jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Octavian-Eugen Ganea | Lagnajit Pattanaik | Connor W. Coley | Regina Barzilay | Klavs F. Jensen | William H. Green | Tommi Jaakkola
Source: Neural Information Processing Systems (article, 2021-12-06)
Field: Materials Science — Materials Chemistry

GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensembles Prediction of a molecule's 3D conformer ensemble from the molecular graph holds a key role in areas of cheminformatics and drug discovery. Existing generative models have several drawbacks including lack of modeling important molecular geometry elements (e.g. torsion angles), separate optimization stages prone to error accumulation, and the need for structure fine-tuning based on approximate classical force-fields or computationally expensive methods such as metadynamics with approximate quantum mechanics calculations at each geometry. We propose GeoMol--an end-to-end, non-autoregressive and SE(3)-invariant machine learning approach to generate distributions of low-energy molecular 3D conformers. Leveraging the power of message passing neural networks (MPNNs) to capture local and global graph information, we predict local atomic 3D structures and torsion angles, avoiding unnecessary over-parameterization of the geometric degrees of freedom (e.g. one angle per non-terminal bond). Such local predictions suffice both for the training loss computation, as well as for the full deterministic conformer assembly (at test time). We devise a non-adversarial optimal transport based loss function to promote diverse conformer generation. GeoMol predominantly outperforms popular open-source, commercial, or state-of-the-art machine learning (ML) models, while achieving significant speed-ups. We expect such differentiable 3D structure generators to significantly impact molecular modeling and related applications. Conformational isomerism ; Computer science ; Maxima and minima ; Differentiable function ; Theoretical computer science ; Artificial intelligence ; Algorithm ; Statistical physics ; Mathematics ; Physics ; Molecule ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Francesca Venturini | Michela Sperti | Umberto Michelucci | Ivo Herzig | Michael Baumgartner | Josep Palau Caballero | Arturo Jiménez | Marco A. Deriu
Source: Foods (article, 2021-05-06)
Field: Chemistry — Analytical Chemistry

Exploration of Spanish Olive Oil Quality with a Miniaturized Low-Cost Fluorescence Sensor and Machine Learning Techniques Extra virgin olive oil (EVOO) is the highest quality of olive oil and is characterized by highly beneficial nutritional properties. The large increase in both consumption and fraud, for example through adulteration, creates new challenges and an increasing demand for developing new quality assessment methodologies that are easier and cheaper to perform. As of today, the determination of olive oil quality is performed by producers through chemical analysis and organoleptic evaluation. The chemical analysis requires advanced equipment and chemical knowledge of certified laboratories, and has therefore limited accessibility. In this work a minimalist, portable, and low-cost sensor is presented, which can perform olive oil quality assessment using fluorescence spectroscopy. The potential of the proposed technology is explored by analyzing several olive oils of different quality levels, EVOO, virgin olive oil (VOO), and lampante olive oil (LOO). The spectral data were analyzed using a large number of machine learning methods, including artificial neural networks. The analysis performed in this work demonstrates the possibility of performing the classification of olive oil in the three mentioned classes with an accuracy of 100%. These results confirm that this minimalist low-cost sensor has the potential to substitute expensive and complex chemical analysis. Olive oil ; Organoleptic ; Quality (philosophy) ; Process engineering ; Computer science ; Biochemical engineering ; Artificial intelligence ; Environmental science ; Pulp and paper industry ; Agricultural engineering ; Machine learning ; Chemistry ; Food science ; Engineering Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Edible Oils Quality and Analysis
Authors: Takamichi Terao
Source: Journal of Physics Condensed Matter (article, 2021-05-07)
Field: Materials Science — Materials Chemistry

Semi-supervised learning for the study of structural formation in colloidal systems via image recognition The analysis of the structural formation of colloidal systems using machine learning techniques has recently attracted much attention. In many of these studies, local bond-order parameters (LBOPs) were employed as descriptors, where such LBOPs are suitable mainly for the detection of crystal structures. On the other hand, image-based convolutional neural networks (CNNs) are quite effective in detecting not only crystals but also random structures, and the author demonstrated their efficiency in a previous paper. However, in supervised learning, it is difficult to obtain a correct result when there is an unexpected new phase that was unknown when training the CNN. In this paper, we propose a hybrid scheme that consists of supervised and unsupervised learning techniques, employing two different approaches: image-based CNN and generalized LBOP. The proposed method was applied to two-dimensional colloidal systems, and its efficiency was demonstrated. Artificial intelligence ; Computer science ; Colloid ; Image (mathematics) ; Pattern recognition (psychology) ; Engineering ; Chemical engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Spectroscopy and Chemometric Analyses
Authors: Pavel N. Zolotarev
Source: Crystal Growth & Design (article, 2021-07-12)
Field: Chemistry — Inorganic Chemistry

Relationships between Changes in Guest Ion Properties and in the Host Framework Topology in Ionic Coordination Polymers In order to identify possible general relationships between changes in the framework topology and changes in the extra framework ion properties, a set consisting of 2202 crystal structures of ionic coordination polymers was extracted from the Cambridge Structural Database. Changes in ion properties served as independent variables for several machine learning models trained to predict the changes in framework dimensionality, topological density, and average ring size of the framework’s tiling. The trained classifiers showed acceptable predictive performance with F1 score in the range 0.4 ÷ 0.6 and were subjected to the validation tests, which confirmed that they fit the data significantly better than by chance. Subsequent feature importance analysis of the classifiers revealed a set of the ion properties being important for prediction of the changes in corresponding framework characteristics in the extracted set of crystal structures. It is shown that in general changes in molecular surface area and molecular flexibility of the guest ions are essential for predicting changes in selected topological characteristics of a framework. Case studies were conducted for several sets of crystal structures with frameworks that are observed to host significant variety of counter ions. The decision tree classifiers allowed us to discover the ion properties determining topological characteristics in particular frameworks. Topology (electrical circuits) ; Flexibility (engineering) ; Ion ; Ionic bonding ; Computer science ; Crystal (programming language) ; Crystal structure ; Variety (cybernetics) ; Set (abstract data type) ; Decision tree ; Data mining ; Materials science ; Machine learning ; Chemistry ; Artificial intelligence ; Mathematics ; Crystallography ; Statistics Metal-Organic Frameworks: Synthesis and Applications ; Polyoxometalates: Synthesis and Applications ; Covalent Organic Framework Applications
Authors: Ryan Jacobs | Mingren Shen | Yuhan Liu | Wei Hao | Xiaoshan Li | Ruoyu He | Jacob R.C. Greaves | Donglin Wang | Zeming Xie | Zitong Huang | Chao Wang | Kevin G. Field | Dane Morgan
Source: arXiv (Cornell University) (preprint, 2021-10-15)
Field: Materials Science — Surfaces, Coatings and Films

Performance, Successes and Limitations of Deep Learning Semantic Segmentation of Multiple Defects in Transmission Electron Micrographs In this work, we perform semantic segmentation of multiple defect types in electron microscopy images of irradiated FeCrAl alloys using a deep learning Mask Regional Convolutional Neural Network (Mask R-CNN) model. We conduct an in-depth analysis of key model performance statistics, with a focus on quantities such as predicted distributions of defect shapes, defect sizes, and defect areal densities relevant to informing modeling and understanding of irradiated Fe-based materials properties. To better understand the performance and present limitations of the model, we provide examples of useful evaluation tests which include a suite of random splits, and dataset size-dependent and domain-targeted cross validation tests. Overall, we find that the current model is a fast, effective tool for automatically characterizing and quantifying multiple defect types in microscopy images, with a level of accuracy on par with human domain expert labelers. More specifically, the model can achieve average defect identification F1 scores as high as 0.8, and, based on random cross validation, have low overall average (+/- standard deviation) defect size and density percentage errors of 7.3 (+/- 3.8)% and 12.7 (+/- 5.3)%, respectively. Further, our model predicts the expected material hardening to within 10-20 MPa (about 10% of total hardening), which is about the same error level as experiments. Our targeted evaluation tests also suggest the best path toward improving future models is not expanding existing databases with more labeled images but instead data additions that target weak points of the model domain, such as images from different microscopes, imaging conditions, irradiation environments, and alloy types. Finally, we discuss the first phase of an effort to provide an easy-to-use, open-source object detection tool to the broader community for identifying defects in new images. Segmentation ; Computer science ; Convolutional neural network ; Deep learning ; Artificial intelligence ; Standard deviation ; Focus (optics) ; Hardening (computing) ; Pattern recognition (psychology) ; Domain knowledge ; Machine learning ; Materials science ; Statistics ; Mathematics ; Optics ; Physics ; Nanotechnology Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Andrew Falkowski | Steven Kauwe | Taylor D. Sparks
Source: ChemRxiv (preprint, 2021-05-28)
Field: Materials Science — Materials Chemistry

Optimizing Fractional Compositions To Achieve Extraordinary Properties Traditional, data-driven materials discovery involves screening chemical systems with machine learning algorithms and selecting candidates that excel in a target property. The number of screening candidates grows infinitely large as the fractional resolution of compositions the number of included elements increases. The computational infeasibility and probability of overlooking a successful candidate grow likewise. Our approach shifts the optimization focus from model parameters to the fractions of each element in a composition. Using a pretrained network, CrabNet, and writing a custom loss function to govern a vector of element fractions, compositions can be optimized such that a predicted property is maximized or minimized. Single and multi-property optimization examples are presented that highlight the capabilities and robustness of this approach to inverse design. Robustness (evolution) ; Property (philosophy) ; Computer science ; Inverse ; Element (criminal law) ; Algorithm ; Function (biology) ; Focus (optics) ; Mathematical optimization ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Process Optimization and Integration
Authors: Akihide Hayashi | Yoshinori Ato | Akira Yamamoto | Hisao Yoshida | Shusuke Yamanaka | Takashi Kawakami | Mitsutaka Okumura
Source: Chemistry Letters (article, 2021-04-14)
Field: Materials Science — Materials Chemistry

Gibbs Energy of Hydrogen Adsorption on Pt Surface by Machine Learning Potential and Metadynamics A machine learning potential that can describe the ethylene hydrogenation reaction on Pt surface has been developed. Using the obtained potential, path integral molecular dynamics of hydrogen on Pt surface and metadynamics of hydrogen adsorption on Pt surface in the presence or absence of ethylene were carried out. The results of metadynamics were compared with adsorption Gibbs energy obtained from a single point calculation. The temperature and pressure dependency of the hydrogen coverage on Pt surface were also calculated with high accuracy. A machine learning potential that can describe the ethylene hydrogenation reaction on Pt surface has been developed. Using the obtained potential, path integral molecular dynamics of hydrogen on Pt surface and meta-dynamics of hydrogen adsorption on Pt surface were carried out. The results of meta-dynamics were compared with adsorption Gibbs energy obtained from a single point calculation. Metadynamics ; Chemistry ; Adsorption ; Hydrogen ; Gibbs free energy ; Potential energy surface ; Molecular dynamics ; Ethylene ; Chemical physics ; Physical chemistry ; Thermodynamics ; Computational chemistry ; Molecule ; Catalysis ; Organic chemistry ; Physics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Hydrogen Storage and Materials
Authors: Tarek Iraki | Lukas Morand | Johannes Dornheim | Norbert Link | Dirk Helm
Source: arXiv (Cornell University) (preprint, 2021-10-27)
Field: Materials Science — Materials Chemistry

A multi-task learning-based optimization approach for finding diverse\n sets of material microstructures with desired properties and its application\n to texture optimization The optimization along the chain processing-structure-properties-performance\nis one of the core objectives in data-driven materials science. In this sense,\nprocesses are supposed to manufacture workpieces with targeted material\nmicrostructures. These microstructures are defined by the material properties\nof interest and identifying them is a question of materials design. In the\npresent paper, we addresse this issue and introduce a generic multi-task\nlearning-based optimization approach. The approach enables the identification\nof sets of highly diverse microstructures for given desired properties and\ncorresponding tolerances. Basically, the approach consists of an optimization\nalgorithm that interacts with a machine learning model that combines multi-task\nlearning with siamese neural networks. The resulting model (1) relates\nmicrostructures and properties, (2) estimates the likelihood of a\nmicrostructure of being producible, and (3) performs a distance preserving\nmicrostructure feature extraction in order to generate a lower dimensional\nlatent feature space to enable efficient optimization. The proposed approach is\napplied on a crystallographic texture optimization problem for rolled steel\nsheets given desired properties.\n Microstructure ; Computer science ; Task (project management) ; Feature (linguistics) ; Optimization problem ; Artificial intelligence ; Texture (cosmology) ; Identification (biology) ; Algorithm ; Machine learning ; Mathematical optimization ; Materials science ; Mathematics ; Image (mathematics) ; Engineering Machine Learning in Materials Science ; Corrosion Behavior and Inhibition ; X-ray Diffraction in Crystallography
Authors: Or Perlman | Bo Zhu | Moritz Zaiß | Matthew S. Rosen | Christian T. Farrar
Source: arXiv (Cornell University) (preprint, 2021-07-10)
Field: Materials Science — Materials Chemistry

An End-to-End AI-Based Framework for Automated Discovery of CEST/MT MR Fingerprinting Acquisition Protocols and Quantitative Deep Reconstruction (AutoCEST) Purpose: To develop an automated machine-learning-based method for the discovery of rapid and quantitative chemical exchange saturation transfer (CEST) MR fingerprinting acquisition and reconstruction protocols. Methods: An MR physics governed AI system was trained to generate optimized acquisition schedules and the corresponding quantitative reconstruction neural-network. The system (termed AutoCEST) is composed of a CEST saturation block, a spin dynamics module, and a deep reconstruction network, all differentiable and jointly connected. The method was validated using a variety of chemical exchange phantoms and an in-vivo mouse brain at 9.4T. Results: The acquisition times for AutoCEST optimized schedules ranged from 35-71s, with a quantitative image reconstruction time of only 29 ms. The resulting exchangeable proton concentration maps for the phantoms were in good agreement with the known solute concentrations for AutoCEST sequences (mean absolute error = 2.42 mM; Pearson's r=0.992 , p$ Pearson product-moment correlation coefficient ; Mean squared error ; Artificial intelligence ; Mean absolute error ; Artificial neural network ; Nuclear medicine ; Computer science ; Algorithm ; Nuclear magnetic resonance ; Chemistry ; Biomedical engineering ; Physics ; Mathematics ; Statistics ; Medicine Lanthanide and Transition Metal Complexes ; Advanced MRI Techniques and Applications ; Electron Spin Resonance Studies
Authors: Andrew E. Sifain | Levi Lystrom | Richard A. Messerly | Justin S. Smith | Benjamin Nebgen | Kipton Barros | Sergei Tretiak | Nicholas Lubbers | Brendan J. Gifford
Source: ChemRxiv (preprint, 2021-06-29)
Field: Materials Science — Materials Chemistry

Predicting Phosphorescence Energies and Inferring Wavefunction Localization with Machine Learning Phosphorescence is commonly utilized for applications including light-emitting diodes and photovoltaics. Machine learning (ML) approaches trained on <i>ab-initio</i> datasets of singlet-triplet energy gaps may expedite the discovery of phosphorescent compounds with the desired emission energies. However, we show that standard ML approaches for modeling potential energy surfaces inaccurately predict singlet-triplet energy gaps due to the failure to account for spatial localities of spin transitions. To solve this, we introduce localization layers in a neural network model that weight atomic contributions to the energy gap, thereby allowing the model to isolate the most determinative chemical environments. Trained on the singlet-triplet energy gaps of organic molecules, we apply our method to an out-of-sample test set of large phosphorescent compounds and demonstrate the substantial improvement that localization layers have on predicting their phosphorescence energies. Remarkably, the inferred localization weights have a strong relationship with the <i>ab-initio</i> spin density of the singlet-triplet transition, and thus infer localities of the molecule that determine the spin transition, despite the fact that no direct electronic information was provided during training. The use of localization layers is expected to improve the modeling of many localized, non-extensive phenomena and could be implemented in any atom-centered neural network model. Phosphorescence ; Singlet state ; Ab initio ; Artificial neural network ; Wave function ; Energy (signal processing) ; Computer science ; Physics ; Chemistry ; Materials science ; Chemical physics ; Artificial intelligence ; Atomic physics ; Excited state ; Quantum mechanics Machine Learning in Materials Science ; Conducting polymers and applications ; Radical Photochemical Reactions
Authors: Nicolò Bonettini | Carlo Andrea Gonano | Paolo Bestagini | Marco Marcon | Bruno Garavelli | Stefano Tubaro
Source: 2021 29th European Signal Processing Conference (EUSIPCO) (article, 2021-08-23)
Field: Chemistry — Analytical Chemistry

Multitask learning for denoising and analysis of X-ray polymer acquisitions X-ray acquisitions are beneficial in food contaminant analysis as they can detect both metallic and non-metallic objects. This paper considers the scenario of single-pixel hyperspectral X-ray acquisitions applied to a series of materials with different characteristics. We propose a method that jointly applies a denoising operation and detects the analysed material in terms of a physical parameterisation. The proposed algorithm is based on a Convolutional Neural Network (CNN) trained with a multitask learning strategy using a custom loss function tailored to the problem at hand. Experimental results on metals and polymers show that the proposed method can also generalise to materials never seen at training time. Convolutional neural network ; Computer science ; Hyperspectral imaging ; Function (biology) ; Pixel ; Artificial intelligence ; Noise reduction ; Pattern recognition (psychology) ; Series (stratigraphy) ; Deep learning ; Task (project management) ; Artificial neural network ; Algorithm ; Machine learning ; Engineering ; Geology Spectroscopy and Chemometric Analyses ; Cultural Heritage Materials Analysis ; Geochemistry and Geologic Mapping
Authors: Kai Hou Yip | Quentin Changeat | Νικόλαος Νικολάου | Mario Morvan | Billy Edwards | I. Waldmann | G. Tinetti
Source:  (preprint, 2021-07-21)
Field: Chemistry — Spectroscopy

Peeking inside the Black Box: Interpreting Deep Learning Models for Exoplanet Atmospheric Retrievals Deep learning algorithms are growing in popularity in the field of exoplanetary science due to their ability to model highly non-linear relations and solve interesting problems in a data-driven manner. Several works have attempted to perform fast retrievals of atmospheric parameters with the use of machine learning algorithms like deep neural networks (DNNs). Yet, despite their high predictive power, DNNs are also infamous for being 'black boxes'. It is their apparent lack of explainability that makes the astrophysics community reluctant to adopt them. What are their predictions based on? How confident should we be in them? When are they wrong and how wrong can they be? In this work, we present a number of general evaluation methodologies that can be applied to any trained model and answer questions like these. In particular, we train three different popular DNN architectures to retrieve atmospheric parameters from exoplanet spectra and show that all three achieve good predictive performance. We then present an extensive analysis of the predictions of DNNs, which can inform us - among other things - of the credibility limits for atmospheric parameters for a given instrument and model. Finally, we perform a perturbation-based sensitivity analysis to identify to which features of the spectrum the outcome of the retrieval is most sensitive. We conclude that for different molecules, the wavelength ranges to which the DNN's predictions are most sensitive, indeed coincide with their characteristic absorption regions. The methodologies presented in this work help to improve the evaluation of DNNs and to grant interpretability to their predictions. Exoplanet ; Credibility ; Computer science ; Deep neural networks ; Machine learning ; Deep learning ; Artificial neural network ; Black box ; Artificial intelligence ; Perturbation (astronomy) ; Popularity ; Sensitivity (control systems) ; Stars ; Physics ; Astronomy Spectroscopy and Laser Applications ; Atmospheric Ozone and Climate ; Stellar, planetary, and galactic studies