jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ziyan Zhang | Aria Mansouri Tehrani | Anton O. Oliynyk | Blake Day | Jakoah Brgoch
Source: ChemRxiv (preprint, 2020-07-15)
Field: Materials Science — Materials Chemistry

Finding the Next Superhard Material through Ensemble Learning We report an ensemble machine-learning method capable of finding new superhard materials by directly predicting the load-dependent Vickers hardness based only on the chemical composition. A total of 1062 experimentally measured load-dependent Vickers hardness data were extracted from the literature and used to train a supervised machine-learning algorithm utilizing boosting, achieving excellent accuracy (R2 = 0.97). This new model was then tested by synthesizing and measuring the load-dependent hardness of several unreported disilicides as well as analyzing the predicted hardness of several classic superhard materials. The trained ensemble method was then employed to screen for superhard materials by examining more than 66,000 compounds in crystal structure databases, which showed that only 68 known materials surpass the superhard threshold. The hardness model was then combined with our data-driven phase diagram generation tool to expand the limited num1 ber of reported compounds. Eleven ternary borocarbide phase spaces were studied, and more than ten thermodynamically favorable compositions with superhard potential were identified, proving this ensemble model’s ability to find previously unknown superhard materials Vickers hardness test ; Boosting (machine learning) ; Materials science ; Ternary operation ; Phase diagram ; Ensemble learning ; Machine learning ; Phase (matter) ; Computer science ; Metallurgy ; Physics ; Microstructure Boron and Carbon Nanomaterials Research ; Semiconductor materials and interfaces ; MXene and MAX Phase Materials
Authors: Jiucheng Cheng | Lifeng Dong | Chunkai Zhang
Source: Research Square (preprint, 2020-12-21)
Field: Materials Science — Materials Chemistry

GeoCGNN: A Geometric-Information-Enhanced Crystal Graph Network for Property Predictions Abstract Graph neural networks (GNNs) have been explored to search for novel crystal materials. But in previous works, geometric structure was not taken into consideration or incompletely. Here, we develop a geometric-information-enhanced crystal graph neural network (GeoCGNN) to predict properties of novel crystal materials. By considering the distance vector between each node and its neighbors, our model can learn full topologic and spatial geometric structure information. Furthermore, we incorporate an effective method based on the mixed basis functions to encode the geometric information into our model, which outperforms other CGNN methods in a variety of databases. As for predicting the formation energy, our model is 30.3%, 14.6% and 13% better than CGCNN, MEGNet and iCGCNN models, respectively. For band gap, our model outperforms respectively 27.6% and 15.2% than CGCNN and MEGNet. Also, we interpret the implied material properties of the learned graph vector in a visible way. Property (philosophy) ; Graph ; Geometric networks ; Computer science ; Spatial network ; Crystal (programming language) ; Theoretical computer science ; Mathematics ; Graph theory ; Combinatorics ; Epistemology ; Philosophy Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: P. Mutti | Franck Cecillon | Yannick Le Goc | Guanghan Song
Source: HAL (Le Centre pour la Communication Scientifique Directe) (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Deep Learning Methods on Neutron Scattering Data Recently, by using deep learning methods, computers are able to surpass or come close to matching human performance on image analysis and pattern recognition. This advanced method could also help interpreting data from neutron scattering experiments. Those data contain rich scientific information about structure and dynamics of materials under investigation, and deep learning could help researchers better understand the link between experimental data and materials properties. We applied deep learning techniques to scientific neutron scattering data. This is a complex problem due to the multi-parameter space we have to deal with. We have used a convolutional neural network-based model to evaluate the quality of experimental neutron scattering images, which can be influenced by instrument configuration, sample and sample environment parameters. Sample structure can be deduced during data collection that can be therefore optimized. The neural network model can predict the experimental parameters to properly setup the instrument and derive the best measurement strategy. This results in a higher quality of data obtained in a shorter time, facilitating data analysis and interpretation. Computer science ; Neutron scattering ; Scattering ; Small-angle neutron scattering ; Neutron ; Deep learning ; Artificial intelligence ; Nuclear physics ; Physics ; Optics Machine Learning in Materials Science ; Nuclear Physics and Applications ; X-ray Diffraction in Crystallography
Authors: Ali Nazari
Source: Neural Computing and Applications (article, 2020-08-25)
Field: Materials Science — Materials Chemistry

Retraction Note: Modeling the correlation between Charpy impact energy and chemical composition of functionally graded steels by artificial neural networks Charpy impact test ; Computational Science and Engineering ; Artificial neural network ; Computer science ; Energy (signal processing) ; Composition (language) ; Correlation ; Biological system ; Artificial intelligence ; Materials science ; Mathematics ; Composite material ; Machine learning ; Toughness ; Statistics ; Biology Material Properties and Failure Mechanisms ; Fatigue and fracture mechanics ; Microstructure and Mechanical Properties of Steels
Authors: Andreas Goetz | Vinícius Wilian D. Cruzeiro | Ronak Roy
Source:  (preprint, 2020-04-30)
Field: Materials Science — Materials Chemistry

Insights into aqueous solvation of atmospheric gases from molecular simulations with machine learning-based many-body potentials Solvation ; Aqueous solution ; Chemical physics ; Chemistry ; Computer science ; Physical chemistry ; Molecule ; Organic chemistry Machine Learning in Materials Science ; Advanced Thermodynamics and Statistical Mechanics
Authors: Andreas Goetz | Vinícius Wilian D. Cruzeiro | Ronak Roy
Source:  (preprint, 2020-04-30)
Field: Materials Science — Materials Chemistry

Insights into aqueous solvation of atmospheric gases from molecular simulations with machine learning-based many-body potentials Solvation ; Aqueous solution ; Molecular dynamics ; Chemical physics ; Chemistry ; Computational chemistry ; Physical chemistry ; Molecule ; Organic chemistry Machine Learning in Materials Science ; Advanced Thermodynamics and Statistical Mechanics
Authors: Prabudhya Roy Chowdhury | Xiulin Ruan
Source: arXiv (Cornell University) (preprint, 2020-12-15)
Field: Materials Science — Materials Chemistry

An iterative machine learning approach for discovering unexpected thermal conductivity enhancement in aperiodic superlattices While machine learning (ML) has shown increasing effectiveness in optimizing materials properties under known physics, its application in challenging conventional wisdom and discovering new physics still remains challenging due to its interpolative nature. In this work, we demonstrate the potential of using ML for such applications by implementing an adaptive ML-accelerated search process that can discover unexpected lattice thermal conductivity ($κ_l$) enhancement instead of reduction in aperiodic superlattices (SLs) as compared to periodic superlattices. We use non-equilibrium molecular dynamics (NEMD) simulations for high-fidelity calculations of $κ_l$ for a small fraction of SLs in the search space, along with a convolutional neural network (CNN) which can rapidly predict $κ_l$ for a large number of structures. To ensure accurate prediction by the CNN for the target unknown structures, we iteratively identify aperiodic SLs containing structural features which lead to locally enhanced thermal transport, and include them as additional training data for the CNN in each iteration. As a result, our CNN can accurately predict the high $κ_l$ of aperiodic SLs that are absent from the initial training dataset, which allows us to identify the previously unseen exceptional structures. The identified RML structures exhibit increased coherent phonon contribution to thermal conductivity owing to the presence of closely spaced interfaces. Our work describes a general purpose machine learning approach for identifying low-probability-of-occurrence exceptional solutions within an extremely large subspace and discovering the underlying physics. Aperiodic graph ; Thermal conductivity ; Subspace topology ; Computer science ; Artificial intelligence ; Lattice (music) ; Superlattice ; Convolutional neural network ; Observable ; Machine learning ; Algorithm ; Materials science ; Physics ; Mathematics ; Optoelectronics ; Thermodynamics ; Combinatorics Machine Learning in Materials Science ; Thermal properties of materials ; Advanced Thermoelectric Materials and Devices
Authors: Priya Jadhav | Dr.Arun Bongale | Satish Kumar | Amit Kumar Tiwari
Source: Insecta mundi (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Artificial Intelligence in Plasma Electrolytic Micro-oxidation for Surface Hardening - Insights from Scholarly Citation Networks and Patents. Objective - The purpose of this article is to analyze the top work areas and patent domains in the field of surface hardening by micro-arc oxidation. Also, it is directed on the opportunities of data analysis by different machine learning tools. Material and methods - The www.lens.org database is used to collect articles from Elsevier, Trans tech publications, Springer New York, MDPI, etc. to review the relevant articles as well as patents related to the topic. The result - A total of 1057 articles were published in 60 different journals and 756 patents in the area of research under various jurisdictions. The top-cited article has 119 scholarly articles citations as well as 19 cited patents. The machine learning analysis is implemented at the testing level for image evaluation in a few articles and to the parameter monitoring of the process in other few articles. The present analysis provides the trend in the area of Plasma Electrolytic Micro-oxidation (PEMO)coating and areas of patentability. Citation ; Metallurgy ; Data science ; Computer science ; Materials science ; World Wide Web Catalytic Processes in Materials Science ; Advanced Machining and Optimization Techniques ; High-Temperature Coating Behaviors
Authors: Schuyler Krawczuk
Source: eScholarship (California Digital Library) (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Data-Efficient Surrogate Models for High-Throughput Density Functional Theory High-throughput screening of compounds for desirable electronic properties can allow for accelerated discovery and design of materials. Density functional theory (DFT) is the popular approach used for these quantum chemical calculations, but it can be computationally expensive on a large scale. Recently, machine learning methods have gained traction as a supplementation to DFT, with well-trained models achieving similar accuracy as DFT itself. However, training a machine learning model to be accurate and generalizable to unseen materials requires a large amount of training data. This work proposes a method to minimize the need for novel data creation for training by using transfer learning and publicly-available databases, allowing for both data-efficient and accurate machine learning to mitigate the computational cost of DFT. Computer science ; Density functional theory ; Throughput ; Machine learning ; Artificial intelligence ; Training set ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Yasemin Basdogan
Source: D-Scholarship@Pitt (University of Pittsburgh) (article, 2020-07-30)
Field: Materials Science — Materials Chemistry

Understanding Solvation Environments in Chemical Systems Molecular level understanding and characterization of solvation environments is often needed across chemistry, biology, and engineering. In many cases, the explicit interactions between molecules with nearby solvents are crucial for molecular-scale understanding. Toward practical modelling of local solvation effects of any solute in any solvent, we developed a general, all-QM, cluster-continuum approach. This approach uses a global optimization procedure to identify low energy molecular clusters with different numbers of explicit solvent molecules and then employs a machine learning algorithm with the help of the Smooth Overlap of Atomic Positions (SOAP) kernel to quantify the similarity between different low-energy solvent environments. From these data, we use a sketch-map non-linear dimensionality reduction technique to obtain a visual representation of the similarity between solvent environments in differently sized microsolvated clusters. After studying the evolution of the local solvation environment around the molecules, we systematically explore reaction pathways using Growing String Method. Without needing either dynamics simulations or a prior knowledge of the local solvation structure, this procedure was used to calculate reaction energies, solvation free energies and barrier heights in solvated systems. We now use this approach to model reaction mechanisms in more complicated reaction environments that are relevant for renewable fuels and chemicals. We reliably predict hydrogenation pathways and calculate barrier heights under electrochemical environments. This approach can be used to study physically significant solvation environments in any solvated system where the solvent molecules affects the quantum level nature of reaction mechanisms. Solvation ; Implicit solvation ; Chemistry ; Molecular dynamics ; Chemical physics ; Molecule ; Curse of dimensionality ; Statistical physics ; Computational chemistry ; Computer science ; Physics ; Artificial intelligence ; Organic chemistry Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Spectroscopy and Quantum Chemical Studies
Source: Прикладная механика и техническая физика (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Optimization Methodology of Artificial Neural Network Models for Predicting Molecular Diffusion Coefficients for Polar and Non-Polar Binary Gases Polar ; Binary number ; Artificial neural network ; Diffusion ; Chemical polarity ; Biological system ; Computer science ; Artificial intelligence ; Physics ; Thermodynamics ; Mathematics ; Biology Catalytic Processes in Materials Science ; Adsorption, diffusion, and thermodynamic properties of materials ; Spectroscopy and Laser Applications
Authors: Thanh-Dung Le | Rita Noumeir | Huu Luong Quach | Ji Hyung Kim | Jung Ho Kim | Ho Min Kim
Source: arXiv (Cornell University) (preprint, 2020-01-29)
Field: Materials Science — Materials Chemistry

Critical Temperature Prediction for a Superconductor: A Variational Bayesian Neural Network Approach Much research in recent years has focused on using empirical machine learning approaches to extract useful insights on the structure-property relationships of superconductor material. Notably, these approaches are bringing extreme benefits when superconductivity data often come from costly and arduously experimental work. However, this assessment cannot be based solely on an open black-box machine learning, which is not fully interpretable, because it can be counter-intuitive to understand why the model may give an appropriate response to a set of input data for superconductivity characteristic analyses, e.g., critical temperature. The purpose of this study is to describe and examine an alternative approach for predicting the superconducting transition temperature $T_c$ from SuperCon database obtained by Japan's National Institute for Materials Science. We address a generative machine-learning framework called Variational Bayesian Neural Network using superconductors chemical elements and formula to predict $T_c$. In such a context, the importance of the paper in focus is twofold. First, to improve the interpretability, we adopt a variational inference to approximate the distribution in latent parameter space for the generative model. It statistically captures the mutual correlation of superconductor compounds and; then, gives the estimation for the $T_c$. Second, a stochastic optimization algorithm, which embraces a statistical inference named Monte Carlo sampler, is utilized to optimally approximate the proposed inference model, ultimately determine and evaluate the predictive performance. Machine learning ; Interpretability ; Artificial intelligence ; Inference ; Artificial neural network ; Computer science ; Bayesian inference ; Context (archaeology) ; Generative model ; Generative grammar ; Bayesian probability Machine Learning in Materials Science ; Topic Modeling ; Nuclear Physics and Applications
Authors: Isac Lazar
Source: Lund University Publications Student Papers (Lund University) (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Principal Component Analysis for STEM-EDS images: Optimal collection parameters STEM-EDS is a common technique used for compositional mapping in materials. The vasts amounts of raw data produced by STEM EDS is suitable for advanced analysis using methods in unsupervised machine learning. One powerful method is Principal Component Analysis (PCA) which can automatically discover significant chemical correlations in a sample. Although more powerful than classical analysis, there are limits to how weak chemical correlations PCA can detect. Faced with a limited data collection time, it is of great importance to know how data collection parameters in the STEM affects PCA performance. In this work, the effect of image size and X-ray counts has been investigated using simulated STEM EDS datasets. It was found that decreasing overall image size while increasing counts per pixel both increases the chances of discovering weak chemical correlations and improves the overall PCA accuracy. Furthermore, use of varimax factor rotations has been investigated as a way to improve interpretability of PCA results. Finally, a real sample originating from a tungsten-carbide cutting tool which had been turned in a titanium alloy was analysed using the presented methods. This analysis confirmed the existence of an expected pure tungsten phase but also lead to the discovery of an unexpected ridge-like structure in the adhered alloy material. Principal component analysis ; Varimax rotation ; Artificial intelligence ; Interpretability ; Pattern recognition (psychology) ; Sample (material) ; Partial least squares regression ; Computer science ; Data mining ; Mathematics ; Statistics ; Machine learning ; Chemistry Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; X-ray Diffraction in Crystallography
Authors: Seyed Mohamad Moosavi | Henglu Xu | Linjiang Chen | Andrew I. Cooper | Berend Smit
Source: ChemRxiv (preprint, 2020-02-06)
Field: Materials Science — Materials Chemistry

Geometric Landscapes for Material Discovery within Energy-Structure-Function Maps Porous molecular crystals are an emerging class of porous materials formed by crystallisation of molecules with weak intermolecular interactions, which distinguishes them from extended nanoporous materials like metal-organic frameworks (MOFs). To aid discovery of porous molecular crystals for desired applications, energy-structure-function (ESF) maps were developed that combine a priori prediction of both the crystal structure and its functional properties. However, it is a challenge to represent the high-dimensional structural and functional landscapes of an ESF map and to identify energetically favourable and functionally interesting polymorphs among the 1,000s-10,000s of structures typically on a single ESF map. Here, we introduce geometric landscapes, a representation for ESF maps based on geometric similarity, quantified by persistent homology. We show that this representation allows the exploration of complex ESF maps, automatically pinpointing interesting crystalline phases available to the molecule. Furthermore, we show that geometric landscapes can serve as an accountable descriptor for porous materials to predict their performance for gas adsorption applications. A machine learning model trained using this geometric similarity could reach a remarkable accuracy in predicting the materials' performance for methane storage applications. Nanoporous ; Representation (politics) ; Intermolecular force ; Similarity (geometry) ; Function (biology) ; Porosity ; Materials science ; Computer science ; Molecule ; Biological system ; Nanotechnology ; Artificial intelligence ; Physics Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; X-ray Diffraction in Crystallography
Authors: Huziel E. Sauceda | Valentin Vassilev Galindo | Stefan Chmiela | Klaus‐Robert Müller | Alexandre Tkatchenko
Source: Bulletin of the American Physical Society (article, 2020-03-04)
Field: Chemistry — Spectroscopy

Nuclear quantum delocalization enhances non-covalent intramolecular interactions: A machine learning and path integral molecular dynamics study Intramolecular force ; Delocalized electron ; Path integral formulation ; Quantum ; Molecular dynamics ; Covalent bond ; Path (computing) ; Chemical physics ; Physics ; Quantum mechanics ; Computer science Advanced NMR Techniques and Applications ; Various Chemistry Research Topics ; Spectroscopy and Quantum Chemical Studies
Authors: Lucian Chan | Garrett M. Morris | Geoffrey Hutchison
Source: ChemRxiv (preprint, 2020-07-20)
Field: Materials Science — Materials Chemistry

Understanding Conformational Entropy in Small Molecules The calculation of the entropy of flexible molecules can be challenging, since the number of possible conformers grows exponentially with molecule size and many low-energy conformers may be thermally accessible. Different methods have been proposed to approximate the contribution of conformational entropy to the molecular standard entropy, including performing thermochemistry calculations with all possible stable conformations, and developing empirical corrections from experimental data. We have performed conformer sampling on over 120,000 small molecules generating some 12 million conformers, to develop models to predict conformational entropy across a wide range of molecules. Using insight into the nature of conformational disorder, our cross-validated physically-motivated statistical model can outperform common machine learning and deep learning methods, with a mean absolute error ≈4.8 J/mol•K, or under 0.4 kcal/mol at 300 K. Beyond predicting molecular entropies and free energies, the model implies a high degree of correlation between torsions in most molecules, often as- sumed to be independent. While individual dihedral rotations may have low energetic barriers, the shape and chemical functionality of most molecules necessarily correlate their torsional degrees of freedom, and hence restrict the number of low-energy conformations immensely. Our simple models capture these correlations, and advance our understanding of small molecule conformational entropy. Conformational isomerism ; Conformational entropy ; Dihedral angle ; Entropy (arrow of time) ; Molecule ; Thermochemistry ; Statistical physics ; Conformational ensembles ; Chemistry ; Small molecule ; Configuration entropy ; Computational chemistry ; Thermodynamics ; Molecular dynamics ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions