jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Takaya Satoh | Masahiko Takei | Fuminori Uematsu
Source: Rapid Communications in Mass Spectrometry (article, 2021-12-14)
Field: Chemistry — Spectroscopy

Development of a peak extraction method using the high‐resolution matrix‐assisted laser desorption/ionization time‐of‐flight mass spectrometry and machine learning techniques: Analysis of peak shapes A machine-learning method for peak extraction from high-resolution MALDI-TOFMS was developed. Single-component peaks of the mass spectrum were distinguished from noise peaks by their peak shapes. Combining with KMD analysis facilitated the identification of minor polymer series in complex mass spectra. Mass spectrum ; Chemistry ; Mass spectrometry ; Analytical Chemistry (journal) ; Ionization ; Time-of-flight mass spectrometry ; Matrix-assisted laser desorption/ionization ; Polymer ; Matrix (chemical analysis) ; Desorption ; Chromatography ; Ion ; Physical chemistry Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Ion-surface interactions and analysis
Authors: Jingrui Wei | Ben Blaiszik | Dane Morgan | Paul M. Voyles
Source: Microscopy and Microanalysis (article, 2021-07-30)
Field: Materials Science — Materials Chemistry

Benchmark tests of atom-locating CNN models with a consistent dataset Modern electron microscopes can resolve single atoms in images of materials, but experimental images may include significant noise, distortions, and poor contrast. Several convolutional neural networks (CNNs) have been developed to automatically identify atom positions in these datasets. However, each network tends to be developed for a specific problem with unknown applicability on data outside the original test dataset. To overcome these problems, we have developed a benchmark dataset and model performance metrics for testing atom-locating approaches covering a range of image quality and various types of crystal lattices. With this benchmark, we have evaluated the performance of two existing CNN models 1,2 , with both models showing excellent performance over different atomic lattices, except for certain cases like overlapping atomic column images. We find that the key factor that limits a model's performance is the quality of STEM images, as a function of quality metrics including the pixel size, contrast, and image resolution, pointing to the need for community agreement on further optimization of these models and benchmark tests for these and related imaging tools. Benchmark (surveying) ; Computer science ; Content (measure theory) ; Atom (system on chip) ; Action (physics) ; Artificial intelligence ; Pattern recognition (psychology) ; Mathematics ; Physics ; Cartography ; Geography ; Quantum mechanics ; Mathematical analysis ; Embedded system Machine Learning in Materials Science ; Nuclear Physics and Applications ; Mass Spectrometry Techniques and Applications
Authors: Jürgen Köfinger | Gerhard Hummer
Source: ChemRxiv (preprint, 2021-07-29)
Field: Materials Science — Materials Chemistry

Empirical optimization of molecular simulation force fields by Bayesian inference The demands on the accuracy of force fields for classical molecular dynamics simulations are steadily growing as larger and more complex systems are studied over longer times. One way to meet these growing demands is to hand over the learning of force fields and their parameters to machines in a systematic (semi-)automatic man- ner. Doing so, we can take full advantage of exascale computing, the increasing availability of experimental data, and advances in quantum-mechanial computations and the calculation of experimental observables from molecular ensembles. Here, we discuss and illustrate the challenges we face in this endeavor and explore a way forward by adapting the Bayesian inference of ensembles (BioEn) method [Hummer and K ̈ofinger, J. Chem. Phys. (2015)] for force field parameterization. In the Bayesian inference of force fields (BioFF) method developed here, the optimization problem is regularized by a simplified prior on the force field parameters and an entropic prior act- ing on the ensemble. The latter compensates for the unavoidable over-simplifications in the parameter prior. We determine optimal force field parameters using an iterative predictor-corrector approach, in which we run simula- tions, determine the reference ensemble using the weighted histogram analysis method (WHAM), and optimize the BioFF posterior. We illustrate this approach for a simple polymer model, using the distance between two labeled sites as the experimental observable. By systematically resolving force field issues, the BioFF corrections extend to observables not included in ensemble reweighting. We envision future force field optimization as a formalized, systematic, and (semi-)automatic machine learning effort that incorporates a wide range of data from experiment and high-level quantum chemical calculations and takes advantage of exascale computing resources. Force field (fiction) ; Observable ; Computer science ; Inference ; Bayesian inference ; Field (mathematics) ; Approximate Bayesian computation ; Bayesian probability ; Bayesian optimization ; Artificial intelligence ; Statistical physics ; Algorithm ; Machine learning ; Mathematics ; Physics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Gaussian Processes and Bayesian Inference
Authors: Shufeng Kong | Dan Guevarra | Carla P. Gomes | John M. Gregoire
Source: arXiv (Cornell University) (preprint, 2021-06-04)
Field: Materials Science — Materials Chemistry

Materials Representation and Transfer Learning for Multi-Property Prediction The adoption of machine learning in materials science has rapidly transformed materials property prediction. Hurdles limiting full capitalization of recent advancements in machine learning include the limited development of methods to learn the underlying interactions of multiple elements, as well as the relationships among multiple properties, to facilitate property prediction in new composition spaces. To address these issues, we introduce the Hierarchical Correlation Learning for Multi-property Prediction (H-CLMP) framework that seamlessly integrates (i) prediction using only a material's composition, (ii) learning and exploitation of correlations among target properties in multi-target regression, and (iii) leveraging training data from tangential domains via generative transfer learning. The model is demonstrated for prediction of spectral optical absorption of complex metal oxides spanning 69 3-cation metal oxide composition spaces. H-CLMP accurately predicts non-linear composition-property relationships in composition spaces for which no training data is available, which broadens the purview of machine learning to the discovery of materials with exceptional properties. This achievement results from the principled integration of latent embedding learning, property correlation learning, generative transfer learning, and attention models. The best performance is obtained using H-CLMP with Transfer learning (H-CLMP(T)) wherein a generative adversarial network is trained on computational density of states data and deployed in the target domain to augment prediction of optical absorption from composition. H-CLMP(T) aggregates multiple knowledge sources with a framework that is well-suited for multi-target regression across the physical sciences. Property (philosophy) ; Machine learning ; Transfer of learning ; Generative grammar ; Computer science ; Artificial intelligence Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Shufeng Kong | Dan Guevarra | Carla P. Gomes | John M. Gregoire
Source: ChemRxiv (preprint, 2021-05-23)
Field: Materials Science — Materials Chemistry

Materials Representation and Transfer Learning for Multi-Property Prediction The adoption of machine learning in materials science has rapidly transformed materials property prediction. Hurdles limiting full capitalization of recent advancements in machine learning include the limited development of methods to learn the underlying interactions of multiple elements, as well as the relationships among multiple properties, to facilitate property prediction in new composition spaces. To address these issues, we introduce the Hierarchical Correlation Learning for Multi-property Prediction (H-CLMP) framework that seamlessly integrates (i) prediction using only a material’s composition, (ii) learning and exploitation of correlations among target properties in multitarget regression, and (iii) leveraging training data from tangential domains via generative transfer learning. The model is demonstrated for prediction of spectral optical absorption of complex metal oxides spanning 69 3-cation metal oxide composition spaces. H-CLMP accurately predicts non-linear composition-property relationships in composition spaces for which no training data is available, which broadens the purview of machine learning to the discovery of materials with exceptional properties. This achievement results from the principled integration of latent embedding learning, property correlation learning, generative transfer learning, and attention models. The best performance is obtained using H-CLMP with Transfer learning (H-CLMP(T)) wherein a generative adversarial network is trained on computational density of states data and deployed in the target domain to augment prediction of optical absorption from composition. H-CLMP(T) aggregates multiple knowledge sources with a framework that is well-suited for multi-target regression across the physical sciences. Property (philosophy) ; Transfer of learning ; Machine learning ; Generative grammar ; Computer science ; Artificial intelligence Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Lindsay R. Merte | Malthe Kjær Bisbo | Igor Sokolović | Martin Setvín | Benjamin Hagman | Mikhail Shipilin | Michael Schmid | Ulrike Diebold | Edvin Lundgren | Bjørk Hammer
Source: ChemRxiv (preprint, 2021-11-22)
Field: Materials Science — Materials Chemistry

Structure of an ultrathin oxide film solved by machine learning enhanced global optimization Determination of the atomic structure of solid surfaces is a challenge that has resisted solution despite advancements in experimental methods. Theory-based global optimization has the potential to revolutionize the field by providing reliable structure models as the basis for interpretation of experiments and for prediction of material properties. So far, however, the approach has been limited by the combinatorial complexity and computational expense of sufficiently accurate energy estimation for surfaces. We demonstrate how an evolutionary algorithm, utilizing machine learning for accelerated energy estimation and diverse population generation, can be used to solve an unknown surface structure—the (4 x 4) surface oxide on Pt3Sn(111)--based on limited experimental input. The algorithm is efficient and robust, and should be broadly applicable in surface studies, where it can replace manual, intuition based model generation. Computer science ; Intuition ; Oxide ; Population ; Surface (topology) ; Mathematical optimization ; Artificial intelligence ; Algorithm ; Materials science ; Mathematics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Authors: Lei Jiang | Huadong Fu | Hongtao Zhang | Jianxin Xie
Source: SSRN Electronic Journal (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Physical Mechanism Interpretation of Polycrystalline Metals’ Yield Strength via a Data-Driven Method: A Novel Hall-Petch Relationship Grain boundary strengthening ; Materials science ; Interpretation (philosophy) ; Crystallite ; Yield (engineering) ; Mechanism (biology) ; Condensed matter physics ; Metallurgy ; Grain boundary ; Physics ; Computer science ; Microstructure Microstructure and mechanical properties ; Microstructure and Mechanical Properties of Steels ; Metallurgy and Material Forming
Authors: Ni Zhan | John R. Kitchin
Source: Molecular Simulation (article, 2021-12-16)
Field: Materials Science — Materials Chemistry

Origin of the Stokes–Einstein deviation in liquid Al–Si One goal in materials simulation is finding relationships between atomic scale and continuum properties. The Stokes–Einstein–Sutherland (SES) equation relates diffusion and viscosity in liquids through the effective diameter d, which is frequently observed to be constant with the ratio T/(Dη) where T is the temperature, D is the diffusion coefficient, and η is the viscosity. The SES has a practical use in estimating diffusion from viscosity or vice versa as they can be difficult to measure experimentally. The constant effective diameter observed in the SES holds for many liquids within a temperature range, but at low temperatures, deviations where d is not constant have been observed. This work investigates SES in liquid Al–Si using molecular dynamics. We analyze the local order using Voronoi polyhedrons and agglomerative clustering. Clustering methods from machine learning allowed us to analyze the large amount of data generated from molecular dynamics trajectories in an efficient manner. We found that clusters have minimal effect on diffusion while increasing viscosity, which is a likely origin of the SES deviation for liquid Al–Si at low temperatures near the melting temperature. Viscosity ; Fick's laws of diffusion ; Diffusion ; Constant (computer programming) ; Molecular dynamics ; Thermodynamics ; Work (physics) ; Range (aeronautics) ; Statistical physics ; Voronoi diagram ; Measure (data warehouse) ; Materials science ; Physics ; Chemistry ; Mathematics ; Computational chemistry ; Geometry Microstructure and mechanical properties ; nanoparticles nucleation surface interactions ; Aluminum Alloy Microstructure Properties
Authors: Yanzhou Wang | Zheyong Fan | Ping Qian | Tapio Ala-Nissilä | Miguel A. Caro
Source: arXiv (Cornell University) (preprint, 2021-09-21)
Field: Materials Science — Materials Chemistry

Structure and Pore Size Distribution in Nanoporous Carbon We study the structural and mechanical properties of nanoporous (NP) carbon materials by extensive atomistic machine-learning (ML) driven molecular dynamics (MD) simulations. To this end, we retrain a ML Gaussian approximation potential (GAP) for carbon by recalculating the a-C structural database of Deringer and Csányi [Phys. Rev. B 2017, 95, 094203] adding van der Waals interactions. Our GAP enables a notable speedup and improves the accuracy of energy and force predictions. We use the GAP to thoroughly study the atomistic structure and pore-size distribution in computational NP carbon samples. These samples are generated by a melt-graphitization-quench MD procedure over a wide range of densities (from 0.5 to 1.7 g/cm$^3$) with structures containing 131,072 atoms. Our results are in good agreement with experimental data for the available observables, and provide a comprehensive account of structural (radial and angular distribution functions, motif and ring counts, X-ray diffraction patterns, pore characterization) and mechanical (elastic moduli and their evolution with density) properties. Our results show relatively narrow pore-size distributions, where the peak position and width of the distributions are dictated by the mass density of the materials. Our data allow further work on computational characterization of NP carbon materials, in particular for energy-storage applications, as well as suggest future experimental characterization of NP carbon-based materials. Nanoporous ; Molecular dynamics ; Materials science ; Diffraction ; van der Waals force ; Carbon fibers ; Characterization (materials science) ; Work (physics) ; Gaussian ; Chemical physics ; Observable ; Nanotechnology ; Composite material ; Computational chemistry ; Physics ; Thermodynamics ; Chemistry ; Molecule ; Optics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Chuanli Wang | Rui Shi | Caofeng Yu | Zhuo Chen | Yu Wang
Source: Journal of Sensors (article, 2021-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Research on Hysteresis Modeling and Compensation Method of Giant Magnetostrictive Force Sensor Linearity is an important index for evaluating the performance of various sensors. Under the Villari effect, there may be some hysteresis between the input force and the output voltage of a force sensor, meaning that the output will be multivalued and nonlinear. To improve the linearity and eliminate the hysteresis of such sensors, an output compensation method using a variable bias current is proposed based on the bidirectional energy conversion mechanism of giant magnetostrictive material. First, the magnetization relationship between the input force, bias current, and flux density is established. Second, a nonlinear neural network model of the force‐magnetization hysteresis and a neural network model for the compensation control of the force sensor are established. These models are trained using the magnetic flux density‐force curve and the magnetic flux density‐current curve, respectively. Taking the optimal linearity as the objective function, the bias current under different input forces is optimized. Finally, a bias current control system is developed and an experimental test platform is built to verify the proposed method. The results show that the proposed variable bias current hysteresis compensation method enables the linearity under the return of the force sensor to reach 1.6%, which is around 48.3% higher than under previous methods. Thus, the proposed variable bias current method effectively suppresses the hysteresis phenomenon and provides improved linearity for giant magnetostrictive force sensors. Magnetostriction ; Hysteresis ; Compensation (psychology) ; Materials science ; Acoustics ; Physics ; Condensed matter physics ; Magnetic field ; Psychology Magnetic Properties and Applications ; Electric Motor Design and Analysis ; Non-Destructive Testing Techniques
Authors: Jian Wang | Christian Hopmann | Ben Liu | Yannik Lockner
Source: Industrial & Engineering Chemistry Research (article, 2021-10-04)
Field: Materials Science — Materials Chemistry

Prediction of Specific Volume of Polypropylene at High Cooling Rates by Artificial Neural Networks The specific volume of polymers is a critical parameter that has offered many highly promising applications in focus as a basic material property. The specific volume (v) as a function of commonly pressure (p) as well as temperature (T), however, does not satisfy accuracy requirements with respect to experimental results. In addition, polymer forming is a rapid cooling process, and the cooling rate (dT/dt) has a significant effect on the pvT behavior of polymers. However, today’s commercial testing devices cannot obtain the pvT data for high cooling rates. Hence, the prediction of specific volume of polymers for rapid cooling applications remains a challenge. This work deals with the potential application of artificial neural networks (ANNs) to model the v in dependence of p, T, and dT/dt within their experimental uncertainty. The experimental pvT data of the semicrystalline polypropylene (PP) at the temperature range of 40–240 °C, the pressure range of 200–2200 bar, and the set cooling rate range of 2–20 °C/min were used for the ANN training. Besides the data of p and T, time (t) and phase-transition temperature (Tt) were also used as input features of the training datasets to improve the extrapolation ability for high cooling rates. A hyperparameter search was conducted to identify the optimized hyperparameter sets. The experimental values of Tt at atmospheric pressure (1 bar) and cooling rates (2–300 000 °C/min) obtained through differential scanning calorimetry (DSC) and flash DSC were used to indirectly validate the prediction accuracy of the ANNs. The newly developed continuous two-domain equations of state (EoS) were also applied in comparison. The ANN approach showed the most reliable predicting results both for interpolations and extrapolations when trying to predict the specific volume beyond the pvT measurement range (the cooling rate is up to 6000 °C/min and the pressure is the atmospheric pressure of 1 bar). Extrapolation ; Volume (thermodynamics) ; Materials science ; Hyperparameter ; Differential scanning calorimetry ; Thermodynamics ; Artificial neural network ; Polypropylene ; Polymer ; Bar (unit) ; Range (aeronautics) ; Computer science ; Biological system ; Mathematics ; Artificial intelligence ; Composite material ; Statistics ; Physics ; Meteorology Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Injection Molding Process and Properties
Authors: Stephan Thaler | Julija Zavadlav
Source: Nature Communications (preprint, 2021-11-25)
Field: Materials Science — Materials Chemistry

Learning neural network potentials from experimental data via Differentiable Trajectory Reweighting In molecular dynamics (MD), neural network (NN) potentials trained bottom-up on quantum mechanical data have seen tremendous success recently. Top-down approaches that learn NN potentials directly from experimental data have received less attention, typically facing numerical and computational challenges when backpropagating through MD simulations. We present the Differentiable Trajectory Reweighting (DiffTRe) method, which bypasses differentiation through the MD simulation for time-independent observables. Leveraging thermodynamic perturbation theory, we avoid exploding gradients and achieve around 2 orders of magnitude speed-up in gradient computation for top-down learning. We show effectiveness of DiffTRe in learning NN potentials for an atomistic model of diamond and a coarse-grained model of water based on diverse experimental observables including thermodynamic, structural and mechanical properties. Importantly, DiffTRe also generalizes bottom-up structural coarse-graining methods such as iterative Boltzmann inversion to arbitrary potentials. The presented method constitutes an important milestone towards enriching NN potentials with experimental data, particularly when accurate bottom-up data is unavailable. Differentiable function ; Granularity ; Observable ; Computer science ; Artificial neural network ; Computation ; Trajectory ; Statistical physics ; Quantum ; Inversion (geology) ; Perturbation (astronomy) ; Algorithm ; Experimental data ; Artificial intelligence ; Physics ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Hydrocarbon exploration and reservoir analysis