jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Lorenzo Ferretti | Andrea Cini | Georgios Zacharopoulos | Cesare Alippi | Laura Pozzi
Source: arXiv (Cornell University) (preprint, 2021-11-29)
Field: Materials Science — Materials Chemistry

A Graph Deep Learning Framework for High-Level Synthesis Design Space\n Exploration The design of efficient hardware accelerators for high-throughput\ndata-processing applications, e.g., deep neural networks, is a challenging task\nin computer architecture design. In this regard, High-Level Synthesis (HLS)\nemerges as a solution for fast prototyping application-specific hardware\nstarting from a behavioural description of the application computational flow.\nThis Design-Space Exploration (DSE) aims at identifying Pareto optimal\nsynthesis configurations whose exhaustive search is often unfeasible due to the\ndesign-space dimensionality and the prohibitive computational cost of the\nsynthesis process. Within this framework, we effectively and efficiently\naddress the design problem by proposing, for the first time in the literature,\ngraph neural networks that jointly predict acceleration performance and\nhardware costs of a synthesized behavioral specification given optimization\ndirectives. The learned model can be used to rapidly approach the Pareto curve\nby guiding the DSE, taking into account performance and cost estimates. The\nproposed method outperforms traditional HLS-driven DSE approaches, by\naccounting for arbitrary length of computer programs and the invariant\nproperties of the input. We propose a novel hybrid control and data flow graph\nrepresentation that enables training the graph neural network on specifications\nof different hardware accelerators; the methodology naturally transfers to\nunseen data-processing applications too. Moreover, we show that our approach\nachieves prediction accuracy comparable with that of commonly used simulators\nwithout having access to analytical models of the HLS compiler and the target\nFPGA, while being orders of magnitude faster. Finally, the learned\nrepresentation can be exploited for DSE in unexplored configuration spaces by\nfine-tuning on a small number of samples from the new target domain.\n Computer science ; Design space exploration ; High-level synthesis ; Compiler ; Data-flow analysis ; Design flow ; Field-programmable gate array ; Artificial neural network ; Computer engineering ; Graph ; Engineering design process ; Computer architecture ; Curse of dimensionality ; Data flow diagram ; Artificial intelligence ; Theoretical computer science ; Embedded system ; Programming language Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Ferroelectric and Negative Capacitance Devices
Authors: Bei Liu | Kai Jin | Jie Tao | Hao Wang | Dan He | Huaguan Li
Source: Polymers for Advanced Technologies (article, 2021-12-28)
Field: Materials Science — Polymers and Plastics

Performance optimization of shape memory epoxy polymers based on machine learning Abstract In this study, we proposed a machine learning (ML) method to optimize the comprehensive performance of shape memory epoxy polymers (SMEPs) based on experimental data as samples. Firstly, a series of SMEPs specimens were prepared, and their properties were evaluated respectively by testing four indexes including the glass transition temperature ( T g ), bending strength, strain fixation rate ( R f ), and strain recovery rate ( R r ). Subsequently, ML used these experimental data as samples for feature learning to investigate the influence of each component on these properties. The results indicated that methyltetrahydrophthalic anhydride was more favorable to T g and bending strength than methylhexahydrophthalic anhydride (MHHPA) as a curing agent. However, a certain amount of MHHPA must be included in the system to guarantee a higher R f and R r . Moreover, the right amount of bisphenol A cyanate ester in the system improved the comprehensive properties of SMEPs, especially the shape memory effect. Finally, a SMEPs system with superior properties was acquired through the optimization of four indexes of T g , bending strength, R f and R r . Therefore, this study shows that ML methods can also be used to investigate SMEPs that require more specific excellent performance. Cyanate ester ; Materials science ; Epoxy ; Composite material ; Glass transition ; Bisphenol A ; Curing (chemistry) ; Bending ; Polymer ; Universal testing machine ; Ultimate tensile strength Polymer composites and self-healing ; Photopolymerization techniques and applications ; Conducting polymers and applications
Authors: Ferhat Slimani | Abdallah Hedir | Mustapha Moudoud | Ali DURMUS | Mounir Amir | M. Megherbi
Source: TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES (article, 2021-06-27)
Field: Materials Science — Materials Chemistry

Prediction of long-term physical properties of low density polyethylene (LDPE) cable insulation materials by artificial neural network modeling approach under environmental constraints This study quantifies long-term physical properties of low density polyethylene (LDPE) cables insulations exposed to environmental constraints such as UV radiation and temperature via both experimental measurements and mathematical modeling approach. For this purpose, tensile test and electrical breakdown test were carried out to determine elongation at break, tensile strength, and dielectric strength of unaged and aged specimens, respectively. Experimental results showed that both UV and temperature exposures affected the LDPE properties, significantly. A supervised artificial neural network (ANN) trained by the Levenberg?Marquardt algorithm was designed for predicting the long-term characteristics of specimens and also for minimizing the experimental procedures. Modeling work showed that the proposed ANN yielded successful estimations and predictions about the service life of thermoplastic cable insulation materials for maintaining the process. Low-density polyethylene ; Artificial neural network ; Materials science ; Ultimate tensile strength ; Polyethylene ; Composite material ; Service life ; Term (time) ; Biological system ; Computer science ; Machine learning High voltage insulation and dielectric phenomena ; Transport Systems and Technology
Authors: Chenru Duan | Shuxin Chen | Michael G. Taylor | Fang Liu | Heather J. Kulik
Source: arXiv (Cornell University) (preprint, 2021-06-24)
Field: Materials Science — Materials Chemistry

Machine learning to tame divergent density functional approximations: a new path to consensus materials design principles Computational virtual high-throughput screening (VHTS) with density functional theory (DFT) and machine-learning (ML)-acceleration is essential in rapid materials discovery. By necessity, efficient DFT-based workflows are carried out with a single density functional approximation (DFA). Nevertheless, properties evaluated with different DFAs can be expected to disagree for the cases with challenging electronic structure (e.g., open shell transition metal complexes, TMCs) for which rapid screening is most needed and accurate benchmarks are often unavailable. To quantify the effect of DFA bias, we introduce an approach to rapidly obtain property predictions from 23 representative DFAs spanning multiple families and "rungs" (e.g., semi-local to double hybrid) and basis sets on over 2,000 TMCs. Although computed properties (e.g., spin-state ordering and frontier orbital gap) naturally differ by DFA, high linear correlations persist across all DFAs. We train independent ML models for each DFA and observe convergent trends in feature importance; these features thus provide DFA-invariant, universal design rules. We devise a strategy to train ML models informed by all 23 DFAs and use them to predict properties (e.g., spin-splitting energy) of over 182k TMCs. By requiring consensus of the ANN-predicted DFA properties, we improve correspondence of these computational lead compounds with literature-mined, experimental compounds over the single-DFA approach typically employed. Both feature analysis and consensus-based ML provide efficient, alternative paths to overcome accuracy limitations of practical DFT. Computer science ; Feature (linguistics) ; Theoretical computer science ; Algorithm Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Geemi P. Wellawatte | Aditi Seshadri | Andrew Dickson White
Source: ChemRxiv (preprint, 2021-09-05)
Field: Materials Science — Materials Chemistry

Model agnostic generation of counterfactual explanations for molecules An outstanding challenge in deep learning in chemistry is its lack of interpretability. The inability of explaining why a neural network makes a prediction is a major barrier to deployment of AI models. This not only dissuades chemists from using deep learning predictions, but also has led to neural networks learning spurious correlations that are difficult to notice. Counterfactuals are a category of explanations that provide a rationale behind a model prediction with satisfying properties like providing chemical structure insights. Yet, counterfactuals are have been previously limited to specific model architectures or required reinforcement learning as a separate process. In this work, we show a universal model-agnostic approach that can explain any black-box model prediction. We demonstrate this method on random forest models, sequence models, and graph neural networks in both classification and regression. Interpretability ; Counterfactual conditional ; Spurious relationship ; Counterfactual thinking ; Artificial intelligence ; Computer science ; Machine learning ; Artificial neural network ; Deep learning ; Reinforcement learning ; Psychology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Caihong Li | Yan Zhao | Wenjing Li | Rongrong Xing | Jing Lü | Rongxia Liu
Source: Industrial Crops and Products (article, 2021-01-30)
Field: Chemistry — Organic Chemistry

UHPLC-UV-Q-Orbitrap HRMS combined with machine learning algorithms reveals the chemical markers of Euodiae Fructus among closely related cultivars Orbitrap ; Chlorogenic acid ; Cultivar ; Chemistry ; Botany ; Chromatography ; Mass spectrometry ; Biology Quinazolinone synthesis and applications ; Phytochemical compounds biological activities ; Phenothiazines and Benzothiazines Synthesis and Activities
Authors: Vipul Mann | Venkat Venkatasubramanian
Source: ChemRxiv (preprint, 2021-04-15)
Field: Materials Science — Materials Chemistry

Retrosynthesis Prediction using Grammar-based Neural Machine Translation: An Information-Theoretic Approach Retrosynthetic prediction is one of the main challenges in chemical synthesis that requires identifying reaction pathways and precursor molecules for synthesizing a target molecule. This requires a search over the space of plausible chemical reactions that often results in complex, multi-step, branched synthesis trees for even moderately complex organic reactions. Here, we propose an approach that performs single-step retrosynthesis prediction using SMILES grammar-based representations in a neural machine translation framework. Information-theoretic analyses of such grammar-representations reveal that they are both superior and well-suited for machine learning tasks due to their underlying redundancy and high information capacity compared to purely character-based representations. We report the top-1 prediction accuracy of 43.8% (top-5 measure of 61.4%) and syntactic validity of 95.6% (top-5 measure of 91.6%) on a standard reaction dataset. Comparing our model's performance with previous work that used purely character-based SMILES representations demonstrate improved accuracy and reduced grammatically invalid predictions. Retrosynthetic analysis ; Computer science ; Artificial intelligence ; Machine translation ; Redundancy (engineering) ; Grammar ; Natural language processing ; Character (mathematics) ; Measure (data warehouse) ; Rule-based machine translation ; Machine learning ; Translation (biology) ; Data mining ; Mathematics ; Linguistics ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Przemysław Talik | Aleksander Mendyk
Source: Applied Sciences (article, 2021-08-23)
Field: Chemistry — Spectroscopy

Machine Learning for the Identification of Hydration Mechanisms of Pharmaceutical-Grade Cellulose Polymers and Their Mixtures with Model Drugs Differently bound water molecules confined in hydrated hydroxypropyl cellulose (HPC) type MF and their mixtures (1:1 w/w) with lowly soluble salicylic acid and highly soluble sodium salicylate were investigated by differential scanning calorimetry (DSC). The obtained ice-melting DSC curves of the HPC/H2O samples were deconvoluted into multiple components, using a specially developed curve decomposition tool. The ice-melting enthalpies of the individual deconvoluted components were used to estimate the amounts of water in three states in the HPC matrix: free water (FW), freezing bound water (FBW), and non-freezing water (NFW). A search for an optimal number of Gaussian functions was carried out among all available samples of data and was based on the analysis of the minimum fitting error vs. the number of Gaussians. Finally, three Gaussians accounting for three fractions of water were chosen for further analysis. The results of the calculations are discussed in detail and compared to previously obtained experimental DSC data. AI/ML tools assisted in theory elaboration and indirect validation of the hypothetical mechanism of the interaction of water with the HPC polymer. Differential scanning calorimetry ; Hydroxypropyl cellulose ; Bound water ; Polymer ; Thermodynamics ; Chemistry ; Materials science ; Molecule ; Organic chemistry Analytical Chemistry and Chromatography ; Microfluidic and Capillary Electrophoresis Applications ; Protein purification and stability
Authors: Somesh Mohapatra | Joyce An | Rafael Gómez‐Bombarelli
Source:  (preprint, 2021-09-08)
Field: Materials Science — Materials Chemistry

GLAMOUR: Graph learning over macromolecule representations The near-infinite chemical diversity of natural and artificial macromolecules arises from the vast range of possible component monomers, linkages, and polymers topologies.This enormous macromolecular diversity contributes to the ubiquity and indispensability of macromolecules but hinders the development of general machine learning methods with macromolecules as input.To address this, we developed GLAMOUR, a framework for chemistry-informed graph representation of macromolecules that enables quantifying structural similarity, and interpretable supervised learning for macromolecules. Macromolecule ; Graph ; Computer science ; Theoretical computer science ; Chemistry ; Biochemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Samare Rostami | Nicola Seriani | S. Alireza Ghasemi | Ralph Gebauer
Source: Physical Review Materials (article, 2021-06-29)
Field: Materials Science — Materials Chemistry

Accurate and flexible neural-network interatomic potential for mixed materials: <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mrow><mml:mi>Ti</mml:mi></mml:mrow><mml:mi>x</mml:mi></mml:msub><mml:msub><mml:mrow><mml:mi>Zr</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:mi>x</mml:mi></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mi mathvariant="normal">O</mml:mi></mml:mrow><mml:mn>2</mml:mn></mml:msub></mml:math> from bulk to clusters and nanoparticles Many interesting systems, such as interfaces, surfaces, grain boundaries, and nanoparticles, contain so many atoms that quantum-mechanical atomistic simulations become inconvenient or outright impossible. It is therefore desirable to develop accurate and flexible general-purpose interatomic potentials to make it possible to explore the potential energy surface of such structures. In this work we generate a neural-network potential through charge equilibration technique (CENT) for ${\text{Ti}}_{x}{\text{Zr}}_{1\ensuremath{-}x}{\text{O}}_{2}$ with $0\ensuremath{\le}x\ensuremath{\le}1$. Optimized symmetry functions for multicomponent systems make it possible to train the potential on less than 10 000 diverse structures containing different cation ratios $x$, from pure ${\text{TiO}}_{2}$ to ${\text{ZrO}}_{2}$, in free and periodic boundary conditions in the framework of density functional theory. The combination of the CENT potential with the symmetry functions generates a flexible and reliable method to reproduce the complexity of the energy landscape of these mixed materials with different boundary conditions at zero pressure. The reliability and transferability of the potential are verified by calculating some properties of bulk and slab configurations. Moreover, in order to investigate the performance of potential for different crystal phases and cluster configurations which are not included in our training data set, we performed a crystal structure search by minima hopping method. Beside reproducing known results in agreement with DFT calculations, we discovered novel crystal structures for bulk ${\text{TiZr}}_{3}{\text{O}}_{8}$ and ${\text{TiZrO}}_{4}$, as well as for small clusters and ${\text{ZrO}}_{2}$ nanoparticles. Materials science ; Grain boundary ; Crystal (programming language) ; Interatomic potential ; Energy (signal processing) ; Artificial neural network ; Cluster (spacecraft) ; Algorithm ; Crystal structure ; Crystallography ; Molecular dynamics ; Physics ; Machine learning ; Thermodynamics ; Computer science ; Quantum mechanics ; Chemistry ; Microstructure Machine Learning in Materials Science ; Nuclear Materials and Properties ; X-ray Diffraction in Crystallography
Authors: Daniel Hedman | Tom Rothe | Gustav Johansson | Fredrik Sandin | J. Andreas Larsson | Yoshiyuki Miyamoto
Source: Carbon Trends (article, 2021-01-21)
Field: Materials Science — Materials Chemistry

Impact of training and validation data on the performance of neural network potentials: A case study on carbon using the CA-9 dataset The use of machine learning to accelerate computer simulations is on the rise. In atomistic simulations, the use of machine learning interatomic potentials (ML-IAPs) can significantly reduce computational costs while maintaining accuracy close to that of ab initio methods. To achieve this, ML-IAPs are trained on large datasets of images, which are atomistic configurations labeled with data from ab initio calculations. Focusing on carbon, we use deep learning to train neural network potentials (NNPs), a form of ML-IAP, based on the state-of-the-art end-to-end NNP architecture SchNet and investigate how the choice of training and validation data affects the performance of the NNPs. Training is performed on the CA-9 dataset, a 9-carbon allotrope dataset constructed using data obtained via ab initio molecular dynamics (AIMD). Our results show that image generation with AIMD causes a high degree of similarity between the generated images, which has a detrimental effect on the performance of the NNPs. But by carefully choosing which images from the dataset are included in the training and validation data, this effect can be mitigated. We conclude by benchmarking our trained NNPs in applications such as relaxation and phonon calculation, where we can reproduce ab initio results with high accuracy. Ab initio ; Benchmarking ; Computer science ; Artificial neural network ; Artificial intelligence ; Relaxation (psychology) ; Similarity (geometry) ; Machine learning ; Ab initio quantum chemistry methods ; Deep learning ; Data mining ; Pattern recognition (psychology) ; Image (mathematics) ; Chemistry ; Psychology Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; X-ray Diffraction in Crystallography
Authors: Yomei Tokuda | Misa Fujisawa | Jinto Ogawa | Yoshikatsu Ueda
Source: AIP Advances (article, 2021-12-01)
Field: Materials Science — Ceramics and Composites

A machine learning approach to the prediction of the dispersion property of oxide glass In this study, we built a model for predicting the optical dispersion property of oxide glasses via machine-learning techniques such as kernel ridge regression, neural networks, and random forests. The models precisely predicted the optical property. Based on the predictions for glasses with doped oxides, we prepared new glasses in our laboratory. The experiments agreed well with the predictions made using kernel ridge regression and neural networks but not with those made using random forests. The results of this study demonstrate that the data-driven approach is a promising route for new material design. Ridge ; Dispersion (optics) ; Artificial neural network ; Regression ; Kernel (algebra) ; Property (philosophy) ; Random forest ; Computer science ; Artificial intelligence ; Machine learning ; Oxide ; Materials science ; Optics ; Mathematics ; Geology ; Statistics ; Physics ; Metallurgy Glass properties and applications ; Pigment Synthesis and Properties ; Phase-change materials and chalcogenides
Authors: Zineb Laabid | Aziz Moumen | Abdelghani Lakhdar | Khalifa Mansouri
Source: IOP Conference Series Materials Science and Engineering (article, 2021-03-01)
Field: Materials Science — Polymers and Plastics

Towards the Numerical Implementation of Neural Network to Predict the Mechanical Characteristics of Bio Composites Abstract Bio composites are a new category of materials using natural based components in their constituents. The study and simulation of the behavior of these innovative materials occupies an important place in the field of scientific research. Discovering and using new methods has always been the goal of researchers. In recent years, artificial intelligence has been very successful and is used in several fields. it represents a big part of today’s industrial revolution. Smart solutions are more and more favored over conventional solutions as they give more precise results in a short time. We can find them in different sectors, such as banking, commerce, transport and industry, especially in materials science.The intersection of the artificial intelligence with materials engineering, gives extraordinary results. This smart method was able to boost the discovery of new materials, and to solve the most complex problems encountered when determining the mechanical properties of bio composites. What characterizes theEco-composites is their light in weight, their sustainable development, and that they are environmentally friendly. However, the determination of their mechanical properties is not obvious. Certainly, solutions based on homogenization methods or even on the finite element method have given good results, but the complexity of the microstructure of these materials limits the determination of their characteristics. In our paper, we hilight the use of Deep Learning that is an artificial intelligence machine learning method that relies on neural networks to predict the mechanical behavior of a polypropylen bioloaded by the natural fibers. Homogenization (climate) ; Artificial neural network ; Field (mathematics) ; Industrial Revolution ; Intersection (aeronautics) ; Computer science ; Artificial intelligence ; Environmentally friendly ; Mechanical engineering ; Smart material ; Materials science ; Industrial engineering ; Engineering ; Composite material ; Mathematics ; Ecology Natural Fiber Reinforced Composites ; Innovations in Concrete and Construction Materials ; Mechanical Behavior of Composites