it becomes not much more expensive than a classical transition-state theory calculation. Instanton ; Semiclassical physics ; Ab initio ; Computer science ; Gaussian process ; Scaling ; Statistical physics ; Cartesian coordinate system ; Gaussian ; Mathematical optimization ; Quantum ; Computational chemistry ; Chemistry ; Physics ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Catalytic Processes in Materials Science
Authors: Akiyasu Yamamoto
Source: IEEJ Transactions on Fundamentals and Materials (article, 2023-12-31)
Field: Materials Science — Materials Chemistry

The Forefront of Informatics Applications in Metallic and Ceramics Materials Technologies In this article, we report recent advances in metallic and ceramics materials technologies. The forefront of the informatics application examples, such as data-mining, machine learning, neural network model, searching for new superconducting materials, and materials/process/measurement informatics are introduced. Materials informatics ; Informatics ; Ceramic ; Process (computing) ; Data science ; Computer science ; Engineering informatics ; Health informatics ; Materials science ; Engineering ; Metallurgy ; Electrical engineering ; Political science ; Health care Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; X-ray Diffraction in Crystallography
Authors: Biao Xu | Haijun Fu | Shasha Huang | Shihua Ma | Yaoxu Xiong | Jun Zhang | Xuepeng Xiang | Wenyu Lu | Ji‐Jung Kai | Shijun Zhao
Source: arXiv (Cornell University) (preprint, 2023-11-28)
Field: Materials Science — Materials Chemistry

Sluggish and Chemically-Biased Interstitial Diffusion in Concentrated Solid Solution Alloys: Mechanisms and Methods Interstitial diffusion is a pivotal process that governs the phase stability and irradiation response of materials in non-equilibrium conditions. In this work, we study sluggish and chemically-biased interstitial diffusion in Fe-Ni concentrated solid solution alloys (CSAs) by combining machine learning (ML) and kinetic Monte Carlo (kMC), where ML is used to accurately and efficiently predict the migration energy barriers on-the-fly. The ML-kMC reproduces the diffusivity that was reported by molecular dynamics results at high temperatures. With this powerful tool, we find that the observed sluggish diffusion and the "Ni-Ni-Ni"-biased diffusion in Fe-Ni alloys are ascribed to a unique "Barrier Lock" mechanism, whereas the "Fe-Fe-Fe"-biased diffusion is influenced by a "Component Dominance" mechanism. Inspired by the mentioned mechanisms, a practical AvgS-kMC method is proposed for conveniently and swiftly determining interstitial-mediated diffusivity by only relying on the mean energy barriers of migration patterns. Combining the AvgS-kMC with the differential evolutionary algorithm, an inverse design strategy for optimizing sluggish diffusion properties is applied to emphasize the crucial role of favorable migration patterns. Kinetic Monte Carlo ; Diffusion ; Thermal diffusivity ; Materials science ; Work (physics) ; Chemical physics ; Thermodynamics ; Atomic diffusion ; Diffusion process ; Kinetic energy ; Metallurgy ; Monte Carlo method ; Chemistry ; Computer science ; Physics ; Mathematics ; Innovation diffusion Nuclear Materials and Properties ; High Temperature Alloys and Creep ; Fusion materials and technologies
Authors: Lisanne Knijff | Thomas Dufils | Yunqi Shao | Chao Zhang
Source: ECS Meeting Abstracts (article, 2023-12-22)
Field: Materials Science — Materials Chemistry

Pinnwall: Heterogeneous Electrode Models from Integrating Machine Learning and Atomistic Simulation Electrochemical energy storage always involves the capacitive process. The prevailing electrode model used to study such polarizable electrode-electrolyte systems is the Siepmann-Sprik model, which has been recently extended to study the metallicity in the electrode model by including the Thomas-Fermi screening length. Nevertheless, a further extension to heterogeneous electrode models is required. Here, we address this challenge by integrating the atomistic machine learning code (PiNN) [1] for generating the base charge [2] and response kernel [3] and the classical molecular dynamics code (MetalWalls). This leads to the development of the PiNNwall [4] interface. Apart from the cases of chemically doped graphene and graphene oxide electrodes as shown in this study, the PiNNwall interface also allows us to probe polarized oxide surfaces in which both the proton charge and the electronic charge can coexist. Therefore, this work opens the door for modelling heterogeneous and complex electrode materials often found in energy storage systems. [1] Shao, Y.; Hellström, M.; Mitev, P. D.; Knijff, L.; Zhang, C. PiNN: A python library for building atomic neural networks of molecules and materials . J. Chem. Inf. Model. 2020 , 60, 1184. [2] Knijff, L.; Zhang, C. Machine learning inference of molecular dipole moment in liquid water. Mach. Learn.: Sci. Technol. 2021 , 2, 03LT03. [3] Shao, Y.; Andersson, L.; Knijff, L.; Zhang, C. Finite-field coupling via learning the charge response kernel. Electron. Struct. 2022 , 4, 014012. [4] Dufils, T., Knijff, L., Shao, Y. and Zhang, C. PiNNwall: heterogeneous electrode models from integrating machine learning and atomistic simulation. arXiv preprint arXiv:2303.15307. 2023 . *This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 Research and Innovation Programme (Grant Agreement No. 949012). We also acknowledge the support from the Centre for Interdisciplinary Mathematics (CIM) and the AI4Research initiative at Uppsala University. Computer science ; Electrode ; Materials science ; Chemistry ; Physical chemistry Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques
Authors: Chen Shen | Tianshu Li | Yixuan Zhang | Teng Long | Nuno Miguel Fortunato | Fei Liang | Mian Dai | Jiahong Shen | Chris Wolverton | Hongbin Zhang
Source: arXiv (Cornell University) (preprint, 2023-05-04)
Field: Materials Science — Materials Chemistry

Accelerated Screening of Ternary Chalcogenides for High-Performance Optoelectronic Materials Chalcogenides, which refer to chalcogen anions, have attracted considerable attention in multiple fields of applications, such as optoelectronics, thermoelectrics, transparent contacts, and thin film transistors. In comparison to oxide counterparts, chalcogenides have demonstrated higher mobility and \textit{p}-type dopability, owing to larger orbital overlaps between metal-X covalent chemical bondings and higher-energy valence bands derived by p-orbitals. Despite the potential of chalcogenides, the number of successfully synthesized compounds remains relatively low compared to oxides, suggesting the presence of numerous unexplored chalcogenides with fascinating physical characteristics. In this study, we implemented a systematic high-throughput screening process combined with first-principles calculations on ternary chalcogenides using 34 crystal structure prototypes. We generated a computational material database containing over 400,000 compounds by exploiting the ion-substitution approach at different atomic sites with elements in the periodic table. The thermodynamic stabilities of the candidates were validated using the chalcogenides included in the Open Quantum Materials Database. Moreover, we trained a model based on Crystal Graph Convolutional Neural Networks to predict the thermodynamic stability of novel materials. Furthermore, we theoretically evaluated the electronic structures of the stable candidates using accurate hybrid functionals. A series of in-depth characteristics, including the carrier effective masses, electronic configuration, and photovoltaic conversion efficiency, was also investigated. Our work provides useful guidance for further experimental research in the synthesis and characterization of such chalcogenides as promising candidates, as well as charting the stability and optoelectronic performance of ternary chalcogenides. Ternary operation ; Chalcogen ; Materials science ; Atomic orbital ; Ionic bonding ; Chemical stability ; Valence (chemistry) ; Electronic structure ; Nanotechnology ; Optoelectronics ; Chemistry ; Computational chemistry ; Computer science ; Crystallography ; Ion ; Physics Machine Learning in Materials Science ; Chalcogenide Semiconductor Thin Films ; Inorganic Chemistry and Materials
Authors: Johannes Drechsel | Henry Barth | Lars Rebenklau
Source:  (article, 2023-09-11)
Field: Materials Science — Materials Chemistry

Partial Discharge Characterization of Ceramic Power Electronics Circuit Carriers Assisted by Machine Learning This paper presents an approach for transferring knowledge about partial discharges in polymer insulators to ceramic insulators with the aid of machine learning. It is shown how various machine-learnable features can be generated from partial discharge measurement data and processed in varying artificial neural networks for classification. It is found that polymer-based partial discharges can be classified using this method. In addition, the Long Short-Term Memory based artificial neural network enables partial discharge cause finding and thus fault detection in ceramic power electronics substrates. Partial discharge ; Artificial neural network ; Ceramic ; Electronics ; Characterization (materials science) ; Fault (geology) ; Power electronics ; Power (physics) ; Computer science ; Electrical engineering ; Materials science ; Electronic engineering ; Artificial intelligence ; Engineering ; Voltage ; Nanotechnology ; Composite material High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Electrostatic Discharge in Electronics
Authors: Maximilian Van Zyl | Leila Pujal | Farnaz Heidar‐Zadeh
Source: Inquiry Queen s Undergraduate Research Conference Proceedings (article, 2023-03-30)
Field: Materials Science — Materials Chemistry

Building Transferable Potential Energy Surfaces with Machine Learning Discovering and characterizing novel compounds to serve society is the overarching goal of chemical sciences. Nowadays, experimental and computational methods provide complementary approaches for navigating the vast diversity of chemical space to identify suitable compounds for a given task. A key component of many computational methods is accurate and efficient mapping of the potential energy surface (PES), which relates the geometry to the energy of a chemical system.1 From a quantum mechanical perspective, the PES can be accurately computed; however, existing methods are infeasible for large systems (e.g., proteins) because their computational cost grows explosively with the size of the system. For this reason, machine learning (ML) approaches are used to circumvent direct computation of the PES of various chemical systems.2 To obtain ML potentials, a supervised algorithm is trained to establish a relationship between the structure of a chemical system, such as a molecule or crystal, and an output, like the system’s total energy and the forces acting on each atom. Even though existing ML potentials can reach excellent accuracy when applied to structures within the training domain (i.e., interpolation), they typically have poor performance for structures outside their applicability domain (i.e., extrapolation). To overcome this, we train NewtonNet,3 a message-passing network for deep learning of interatomic potentials, with more chemically-inspired data to the ML algorithm than has been traditionally used. Our goal is to allow this ML potential to learn transferable chemical properties and test its performance when applied to systems beyond the scope of the training set. References Journal of Computational Chemistry, 2003, 24, 1514-1527. Chemical Reviews, 2021, 121, 9816-9872. Digital Discovery, 2022, 1, 333-343. Extrapolation ; Computer science ; Chemical space ; Interpolation (computer graphics) ; Domain (mathematical analysis) ; Computation ; Artificial intelligence ; Energy (signal processing) ; Applicability domain ; Machine learning ; Algorithm ; Chemistry ; Mathematics ; Image (mathematics) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Jarno Laakso | Lauri Himanen | Henrietta Homm | Eiaki V. Morooka | Marc O. J. Jäger | Milica Todorović | Patrick Rinke
Source: arXiv (Cornell University) (preprint, 2023-03-24)
Field: Materials Science — Materials Chemistry

Updates to the DScribe Library: New Descriptors and Derivatives We present an update of the DScribe package, a Python library for atomistic descriptors. The update extends DScribe's descriptor selection with the Valle-Oganov materials fingerprint and provides descriptor derivatives to enable more advanced machine learning tasks, such as force prediction and structure optimization. For all descriptors, numeric derivatives are now available in DSribe. For the many-body tensor representation (MBTR) and the Smooth Overlap of Atomic Positions (SOAP), we have also implemented analytic derivatives. We demonstrate the effectiveness of the descriptor derivatives for machine learning models of Cu clusters and perovskite alloys. Python (programming language) ; Computer science ; Representation (politics) ; Artificial intelligence ; Tensor (intrinsic definition) ; Cheminformatics ; Machine learning ; Mathematics ; Chemistry ; Computational chemistry ; Programming language ; Geometry Machine Learning in Materials Science ; Physics of Superconductivity and Magnetism ; Catalysis and Oxidation Reactions
Source: NanoWorld Journal (article, 2023-11-03)
Field: Chemistry — Inorganic Chemistry

Improving TiO2 Nano Powder Synthesis Using High-energy Milling and Neural Network Optimization Sumanth Ratna Kandavalli, Chandravathi Chandrasekaran, Amuthakkannan Rajakannu, Vimalanathan Palanikumar, Srinivasan Raju, Venkatesan Srinivasan and Mayakannan Selvaraju Using an artificial-neural-network (ANN) model trained on milling factors like milling duration (T) and balls-to-powder ratio (R), the crystalline size of TiO2 (PS) Nano powder was predicted. High-energy mechanical milling was used to produce this Nano powder, and the results of the experiments served as the essential training data. Ball mill ; Materials science ; Nano- ; Artificial neural network ; Nanoparticle ; Diffraction ; High energy ; Powder diffraction ; Composite material ; Chemical engineering ; Metallurgy ; Nanotechnology ; Computer science ; Machine learning ; Crystallography ; Optics ; Engineering ; Engineering physics Pigment Synthesis and Properties ; X-ray Diffraction in Crystallography ; Machine Learning in Materials Science
Authors: Robert M. Hazen | Henderson James Cleaves | Grethe Hystad | Anirudh Prabhu | Michael L. Wong | George D. Cody | Sophia Economon
Source:  (article, 2023-01-01)
Field: Chemistry — Analytical Chemistry

A Robust Molecular Biosignature Based on Machine Learning Applied to Three-Dimensional Pyrolysis GCMS Data Computer science ; Pyrolysis ; Artificial intelligence ; Astrobiology ; Chemical engineering ; Physics ; Engineering Spectroscopy and Chemometric Analyses
Authors: Helen Uchenna Modekwe | Ayooluwa Tomiwa Akintola | Olusola Olaitan Ayeleru | Messai A. Mamo | Michael O. Daramola | Kapil Moothi
Source: Research Square (preprint, 2023-03-14)
Field: Materials Science — Materials Chemistry

Artificial neural network for predicting the performance of waste polypropylene plastic-derived carbon nanotubes Polypropylene ; Carbon nanotube ; Plastic waste ; Artificial neural network ; Materials science ; Carbon fibers ; Composite material ; Polymer science ; Waste management ; Computer science ; Artificial intelligence ; Engineering ; Composite number Carbon Nanotubes in Composites ; Nanotechnology research and applications ; Polymer Nanocomposite Synthesis and Irradiation
Authors: Roshan Patel | Sophia Colmenares | Michael A. Webb
Source: ChemRxiv (preprint, 2023-04-25)
Field: Materials Science — Materials Chemistry

Sequence Patterning, Topology, and Morphology in Single-chain Nanoparticles: Insights from Graph Theory and Machine Learning Single-chain nanoparticles are intriguing materials inspired by proteins that consist of a single precursor polymer chain that has collapsed into a stable structure. In many prospective applications, such as catalysis, the utility of a single-chain nanoparticle will intricately depend on the formation of a mostly specific structure or morphology. However, it is not generally well understood how to reliably control the morphology of single-chain nanoparticles. To address this knowledge gap, we simulate the formation of 7,680 distinct single-chain nanoparticles from precursor chains that span a wide range of, in principle, tunable patterning characteristics of cross-linking moieties. Using a combination of molecular simulation and machine learning analyses, we show how the overall fraction of functionalization and blockiness of cross-linking moieties biases the formation of certain local and global morphological characteristics. Importantly, using the total volume of simulation data, we illustrate and quantify the dispersity of morphologies that arise due to both the stochastic nature of collapse from a well-defined sequence as well as from the ensemble of sequences that correspond to a given specification of precursor parameters. Moreover, we also examine the efficacy of precise sequence control in achieving morphological outcomes in different regimes of precursor parameters. Overall, this work critically assesses how precursor chains might be feasibly tailored to achieve given SCNP morphologies and provides a platform to pursue future sequence-based design. Single chain ; Dispersity ; Nanoparticle ; Sequence (biology) ; Chain (unit) ; Nanotechnology ; Computer science ; Polymer ; Materials science ; Topology (electrical circuits) ; Biological system ; Chemistry ; Mathematics ; Physics ; Polymer chemistry Machine Learning in Materials Science ; Supramolecular Self-Assembly in Materials ; Surface Chemistry and Catalysis