jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yingying Xu | Xinyi Liu | Jifen Wang
Source: Journal of Applied Physics (article, 2024-02-15)
Field: Materials Science — Materials Chemistry

Prediction of thermoelectric-figure-of-merit based on autoencoder and light gradient boosting machine The evaluation of thermoelectric materials relies significantly on the thermoelectric figure of merit, ZT, which serves as a crucial parameter in assessing their properties. The accurate prediction of ZT values can be accomplished by utilizing machine learning models to learn material characteristics. However, factors such as the size of the dataset, model hyperparameters, and data quality can all impact the accuracy of machine learning. In contrast to previous research where high-dimensional features were simply discarded to transform them into low-dimensional ones, deep learning models such as autoencoder can extract more effective information. Therefore, in this article, the combination of autoencoders and the Light Gradient Boosting Machine (LightGBM) is employed to learn the chemical characteristics and ZT values of various materials. The reliability of the model was confirmed by achieving an R2 score of 0.94 during tenfold cross-validation. 130 000 materials were predicted and screened, the temperature dependence of the screened materials was studied in depth, and 13 materials with high ZT values were identified. Four of the 13 most promising candidates identified are existing thermoelectric materials, while nine are ideal candidates for future experimental studies and validation. This work utilizes autoencoders for extensive prediction and screening of promising materials, providing an effective approach for handling high-dimensional material data. Autoencoder ; Artificial intelligence ; Boosting (machine learning) ; Figure of merit ; Gradient boosting ; Machine learning ; Thermoelectric effect ; Computer science ; Hyperparameter ; Deep learning ; Materials science ; Physics ; Random forest Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Thermal properties of materials
Authors: Jas Kalayan | Ismaeel Ramzan | Christopher D. Williams | Richard A. Bryce | Neil A. Burton
Source: Journal of Computational Chemistry (article, 2024-01-29)
Field: Materials Science — Materials Chemistry

A neural network potential based on pairwise resolved atomic forces and energies Molecular simulations have become a key tool in molecular and materials design. Machine learning (ML)-based potential energy functions offer the prospect of simulating complex molecular systems efficiently at quantum chemical accuracy. In previous work, we have introduced the ML-based PairF-Net approach to neural network potentials, that adopts a pairwise interatomic scheme to predicting forces within a molecular system. Here, we further develop the PairF-Net model to intrinsically incorporate energy conservation and couple the model to a molecular mechanical (MM) environment within the OpenMM package. The updated PairF-Net model yields energy and force predictions and dynamical distributions in good agreement with the rMD17 dataset of ten small organic molecules in the gas-phase. We further show that these in vacuo ML models of small molecules can be applied to force predictions in aqueous solution via hybrid ML/MM simulations. We present a new benchmark dataset for these ten molecules in solution, obtained from QM/MM simulations, which we denote as rMD17-aq (https://zenodo.org/records/10048644); and assess the ability of PairF-Net to reproduce the molecular energy, atomic forces and dynamical distributions of these solution conformations via ML/MM simulations. Pairwise comparison ; Molecular dynamics ; Benchmark (surveying) ; Work (physics) ; Statistical physics ; Potential energy ; Artificial neural network ; Molecule ; Energy (signal processing) ; Biological system ; Computer science ; Chemistry ; Physics ; Computational chemistry ; Artificial intelligence ; Thermodynamics ; Atomic physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Byung Do Lee | Deepak S. Gavali | Hee-jeong Kim | Seonghwan Kim | Min Young Cho | Kyunglim Pyo | Young‐Kook Lee | Woon Bae Park | Kee‐Sun Sohn
Source: Journal of Materials Chemistry A (article, 2024-11-18)
Field: Materials Science — Materials Chemistry

Discovering virtual Na-based argyrodites as solid-state electrolytes using DFT, AIMD, and machine learning techniques In light of the current absence of experimentally synthesized Na-based argyrodites, we systematically investigated a large compositional space comprising 4375 hypothetical Na-based argyrodite structures through DFT and AIMD calculations. Virtual screening ; Computer science ; Solid-state ; Electrolyte ; Materials science ; Artificial intelligence ; Chemistry ; Physical chemistry ; Computational chemistry ; Molecular dynamics Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Chemical Synthesis and Characterization
Authors: Ryan Jacobs | Maciej P. Polak | Lane E. Schultz | Hamed Mahdavi | Vasant Honavar | Dane Morgan
Source: arXiv (Cornell University) (preprint, 2024-09-09)
Field: Materials Science — Materials Chemistry

Regression with Large Language Models for Materials and Molecular Property Prediction We demonstrate the ability of large language models (LLMs) to perform material and molecular property regression tasks, a significant deviation from the conventional LLM use case. We benchmark the Large Language Model Meta AI (LLaMA) 3 on several molecular properties in the QM9 dataset and 24 materials properties. Only composition-based input strings are used as the model input and we fine tune on only the generative loss. We broadly find that LLaMA 3, when fine-tuned using the SMILES representation of molecules, provides useful regression results which can rival standard materials property prediction models like random forest or fully connected neural networks on the QM9 dataset. Not surprisingly, LLaMA 3 errors are 5-10x higher than those of the state-of-the-art models that were trained using far more granular representation of molecules (e.g., atom types and their coordinates) for the same task. Interestingly, LLaMA 3 provides improved predictions compared to GPT-3.5 and GPT-4o. This work highlights the versatility of LLMs, suggesting that LLM-like generative models can potentially transcend their traditional applications to tackle complex physical phenomena, thus paving the way for future research and applications in chemistry, materials science and other scientific domains. Property (philosophy) ; Regression ; Computer science ; Regression analysis ; Natural language processing ; Econometrics ; Mathematics ; Statistics ; Machine learning ; Philosophy Machine Learning in Materials Science
Authors: Lifeng Xu | Jian Jiang
Source: Journal of Chemical Theory and Computation (article, 2024-09-12)
Field: Materials Science — Materials Chemistry

Synergistic Integration of Physical Embedding and Machine Learning Enabling Precise and Reliable Force Field Machine-learning force fields have achieved significant strides in accurately reproducing the potential energy surface with quantum chemical accuracy. However, this approach still faces several challenges, e.g., extrapolating to uncharted chemical spaces, interpreting long-range electrostatics, and mapping complex macroscopic properties. To address these issues, we advocate for a synergistic integration of physical principles and machine learning techniques within the framework of a physically informed neural network (PINN). This approach involves incorporating physical knowledge into the parameters of the neural network, coupled with an efficient global optimizer, the Tabu-Adam algorithm, proposed in this work to augment optimization under strict physical constraint. We choose the AMOEBA+ force field as the physics-based model for embedding and then train and test it using the diethylene glycol dimethyl ether (DEGDME) data set as a case study. The results reveal a breakthrough in constructing a precise and noise-robust machine learning force field. Utilizing two training sets with hundreds of samples, our model exhibits remarkable generalization and density functional theory (DFT) accuracy in describing molecular interactions and enables a precise prediction of the macroscopic properties such as the diffusion coefficient with minimal cost. This work provides valuable insight into establishing a fundamental framework of the PINN force field. Computer science ; Embedding ; Field (mathematics) ; Force field (fiction) ; Artificial intelligence ; Nanotechnology ; Human–computer interaction ; Data science ; Materials science ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Software Engineering Research
Authors: Shuo Liu | Xiuguo Chen | Tianjuan Yang | Jiahao Zhang | Shiyuan Liu
Source: Optics Express (article, 2024-05-09)
Field: Materials Science — Surfaces, Coatings and Films

Inverse optical scatterometry using sketch-guided deep learning Optical scatterometry, also referred to as optical critical dimension (OCD) metrology, is a widely used technique for characterizing nanostructures in semiconductor industry. As a model-based optical metrology, the measurement in optical scatterometry is not straightforward but involves solving a complicated inverse problem. So far, the methods for solving the inverse scattering problem, whether traditional or deep-learning-based, necessitate a predefined geometric model, but they are also constrained by this model with poor applicability. Here, we demonstrate a sketch-guided neural network (SGNN) for nanostructure reconstruction in optical scatterometry. By learning from training data based on the designed generic profile model, the neural network acquires not only scattering knowledge but also sketching techniques, that allows it to draw the profiles corresponding to the input optical signature, regardless of whether the sample structure is the same as the generic profile model or not. The accuracy and strong generalizability of proposed approach is validated by using a series of one-dimensional gratings. Experiments have also demonstrated that it is comparable to nonlinear regression methods and outperforms traditional deep learning methods. To our best knowledge, this is the first time that the concept of sketching has been introduced into deep learning for solving the inverse scattering problem. We believe that our method will provide a novel solution for semiconductor metrology, enabling fast and accurate reconstruction of nanostructures. Metrology ; Computer science ; Inverse problem ; Artificial neural network ; Inverse scattering problem ; Deep learning ; Generalizability theory ; Artificial intelligence ; Optics ; Scattering ; Physics ; Mathematics Optical Coatings and Gratings ; Near-Field Optical Microscopy ; Photonic and Optical Devices
Authors: Ryosuke Jinnouchi
Source: Journal of The Electrochemical Society (article, 2024-08-23)
Field: Chemistry — Electrochemistry

Grand-Canonical First Principles-Based Calculations of Electrochemical Reactions This article introduces the first principles-based grand-canonical formalisms of several representative electronic structure calculation methods in electrochemistry, which are essential for elucidating the atomic-scale mechanisms of electrochemical reactions and discovering the guiding principles for designing advanced materials. While most applications still rely on approximate structures obtained by static calculations at absolute zero, the foundational theories of more rigorous molecular dynamics simulations are also developing. I discuss methods that combine these theories with emerging machine-learning interatomic potentials, suggesting that this approach could pave the way to predict the thermodynamics and kinetics of electrochemical reactions at finite temperatures purely from first principles. Electrochemistry ; Computational chemistry ; Non canonical ; Materials science ; Chemistry ; Statistical physics ; Physics ; Physical chemistry ; Electrode Electrochemical Analysis and Applications ; Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Jeļena Fiošina | Philipp Sievers | Marco Drache | Sabine Beuermann
Source: ACS Polymers Au (article, 2024-07-26)
Field: Materials Science — Materials Chemistry

AI-Based Forecasting of Polymer Properties for High-Temperature Butyl Acrylate Polymerizations High-temperature polymerizations involving self-initiation of the monomer are attractive because of high reaction rate, comparable lower viscosities, and no need for an additional initiator. However, the polymers obtained show a more complex microstructure, e.g., with specific branching levels or significant amounts of macromonomer. Simulations of the polymerization processes are powerful tools to gain a deeper understanding of the processes and the elemental reactions at the molecular level. However, simulations can be computationally demanding, requiring significant time and memory resources. Therefore, this study aims at applying AI-based forecasting of tailored polymer properties and using a kinetic Monte Carlo simulator for the generation of training and test data. The applied machine learning (ML) models (random forest and kernel density (KD) regression) predict monomer concentration, macromonomer content, and full molar mass distributions as a function of time, as well as the average branching level with an excellent performance (<i>R</i> <sup>2</sup> (coefficient of determination) > 0.99, MAE (mean absolute error) < 1% for kernel density regression). This study explores the number of training data needed for reliable and accurate predictions in ML models. Explainability methods reveal that the importance of input variables in ML models aligns with expert knowledge. Acrylate ; Polymer ; Materials science ; Butyl acrylate ; Polymer chemistry ; Polymer science ; Chemical engineering ; Copolymer ; Composite material ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Shuhei Kanesato | Katsuhisa Yasoshima | Kentaro Matsumoto | Nana Misawa | Yūichi Suzuki | Nobuaki Koga | Masataka Nagaoka
Source: The Journal of Physical Chemistry B (article, 2024-06-07)
Field: Chemistry — Organic Chemistry

Atomistic Simulation of Hf-Pyridyl Amido-Catalyzed Chain Transfer Alkene Polymerization Reaction and Its Machine Learning for Extraction of Essential Descriptors: Effect of Microscopic Steric Hindrance on the Monomer Insertion Process The microscopic effects of each substituent of the Hf catalyst and the growing polymer on the monomer insertion process were investigated for Hf-pyridyl amido-catalyzed coordinative chain transfer polymerization using the Red Moon method. Since the Hf catalyst has two reaction sites, <i>cis</i>- and <i>trans</i>-sites, we separately applied the appropriate analysis methods to each one, revealing that the naphthalene ring influenced monomer insertion at the <i>cis</i>-one, while the <i>i</i>-Pr group and the hexyl group of the adjacent 1-octene unit did the <i>trans</i>-one. It was interesting to find that the hexyl group of the 1-octene-inserted catalyst (oHfCat) pushes the naphthalene ring toward the <i>cis</i>-site and narrows the space at the <i>cis</i>-site, thus indirectly creating a steric hindrance to <i>cis</i>-insertions. Further, the relative position of the Hf catalyst and the growing polymer was found to be strongly influenced by the patterns of insertion reactions, i.e., <i>cis</i>- or <i>trans</i>-insertions. In particular, it was clarified that, after <i>trans</i>-insertions, the growing polymer on the Hf atom covers the <i>cis</i>-site, making <i>cis</i>-insertion less likely to occur. These studies reveal the microscopic effects of the catalyst substituents and the growing polymer on the catalyst during the polymerization reaction process; these microscopic analyses using the RM method should provide atomistic insights that are not easy to obtain experimentally for advanced catalyst design and polymerization control. Chain transfer ; Alkene ; Polymerization ; Monomer ; Catalysis ; Steric effects ; Chemistry ; Polymer chemistry ; Substituent ; Polymer ; Photochemistry ; Radical polymerization ; Organic chemistry Organometallic Complex Synthesis and Catalysis ; Advanced Polymer Synthesis and Characterization ; Synthetic Organic Chemistry Methods
Authors: Baoqin Fu | Yandong Sun | Wanrun Jiang | Fu Wang | Linfeng Zhang | Han Wang | Ben Xu
Source: Journal of Nuclear Materials (article, 2024-01-06)
Field: Materials Science — Materials Chemistry

Determining the thermal conductivity and phonon behavior of SiC materials with quantum accuracy via deep learning interatomic potential model Thermal conductivity ; Phonon ; Materials science ; Interatomic potential ; Molecular dynamics ; Microstructure ; Density functional theory ; Phonon scattering ; Thermal ; Semiconductor ; Scattering ; Lattice (music) ; Condensed matter physics ; Statistical physics ; Thermodynamics ; Physics ; Optoelectronics ; Chemistry ; Computational chemistry ; Composite material ; Optics Thermal properties of materials ; Machine Learning in Materials Science ; Silicon Carbide Semiconductor Technologies
Authors: Minghao Yuan | Qinglang Guo | Yingxue Wang
Source: Progress in Natural Science Materials International (article, 2024-08-27)
Field: Materials Science — Materials Chemistry

The current research status and prospects of AI in chemical science Materials science ; Current (fluid) ; Engineering ethics ; Nanotechnology ; Engineering physics ; Thermodynamics ; Engineering ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Zhiqiang Li | Jian Wang | Haoyu Dong | Yanguang Zhou | Linhua Liu | Jia‐Yue Yang
Source: International Journal of Heat and Mass Transfer (article, 2024-09-14)
Field: Materials Science — Materials Chemistry

Mechanistic insights into water filling effects on thermal transport of carbon nanotubes from machine learning molecular dynamics Carbon nanotube ; Materials science ; Molecular dynamics ; Thermal ; Chemical physics ; Water transport ; Dynamics (music) ; Nanotechnology ; Thermodynamics ; Environmental science ; Water flow ; Chemistry ; Computational chemistry ; Physics Machine Learning in Materials Science ; Thermal properties of materials ; Nanopore and Nanochannel Transport Studies
Authors: Frida Bilén | Pernilla Ekborg-Tanner | Antoine Balzano | M. Ughetto | Róbson Rosa da Silva | Hannes Schomaker | Paul Erhart | Kasper Moth‐Poulsen | Romain Bordes
Source: The Journal of Physical Chemistry C (article, 2024-08-08)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine Learning-Based Interpretation of Optical Properties of Colloidal Gold with Convolutional Neural Networks Gold nanoparticles are used in a range of applications, but their properties depend on their shape, size, and polydispersity. A quick, easy, and accurate characterization of the particles is therefore of high importance, especially in flow synthesis settings where continuous monitoring of the characteristics is desired. Our hypothesis was that convolutional neural networks can be used to extract detailed information about structural parameters of gold nanoparticles from their UV-vis spectra, and we have shown that this is possible by predicting size distributions from in silico UV-vis spectra for colloidal gold with high accuracy. Here this was done for both spherical and rod-shaped gold nanoparticles. We also show that the addition of noise makes the prediction of diameter polydispersity more challenging, but the average diameter, and for rods also aspect ratio distribution, can be accurately predicted even with the highest evaluated level of noise. The model structure is promising and worthy of implementation to enable predictions beyond in silico generated spectra. The model, for instance, can find application in flow synthesis settings to create a machine learning-driven feedback loop for automated synthesis. Convolutional neural network ; Interpretation (philosophy) ; Computer science ; Artificial intelligence ; Colloidal gold ; Artificial neural network ; Machine learning ; Nanotechnology ; Materials science ; Nanoparticle Gold and Silver Nanoparticles Synthesis and Applications ; Machine Learning in Materials Science ; Nonlinear Optical Materials Studies
Authors: Hongshuo Huang | Amir Barati Farimani
Source: Journal of Applied Physics (article, 2024-04-24)
Field: Materials Science — Materials Chemistry

Multimodal learning of heat capacity based on transformers and crystallography pretraining Thermal properties of materials are essential to many applications of thermal electronic devices. Density functional theory (DFT) has shown capability in obtaining an accurate calculation. However, the expensive computational cost limits the application of the DFT method for high-throughput screening of materials. Recently, machine learning models, especially graph neural networks (GNNs), have demonstrated high accuracy in many material properties’ prediction, such as bandgap and formation energy, but fail to accurately predict heat capacity(CV) due to the limitation in capturing crystallographic features. In our study, we have implemented the material informatics transformer (MatInFormer) framework, which has been pretrained on lattice reconstruction tasks. This approach has shown proficiency in capturing essential crystallographic features. By concatenating these features with human-designed descriptors, we achieved a mean absolute error of 4.893 and 4.505 J/(mol K) in our predictions. Our findings underscore the efficacy of the MatInFormer framework in leveraging crystallography, augmented with additional information processing capabilities. Materials science ; Transformer ; Heat capacity ; Engineering ; Physics ; Electrical engineering ; Thermodynamics ; Voltage Machine Learning in Materials Science ; Iron and Steelmaking Processes ; Thermography and Photoacoustic Techniques
Authors: Dennis Svatunek
Source: Topics in Current Chemistry (article, 2024-05-10)
Field: Chemistry — Organic Chemistry

Computational Organic Chemistry: The Frontier for Understanding and Designing Bioorthogonal Cycloadditions Computational organic chemistry has become a valuable tool in the field of bioorthogonal chemistry, offering insights and aiding in the progression of this branch of chemistry. In this review, I present an overview of computational work in this field, including an exploration of both the primary computational analysis methods used and their application in the main areas of bioorthogonal chemistry: (3 + 2) and [4 + 2] cycloadditions. In the context of (3 + 2) cycloadditions, detailed studies of electronic effects have informed the evolution of cycloalkyne/1,3-dipole cycloadditions. Through computational techniques, researchers have found ways to adjust the electronic structure via hyperconjugation to enhance reactions without compromising stability. For [4 + 2] cycloadditions, methods such as distortion/interaction analysis and energy decomposition analysis have been beneficial, leading to the development of bioorthogonal reactants with improved reactivity and the creation of orthogonal reaction pairs. To conclude, I touch upon the emerging fields of cheminformatics and machine learning, which promise to play a role in future reaction discovery and optimization. Chemistry ; Bioorthogonal chemistry ; Frontier ; Biochemical engineering ; Nanotechnology ; Combinatorial chemistry ; Click chemistry Click Chemistry and Applications ; Chemical Synthesis and Analysis ; Cyclopropane Reaction Mechanisms