jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Dinis O. Abranches | Edward J. Maginn | Yamil J. Colón
Source: Proceedings of the National Academy of Sciences (article, 2024-07-23)
Field: Materials Science — Materials Chemistry

Stochastic machine learning via sigma profiles to build a digital chemical space This work establishes a different paradigm on digital molecular spaces and their efficient navigation by exploiting sigma profiles. To do so, the remarkable capability of Gaussian processes (GPs), a type of stochastic machine learning model, to correlate and predict physicochemical properties from sigma profiles is demonstrated, outperforming state-of-the-art neural networks previously published. The amount of chemical information encoded in sigma profiles eases the learning burden of machine learning models, permitting the training of GPs on small datasets which, due to their negligible computational cost and ease of implementation, are ideal models to be combined with optimization tools such as gradient search or Bayesian optimization (BO). Gradient search is used to efficiently navigate the sigma profile digital space, quickly converging to local extrema of target physicochemical properties. While this requires the availability of pretrained GP models on existing datasets, such limitations are eliminated with the implementation of BO, which can find global extrema with a limited number of iterations. A remarkable example of this is that of BO toward boiling temperature optimization. Holding no knowledge of chemistry except for the sigma profile and boiling temperature of carbon monoxide (the worst possible initial guess), BO finds the global maximum of the available boiling temperature dataset (over 1,000 molecules encompassing more than 40 families of organic and inorganic compounds) in just 15 iterations (i.e., 15 property measurements), cementing sigma profiles as a powerful digital chemical space for molecular optimization and discovery, particularly when little to no experimental data is initially available. Maxima and minima ; Chemical space ; Sigma ; Hyperparameter ; Computer science ; Artificial intelligence ; Machine learning ; Stochastic gradient descent ; Bayesian optimization ; Algorithm ; Gradient descent ; Artificial neural network ; Chemistry ; Mathematics ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Sen Lu | Pei Song | Zepeng Jia | Zhikai Gao | Zhiguo Wang | Tiren Peng | Xue Bai | Qi Jiang | Hong Cui | Weizhi Tian | Rong Feng | Zhiyong Liang | Qin Kang | Lingxia Jin | Hongkuan Yuan
Source: International Journal of Hydrogen Energy (article, 2024-08-24)
Field: Materials Science — Materials Chemistry

Symbolic transform optimized convolutional neural network model for high-performance prediction and analysis of MXenes hydrogen evolution reaction catalysts MXenes ; Convolutional neural network ; Computer science ; Catalysis ; Artificial neural network ; Hydrogen ; Chemistry ; Artificial intelligence ; Biological system ; Biology ; Organic chemistry MXene and MAX Phase Materials ; Advanced Photocatalysis Techniques ; Ammonia Synthesis and Nitrogen Reduction
Authors: Basile Herzog | Alejandro Gallo | Felix Hummel | Michaël Badawi | Tomáš Bučko | Sébastien Lebègue∥ | Andreas Grüneis | Dario Rocca
Source: npj Computational Materials (article, 2024-04-04)
Field: Materials Science — Materials Chemistry

Coupled cluster finite temperature simulations of periodic materials via machine learning Abstract Density functional theory is the workhorse of materials simulations. Unfortunately, the quality of results often varies depending on the specific choice of the exchange-correlation functional, which significantly limits the predictive power of this approach. Coupled cluster theory, including single, double, and perturbative triple particle-hole excitation operators, is widely considered the ‘gold standard' of quantum chemistry as it can achieve chemical accuracy for non-strongly correlated applications. Because of the high computational cost, the application of coupled cluster theory in materials simulations is rare, and this is particularly true if finite-temperature properties are of interest for which molecular dynamics simulations have to be performed. By combining recent progress in machine learning models with low data requirements for energy surfaces and in the implementation of coupled cluster theory for periodic materials, we show that chemically accurate simulations of materials are practical and could soon become significantly widespread. As an example of this numerical approach, we consider the calculation of the enthalpy of adsorption of CO 2 in a porous material. Coupled cluster ; Density functional theory ; Cluster (spacecraft) ; Statistical physics ; Computer science ; Molecular dynamics ; Algorithm ; Materials science ; Computational chemistry ; Physics ; Chemistry ; Molecule ; Quantum mechanics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Advanced Chemical Physics Studies
Authors: Masaki Fushimi | Devaiah Damma
Source: The Journal of Physical Chemistry C (article, 2024-04-12)
Field: Materials Science — Materials Chemistry

The Role of External Donors in Ziegler–Natta Catalysts through Nudged Elastic Band Simulations on Realistic-Scale Models Employing a Universal Neural Network Potential This study undertakes a thorough computational exploration of Ziegler–Natta catalysis, emphasizing the role of external donors, particularly dicyclopentyldimethoxysilane (D donor), in the production of polypropylene. Employing the PreFerred Potential (PFP) model within the Nudged Elastic Band (NEB) method and Universal Neural Network Potentials (UNNP), we meticulously assessed the structural integrity of MgCl2 crystals, the dynamics of TiCl4 adsorption, and the kinetics of propylene insertion reactions. Our results demonstrated the precision of the PFP model in accurately replicating the crystalline structures and reaction mechanisms inherent in Ziegler–Natta catalyst systems. A pivotal finding from our research is the significant reduction in activation energy for isotactic propylene insertion, attributed to the presence of at least two D donors around a single Ti active site. Additionally, our computational approach, characterized by its speed and efficiency, successfully incorporates realistic catalyst models, encompassing a range of donor compounds, thereby bridging the gap between theoretical predictions and experimental practices. Our study not only corroborated the existing computational models but also provided novel insights into the mechanistic roles of external donors in Ziegler–Natta catalysis. The implications of these findings extend beyond theoretical studies, offering practical applications in the field of catalytic science and propylene polymerization. This research paves the way for future investigations, potentially transforming our understanding and utilization of Ziegler–Natta catalysts in industrial applications. Natta ; Catalysis ; Tacticity ; Materials science ; Polypropylene ; Polymerization ; Molecular dynamics ; Bridging (networking) ; Nanotechnology ; Biochemical engineering ; Chemistry ; Polymer science ; Computational chemistry ; Computer science ; Engineering ; Organic chemistry ; Composite material ; Polymer Machine Learning in Materials Science ; Corrosion Behavior and Inhibition ; Fuel Cells and Related Materials
Authors: Yuzhuo Chen | Sebastian V. Pios | Maxim F. Gelin | Lipeng Chen
Source: Journal of Chemical Theory and Computation (article, 2024-06-03)
Field: Materials Science — Materials Chemistry

Accelerating Molecular Vibrational Spectra Simulations with a Physically Informed Deep Learning Model In recent years, machine learning (ML) surrogate models have emerged as an indispensable tool to accelerate simulations of physical and chemical processes. However, there is still a lack of ML models that can accurately predict molecular vibrational spectra. Here, we present a highly efficient multitask ML surrogate model termed Vibrational Spectra Neural Network (VSpecNN), to accurately calculate infrared (IR) and Raman spectra based on dipole moments and polarizabilities obtained on-the-fly via ML-enhanced molecular dynamics simulations. The methodology is applied to pyrazine, a prototypical polyatomic chromophore. The VSpecNN-predicted energies are well within the chemical accuracy (1 kcal/mol), and the errors for VSpecNN-predicted forces are only half of those obtained from a popular high-performance ML model. Compared to the ab initio reference, the VSpecNN-predicted frequencies of IR and Raman spectra differ only by less than 5.87 cm<sup>-1</sup>, and the intensities of IR spectra and the depolarization ratios of Raman spectra are well reproduced. The VSpecNN model developed in this work highlights the importance of constructing highly accurate neural network potentials for predicting molecular vibrational spectra. Raman spectroscopy ; Spectral line ; Polyatomic ion ; Dipole ; Molecular vibration ; Infrared ; Molecular dynamics ; Infrared spectroscopy ; Chemistry ; Molecular physics ; Computational chemistry ; Physics ; Molecule ; Optics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Spectroscopy and Quantum Chemical Studies
Authors: Joonyoung F. Joung | Mun Hong Fong | Jihye Roh | Zhengkai Tu | John Bradshaw | Connor W. Coley
Source: Angewandte Chemie International Edition (article, 2024-07-12)
Field: Materials Science — Materials Chemistry

Reproducing Reaction Mechanisms with Machine‐Learning Models Trained on a Large‐Scale Mechanistic Dataset Mechanistic understanding of organic reactions can facilitate reaction development, impurity prediction, and in principle, reaction discovery. While several machine learning models have sought to address the task of predicting reaction products, their extension to predicting reaction mechanisms has been impeded by the lack of a corresponding mechanistic dataset. In this study, we construct such a dataset by imputing intermediates between experimentally reported reactants and products using expert reaction templates and train several machine learning models on the resulting dataset of 5,184,184 elementary steps. We explore the performance and capabilities of these models, focusing on their ability to predict reaction pathways and recapitulate the roles of catalysts and reagents. Additionally, we demonstrate the potential of mechanistic models in predicting impurities, often overlooked by conventional models. We conclude by evaluating the generalizability of mechanistic models to new reaction types, revealing challenges related to dataset diversity, consecutive predictions, and violations of atom conservation. Generalizability theory ; Computer science ; Artificial intelligence ; Machine learning ; Construct (python library) ; Scale (ratio) ; Psychology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Pedro J. M. A. Carriço | Márcio Ferreira | Tiago F. T. Cerqueira | Fernando Nogueira | Pedro Borlido
Source: Physical Review Materials (article, 2024-01-03)
Field: Materials Science — Materials Chemistry

High-refractive-index materials screening from machine learning and <i>ab initio</i> methods In this study we analyze the dielectric properties of a recently published dataset to identify high-refractive-index and high-band-gap materials that are crucial for modern optoelectronic applications. We employ advanced crystal graph convolutional neural networks and density functional perturbation theory calculations to accelerate the discovery of such materials. Our analysis confirms the traditional inverse relationship between band gap and dielectric constant, which persists even in this large dataset. However, our study reveals several promising materials that possess competitive properties compared to current industry standards. Our findings provide valuable insights into the field of dielectric materials and demonstrate the potential of advanced machine learning and computational techniques for accelerating materials discovery. Materials science ; Dielectric ; Refractive index ; Band gap ; Ab initio ; Inverse ; Perturbation theory (quantum mechanics) ; Convolutional neural network ; Machine learning ; Optoelectronics ; Computer science ; Physics ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices ; Electronic and Structural Properties of Oxides
Authors: Zhiwen Wang | Xianjia Chen | Jici Wen | Yujie Wei
Source: Extreme Mechanics Letters (article, 2024-07-30)
Field: Materials Science — Materials Chemistry

Determining plastic slips in rate-independent crystal plasticity models through machine learning algorithms Crystal plasticity ; Plasticity ; Algorithm ; Computer science ; Materials science ; Artificial intelligence ; Composite material Microstructure and mechanical properties ; Hydrogen embrittlement and corrosion behaviors in metals ; Mineral Processing and Grinding
Authors: Pengfei Hou | Yumiao Tian | Xing Meng
Source: Chemistry - A European Journal (article, 2024-06-14)
Field: Materials Science — Materials Chemistry

Improving Molecular‐Dynamics Simulations for Solid–Liquid Interfaces with Machine‐Learning Interatomic Potentials Emerging developments in artificial intelligence have opened infinite possibilities for material simulation. Depending on the powerful fitting of machine learning algorithms to first-principles data, machine learning interatomic potentials (MLIPs) can effectively balance the accuracy and efficiency problems in molecular dynamics (MD) simulations, serving as powerful tools in various complex physicochemical systems. Consequently, this brings unprecedented enthusiasm for researchers to apply such novel technology in multiple fields to revisit the major scientific problems that have remained controversial owing to the limitations of previous computational methods. Herein, we introduce the evolution of MLIPs, provide valuable application examples for solid-liquid interfaces, and present current challenges. Driven by solving multitudinous difficulties in terms of the accuracy, efficiency, and versatility of MLIPs, this booming technique, combined with molecular simulation methods, will provide an underlying and valuable understanding of interdisciplinary scientific challenges, including materials, physics, and chemistry. Molecular dynamics ; Interatomic potential ; Dynamics (music) ; Computer science ; Chemical physics ; Materials science ; Statistical physics ; Physics ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Catalysis and Oxidation Reactions
Authors: Selva Chandrasekaran Selvaraj | Volodymyr Koverga | Anh T. Ngo
Source: Journal of The Electrochemical Society (preprint, 2024-05-01)
Field: Materials Science — Materials Chemistry

Exploring Li-Ion Transport Properties of Li<sub>3</sub>TiCl<sub>6</sub>: A Machine Learning Molecular Dynamics Study We performed large-scale molecular dynamics simulations based on a machine-learning force field (MLFF) to investigate the Li-ion transport mechanism in cation-disordered Li 3 TiCl 6 cathode at six different temperatures, ranging from 25°C to 100°C. In this work, deep neural network method and data generated by ab − initio molecular dynamics (AIMD) simulations were deployed to build a high-fidelity MLFF. Radial distribution functions, Li-ion mean square displacements (MSD), diffusion coefficients, ionic conductivity, activation energy, and crystallographic direction-dependent migration barriers were calculated and compared with corresponding AIMD and experimental data to benchmark the accuracy of the MLFF. From MSD analysis, we captured both the self and distinct parts of Li-ion dynamics. The latter reveals that the Li-ions are involved in anti-correlation motion that was rarely reported for solid-state materials. Similarly, the self and distinct parts of Li-ion dynamics were used to determine Haven’s ratio to describe the Li-ion transport mechanism in Li 3 TiCl 6 . Obtained trajectory from molecular dynamics infers that the Li-ion transportation is mainly through interstitial hopping which was confirmed by intra- and inter-layer Li-ion displacement with respect to simulation time. Ionic conductivity (1.06 mS/cm) and activation energy (0.29eV) calculated by our simulation are highly comparable with that of experimental values. Overall, the combination of machine-learning methods and AIMD simulations explains the intricate electrochemical properties of the Li 3 TiCl 6 cathode with remarkably reduced computational time. Thus, our work strongly suggests that the deep neural network-based MLFF could be a promising method for large-scale complex materials. Electrochemistry ; Dynamics (music) ; Molecular dynamics ; Chemical physics ; Materials science ; Computer science ; Nanotechnology ; Chemistry ; Computational chemistry ; Physical chemistry ; Psychology ; Pedagogy ; Electrode Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Yinzhou Liu | Weidong Zheng | Haoqiang Ai | Lin Cheng | Ruiqiang Guo | Xiaohan Song
Source: Energy and AI (article, 2024-11-08)
Field: Materials Science — Materials Chemistry

Predicting the thermal conductivity of polymer composites with one-dimensional oriented fillers using the combination of deep learning and ensemble learning • Deep learning (CNN) and ensemble learning (RFR) was successfully used to accurately predict the thermal conductivity of polymer composites with one-dimensional oriented fillers. • Introduced a new descriptor, "Orientation degree (Od)," to quantitatively describe the spatial distribution of one-dimensional fillers, significantly enhancing prediction accuracy. • The mathematical expressions between the different descriptors and the thermal conductivity of the composite were obtained by using the symbolic regression algorithm. • The proposed method reduces the need for extensive retraining of models, offering a practical solution for material property prediction in advanced materials design and energy efficiency applications. Polymer composites with one-dimensional (1D) oriented fillers, recognized for their high thermal conductivity (TC), are extensively utilized in cooling electronic components. However, the prediction of the TC of composites with 1D oriented fillers poses a challenge due to the significant impact of filler orientation on composite TC. In this paper, we use a strategy that combines deep learning and ensemble learning to efficiently and quickly predict the TC of composites with 1D oriented fillers. First, as a control, we used convolutional neural network (CNN) model to predict the TC of 1D carbon fiber-epoxy composite, and the R-squared (R 2 ) on the test set reached 0.924. However, for composites consist of different matrices and fillers, the CNN model needs to be retrained, which greatly wastes computing resources. Therefore, we define a descriptor ‘Orientation degree ( O d )’ to quantitatively describe the spatial distribution of the 1D fillers. CNN model was used to predict this structural parameter, the accuracy R 2 can reach 0.950. Using O d as a feature, random forest regression (RFR) was used to predict the TC, and the accuracy R 2 reached 0.954, which was higher than that of CNN control group. We further successfully extended this strategy to composites consist of different 1D fillers and matrices, and only one CNN model and one RFR model needed to be trained to achieve fast and accurate TC prediction. This strategy provides valuable insights and guidance for machine learning-based material property prediction. Composite material ; Thermal conductivity ; Materials science ; Polymer ; Filler (materials) ; Ensemble learning ; Artificial intelligence ; Computer science Thermal properties of materials ; Composite Material Mechanics ; Machine Learning in Materials Science
Authors: Masaki Mito | Narimichi Mokutani | Yongpeng Tang | Kaname Matsumoto | Takayuki Tajiri | Zenji Horita
Source: Journal of Materials Science (article, 2024-02-15)
Field: Materials Science — Materials Chemistry

Achieving high-Tc superconductivity in Magnéli phase based on Ti oxides: prediction by machine learning and material synthesis by high-pressure torsion processing We explored superconductors with high superconducting transition temperatures (Tc) tuning the stability of Magnéli phase through high-pressure torsion (HPT). This study has started from exploring superconducting states in the Al–Mg–O ternary system along with the prediction using machine learning. We successfully found superconducting states with Tc = 4.0 and 7.3 K for a composition of Al:Ti = 1:2 in the mixture of Al and surface-oxidized Ti powders. Another magnetic anomaly was also observed at ~ 93 K, being supported by the Tc prediction using the machine learning for the Al–Ti–O system. In this study, the HPT processing was also performed on a Magnéli material Ti4O7 and such mixtures of stable materials as Al + TiO2, Al2O3 + Ti, Al2O3 + TiO2, and Al + Ti4O7. In HPT-processed Ti4O7, the metal–insulator transition was maintained even after the HPT processing. HPT experiments using stable oxides indicate the difficulty of newly stabilizing the Magnéli phase starting from thermodynamically stable materials under severe plastic deformation. A series of attempts reveals that the superconducting state in the mixture with ratio Al:Ti = 1:2 is attributed to both strained Ti-oxide created on the surface of the Ti powder and its reaction with Al under HPT processing, resulting in the stabilization of a Magnéli phase. Materials science ; Torsion (gastropod) ; High pressure ; Superconductivity ; Solid mechanics ; High-temperature superconductivity ; Phase (matter) ; Composite material ; Chemical engineering ; Condensed matter physics ; Engineering physics ; Organic chemistry Electronic and Structural Properties of Oxides ; Metal and Thin Film Mechanics ; Advanced materials and composites
Authors: Qiang Zhang | Yuanhao Wang | Jia Zhang | Yue Yang | Guangren Qian
Source: Applied Catalysis A General (article, 2024-05-31)
Field: Materials Science — Materials Chemistry

Application of machine learning in designing and understanding effective catalyst for selective catalytic reduction of nitrogen oxide Catalysis ; Selective catalytic reduction ; Nitrogen oxide ; Reduction (mathematics) ; Oxide ; Selective reduction ; Computer science ; Materials science ; Chemistry ; Nanotechnology ; NOx ; Organic chemistry ; Mathematics Catalytic Processes in Materials Science ; Machine Learning in Materials Science ; Machine Learning and ELM