jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ralf Wanzenböck | Florian Buchner | Péter Kovács | Georg K. H. Madsen | Jesús Carrete
Source: Computer Physics Communications (article, 2023-12-21)
Field: Materials Science — Materials Chemistry

Clinamen2: Functional-style evolutionary optimization in Python for atomistic structure searches Clinamen2 is a versatile functional-style Python implementation of the covariance matrix adaptation evolution strategy (CMA-ES) utilizing Cholesky decomposition. On top of a problem-agnostic core algorithm, the software package offers a suite of utilities and library code enabling applications to important atomistic structure searches. Features include massively distributed computation and the BI-Population restart scheme. This article details the general code structure and introduces examples that illustrate some relevant applications for the materials science and chemistry worlds, including interfacing to density-functional-theory codes and machine-learned surrogate models. The functional design renders the code modular and adaptable, and makes the creation of interfaces to other atomistic software straightforward. Program Title: Clinamen2 CPC Library link to program files: https://doi.org/10.17632/x7syr2txsd.1 Developer's repository link: https://github.com/Madsen-s-research-group/clinamen2-public-releases Code Ocean capsule: https://codeocean.com/capsule/4950229 Licensing provisions: Apache-2.0 Programming language: Python Supplementary material: Nature of problem: Find optimal atomistic structures retaining full flexibility in the choice of the optimization target, the methodological approach and its implementation (e.g. CPU- vs. GPU-heavy calculations). Enable interfacing with relevant software, including but not limited to density-functional-theory (DFT) codes and machine-learning (ML) solutions. Solution method: The covariance matrix adaptation evolution strategy (CMA-ES) algorithm is implemented using Cholesky decompositions for efficiency. The core algorithm and application examples for specific problems are implemented in functional-style Python free of side effects, with data classes to keep track of the state of the evolution. Dask is used for job control to enable highly distributed workflows. Advanced strategies like BI-Population CMA-ES are easy to implement and illustrated in the examples. Python (programming language) ; Computer science ; Cholesky decomposition ; CMA-ES ; Interfacing ; Programming style ; Computational science ; Data structure ; Software ; Programming language ; Theoretical computer science ; Parallel computing ; Evolutionary algorithm ; Evolution strategy ; Artificial intelligence Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Surface Chemistry and Catalysis
Authors: Andrea Rizzi | Paolo Carloni | Michele Parrinello
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2023-07-05)
Field: Materials Science — Surfaces, Coatings and Films

Multimap targeted free energy estimation Input files, molecular dynamics and OPES trajectories, CGenFF and DFTB3/3ob potential energies, and normalizing flow neural network models generated for the journal article: "Multimap targeted free energy estimation." Rizzi, Andrea, Paolo Carloni, and Michele Parrinello. arXiv preprint arXiv:2302.07683 (2023). Estimation ; Energy (signal processing) ; Computer science ; Statistics ; Engineering ; Mathematics ; Systems engineering Electron and X-Ray Spectroscopy Techniques
Authors: Sriram Devata | Henderson James Cleaves | John Dimandja | Christopher A. Heist | Markus Meringer
Source: Journal of the American Society for Mass Spectrometry (article, 2023-06-30)
Field: Chemistry — Spectroscopy

Comparative Evaluation of Electron Ionization Mass Spectral Prediction Methods During the past decade promising methods for computational prediction of electron ionization mass spectra have been developed. The most prominent ones are based on quantum chemistry (QCEIMS) and machine learning (CFM-EI, NEIMS). Here we provide a threefold comparison of these methods with respect to spectral prediction and compound identification. We found that there is no unambiguous way to determine the best of these three methods. Among other factors, we find that the choice of spectral distance functions play an important role regarding the performance for compound identification. Chemistry ; Ionization ; Electron ionization ; Identification (biology) ; Mass spectrum ; Mass spectrometry ; Electron ; Spectral analysis ; Spectral line ; Ionization energy ; Computational chemistry ; Analytical Chemistry (journal) ; Chromatography ; Ion ; Spectroscopy ; Physics ; Organic chemistry ; Quantum mechanics Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Advanced Chemical Sensor Technologies
Authors: Kai Tang | Azam Rasouli | Jafar Safarian | Xiang Ma | Gabriella Tranell
Source: Materials (article, 2023-05-31)
Field: Materials Science — Materials Chemistry

Magnesiothermic Reduction of Silica: A Machine Learning Study Fundamental studies have been carried out experimentally and theoretically on the magnesiothermic reduction of silica with different Mg/SiO<sub>2</sub> molar ratios (1-4) in the temperature range of 1073 to 1373 K with different reaction times (10-240 min). Due to the kinetic barriers occurring in metallothermic reductions, the equilibrium relations calculated by the well-known thermochemical software FactSage (version 8.2) and its databanks are not adequate to describe the experimental observations. The unreacted silica core encapsulated by the reduction products can be found in some parts of laboratory samples. However, other parts of samples show that the metallothermic reduction disappears almost completely. Some quartz particles are broken into fine pieces and form many tiny cracks. Magnesium reactants are able to infiltrate the core of silica particles via tiny fracture pathways, thereby enabling the reaction to occur almost completely. The traditional unreacted core model is thus inadequate to represent such complicated reaction schemes. In the present work, an attempt is made to apply a machine learning approach using hybrid datasets in order to describe complex magnesiothermic reductions. In addition to the experimental laboratory data, equilibrium relations calculated by the thermochemical database are also introduced as boundary conditions for the magnesiothermic reductions, assuming a sufficiently long reaction time. The physics-informed Gaussian process machine (GPM) is then developed and used to describe hybrid data, given its advantages when describing small datasets. A composite kernel for the GPM is specifically developed to mitigate the overfitting problems commonly encountered when using generic kernels. Training the physics-informed Gaussian process machine (GPM) with the hybrid dataset results in a regression score of 0.9665. The trained GPM is thus used to predict the effects of Mg-SiO<sub>2</sub> mixtures, temperatures, and reaction times on the products of a magnesiothermic reduction, that have not been covered by experiments. Additional experimental validation indicates that the GPM works well for the interpolates of the observations. Overfitting ; Reduction (mathematics) ; Materials science ; Core (optical fiber) ; Range (aeronautics) ; Gaussian process ; Computer science ; Gaussian ; Machine learning ; Mathematics ; Physics ; Composite material ; Artificial neural network ; Geometry Machine Learning in Materials Science ; Metallurgical Processes and Thermodynamics ; Mineral Processing and Grinding
Authors: K. L. Liu | Ruilin Xiao | Ying Ruan | B. Wei
Source: Physical review. E (article, 2023-11-21)
Field: Materials Science — Materials Chemistry

Active learning prediction and experimental confirmation of atomic structure and thermophysical properties for liquid <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msub><mml:mi>Hf</mml:mi><mml:mn>76</mml:mn></mml:msub><mml:mspace width="0.16em"/><mml:msub><mml:mi mathvariant="normal">W</mml:mi><mml:mn>24</mml:mn></mml:msub></mml:mrow></mml:math> refractory alloy The determination of liquid atomic structure and thermophysical properties is essential for investigating the physical characteristics and phase transitions of refractory alloys. However, due to the stringent experimental requirements and underdeveloped interatomic potentials, acquiring such information through experimentation or simulation remains challenging. Here, an active learning method incorporating a deep neural network was established to generate the interatomic potential of the Hf_{76}W_{24} refractory alloy. Then the achieved potential was applied to investigate the liquid atomic structure and thermophysical properties of this alloy over a wide temperature range. The simulation results revealed the distinctive bonding preferences among atoms, that is, Hf atoms exhibited a strong tendency for conspecific bonding, while W atoms preferred to form an interspecific bonding. The analysis of short-range order (SRO) in the liquid alloy revealed a significant proportion of icosahedral (ICO) and distorted ICO structures, which even exceeded 30% in the undercooled state. As temperature decreased, SRO structures demonstrated an increase in larger coordination number (CN) clusters and a decrease in smaller CNs. The alterations of the atomic structure indicated that the liquid alloy becomes more ordered, densely packed, and energetically favorable with decreasing temperature, consistent with the obtained fact: Both density and surface tension increase linearly. The simulated thermophysical properties were close to experimental values with minor deviations of 2.8% for density and 3.4% for surface tension. The consistency of the thermophysical properties further attested to the accuracy and reliability of active learning simulation. Icosahedral symmetry ; Materials science ; Alloy ; Interatomic potential ; Thermodynamics ; Phase (matter) ; Surface tension ; Coordination number ; Molecular dynamics ; Crystallography ; Physics ; Chemistry ; Computational chemistry ; Metallurgy ; Ion Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; X-ray Diffraction in Crystallography
Authors: Yong Lu | Xiaoyi Huang | Zhiyuan Ren | Dan Sun | Yihui Guo | Xingjun Liu | Cuiping Wang
Source: Journal of Nuclear Materials (article, 2023-06-01)
Field: Materials Science — Materials Chemistry

A prediction model for thermal conductivity of metallic nuclear fuel based on multiple machine learning models Thermal conductivity ; Uranium ; Nuclear fuel ; Materials science ; Experimental data ; Work (physics) ; Multilayer perceptron ; Artificial intelligence ; Reliability (semiconductor) ; Thermal ; Machine learning ; Thermodynamics ; Artificial neural network ; Computer science ; Nuclear engineering ; Metallurgy ; Composite material ; Physics ; Statistics ; Mathematics ; Engineering Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Machine Learning in Materials Science
Authors: Arthur Kosmala | Johannes Gasteiger | Nicholas Gao | Stephan Günnemann
Source: arXiv (Cornell University) (preprint, 2023-03-08)
Field: Materials Science — Materials Chemistry

Ewald-based Long-Range Message Passing for Molecular Graphs Neural architectures that learn potential energy surfaces from molecular data have undergone fast improvement in recent years. A key driver of this success is the Message Passing Neural Network (MPNN) paradigm. Its favorable scaling with system size partly relies upon a spatial distance limit on messages. While this focus on locality is a useful inductive bias, it also impedes the learning of long-range interactions such as electrostatics and van der Waals forces. To address this drawback, we propose Ewald message passing: a nonlocal Fourier space scheme which limits interactions via a cutoff on frequency instead of distance, and is theoretically well-founded in the Ewald summation method. It can serve as an augmentation on top of existing MPNN architectures as it is computationally inexpensive and agnostic to architectural details. We test the approach with four baseline models and two datasets containing diverse periodic (OC20) and aperiodic structures (OE62). We observe robust improvements in energy mean absolute errors across all models and datasets, averaging 10% on OC20 and 16% on OE62. Our analysis shows an outsize impact of these improvements on structures with high long-range contributions to the ground truth energy. Computer science ; Range (aeronautics) ; Focus (optics) ; Limit (mathematics) ; Locality ; Statistical physics ; Message passing ; Fourier transform ; Energy (signal processing) ; Theoretical computer science ; Artificial intelligence ; Algorithm ; Physics ; Mathematics ; Quantum mechanics ; Distributed computing Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Electron and X-Ray Spectroscopy Techniques
Authors: Xiangyu Chen | William Shao | Nam Q. Le | Paulette Clancy
Source: Journal of Chemical Theory and Computation (article, 2023-10-26)
Field: Materials Science — Materials Chemistry

Transferable Force Field for Gallium Nitride Crystal Growth from the Melt Using On-The-Fly Active Learning Atomic-scale simulations of reactive processes have been stymied by two factors: the lack of a suitable semiempirical force field on one hand and the impractically large computational burden of using ab initio molecular dynamics on the other hand. In this paper, we use an "on-the-fly" active learning technique to develop a nonparameterized force field that, in essence, exhibits the accuracy of density functional theory and the speed of a classical molecular dynamics simulation. We developed a force field capable of capturing the crystallization of gallium nitride (GaN) during a novel additive manufacturing process featuring the reaction of liquid Ga and gaseous nitrogen precursors to grow crystalline GaN thin films. We show that this machine learning model is capable of producing a single force field that can model solid, liquid, and gas phases involved in the process. We verified our computational predictions against a range of experimental measurements relevant to each phase and against ab initio calculations, showing that this nonparametric force field produces properties with excellent accuracy as well as exhibits computationally tractable efficiency. The force field is capable of allowing us to simulate the solid-liquid coexistence interface and the crystallization of GaN from the melt. The development of this transferable force field opens the opportunity to simulate the liquid-phase epitaxial growth more accurately than before to analyze reaction and diffusion processes and ultimately to establish a growth model of the additive manufacturing process to create the gallium nitride thin films. Force field (fiction) ; Materials science ; Gallium nitride ; Molecular dynamics ; Crystallization ; Gallium ; Field (mathematics) ; Ab initio ; Nitride ; Chemical physics ; Epitaxy ; Nanotechnology ; Phase (matter) ; Computer science ; Computational chemistry ; Chemistry ; Artificial intelligence ; Thermodynamics ; Physics Machine Learning in Materials Science ; Semiconductor materials and devices ; Ion-surface interactions and analysis
Authors: Valeria Fonseca Díaz | Ben Aernouts | Els Bobelyn | Bart Nicolaı̈ | Wouter Saeys
Source: Chemometrics and Intelligent Laboratory Systems (article, 2023-06-07)
Field: Chemistry — Analytical Chemistry

Monitoring of multivariate calibration models in the absence of new reference values: The regression case Calibration ; Chemometrics ; Computer science ; Multivariate statistics ; Data mining ; Software ; Machine learning ; Regression ; Domain (mathematical analysis) ; Focus (optics) ; Pipeline transport ; Artificial intelligence ; Statistics ; Mathematics ; Engineering Spectroscopy and Chemometric Analyses ; Pesticide Residue Analysis and Safety ; Fault Detection and Control Systems
Authors: Wei Yu | Zhaofu Zhang | Xuhao Wan | Jinhao Su | Qingzhong Gui | Hailing Guo | Hongxia Zhong | John Robertson | Yuzheng Guo
Source: Chemistry of Materials (article, 2023-08-14)
Field: Materials Science — Materials Chemistry

High-Accuracy Machine-Learned Interatomic Potentials for the Phase Change Material Ge<sub>3</sub>Sb<sub>6</sub>Te<sub>5</sub> Ge3Sb6Te5 with an emerging off-stoichiometric composition has been proven to have characteristic properties of phase change materials (PCMs) by experiments. However, the detailed mechanism of the phase transition and the highly temperature-dependent kinetics of its crystallization process have yet to be resolved at the atomic scale. In this work, we develop an artificial neural network-based potential (NNP) to accelerate the molecular dynamics (MD) simulation of Ge3Sb6Te5 without sacrificing the quantum mechanical accuracy. Overall, the comprehensive structural information predicted by NNP shows an excellent agreement with that of ab initio MD (AIMD), indicating the reliability of the proposed method. Based on the well-trained NNP, an MD simulation can be adopted to simulate Ge3Sb6Te5 with over 10,000 atoms with high efficiency and accuracy, which is beyond the reach of AIMD. Subsequently, a further NNP-based MD simulation with long timescales is carried out, which successfully captures the rapid transition of the crystallization and perfectly reproduces the crystallization process consistent with experiments. This work provides a novel atomic-level simulation and analysis approach to the complex Ge3Sb6Te5 and makes it possible to simulate the real nonvolatile memory device. Crystallization ; Molecular dynamics ; Materials science ; Reliability (semiconductor) ; Work (physics) ; Phase (matter) ; Stoichiometry ; Process (computing) ; Phase transition ; Atomic units ; Ab initio ; Chemical physics ; Statistical physics ; Computer science ; Thermodynamics ; Chemistry ; Physical chemistry ; Computational chemistry ; Physics Phase-change materials and chalcogenides ; Machine Learning in Materials Science ; Chalcogenide Semiconductor Thin Films
Source: International Journal of Corrosion and Scale Inhibition (article, 2023-12-24)
Field: Materials Science — Materials Chemistry

Insights into corrosion inhibition development through QSAR and machine learning: Application to benzimidazole derivatives As corrosion inhibitors for mild steel in 1 M hydrochloric acid, benzimidazole derivatives were studied by applying the quantitative structure-activity relationships (QSAR).A total of nine models were built based on selected variables, and a further nine models were built using 2D descriptors derived by Molecular Operating Environment (MOE) software.The models Multiple linear regression (MLR), Support Vector Machine Regression (SVR), Back Propagation Artificial Neural Networks (BPANN), Partial Least Square (PLS), and others were created to forecast the inhibitory effectiveness of the new benzimidazole compounds.In addition, the predictive model's calibration and test results' coefficients of determination (R 2 ) and root mean squared (RMSE) were evaluated.Despite most models showing adequate performance, the SVR model was chosen as the best model due to its successful calibration and test results represented in higher coefficient of determination of calibration and test, which are 97.83% and 94.54%, respectively, and the lower RMSE of calibration and test which are 2.45 and 2.96.Furthermore, plotting the anticipated inhibition efficiency versus the experimental value revealed a good degree of dependability for the predictions made by the SVR model for the novel inhibitors.The selection of the descriptors and their effects on the effectiveness of inhibition were essential factors in creating and developing new benzimidazole derivatives as Benzimidazole ; Quantitative structure–activity relationship ; Corrosion ; Corrosion inhibitor ; Combinatorial chemistry ; Chemistry ; Computer science ; Stereochemistry ; Organic chemistry Corrosion Behavior and Inhibition ; Computational Drug Discovery Methods
Authors: Tianzhe Li | Julio Cárdenas‐Rodríguez | Priya N. Trakru | Mark D. Pagel
Source: NMR in Biomedicine (article, 2023-06-06)
Field: Materials Science — Materials Chemistry

A machine learning approach that measures pH using acidoCEST MRI of iopamidol Tumor acidosis is an important biomarker for aggressive tumors, and extracellular pH (pHe) of the tumor microenvironment can be used to predict and evaluate tumor responses to chemotherapy and immunotherapy. AcidoCEST MRI measures tumor pHe by exploiting the pH-dependent chemical exchange saturation transfer (CEST) effect of iopamidol, an exogenous CT agent repurposed for CEST MRI. However, all pH fitting methodologies for acidoCEST MRI data have limitations. Herein we present results of the application of machine learning for extracting pH values from CEST Z-spectra of iopamidol. We acquired 36,000 experimental CEST spectra from 200 phantoms of iopamidol prepared at five concentrations, five T<sub>1</sub> values, and eight pH values at five temperatures, acquired at six saturation powers and six saturation times. We also acquired T<sub>1</sub> , T<sub>2</sub> , B<sub>1</sub> RF power, and B<sub>0</sub> magnetic field strength supplementary MR information. These MR images were used to train and validate machine learning models for the tasks of pH classification and pH regression. Specifically, we tested the L1-penalized logistic regression classification (LRC) model and the random forest classification (RFC) model for classifying the CEST Z-spectra for thresholds at pH 6.5 and 7.0. Our results showed that both RFC and LRC were effective for pH classification, although the RFC model achieved higher predictive value, and improved the accuracy of classification accuracy with CEST Z-spectra with a more limited set of saturation frequencies. Furthermore, we used LASSO and random forest regression (RFR) models to explore pH regression, which showed that the RFR model achieved higher accuracy and precision for estimating pH across the entire pH range of 6.2-7.3, especially when using a more limited set of features. Based on these results, machine learning for analysis of acidoCEST MRI is promising for eventual in vivo determination of tumor pHe. Random forest ; Artificial intelligence ; Support vector machine ; Machine learning ; Iopamidol ; Logistic regression ; Chemistry ; Nuclear magnetic resonance ; Computer science ; Medicine ; Radiology ; Physics Lanthanide and Transition Metal Complexes ; Magnetism in coordination complexes ; Electron Spin Resonance Studies