jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hantong Jiang | Xuanjie Lin | Liquan Wang | Yongsheng Ren | Shu Zhan | Wenhui Ma
Source: Crystal Research and Technology (article, 2022-06-17)
Field: Materials Science — Materials Chemistry

Predicting Material Properties by Deep Graph Networks Abstract Nodes and edges constitute crystal structures, so crystal structures are easy to express as graphs. Using graph networks to predict material properties has been a prevailing view in the past years. Neural networks are used to fit the density functional theory (DFT) calculation results to predict the properties of various materials. Based on this train of thought, the authors develop graph neural network models for predicting material properties with crystal structure data, and the model gets competitive results with Material Project datasets. It is demonstrated that the authors’ model outperforms other ML methods. Like MEGNet, this model makes input data three parts: nodes, edges, and global. Using Graph Attention Network (GAT) can aggregate node data with edge information and get attention to important areas in graphs. Integrating three features retains structural information and is more suitable for mapping the original distribution of crystal structures. Computer science ; Graph ; Theoretical computer science ; Artificial neural network ; Data mining ; Enhanced Data Rates for GSM Evolution ; Aggregate (composite) ; Graph theory ; Algorithm ; Artificial intelligence ; Mathematics ; Materials science ; Combinatorics ; Nanotechnology Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Daniel Sauceda | Prashant Singh | Raymundo Arróyave
Source: Scientific Reports (article, 2022-07-29)
Field: Materials Science — Materials Chemistry

A theoretical investigation of the effect of Ga alloying on thermodynamic stability, electronic-structure, and oxidation resistance of Ti2AlC MAX phase Abstract We present a systematic investigation of thermodynamic stability, phase-reaction, and chemical activity of Al containing disordered Ti 2 (Al-Ga)C MAX phases using machine-learning driven high-throughput framework to understand the oxidation resistance behavior with increasing temperature and exposure to static oxygen. The A-site (at Al) disordering in Ti 2 AlC MAX (M=Ti, A=Al, X=C) with Ga shows significant change in the chemical activity of Al with increasing temperature and exposure to static oxygen, which is expected to enable surface segregation of Al, thereby, the formation of Al 2 O 3 and improved oxidation resistance. We performed in-depth convex hull analysis of ternary Ti–Al–C, Ti–Ga–C, and Ti–Al–Ga–C based MAX phase, and provide detailed contribution arising from electronic, chemical and vibrational entropies. The thermodynamic analysis shows change in the Gibbs formation enthalpy (Δ G form ) at higher temperatures, which implies an interplay of temperature-dependent enthalpy and entropic contributions in oxidation resistance Ga doped Ti 2 AlC MAX phases. A detailed electronic structure and chemical bonding analysis using crystal orbital Hamilton population method reveal the origin of change in phases stability and in oxidation resistance in disorder Ti 2 (Al 1−x Ga x )C MAX phases. Our electronic structure analysis correlate well with the change in oxidation resistance of Ga doped MAX phases. We believe our study provides a useful guideline to understand to role of alloying on electronic, thermodynamic, and oxidation related mechanisms of bulk MAX phases, which can work as a precursor to understand oxidation behavior of two-dimensional MAX phases, i.e., MXenes (transition metal carbides, carbonitrides and nitrides). Chemical stability ; Enthalpy ; Ternary operation ; Materials science ; Crystal structure ; Gibbs free energy ; Chemical bond ; Thermodynamics ; Electronic structure ; Physical chemistry ; Chemistry ; Crystallography ; Computational chemistry ; Organic chemistry MXene and MAX Phase Materials ; Aluminum Alloys Composites Properties ; Advanced ceramic materials synthesis
Authors: James Damewood | Daniel Schwalbe‐Koda | Rafael Gómez‐Bombarelli
Source: npj Computational Materials (article, 2022-04-08)
Field: Materials Science — Materials Chemistry

Sampling lattices in semi-grand canonical ensemble with autoregressive machine learning Abstract Calculating thermodynamic potentials and observables efficiently and accurately is key for the application of statistical mechanics simulations to materials science. However, naive Monte Carlo approaches, on which such calculations are often dependent, struggle to scale to complex materials in many state-of-the-art disciplines such as the design of high entropy alloys or multi-component catalysts. To address this issue, we adapt sampling tools built upon machine learning-based generative modeling to the materials space by transforming them into the semi-grand canonical ensemble. Furthermore, we show that the resulting models are transferable across wide ranges of thermodynamic conditions and can be implemented with any internal energy model U , allowing integration into many existing materials workflows. We demonstrate the applicability of this approach to the simulation of benchmark systems (AgPd, CuAu) that exhibit diverse thermodynamic behavior in their phase diagrams. Finally, we discuss remaining challenges in model development and promising research directions for future improvements. Computer science ; Statistical ensemble ; Statistical physics ; Benchmark (surveying) ; Canonical ensemble ; Machine learning ; Grand canonical ensemble ; Ensemble learning ; Artificial intelligence ; Workflow ; Monte Carlo method ; Statistical mechanics ; Observable ; Entropy (arrow of time) ; Key (lock) ; Mathematics ; Physics ; Thermodynamics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Catalysis and Oxidation Reactions
Authors: Joydeep Munshi | Alexander Rakowski | Benjamin H. Savitzky | Steven E. Zeltmann | Jim Ciston | Matthew Henderson | Shreyas Cholia | Andrew M. Minor | Maria K. Y. Chan | Colin Ophus
Source: arXiv (Cornell University) (preprint, 2022-02-01)
Field: Materials Science — Materials Chemistry

Disentangling multiple scattering with deep learning: application to strain mapping from electron diffraction patterns Implementation of a fast, robust, and fully-automated pipeline for crystal structure determination and underlying strain mapping for crystalline materials is important for many technological applications. Scanning electron nanodiffraction offers a procedure for identifying and collecting strain maps with good accuracy and high spatial resolutions. However, the application of this technique is limited, particularly in thick samples where the electron beam can undergo multiple scattering, which introduces signal nonlinearities. Deep learning methods have the potential to invert these complex signals, but previous implementations are often trained only on specific crystal systems or a small subset of the crystal structure and microscope parameter phase space. In this study, we implement a Fourier space, complex-valued deep neural network called FCU-Net, to invert highly nonlinear electron diffraction patterns into the corresponding quantitative structure factor images. We trained the FCU-Net using over 200,000 unique simulated dynamical diffraction patterns which include many different combinations of crystal structures, orientations, thicknesses, microscope parameters, and common experimental artifacts. We evaluated the trained FCU-Net model against simulated and experimental 4D-STEM diffraction datasets, where it substantially out-performs conventional analysis methods. Our simulated diffraction pattern library, implementation of FCU-Net, and trained model weights are freely available in open source repositories, and can be adapted to many different diffraction measurement problems. Diffraction ; Pipeline (software) ; Computer science ; Electron diffraction ; Optics ; Scattering ; Crystal (programming language) ; Artificial intelligence ; Materials science ; Physics X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques
Authors: Jingli Wang | Jingxiang Gao | Jiaqi Huang | Qinghui Qi | Xinqi Mao | Wang Cao | Ruibo Ding | Yachun Mao
Source: Canadian Journal of Remote Sensing (article, 2022-04-21)
Field: Chemistry — Analytical Chemistry

Quantitative Inversion Modeling Method for Grading Deerni Copper Deposits Based on Visible and Near-Infrared Hyperspectral Data Quantitative metal grade inversion based on hyperspectral data is an effective approach to achieve the real-time in situ determination of ore body grades and has the advantages of low cost compared with traditional chemical analysis methods. However, the redundant nature of hyperspectral data and the parameter-limiting nature of machine learning algorithms reduce the modeling accuracy and precision, resulting in severe limitations on the application of hyperspectral techniques for the grade inversion of Deerni copper ore bodies. In this paper, we first obtained visible-NIR hyperspectral data for 190 ore samples using a spectrometer and determined the copper content of the sample set using chemical analysis; then, we processed the raw hyperspectral data using three dimensionality reduction algorithms and optimized a BP neural network based on an evolutionary algorithm. Finally, a Deerni copper grade inversion model was established using the hyperspectral data before and after dimensionality reduction, and the inversion accuracy and precision was compared and analyzed with that obtained by the BP neural network, the random forest and the variable hidden layer nodes models. The combination of the LLE dimensionality reduction algorithm and the optimized BP neural network algorithm achieves the highest modeling precision, with an R2 of 0.950. Hyperspectral imaging ; Dimensionality reduction ; Curse of dimensionality ; Artificial neural network ; Inversion (geology) ; Artificial intelligence ; Computer science ; Pattern recognition (psychology) ; Data set ; Imaging spectrometer ; Algorithm ; Remote sensing ; Data mining ; Spectrometer ; Geology ; Optics Spectroscopy and Chemometric Analyses ; Remote-Sensing Image Classification ; Geochemistry and Geologic Mapping
Authors: Marian Arale Brännvall | Davide Gambino | Rickard Armiento | Björn Alling
Source: Physical review. B./Physical review. B (article, 2022-04-13)
Field: Materials Science — Materials Chemistry

Machine learning approach for longitudinal spin fluctuation effects in bcc Fe at<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>T</mml:mi><mml:mi>c</mml:mi></mml:msub></mml:math>and under Earth-core conditions We propose a machine learning approach to predict the shapes of the longitudinal spin fluctuation (LSF) energy landscapes for each local magnetic moment. This approach allows the inclusion of the effects of LSFs in, e.g., the simulation of a magnetic material with ab initio molecular dynamics in an effective way. This type of simulation requires knowledge of the reciprocal interaction between atoms and moments, which, in principle, would entail calculating the energy landscape of each atom at every instant in time. The machine learning approach is based on the kernel ridge regression method and developed using bcc Fe at the Curie temperature and ambient pressure as a test case. We apply the trained machine learning models in a combined atomistic spin dynamics and ab initio molecular dynamics (ASD-AIMD) simulation, where they are used to determine the sizes of the magnetic moments of every atom at each time step. In addition to running an ASD-AIMD simulation with the LSF machine learning approach for bcc Fe at the Curie temperature, we also simulate Fe at temperature and pressure comparable to the conditions at the Earth's inner solid core. The latter simulation serves as a critical test of the generality of the method and demonstrates the importance of the magnetic effects in Fe in the Earth's core despite its extreme temperature and pressure. Atom (system on chip) ; Ab initio ; Machine learning ; Magnetic moment ; Molecular dynamics ; Curie temperature ; Artificial intelligence ; Physics ; Computer science ; Materials science ; Algorithm ; Statistical physics ; Condensed matter physics ; Quantum mechanics ; Ferromagnetism Machine Learning in Materials Science ; Theoretical and Computational Physics ; Magnetic Properties and Applications
Authors: GuanYa Yang | Wai Yuet Chiu | Jiang Wu | Yi Zhou | ShuGuang Chen | Weijun Zhou | Jiaqi Fan | GuanHua Chen
Source: The Journal of Physical Chemistry A (article, 2022-09-02)
Field: Materials Science — Materials Chemistry

Predicting Experimental Heats of Formation via Deep Learning with Limited Experimental Data When it comes to predicting experimental values of molecular properties with deep learning, the key problem is the lack of sufficient experimental data for training. We propose a method that consists of pretraining a graph neural network that aims to reproduce first-principles quantum mechanical results, followed by fine-tuning of a fully connected neural network against experimental results. The combined pretraining and fine-tuning model is expected to yield molecular properties close to experimental accuracy. This is made possible because first-principles quantum mechanical methods are often qualitatively correct or semiquantitatively accurate; thus, a calibration of the calculation results against high-precision but limited experiment data can improve accuracy greatly. Moreover, the method is highly efficient, as first-principles quantum mechanical calculation is bypassed. To demonstrate this, we apply the combined model to determine the experimental heats of formation of organic molecules made of H, C, O, N, or F atoms (up to 30 atoms), where mere 405 experimental data are used. The overall mean absolute error is 1.8 kcal/mol for these molecules. Experimental data ; Artificial neural network ; Quantum ; Calibration ; Computer science ; Quantum chemical ; Graph ; Molecule ; Biological system ; Statistical physics ; Algorithm ; Artificial intelligence ; Physics ; Mathematics ; Quantum mechanics ; Theoretical computer science ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Aditya Nandy | Chenru Duan | Conrad Goffinet | Heather J. Kulik
Source: ChemRxiv (preprint, 2022-02-25)
Field: Materials Science — Materials Chemistry

New Strategies for Direct Methane-to-Methanol Conversion from Active Learning Exploration of 16 Million Catalysts Despite decades of effort, no earth abundant homogeneous catalysts have been discovered that can selectively oxidize methane to methanol. We exploit active learning to simultaneously optimize methane activation and methanol release calculated with machine learning (ML)-accelerated density functional theory (DFT) in a space of 16M candidate catalysts with novel macrocycles. By constructing these macrocycles from fragments inspired by synthesized compounds, we ensure synthetic realism in our computational search. Our large-scale search reveals that low spin Fe(II) compounds paired with strong field (e.g. P or S-coordinating) ligands have the best energetic tradeoff between hydrogen atom transfer (HAT) and methanol release. This observation is distinct from prior efforts that have focused on high spin Fe(II) with weak field ligands. By decoupling equatorial and axial ligand effects, we determine that negatively charged axial ligands are critical for more rapid release of methanol and, higher valency metals (i.e., M(III) vs M(II)) are unlikely to be suitable for methanol release. With full characterization of barrier heights, we confirmed that optimizing for HAT did not lead to large oxo formation barriers. Energetic span analysis revealed designs for an intermediate spin Mn(II) catalyst and a low spin Fe(II) catalyst that would lead to good turnover frequencies. This active learning approach is expected to be beneficial for search of large catalyst spaces where no prior designs have been identified and where linear scaling relationships between reaction energies or barriers may be limited or unknown. Methanol ; Catalysis ; Chemistry ; Density functional theory ; Methane ; Ligand (biochemistry) ; Chemical physics ; Computational chemistry ; Organic chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Asymmetric Hydrogenation and Catalysis
Authors: Mohamed Nadir Boucherit | Sid Ahmed Amzert | Fahd Arbaoui
Source: Anti-Corrosion Methods and Materials (article, 2022-03-15)
Field: Materials Science — Metals and Alloys

Application of machine learning in predicting pitting corrosion – electrochemical data around the open circuit potential Purpose The purpose of this study is to confirm the idea that observing the electrochemical data of a steel polarized around its open circuit potential can provide insight into its performance against pitting corrosion. To confirm this idea a two-step work was carried out. The authors collected electrochemical data through experiments and exploited them through machine learning by building neural networks capable of predicting the behaviour of the steel against the pitting corrosion. Design/methodology/approach The electrochemical experiments consist in plotting voltammograms of the steel in chemical solutions of various degrees of corrosiveness. For each experiment, the authors observe how the open-circuit potential evolves over a period of 1 min, and following this, the authors observe the current evolution when they impose a potential scan that starts from the open-circuit potential. For each of these situations, the pitting potential Epit is noted. The authors then build different artificial neural networks, which after learning, can, by receiving electrochemical data, calculate a pitting potential Epit′. The performance of the neural networks is evaluated by the correlation of Epit and Epit′. Findings Through this work, different types of networks were compared. The results show that recurrent or convolutional networks can better capture the temporal nature of the input data. Originality/value The results of this work support the idea that the measurable electrochemical data around the free potential of a material can be correlated with its behaviour at more anodic potentials, particularly the initiation of pits. Pitting corrosion ; Convolutional neural network ; Open-circuit voltage ; Computer science ; Artificial neural network ; Work (physics) ; Electrochemistry ; Corrosion ; Electrochemical noise ; Anode ; Artificial intelligence ; Materials science ; Metallurgy ; Chemistry ; Engineering ; Electrode ; Mechanical engineering ; Voltage ; Electrical engineering Hydrogen embrittlement and corrosion behaviors in metals ; Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability
Authors: Shantanu Maheshwari | Sharankumar Shetty | Ram R. Ratnakar | Suchismita Sanyal
Source: Journal of the Indian Institute of Science (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Role of Computational Science in Materials and Systems Design for Sustainable Energy Applications: An Industry Perspective Perspective (graphical) ; Sustainable energy ; Design science ; Engineering ethics ; Management science ; Engineering ; Architectural engineering ; Engineering physics ; Biochemical engineering ; Computer science ; Mechanical engineering ; Renewable energy Machine Learning in Materials Science ; Fuel Cells and Related Materials ; CO2 Reduction Techniques and Catalysts
Authors: Yoshiyuki Kobayashi | Kenichi Yoshida
Source: Procedia Computer Science (article, 2022-01-01)
Field: Chemistry — Spectroscopy

Automated retention time prediction of new psychoactive substances in gas chromatography New psychoactive substances (NPSs) are drugs that exhibit chemical structures and pharmacological actions similar to illegal drugs. Because the health hazards caused by these drugs of abuse have become a social problem, the quick identification of NPSs is necessary for rapid regulation. We developed an automated machine learning system to predict the retention time of the gas chromatography-mass spectrometry for NPSs. The use of an automated machine learning system and Mordred that calculates molecular descriptors enables accurate retention time prediction. Because the retention time prediction is crucial for identifying NPSs, our research can contribute to the prevention of health hazards. Computer science ; Retention time ; Identification (biology) ; Drugs of abuse ; Gas chromatography–mass spectrometry ; Mass spectrometry ; Machine learning ; Artificial intelligence ; Data mining ; Drug ; Chromatography ; Chemistry ; Medicine ; Psychiatry Analytical Chemistry and Chromatography ; Advanced Chemical Sensor Technologies ; Mass Spectrometry Techniques and Applications
Authors: Nghia T. Nguyen | Steph-Yves Louis | Lai Wei | Kamal Choudhary | Ming Hu | Jianjun Hu
Source: ACS Omega (article, 2022-07-21)
Field: Materials Science — Materials Chemistry

Predicting Lattice Vibrational Frequencies Using Deep Graph Neural Networks Lattice vibrational frequencies are related to many important materials properties such as thermal and electrical conductivity as well as superconductivity. However, computational calculation of vibrational frequencies using density functional theory methods is computationally too demanding for large number of samples in materials screening. Here we propose a deep graph neural network based algorithm for predicting crystal vibrational frequencies from crystal structures. Our algorithm addresses the variable dimension of vibrational frequency spectrum using the zero padding scheme. Benchmark studies on two data sets with 15,000 mixed-structure and 35,552 rhombohedra samples show that the aggregated <i>R</i> <sup>2</sup> scores of the prediction reach 0.554 and 0.724. We also evaluate the structural transferability by predicting the vibration frequencies for 239 individual cubic target structures. The <i>R</i> <sup>2</sup> scores for more than 40% of the targets are greater than 0.8 and can reach as high as 0.98 for the model trained with mixed samples, while the average mean absolute error is 43.69 Thz showing low transferability across structure types. Our work demonstrates the capability of deep graph neural networks to learn to predict lattice vibration frequency when sufficient number of training samples are available. Transferability ; Artificial neural network ; Computation ; Lattice (music) ; Molecular vibration ; Algorithm ; Crystal structure ; Vibration ; Graph ; Materials science ; Computer science ; Artificial intelligence ; Statistical physics ; Physics ; Machine learning ; Theoretical computer science ; Molecule ; Chemistry ; Acoustics ; Quantum mechanics ; Crystallography Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Inorganic Chemistry and Materials
Authors: Faisal Alboqami | Amjad Ali Pasha | Mohammad Irfan Alam | Abdulazeez Abdulraheem | Abdul Gani Abdul Jameel
Source: Arabian Journal for Science and Engineering (article, 2022-12-19)
Field: Chemistry — Organic Chemistry

Prediction of Yield Sooting Index Utilizing Artificial Neural Networks and Adaptive-Network-Based Fuzzy Inference Systems Adaptive neuro fuzzy inference system ; Soot ; Artificial neural network ; Yield (engineering) ; Combustion ; Artificial intelligence ; Inference system ; Test set ; Computer science ; Biological system ; Machine learning ; Fuzzy logic ; Chemistry ; Materials science ; Fuzzy control system ; Organic chemistry Chemical Thermodynamics and Molecular Structure ; Atmospheric chemistry and aerosols ; Vehicle emissions and performance