jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kevin Maik Jablonka | Giriprasad Melpatti Jothiappan | Shefang Wang | Berend Smit | Brian Yoo
Source: ChemRxiv (preprint, 2020-11-09)
Field: Materials Science — Materials Chemistry

Bias Free Multiobjective Active Learning for Materials Design and Discovery &lt;div&gt;The design rules for materials are clear for applications with a single objective. For most applications, however, there are often multiple, sometimes competing objectives where there is no single best material, and the design rules change to finding the set of Pareto optimal materials. &lt;/div&gt;&lt;div&gt;In this work, we introduce an active learning algorithm that directly uses the Pareto dominance relation to compute the set of Pareto optimal materials with desirable accuracy. &lt;br&gt;&lt;/div&gt;&lt;div&gt;We apply our algorithm to de novo polymer design with a prohibitively large search space.&lt;/div&gt;&lt;div&gt;Using molecular simulations, we compute key descriptors for dispersant applications and reduce the number of materials that need to be evaluated to reconstruct the Pareto front with a desired confidence by over 98% compared to random search.&lt;/div&gt;&lt;div&gt;This work showcases how simulation and machine learning techniques can be coupled to discover materials within a design space that would be intractable using conventional screening approaches.&lt;/div&gt; Pareto principle ; Pareto optimal ; Computer science ; Set (abstract data type) ; Multi-objective optimization ; Key (lock) ; Chemical space ; Space (punctuation) ; Machine learning ; Mathematical optimization ; Artificial intelligence ; Mathematics ; Drug discovery ; Biology ; Bioinformatics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Multi-Objective Optimization Algorithms
Authors: Kan Hatakeyama‐Sato | Kenichi Oyaizu
Source: Communications Materials (article, 2020-07-30)
Field: Materials Science — Materials Chemistry

Integrating multiple materials science projects in a single neural network Abstract In data-intensive science, machine learning plays a critical role in processing big data. However, the potential of machine learning has been limited in the field of materials science because of the difficulty in treating complex real-world information as a digital language. Here, we propose to use graph-shaped databases with a common format to describe almost any materials science experimental data digitally, including chemical structures, processes, properties, and natural languages. The graphs can express real world’s data with little information loss. In our approach, a single neural network treats the versatile materials science data collected from over ten projects, whereas traditional approaches require individual models to be prepared to process each individual database and property. The multitask learning of miscellaneous factors increases the prediction accuracy of parameters synergistically by acquiring broad knowledge in the field. The integration is beneficial for developing general prediction models and for solving inverse problems in materials science. Computer science ; Field (mathematics) ; Artificial neural network ; Property (philosophy) ; Network science ; Artificial intelligence ; Process (computing) ; Data science ; Machine learning ; Graph ; Big data ; Theoretical computer science ; Data mining ; Complex network ; World Wide Web ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Asif Chowdhury | Wenqiang Yang | Kareem Abdelfatah | Mehdi Zare | Andreas Heyden | Gabriel Terejanu
Source: Journal of Chemical Theory and Computation (article, 2020-01-21)
Field: Materials Science — Materials Chemistry

A Multiple Filter Based Neural Network Approach to the Extrapolation of Adsorption Energies on Metal Surfaces for Catalysis Applications Computational catalyst discovery involves the development of microkinetic reactor models based on estimated parameters determined from density functional theory (DFT). For complex surface chemistries, the number of reaction intermediates can be very large, and the cost of calculating the adsorption energies by DFT for all surface intermediates even for one active site model can become prohibitive. In this paper, we have identified appropriate descriptors and machine learning models that can be used to predict a significant part of these adsorption energies given data on the rest of them. Moreover, our investigations also included the case when the species data used to train the predictive model are of different size relative to the species the model tries to predict-this is an extrapolation in the data space which is typically difficult with regular machine learning models. Due to the relative size of the available data sets, we have attempted to extrapolate from the larger species to the smaller ones in the current work. Here, we have developed a neural network based predictive model that combines an established additive atomic contribution based model with the concepts of a convolutional neural network that, when extrapolating, achieves a statistically significant improvement over the previous models. Extrapolation ; Artificial neural network ; Computer science ; Chemical space ; Convolutional neural network ; Adsorption ; Filter (signal processing) ; Surface (topology) ; Work (physics) ; Density functional theory ; Machine learning ; Biological system ; Artificial intelligence ; Chemistry ; Computational chemistry ; Physics ; Mathematics ; Physical chemistry ; Thermodynamics ; Drug discovery ; Statistics Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysis and Hydrodesulfurization Studies
Authors: Zhenqin Wu | Daniel Serie | Gege Xu | James Zou
Source: Journal of Proteomics (article, 2020-05-13)
Field: Chemistry — Spectroscopy

PB-Net: Automatic peak integration by sequential deep learning for multiple reaction monitoring Computer science ; Throughput ; Artificial neural network ; Annotation ; Artificial intelligence ; Deep learning ; Ground truth ; Data mining ; Machine learning ; Pattern recognition (psychology) Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Danny E. P. Vanpoucke | Onno S. J. van Knippenberg | Ko Hermans | Katrien V. Bernaerts | Siamak Mehrkanoon
Source: Journal of Applied Physics (article, 2020-08-03)
Field: Materials Science — Materials Chemistry

Small data materials design with machine learning: When the average model knows best Machine learning is quickly becoming an important tool in modern materials design. Where many of its successes are rooted in huge datasets, the most common applications in academic and industrial materials design deal with datasets of at best a few tens of data points. Harnessing the power of machine learning in this context is, therefore, of considerable importance. In this work, we investigate the intricacies introduced by these small datasets. We show that individual data points introduce a significant chance factor in both model training and quality measurement. This chance factor can be mitigated by the introduction of an ensemble-averaged model. This model presents the highest accuracy, while at the same time, it is robust with regard to changing the dataset size. Furthermore, as only a single model instance needs to be stored and evaluated, it provides a highly efficient model for prediction purposes, ideally suited for the practical materials scientist. Computer science ; Machine learning ; Context (archaeology) ; Artificial intelligence ; Quality (philosophy) ; Small data ; Factor (programming language) ; Data modeling ; Ensemble learning ; Data mining ; Software engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Nikolay Borodinov | Matthias Lorenz | Steven T. King | Anton V. Ievlev | Olga S. Ovchinnikova
Source: npj Computational Materials (article, 2020-06-26)
Field: Chemistry — Spectroscopy

Toward nanoscale molecular mass spectrometry imaging via physically constrained machine learning on co-registered multimodal data Abstract Mass spectrometry imaging (MSI) plays a pivotal role in investigating the chemical nature of complex systems that underly our understanding in biology and medicine. Multiple fields of life science such as proteomics, lipidomics and metabolomics benefit from the ability to simultaneously identify molecules and pinpoint their distribution across a sample. However, achieving the necessary submicron spatial resolution to distinguish chemical differences between individual cells and generating intact molecular spectra is still a challenge with any single imaging approach. Here, we developed an approach that combines two MSI techniques, matrix-assisted laser desorption/ionization (MALDI) and time-of-flight secondary ion mass spectrometry (ToF-SIMS), one with low spatial resolution but intact molecular spectra and the other with nanometer spatial resolution but fragmented molecular signatures, to predict molecular MSI spectra with submicron spatial resolution. The known relationships between the two MSI channels of information are enforced via a physically constrained machine-learning approach and directly incorporated in the data processing. We demonstrate the robustness of this method by generating intact molecular MALDI-type spectra and chemical maps at ToF-SIMS resolution when imaging mouse brain thin tissue sections. This approach can be readily adopted for other types of bioimaging where physical relationships between methods have to be considered to boost the confidence in the reconstruction product. Mass spectrometry imaging ; Mass spectrometry ; MALDI imaging ; Image resolution ; Chemistry ; Metabolomics ; Molecular imaging ; Resolution (logic) ; Chemical imaging ; Lipidomics ; Analytical Chemistry (journal) ; Biological system ; Nanotechnology ; Computer science ; Matrix-assisted laser desorption/ionization ; Desorption ; Artificial intelligence ; Materials science ; Chromatography ; Biology ; Hyperspectral imaging Mass Spectrometry Techniques and Applications ; Ion-surface interactions and analysis ; Advanced Proteomics Techniques and Applications
Authors: Shingo Urata | Nobuhiro Nakamura | Kento Aiba | Tomofumi Tada | Hideo Hosono
Source: Materials & Design (article, 2020-10-08)
Field: Materials Science — Ceramics and Composites

How fluorine minimizes density fluctuations of silica glass: Molecular dynamics study with machine-learning assisted force-matching potential Transparent silica glass is an indispensable material for today's information technology society as an ultra-low loss optical fiber. Recently, further low-loss optical fibers are demanded due to rapidly expanding information. A small amount of fluorine doping is known to reduce the Rayleigh scattering further by reducing the density fluctuations of silica glass, even though the fluorine is an impurity for the pristine silica glass. This study devoted to answer the riddle of F-doping and an accurate force-matching potential optimized by machine-learning enables us to understand how fluorine remedies the disorders in silica glass. It was found that fluorine mainly replaces an oxygen in tetrahedral SiO4 unit, but also forms five-fold Si, such as SiO4F and SiO3F2 units. The former disrupts silica network, while the latter attenuates the network rigidity by loosening the five Si-O(F) bonds. Since the two local effects of fluorine spreads through the glass network, F-doping prompts silica glass to relax further even at temperature lower than the glass transition (fictive) temperature. Consequently, fluorine minimizes the density fluctuation (thus, Rayleigh scattering) of silica glass at around 1 wt%, although fluorine itself is an impurity for silica glass. Silica glass ; Materials science ; Fluorine ; Rayleigh scattering ; Doping ; Glass transition ; Impurity ; Rigidity (electromagnetism) ; Composite material ; Chemical engineering ; Optics ; Organic chemistry ; Chemistry ; Optoelectronics ; Polymer ; Metallurgy Glass properties and applications ; Thin-Film Transistor Technologies ; Material Dynamics and Properties
Authors: Joshua Kammeraad | Jack Goetz | Eric A. Walker | Ambuj Tewari | Paul M. Zimmerman
Source: Journal of Chemical Information and Modeling (article, 2020-02-24)
Field: Materials Science — Materials Chemistry

What Does the Machine Learn? Knowledge Representations of Chemical Reactivity In a departure from conventional chemical approaches, data-driven models of chemical reactions have recently been shown to be statistically successful using machine learning. These models, however, are largely black box in character and have not provided the kind of chemical insights that historically advanced the field of chemistry. To examine the knowledgebase of machine-learning models-what does the machine learn-this article deconstructs black-box machine-learning models of a diverse chemical reaction data set. Through experimentation with chemical representations and modeling techniques, the analysis provides insights into the nature of how statistical accuracy can arise, even when the model lacks informative physical principles. By peeling back the layers of these complicated models we arrive at a minimal, chemically intuitive model (and no machine learning involved). This model is based on systematic reaction-type classification and Evans-Polanyi relationships within reaction types which are easily visualized and interpreted. Through exploring this simple model, we gain deeper understanding of the data set and uncover a means for expert interactions to improve the model's reliability. Artificial intelligence ; Computer science ; Machine learning ; Black box ; Set (abstract data type) ; Field (mathematics) ; Simple (philosophy) ; Experimental data ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Text Analysis Techniques
Authors: Bouchaib Zazoum | Ennouri Triki | Abdel Bachri
Source: Materials (article, 2020-09-25)
Field: Materials Science — Polymers and Plastics

Modeling of Mechanical Properties of Clay-Reinforced Polymer Nanocomposites Using Deep Neural Network Due to the non-linear characteristics of the processing parameters, predicting the desired properties of nanocomposites using the conventional regression approach is often unsatisfactory. Thus, it is essential to use a machine learning approach to determine the optimum processing parameters. In this study, a backpropagation deep neural network (DNN) with nanoclay and compatibilizer content, and processing parameters as input, was developed to predict the mechanical properties, including tensile modulus and tensile strength, of clay-reinforced polyethylene nanocomposites. The high accuracy of the developed model proves that DNN can be used as an efficient tool for predicting mechanical properties of the nanocomposites in terms of four independent parameters. Ultimate tensile strength ; Materials science ; Nanocomposite ; Artificial neural network ; Composite material ; Backpropagation ; Polyethylene ; Modulus ; Computer science ; Artificial intelligence Polymer Nanocomposites and Properties ; Polymer Nanocomposite Synthesis and Irradiation ; Dielectric materials and actuators
Authors: Maximilian Amsler | Samare Rostami | Hossein Tahmasbi | Ehsan Rahmatizad Khajehpasha | Somayeh Faraji | Robabe Rasoulkhani | S. Alireza Ghasemi
Source: Computer Physics Communications (article, 2020-06-02)
Field: Materials Science — Materials Chemistry

FLAME: A library of atomistic modeling environments Maxima and minima ; Metastability ; Saddle point ; Molecular dynamics ; Ab initio ; Range (aeronautics) ; Energy landscape ; Statistical physics ; Computer science ; Potential energy surface ; Artificial neural network ; Potential energy ; Software ; Computational science ; Ground state ; Materials science ; Chemical physics ; Physics ; Chemistry ; Computational chemistry ; Atomic physics ; Thermodynamics ; Mathematics ; Artificial intelligence Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Catalysis and Oxidation Reactions
Authors: Alan M. Bond
Source: Journal of Solid State Electrochemistry (article, 2020-05-29)
Field: Chemistry — Electrochemistry

A perceived paucity of quantitative studies in the modern era of voltammetry: prospects for parameterisation of complex reactions in Bayesian and machine learning frameworks Bayesian probability ; Voltammetry ; Cyclic voltammetry ; Computer science ; Bayesian inference ; Machine learning ; Artificial intelligence ; Electrochemistry ; Nanotechnology ; Materials science ; Biological system ; Chemistry ; Physical chemistry Electrochemical Analysis and Applications ; Electrochemical sensors and biosensors ; Advanced Chemical Sensor Technologies
Authors: Gentoku Takasao | Toru Wada | Ashutosh Thakur | Patchanee Chammingkwan | Minoru Terano | Toshiaki Taniike
Source: Journal of Catalysis (article, 2020-11-17)
Field: Chemistry — Inorganic Chemistry

Insight into structural distribution of heterogeneous Ziegler–Natta catalyst from non-empirical structure determination Chemistry ; Catalysis ; Natta ; Steric effects ; Ziegler–Natta catalyst ; Adsorption ; Polymer ; Ethylene ; Chemical engineering ; Physical chemistry ; Organic chemistry ; Polymerization Inorganic Chemistry and Materials ; Carbon dioxide utilization in catalysis ; Ammonia Synthesis and Nitrogen Reduction
Authors: Chaochao Yan | Qianggang Ding | Peilin Zhao | Shuangjia Zheng | Jinyu Yang | Yang Yu | Junzhou Huang
Source: ChemRxiv (preprint, 2020-06-11)
Field: Materials Science — Materials Chemistry

RetroXpert: Decompose Retrosynthesis Prediction like A Chemist Retrosynthesis is the process of recursively decomposing target molecules into available building blocks. It plays an important role in solving problems in organic synthesis planning. To automate the retrosynthesis analysis, many retrosynthesis prediction methods have been proposed.However, most of them are cumbersome and lack interpretability about their predictions.In this paper, we devise a novel template-free algorithm, RetroXpert, for automatic retrosynthetic expansion by automating the procedure that chemists used to do.Our method disassembles retrosynthesis into two steps: i) we identify the potential reaction center within the target molecule through a graph neural network and generate intermediate synthons; and ii) we predict the associated reactants based on the obtained synthons via a reactant generation model. While outperforming the state-of-the-art baselines by a significant margin, our model also provides chemically reasonable interpretation. Retrosynthetic analysis ; Interpretability ; Synthon ; Computer science ; Artificial intelligence ; Margin (machine learning) ; Process (computing) ; Stepping stone ; Interpretation (philosophy) ; Machine learning ; Chemistry ; Programming language ; Stereochemistry Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Chemical Synthesis and Analysis
Authors: Sergey Gusarov | Stanislav R. Stoyanov | Samira Siahrostami
Source: The Journal of Physical Chemistry C (article, 2020-04-13)
Field: Materials Science — Materials Chemistry

Development of Fukui Function Based Descriptors for a Machine Learning Study of CO<sub>2</sub>Reduction Developing novel methods that capture chemical properties quickly and with reasonable accuracy has emerged as an attractive way to replace time-consuming density functional theory (DFT) calculations. In this study, we propose a new type of machine learning (ML) enhanced descriptors based on the Fukui function (FF) projected onto the Connolly surface. The FF contains information about the local system’s response to the perturbation and could be used as a descriptor of the chemical properties of a surfaces. We show that the FF, augmented by a general characteristic of the electronic structure of the surface, such as a work function, is well correlated to the mapped adsorption energy of CO. Therefore, this combination might replace the computationally expensive mapping of the adsorption energy of small molecules as an indicator of catalytic activity. Potential extensions of the proposed methodology are briefly discussed. Fukui function ; Density functional theory ; Adsorption ; Computer science ; Reduction (mathematics) ; Molecule ; Function (biology) ; Perturbation theory (quantum mechanics) ; Surface (topology) ; Biological system ; Energy (signal processing) ; Computational chemistry ; Chemistry ; Chemical physics ; Materials science ; Mathematics ; Physical chemistry ; Catalysis ; Physics ; Quantum mechanics ; Statistics Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Computational Drug Discovery Methods
Authors: Changhee Song | Sanghoon Lee | Bonhyun Gu | Ikwhang Chang | Gu Young Cho | Jong Dae Baek | Suk Won
Source: Energies (article, 2020-04-02)
Field: Materials Science — Materials Chemistry

A Study of Anode-Supported Solid Oxide Fuel Cell Modeling and Optimization Using Neural Network and Multi-Armed Bandit Algorithm Anode-supported solid oxide fuel cells (SOFCs) model based on artificial neural network (ANN) and optimized design variables were modeled. The input parameters of the anode-supported SOFC model developed in this study are as follows: current density, temperature, electrolyte thickness, anode thickness, anode porosity, and cathode thickness. Voltage was estimated from the SOFC model with the input parameters. Numerical results show that the SOFC model constructed in this study can represent the actual SOFC characteristics very well. There are four design parameters to be optimized: electrolyte, anode, cathode thickness, and anode porosity. To derive the optimal combination of the design parameters, we have used a multi-armed bandit algorithm (MAB), and developed a methodology for deriving near-optimal parameter set without searching for all possible parameter sets. Anode ; Cathode ; Electrolyte ; Solid oxide fuel cell ; Artificial neural network ; Porosity ; Materials science ; Algorithm ; Oxide ; Voltage ; Current density ; Computer science ; Biological system ; Chemical engineering ; Composite material ; Engineering ; Chemistry ; Electrical engineering ; Physics ; Metallurgy ; Electrode ; Artificial intelligence Advancements in Solid Oxide Fuel Cells ; Fuel Cells and Related Materials ; Electrocatalysts for Energy Conversion
Authors: Rocío Mercado | Tobias Rastemo | Edvard Lindelöf | Günter Klambauer | Ola Engkvist | Hongming Chen | Esben Jannik Bjerrum
Source: Applied AI Letters (article, 2020-12-01)
Field: Materials Science — Materials Chemistry

Practical notes on building molecular graph generative models Abstract Here are presented technical notes and tips on developing graph generative models for molecular design. Although this work stems from the development of GraphINVENT, a Python platform for iterative molecular generation using graph neural networks, this work is relevant to researchers studying other architectures for graph‐based molecular design. In this work, technical details that could be of interest to researchers developing their own molecular generative models are discussed, including an overview of previous work in graph‐based molecular design and strategies for designing new models. Advice on development and debugging tools which are helpful during code development is also provided. Finally, methods that were tested but which ultimately did not lead to promising results in the development of GraphINVENT are described here in the hope that this will help other researchers avoid pitfalls in development and instead focus their efforts on more promising strategies for graph‐based molecular generation. Generative grammar ; Computer science ; Debugging ; Graph ; Python (programming language) ; Data science ; Software engineering ; Theoretical computer science ; Artificial intelligence ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering