jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kento Mitsui | Yutaka Sasaki | Ryoji Asahi
Source: Science and Technology of Advanced Materials Methods (article, 2023-05-02)
Field: Materials Science — Materials Chemistry

Automatic knowledge acquisition from superconductivity information in literature In this study, we developed a natural language processing model for extracting information solely from the abstracts of literature on superconducting materials, with the aim of making predictions for materials science. Using a dataset of tagged documents (annotations) on superconductivity, the DyGIE++ framework was employed for the simultaneous extraction of the named entities, relations, and events. Additionally, a model was developed for classifying the subject material in the abstracts. After training with 1,000 annotated abstracts, the model extracted information, such as the material composition, superconducting transition temperature, doping information, and process information, automatically from 48,565 abstracts registered in the Scopus database since 1937. The numbers of extracted entries concerning superconducting materials and transition temperatures were 43,944 and 24,075, respectively, i.e. equivalent to the number of entries in the existing databases. Machine learning models were constructed to predict physical and chemical properties. For example, the superconducting transition temperatures were predicted for compositions, with a mean absolute error of 15 K. In addition, the doping information indicated that the superconducting transition temperature was correlated with the choice of dopant and doping site. Superconductivity ; Computer science ; Physics ; Condensed matter physics Machine Learning in Materials Science ; Topic Modeling ; Expert finding and Q&A systems
Authors: Gian-Luca Geuken | Jörn Mosler | Patrick Kurzeja
Source: arXiv (Cornell University) (preprint, 2023-11-10)
Field: Materials Science — Materials Chemistry

Incorporating sufficient physical information into artificial neural networks: a guaranteed improvement via physics-based Rao-Blackwellization The concept of Rao-Blackwellization is employed to improve predictions of artificial neural networks by physical information. The error norm and the proof of improvement are transferred from the original statistical concept to a deterministic one, using sufficient information on physics-based conditions. The proposed strategy is applied to material modeling and illustrated by examples of the identification of a yield function, elasto-plastic steel simulations, the identification of driving forces for quasi-brittle damage and rubber experiments. Sufficient physical information is employed, e.g., in the form of invariants, parameters of a minimization problem, dimensional analysis, isotropy and differentiability. It is proven how intuitive accretion of information can yield improvement if it is physically sufficient, but also how insufficient or superfluous information can cause impairment. Opportunities for the improvement of artificial neural networks are explored in terms of the training data set, the networks' structure and output filters. Even crude initial predictions are remarkably improved by reducing noise, overfitting and data requirements. Artificial neural network ; Overfitting ; Identification (biology) ; Computer science ; Algorithm ; Norm (philosophy) ; Differentiable function ; Physical law ; Set (abstract data type) ; Artificial intelligence ; Mathematics ; Physics ; Mathematical analysis Machine Learning in Materials Science ; Non-Destructive Testing Techniques ; Fault Detection and Control Systems
Authors: Yuki K. Wakabayashi | Takuma Otsuka | Yoshiharu Krockenberger | Hiroshi Sawada | Yoshitaka Taniyasu | Hideki Yamamoto
Source: arXiv (Cornell University) (preprint, 2023-03-02)
Field: Materials Science — Materials Chemistry

Stoichiometric growth of SrTiO3 films via Bayesian optimization with adaptive prior mean Perovskite insulator SrTiO3 is expected to be applied to the next generation of electronic and photonic devices as high-k capacitors and photocatalysts. However, reproducible growth of highly insulating stoichiometric SrTiO3 films remains challenging due to the difficulty of the precise stoichiometry control in perovskite oxide films. Here, to grow stoichiometric SrTiO3 thin films by fine-tuning multiple growth conditions, we developed a new Bayesian optimization (BO)-based machine learning method that encourages the exploration of the search space by varying the prior mean to get out of suboptimal growth condition parameters. Using simulated data, we demonstrate the efficacy of the new BO method, which reproducibly reaches the global best conditions. With the BO method implemented in machine-learning-assisted molecular beam epitaxy (ML-MBE), highly insulating stoichiometric SrTiO3 film with no absorption in the band gap was developed in only 44 MBE growth runs. The proposed algorithm provides an efficient experimental design platform that is not as dependent on the experience of individual researchers and will accelerate not only oxide electronics but also various material syntheses. Stoichiometry ; Bayesian optimization ; Materials science ; Molecular beam epitaxy ; Perovskite (structure) ; Band gap ; Thin film ; Oxide ; Electronics ; Optoelectronics ; Computer science ; Nanotechnology ; Epitaxy ; Machine learning ; Chemical engineering ; Chemistry Electronic and Structural Properties of Oxides ; Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices
Authors: Enzo Raffaele Moretti | Ragnar Kiebach | Mikkel Rykær Kraglund | Nis Fisker‐Bødker | Tejs Vegge
Source: ECS Meeting Abstracts (article, 2023-08-28)
Field: Materials Science — Materials Chemistry

Autonomous Catalyst Development for Alkaline Electrolysis NiFe LDH based materials are promising candidates for cheap, high performance catalysts for alkaline OER. While suffering from degradation effects, the presence of Fe, even in trace amounts, increases their OER activity manifold compared to plain Ni hydroxide. Although discovered in the mid-80s, the exact catalytic mechanism of this “Fe effect” is still not fully understood. Additionally, other doping elements like Co, Cr, Al and others have shown to both further improve OER activity and increase stability. The properties of those materials are often highly dependent on the doping ratio, resulting in an enormous number of possible material combinations. Modern techniques like DFT simulations and machine learning can help to speed up the search for the optimal compositions, but ultimately manual experimentation often limits how fast better materials can be discovered, as the process remains both resource and time-consuming. In this work, we combine an autonomous high-throughput experiment with machine learning assisted optimization of material compositions to a closed-loop system in an attempt to substantially accelerate the search for high performance alkaline OER catalysts. The system screens an eight variable phase space of doped NiFe LDH for compositions that yield optimal OER performance. Its centerpiece is a 4-axis robotic arm from North Robotics with sample, vial and liquid handling capabilities. The samples are prepared by means of a dip-coating process on Ni foam substrates that allows for doping with different metals. Electrochemical investigations are carried out in 1M KOH in an automated test cell, including EIS, CV and CP measurements at current densities of up to 200mA/cm 2 . From these results, a figure of merit for the OER activity is calculated and fed into a bayesian optimizer (Dragonfly). This algorithm determines the most promising next test candidate based on the principles of exploration and exploitation and initiates the next experiment accordingly. The experimental procedure can be divided into independent sub-steps, allowing for smart scheduling. This can reduce downtime of the robotic arm and therefore increase the throughput significantly, such that up to 1000 experiments per week can be achieved. We present the first results of a screening study conducted with this system and compare it to a manual study in terms of accuracy, reproducibility and throughput. Selected, particularly high performing compositions found in this study are also tested in a flow cell setup under industrially relevant conditions (30 wt. % KOH, 60 °C) for several hundred hours. Catalysis ; Materials science ; Doping ; Chemical engineering ; Nanotechnology ; Computer science ; Process engineering ; Chemistry ; Organic chemistry ; Engineering Catalytic Processes in Materials Science ; Catalysis and Hydrodesulfurization Studies ; Electrocatalysts for Energy Conversion
Authors: Hovan Lee | Zelong Zhao | George H. Booth | Weifeng Ge | Cédric Weber
Source: arXiv (Cornell University) (preprint, 2023-06-12)
Field: Materials Science — Materials Chemistry

A language-inspired machine learning approach for solving strongly correlated problems with dynamical mean-field theory We present SCALINN -- Strongly Correlated Approach with Language Inspired Neural Network -- as a method for solving the Anderson impurity model and reducing the computational cost of dynamical mean-field theory calculations. Inspired by the success of generative Transformer networks in natural language processing, SCALINN utilizes an in-house modified Transformer network in order to learn correlated Matsubara Green's functions, which act as solutions to the impurity model. This is achieved by providing the network with low-cost Matsubara Green's functions, thereby overcoming the computational cost of high accuracy solutions. Across different temperatures and interaction strengths, the performance of SCALINN is demonstrated in both physical observables (spectral function, Matsubara Green's functions, quasi-particle weight), and the mean squared error cost values of the neural network, showcasing the network's ability to accelerate Green's function based calculations of correlated materials. Artificial neural network ; Transformer ; Observable ; Mean field theory ; Computer science ; Generative grammar ; Artificial intelligence ; Mean squared error ; Function (biology) ; Mathematics ; Quantum mechanics ; Physics ; Voltage ; Statistics Machine Learning in Materials Science ; Quantum, superfluid, helium dynamics ; Quantum many-body systems
Authors: Ao Wu | Jiangxue Huang | Qijun Huang | Jin He | Hao Wang | Sheng Chang
Source: arXiv (Cornell University) (preprint, 2023-11-30)
Field: Materials Science — Materials Chemistry

A Novel Interface Database of Graphene Nanoribbon from Density Functional Theory Interfaces play a crucial role in determining the overall performance and functionality of electronic devices and systems. Driven by the data science, machine learning (ML) reveals excellent guidance for material selection and device design, in which an advanced database is crucial for training models with state-of-the-art (SOTA) precision. However, a systematic database of interfaces is still in its infancy due to the difficulties in collecting raw data in experiment and the expensive first-principles computational cost in density functional theory (DFT). In this paper, we construct ample interface structures of graphene nanoribbons (GNR), whose interfacial morphology can be precisely fabricated based on specific molecular precursors. The GNR interfaces serve as promising candidates since their bandgaps can be modulated. Their physical properties including energy bands and density of states (DOS) maps are obtained under reasonable calculation parameters. This database can provide theoretical guidance for the design of electronic devices and accelerate the ML study of various physical quantities. Interface (matter) ; Density functional theory ; Graphene ; Construct (python library) ; Computer science ; Materials science ; Database ; Nanotechnology ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Graphene research and applications ; Surface Chemistry and Catalysis
Authors: Ziyue Zou | Pratyush Tiwary
Source: arXiv (Cornell University) (preprint, 2023-10-11)
Field: Materials Science — Materials Chemistry

Enhanced sampling of Crystal Nucleation with Graph Representation Learnt Variables In this study, we present a graph neural network-based learning approach using an autoencoder setup to derive low-dimensional variables from features observed in experimental crystal structures. These variables are then biased in enhanced sampling to observe state-to-state transitions and reliable thermodynamic weights. Our approach uses simple convolution and pooling methods. To verify the effectiveness of our protocol, we examined the nucleation of various allotropes and polymorphs of iron and glycine from their molten states. Our graph latent variables when biased in well-tempered metadynamics consistently show transitions between states and achieve accurate free energy calculations in agreement with experiments, both of which are indicators of dependable sampling. This underscores the strength and promise of our graph neural net variables for improved sampling. The protocol shown here should be applicable for other systems and with other sampling methods. Metadynamics ; Graph ; Latent variable ; Sampling (signal processing) ; Autoencoder ; Computer science ; Statistical physics ; Nucleation ; Crystal (programming language) ; Pooling ; Artificial neural network ; Artificial intelligence ; Molecular dynamics ; Theoretical computer science ; Computational chemistry ; Chemistry ; Physics ; Thermodynamics ; Detector Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Sergi Vela Llausí | María Fumanal Quintana | Carmen Sousa Romero
Source: Dipòsit Digital de la Universitat de Barcelona (Universitat de Barcelona) (article, 2023-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Understanding Kinetically Controlled Spin Transitions in Bistable Spin Crossover Materials Spin crossover (SCO) materials can be kinetically trapped in a photo-excited metastable state in the so-called LIESST and reverse-LIESST processes. Under these conditions, SCO molecules are excellent light-responsive bistable molecular switches. However, above a certain temperature (TLIESST and Tr-LIESST, respectively), the relaxation to the ground state becomes favorable and their bistability is suppressed. Understanding the mechanism of these processes, and being able to predict their kinetics, is key to designing SCO switches that are able to operate at room temperature. Herein, we reveal the mechanism of thermally induced spin transitions of the [FeII(1-bpp)2]2+ SCO complex, and we predict its TLIESST (as well as its T1/2) with unprecedented accuracy. This is possible here thanks to the efficient reconstruction of the low-spin (LS, S = 0), high-spin (HS, S = 2) and intermediate (IS, S = 1) state Free energy surfaces (FESs) with ab initio and machine-learning methods, and the characterization of the minimum energy crossing points (MECPs) connecting those FESs. This approach paves the way for the systematic investigation of molecular features determining the mechanism of kinetically controlled transitions in SCO materials, as well as their temperature-dependent rate constants. Bistability ; Spin crossover ; Metastability ; Ab initio ; Relaxation (psychology) ; Spin states ; Molecule ; Ground state ; Spin (aerodynamics) Magnetism in coordination complexes ; Metal-Catalyzed Oxygenation Mechanisms ; Organic Light-Emitting Diodes Research
Authors: Emil Pitz | Kishore Pochiraju
Source: arXiv (Cornell University) (preprint, 2023-04-16)
Field: Materials Science — Materials Chemistry

A Neural Network Transformer Model for Composite Microstructure Homogenization Heterogeneity and uncertainty in a composite microstructure lead to either computational bottlenecks if modeled rigorously or to solution inaccuracies in the stress field and failure predictions if approximated. Although methods suitable for analyzing arbitrary and non-linear microstructures exist, their computational cost makes them impractical to use in large-scale structural analysis. Surrogate models or Reduced Order Models (ROMs) commonly enhance efficiencies but are typically calibrated with a single microstructure. Homogenization methods, such as the Mori-Tanaka method, offer rapid homogenization for a wide range of constituent properties. However, simplifying assumptions, like stress and strain averaging in phases, render the consideration of both deterministic and stochastic variations in microstructure infeasible. This paper illustrates a transformer neural network architecture that captures the knowledge of various microstructures and constituents, enabling it to function as a computationally efficient homogenization surrogate model. Given an image or an abstraction of an arbitrary composite microstructure of linearly elastic fibers in an elastoplastic matrix, the transformer network predicts the history-dependent, non-linear, and homogenized stress-strain response. Two methods for encoding microstructure features were tested: calculating two-point statistics using Principal Component Analysis (PCA) for dimensionality reduction and employing an autoencoder with a Convolutional Neural Network (CNN). Both methods accurately predict the homogenized material response. The developed transformer neural network offers an efficient means for microstructure-to-property translation, generalizable and extendable to a variety of microstructures. The paper describes the network architecture, training and testing data generation, and performance under cycling and random loadings. Homogenization (climate) ; Microstructure ; Computer science ; Transformer ; Artificial neural network ; Curse of dimensionality ; Discretization ; Algorithm ; Materials science ; Artificial intelligence ; Mathematics ; Composite material ; Mathematical analysis ; Physics ; Voltage Machine Learning in Materials Science ; Composite Material Mechanics ; Material Properties and Failure Mechanisms
Authors: Abir Boublia | Zahir Guezzout | N. Haddaoui | Michaël Badawi | Ahmad S. Darwish | Tarek Lemaoui | Fawzi Banat | Inas M. AlNashef | Krishna Kumar Yadav | Mohd Sajid Ali | Byong‐Hun Jeon | Yacine Benguerba
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Polymers and Plastics

Machine Learning Prediction Models for PANI/Graphene Nanocomposites: Electrical Conductivity and Gas Sensing Performance Graphene ; Materials science ; Nanocomposite ; Electrical resistivity and conductivity ; Conductivity ; Composite material ; Nanotechnology ; Physics ; Electrical engineering ; Engineering Conducting polymers and applications ; Gas Sensing Nanomaterials and Sensors ; Advanced Sensor and Energy Harvesting Materials
Authors: Zhenning Liu | Peng Gao | Qian Wu | Zhenguo Wang | Jianyang Li | Kunyang Wang | Guiwei Li | Xueli Zhou | Zhengyi Song | Bingqian Li | Qingping Liu
Source: Smart Manufacturing (article, 2023-06-23)
Field: Materials Science — Polymers and Plastics

4D printing of PLA/PCL composites with tunable shape memory properties via printing and programming parameters 4D printing of biocompatible shape memory polymer materials is of great significance in the biomedical field. The addition of PCL material into PLA matrix is capable of modulating the elastic modulus of the material while remaining a reduced degradation rate. However, the influence of 4D printing parameters and shape programming parameters on their shape memory properties had not been systematically investigated. Here, we examine the effects of 4D printing process parameters and shape programming parameters on the shape memory properties of PLA/PCL composites. We first explore the effects of key printing parameters on the print quality and shape memory properties of pure PLA materials, and select composites with a PCL mass fraction of 15% to fully characterize their thermodynamic properties. The effects of programming stress and programming temperature on the shape memory properties of the PCL15 composites are analyzed. Finally, the artificial neural network algorithm is employed to optimize its programming parameters, and brings 5.76% enhancement of shape recovery ratio compared with that before optimization. This work provides a systematic study of the shape memory properties of 4D printed PLA/PCL composites, paving the way for promoting their application in the biomedical industry. Shape-memory polymer ; Materials science ; Shape-memory alloy ; Composite material ; 3D printing ; Mass fraction ; Smart material ; Modulus ; Computer science Polymer composites and self-healing ; Bone Tissue Engineering Materials ; Additive Manufacturing and 3D Printing Technologies
Authors: Nir Goldman | Laurence E. Fried | Rebecca Lindsey | C. Huy Pham | R. Dettori
Source: arXiv (Cornell University) (preprint, 2023-01-04)
Field: Materials Science — Materials Chemistry

Enhancing the Accuracy of Density Functional Tight Binding Models Through ChIMES Many-body Interaction Potentials Semi-empirical quantum models such as Density Functional Tight Binding (DFTB) are attractive methods for obtaining quantum simulation data at longer time and length scales than possible with standard approaches. However, application of these models can require lengthy effort due to the lack of a systematic approach for their development. In this work, we discuss use of the Chebyshev Interaction Model for Efficient Simulation (ChIMES) to create rapidly parameterized DFTB models which exhibit strong transferability due to the inclusion of many-body interactions that might otherwise be inaccurate. We apply our modeling approach to silicon polymorphs and review previous work on titanium hydride. We also review creation of a general purpose DFTB/ChIMES model for organic molecules and compounds that approaches hybrid functional and coupled cluster accuracy with two orders of magnitude fewer parameters than similar neural network approaches. In all cases, DFTB/ChIMES yields similar accuracy to the underlying quantum method with orders of magnitude improvement in computational cost. Our developments provide a way to create computationally efficient and highly accurate simulations over varying extreme thermodynamic conditions, where physical and chemical properties can be difficult to interrogate directly and there is historically a significant reliance on theoretical approaches for interpretation and validation of experimental results. Parameterized complexity ; Tight binding ; Computer science ; Quantum ; Transferability ; Statistical physics ; Algorithm ; Theoretical computer science ; Machine learning ; Computational chemistry ; Physics ; Electronic structure ; Chemistry ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Catalysis and Oxidation Reactions