jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yu Okano | Takeshi Kaneshita | Katsuki Okuno | Shimpei Takemoto | Yoshishige Okuno
Source: Research Square (preprint, 2023-11-07)
Field: Materials Science — Materials Chemistry

Thermodynamic Equilibrium State Prediction By Deep Learning Method Abstract The method of calculation of phase diagrams (CALPHAD) is a calculation method that searches for a state of providing minimal Gibbs energy as an equilibrium state. To perform a thermodynamic equilibrium calculation for a single material composition and to predict a phase diagram, we can complete the CALPHAD method calculation within a realistic time. However, screening many material compositions associated with predicting the corresponding phase diagrams takes much time. For alloy materials, for example, it would take 161 hours to calculate phase diagrams of all alloy compositions to screen 10,000 sets of explanatory variables, i.e., compositions and manufacturing conditions, since it takes 58 seconds to calculate each set. The present study aims to provide a calculation device, method, and program for quickly predicting the thermodynamic equilibrium state. We developed a deep learning model based on the Transformer architecture to achieve this objective, primarily used for various natural language processing tasks, such as machine translation, text summarization, question answering, and sentiment analysis. The encoder part of our developed model extracts the necessary features for phase diagram prediction from the inputted alloying elements. In contrast, the decoder part predicts a phase diagram for each temperature based on the results from the encoder. We calculated 800,000 species using the CALPHAD method and employed these data to train our developed model. Our trained model can calculate thermodynamic equilibrium states more than 100 times faster than the CALPHAD method and correctly reproduce the phase diagrams of ground truths. Based on the present result, we could invent a calculation device, a calculation method, and a calculation program for predicting the thermodynamic equilibrium state in a short time. CALPHAD ; Phase diagram ; Computer science ; Set (abstract data type) ; Thermodynamics ; Thermodynamic equilibrium ; Phase (matter) ; Algorithm ; Chemistry ; Physics ; Programming language Machine Learning in Materials Science
Authors: Xie, Yu
Source: Digital Access to Scholarship at Harvard (DASH) (Harvard University) (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning Bayesian Force Fields and Applications to Phase Transformations This thesis develops machine learning Bayesian force fields for efficient and accurate molecular dynamics simulations of materials. The Gaussian process regression model provides uncertainty quantification, enabling Bayesian active learning to iteratively collect first-principle data and improve model accuracy. To overcome unfavorable scaling with training set size, the Gaussian process forces and uncertainties are mapped to low-dimensional spline functions or polynomial forms to enable high efficiency at inference time. To address the unfavorable scaling with the number of chemical elements of the descriptor dimensionality, we devised an embedding method for dimension reduction. We also parallelized the sparse GP force field using MPI, allowing for efficient use of large computational resources during training. These advances enable large-scale molecular dynamics studies of complex processes like phase transformations. The thesis investigates the 2D to 3D transition of stanene, the high-pressure phase transition of SiC, and the high-temperature decomposition and incongruent melting of SiC. The Bayesian force fields demonstrate computational speeds comparable to empirical potentials, while achieving accuracy on par with density functional theory. The atomic-level insights into stability, structure, and dynamics align well with experiments. Overall, this thesis puts forth a framework for developing transferable and systematically improvable machine learning Bayesian force fields. The integration of active learning, dimensionality reduction, and high performance computing pushes the boundaries of what is tractable in large-scale atomistic simulations. The applications showcase the ability to model complex material phenomena beyond the reach of conventional computational methods. Gaussian process ; Bayesian optimization ; Curse of dimensionality ; Bayesian probability ; Bayesian inference ; Uncertainty quantification ; Force field (fiction) ; Dimensionality reduction ; Kriging Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference ; Model Reduction and Neural Networks
Authors: Cameron J. Foss | Zlatan Akšamija
Source: arXiv (Cornell University) (preprint, 2023-01-30)
Field: Materials Science — Materials Chemistry

Machine learning enables robust prediction of thermal boundary conductance of 2D substrate interfaces Two-dimensional van der Waals (vdW) materials exhibit a broad palette of unique and superlative properties, including high electrical and thermal conductivities, paired with the ability to exfoliate or grow and transfer single layers onto a variety of substrates thanks to the relatively weak vdW interlayer bonding. However, the same vdW bonds also lead to relatively low thermal boundary conductance (TBC) between the 2D layer and its 3D substrate, which is the main pathway for heat removal and thermal management in devices, leading to a potential thermal bottleneck and dissipation-driven performance degradation. Here we use first-principles phonon dispersion with our 2D-3D Boltzmann phonon transport model to compute the TBC of 156 unique 2D/3D interface pairs, many of which are not available in the literature. We then employ machine learning (ML) to develop streamlined predictive models, of which Neural Network and Gaussian process display the highest predictive accuracy (RMSE $&lt;$ 5 MWm$^{-2}$K$^{-1}$ and $R^2&gt;$0.99) on the complete descriptor set. Then we perform sensitivity analysis to identify the most impactful descriptors, consisting of the vdW spring coupling constant, 2D thermal conductivity, ZA phonon bandwidth, the ZA phonon resonance gap, and the frequency of the first van Hove singularity or Boson peak. On that reduced set, we find that a decision-tree algorithm can make accurate predictions (RMSE $&lt;$ 20 MWm$^{-2}$K$^{-1}$ and $R^2&gt;$0.9) on materials it has not been trained on by performing a transferability analysis. Our model allows optimal selection of 2D-substrate pairings to maximize heat transfer and will improve thermal management in future 2D nanoelectronics. Thermal conductivity ; Phonon ; Materials science ; Machine learning ; van der Waals force ; Artificial intelligence ; Computer science ; Condensed matter physics ; Physics ; Quantum mechanics Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Graphene research and applications
Authors: Víctor Posligua | José J. Plata | Antonio M. Márquez | Javier Fdez. Sanz | Ricardo Grau‐Crespo
Source: arXiv (Cornell University) (preprint, 2023-09-08)
Field: Materials Science — Materials Chemistry

Theoretical investigation of the lattice thermal conductivities of II-IV-V2 pnictide semiconductors Ternary pnictides semiconductors with II-IV-V2 stoichiometry hold potential as cost effective thermoelectric materials with suitable electronic transport properties, but their lattice thermal conductivities ($κ$) are typically too high. Gaining insight into their vibrational properties is therefore crucial to finding strategies to reduce $κ$ and achieve improved thermoelectric performance. We present a theoretical exploration of the lattice thermal conductivities for a set of pnictide semiconductors with ABX2 composition (A = Zn, Cd; B = Si, Ge, Sn; and X = P, As), using machine-learning based regression algorithms to extract force constants from a reduced number of density functional theory simulations, and then solving the Boltzmann transport equation for phonons. Our results align well available experimental data, decreasing the mean absolute error by ~3 Wm-1K-1 with respect to the best previous set of theoretical predictions. Zn-based ternary pnictides have, on average, more than double the thermal conductivity of the Cd-based compounds. Anisotropic behaviour increases with the mass difference between A and B cations, but while the nature of the anion does not affect the structural anisotropy, the thermal conductivity anisotropy is typically higher for arsenides than for phosphides. We identify compounds, like CdGeAs2, for which nanostructuring to an affordable range of particle sizes could lead to values low enough for thermoelectric applications. Pnictogen ; Ternary operation ; Thermoelectric effect ; Thermal conductivity ; Anisotropy ; Condensed matter physics ; Materials science ; Phonon ; Semiconductor ; Lattice (music) ; Density functional theory ; Boltzmann equation ; Thermoelectric materials ; Thermodynamics ; Computational chemistry ; Chemistry ; Physics ; Superconductivity ; Quantum mechanics ; Optoelectronics ; Computer science Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Chalcogenide Semiconductor Thin Films
Authors: Arturo Alvarez | Víctor H. Alvarez
Source: SHILAP Revista de lepidopterología (article, 2023-09-01)
Field: Materials Science — Materials Chemistry

PREDICTION OF PHYSICOCHEMICAL PROPERTIES BY ARTIFICIAL NEURAL NETS In this study, we employed the Artificial Neural Network-Group COSMO sigma profile method to estimate critical pressure, critical temperature, enthalpy of fusion, and melting temperature of pure chemical compounds. Utilizing a comprehensive database containing 1400 data points of various pure compounds, we developed a robust predictive model. The method demonstrated high accuracy, yielding root mean square errors of 5 bar for critical pressure, 25 K for critical temperature, 2030 kJ/mol for enthalpy of fusion, and 23 K for melting temperature. These results underscore the potential of the Artificial Neural Network-Group COSMO sigma profile method as a reliable tool for predicting critical thermodynamic properties, contributing valuable insights to the field of chemical engineering and material science. Artificial neural network ; Enthalpy ; Sigma ; Field (mathematics) ; Bar (unit) ; Melting temperature Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Chemical Thermodynamics and Molecular Structure
Authors: Liuzhou Gao | Xinyi Liang | Linke He | Guoao Li | Shengda Chen | Jia Cao | Jing Ma | Guoqiang Wang | Shuhua Li
Source: ChemRxiv (preprint, 2023-04-12)
Field: Chemistry — Organic Chemistry

Enhancing the nucleophilicity of benzylic boronates enables the divergent formation of C−C and C−X bonds Tetracoordinated boron species generated from the complexation of organoboron compounds with bases are highly important intermediates in molecular fragments coupling. The type of base significantly impacts both the reaction activity and mechanism of organoboron compounds. Herein, our structural and mechanistic insights show that the full heterolytic cleavage of the C−B bond in benzylic boronates, leading to the formation of benzyl potassium species, can be achieved by increasing the amount of alkoxides. This, in turn, significantly enhances the nucleophilicity of both secondary and tertiary benzylic boronates. This mechanistic insight has proven valuable in the divergent construction of C(sp3)−C(sp3 or sp2) and other C−X bonds (X = Si, Ge, Sn, S, Se), as demonstrated through the integration of base-catalyzed silylboration/diborylation reactions. Through machine-learning-assisted screening of approximately 104 substrate combinations, we have expanded the scope of electrophiles for this transformation to include challenging aromatic heterocycles. Moreover, the synthetic potential of this protocol was demonstrated through the construction of drug-relevant molecules that contain 1,1-diaryl pharmacophores. Heterolysis ; Chemistry ; Nucleophile ; Electrophile ; Combinatorial chemistry ; Bond cleavage ; Molecule ; Stereochemistry ; Substrate (aquarium) ; Catalysis ; Organic chemistry Organoboron and organosilicon chemistry ; Catalytic Cross-Coupling Reactions ; Chemical Synthesis and Analysis
Authors: Giacomo Baccolo | Huiwen Yu | Cecile Valsecchi | Davide Ballabio | Rasmus Bro
Source: Chemometrics and Intelligent Laboratory Systems (article, 2023-10-07)
Field: Chemistry — Spectroscopy

Comparison of machine learning approaches for the classification of elution profiles Hyphenated chromatography is among the most popular analytical techniques in omics related research. While great advancements have been achieved on the experimental side, the same is not true for the extraction of the relevant information from chromatographic data. Extensive signal preprocessing is required to remove the signal of the baseline, resolve the time shifts of peaks from sample to sample and to properly estimate the spectra and concentrations of co-eluting compounds. Among several available strategies, curve resolution approaches, such as PARAFAC2, ease the deconvolution and the quantification of chemicals. However, not all resolved profiles are relevant. For example, some take into account the baseline, others the chemical compounds. Thus, it is necessary to distinguish the profiles describing relevant chemistry. With the aim to assist researchers in this selection phase, we have tried three different classification algorithms (convolutional and recurrent neural networks, k-nearest neighbours) for the automatic identification of GC-MS elution profiles resolved by PARAFAC2. To this end, we have manually labelled more than 170,000 elution profiles in the following four classes: ‘Peak’, ‘Cutoff peak’,’ Baseline’ and ‘Others’ in order to train, validate and test the classification models. The results highlight two main points: i) neural networks seem to be the best solution for this specific classification task confirmed by the overall quality of the classification, ii) the quality of the input data is crucial to maximize the modelling performances. • A new approach to automatically label curve-resolved elution profiles has been developed. • 170.000 elution profiles labelled. • Three difference classification methods developed. • Results significantly improved compared to state-of-the-art. Elution ; Artificial intelligence ; Computer science ; Machine learning ; Pattern recognition (psychology) ; Chromatography ; Chemistry Analytical Chemistry and Chromatography ; Metabolomics and Mass Spectrometry Studies ; Spectroscopy and Chemometric Analyses
Authors: Alexander Lukin | Oğuz Gülseren
Source: Preprints.org (preprint, 2023-12-27)
Field: Materials Science — Materials Chemistry

Data-Driven Inverse Design of Low-Dimensional Nanocarbons: Revealing Hidden Growth-Properties Relationships and Identifying Universal Descriptors Recent advances in nanomaterials have been heavily influenced by low-dimensional nanocarbon allotropes. In particular, carbyne has attracted attention for its potential as a true one-dimensional carbon chain with sp1 hybridization. To maximize the capabilities of this material, we employ a focused data-driven inverse design approach based on the carbon nanomaterials genome concept. This involves using deep learning neural network models to identify key descriptors tied to desired properties, enabling property prediction and reverse engineering of nanocarbons. Our iterative approach entails: (i) gathering growth/property data on nanostructures; (ii) identifying informative numerical/categorical predictors; (iii) developing deep learning models mapping descriptors to properties; (iv) refining models with new insights; (v) determining required descriptors/conditions for target properties via inverse mapping; (vi) validating models by synthesizing predicted nanostructures; and (vii) enhancing models with validation data. This allows uncovering hidden growth-property connections, precisely tuning nanocarbons for desired attributes. We introduce new methodologies including exciting synergistic effects, synchronizing atomic vibrations, active screen plasma, energy-driven transformations, surface acoustic micro/nano-manipulation, doping and directed self-assembly to expose relationships and integrate insights into the inverse design flow. This research promises to accelerate discovery of next-generation low-dimensional nanocarbons with exceptional properties and applications. Inverse ; Nanotechnology ; Synchronizing ; Computer science ; Property (philosophy) ; Deep learning ; Materials science ; Artificial intelligence ; Mathematics Machine Learning in Materials Science ; Fullerene Chemistry and Applications ; Surface Chemistry and Catalysis
Authors: Rouven Koch | David van Driel | Alberto Bordin | José L. Lado | Eliška Greplová
Source: arXiv (Cornell University) (preprint, 2023-04-21)
Field: Materials Science — Materials Chemistry

Adversarial Hamiltonian learning of quantum dots in a minimal Kitaev chain Determining Hamiltonian parameters from noisy experimental measurements is a key task for the control of experimental quantum systems. An experimental platform that recently emerged, and where knowledge of Hamiltonian parameters is crucial to fine-tune the system, is that of quantum dot-based Kitaev chains. In this work, we demonstrate an adversarial machine learning algorithm to determine the parameters of a quantum dot-based Kitaev chain. We train a convolutional conditional generative adversarial neural network (Conv-cGAN) with simulated differential conductance data and use the model to predict the parameters at which Majorana bound states are predicted to appear. In particular, the Conv-cGAN model facilitates a rapid, numerically efficient exploration of the phase diagram describing the transition between elastic co-tunneling and crossed Andreev reflection regimes. We verify the theoretical predictions of the model by applying it to experimentally measured conductance obtained from a minimal Kitaev chain consisting of two spin-polarized quantum dots coupled by a superconductor-semiconductor hybrid. Our model accurately predicts, with an average success probability of $97$\%, whether the measurement was taken in the elastic co-tunneling or crossed Andreev reflection-dominated regime. Our work constitutes a stepping stone towards fast, reliable parameter prediction for tuning quantum-dot systems into distinct Hamiltonian regimes. Ultimately, our results yield a strategy to support Kitaev chain tuning that is scalable to longer chains. Hamiltonian (control theory) ; Andreev reflection ; Quantum dot ; Physics ; Quantum ; Quantum tunnelling ; Quantum mechanics ; Statistical physics ; Condensed matter physics ; Conductance ; Mathematics Electronic and Structural Properties of Oxides ; Ferroelectric and Negative Capacitance Devices ; Machine Learning in Materials Science
Authors: Shih-Kuang | Lee | Sun-Ting Tsai | Sharon C. Glotzer
Source: arXiv (Cornell University) (preprint, 2023-12-19)
Field: Materials Science — Materials Chemistry

Classification of complex local environments in systems of particle shapes through shape-symmetry encoded data augmentation Detecting and analyzing the local environment is crucial for investigating the dynamical processes of crystal nucleation and shape colloidal particle self-assembly. Recent developments in machine learning provide a promising avenue for better order parameters in complex systems that are challenging to study using traditional approaches. However, the application of machine learning to self-assembly on systems of particle shapes is still underexplored. To address this gap, we propose a simple, physics-agnostic, yet powerful approach that involves training a multilayer perceptron (MLP) as a local environment classifier for systems of particle shapes, using input features such as particle distances and orientations. Our MLP classifier is trained in a supervised manner with a shape symmetry-encoded data augmentation technique without the need for any conventional roto-translations invariant symmetry functions. We evaluate the performance of our classifiers on four different scenarios involving self-assembly of cubic structures, 2-dimensional and 3-dimensional patchy particle shape systems, hexagonal bipyramids with varying aspect ratios, and truncated shapes with different degrees of truncation. The proposed training process and data augmentation technique are both straightforward and flexible, enabling easy application of the classifier to other processes involving particle orientations. Our work thus presents a valuable tool for investigating self-assembly processes on systems of particle shapes, with potential applications in structure identification of any particle-based or molecular system where orientations can be defined. Classifier (UML) ; Computer science ; Artificial intelligence ; Particle system ; Invariant (physics) ; Physics Pickering emulsions and particle stabilization ; Machine Learning in Materials Science
Authors: Alexander Bukharin | Tianyi Liu | Shengjie Wang | Simiao Zuo | Weihao Gao | Yan Wen | Tuo Zhao
Source: arXiv (Cornell University) (preprint, 2023-06-05)
Field: Materials Science — Materials Chemistry

Machine Learning Force Fields with Data Cost Aware Training Machine learning force fields (MLFF) have been proposed to accelerate molecular dynamics (MD) simulation, which finds widespread applications in chemistry and biomedical research. Even for the most data-efficient MLFFs, reaching chemical accuracy can require hundreds of frames of force and energy labels generated by expensive quantum mechanical algorithms, which may scale as $O(n^3)$ to $O(n^7)$, with $n$ proportional to the number of basis functions. To address this issue, we propose a multi-stage computational framework -- ASTEROID, which lowers the data cost of MLFFs by leveraging a combination of cheap inaccurate data and expensive accurate data. The motivation behind ASTEROID is that inaccurate data, though incurring large bias, can help capture the sophisticated structures of the underlying force field. Therefore, we first train a MLFF model on a large amount of inaccurate training data, employing a bias-aware loss function to prevent the model from overfitting tahe potential bias of this data. We then fine-tune the obtained model using a small amount of accurate training data, which preserves the knowledge learned from the inaccurate training data while significantly improving the model's accuracy. Moreover, we propose a variant of ASTEROID based on score matching for the setting where the inaccurate training data are unlabeled. Extensive experiments on MD datasets and downstream tasks validate the efficacy of ASTEROID. Our code and data are available at https://github.com/abukharin3/asteroid. Overfitting ; Computer science ; Code (set theory) ; Machine learning ; Force field (fiction) ; Artificial intelligence ; Field (mathematics) ; Matching (statistics) ; Energy (signal processing) ; Asteroid ; Artificial neural network Machine Learning in Materials Science ; Mass Spectrometry Techniques and Applications ; Protein Structure and Dynamics