jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Г. В. Казаков | Kirill Ivanov | Alexey Penenko | М. А. Марченко
Source:  (article, 2024-07-19)
Field: Materials Science — Materials Chemistry

Optimization of Computations in Simulating Chemical Kinetics Using Surrogate Neural Models We investigate how deep learning-based methods can be applied to solve stiff systems of ordinary differential equations modeling complex chemical kinetic systems. We use artificial neural networks (ANN) as a time-step integrator: ANN reproduces the evolution of a given chemical state for a short period of time. Thus, it can potentially replace computationally expensive stiff solvers, giving a substantial speed-up. This improvement is crucial in simulating a large number of simultaneous reactions that can happen, for example, in almost all combustion problems in aircraft and rocket jet engines, industrial installations for the pyrogenic synthesis of nanomaterials, processes in chemical and technological industrial installations, for example, in chemical reactors. In order to achieve an acceptable improvement in inference time while maintaining prediction accuracy, different techniques are applied to help ANNs assimilate chemical system structure: clustering of phase space and auto-regression. The last one is enhanced with curriculum learning methods. The described approach is tested on a basic Robertson problem. The approach may be promising to further speed up of numerical modeling while maintaining acceptable accuracy of ODE solution. Computer science ; Computation ; Surrogate model ; Kinetics ; Artificial intelligence ; Algorithm ; Machine learning ; Physics Machine Learning in Materials Science
Authors: Soham Chattopadhyay | Dallas R. Trinkle
Source: arXiv (Cornell University) (preprint, 2024-01-11)
Field: Materials Science — Materials Chemistry

Quantifying the contributions to diffusion in complex materials Using machine learning with a variational formula for diffusivity, we recast diffusion as a sum of individual contributions to diffusion--called "kinosons"--and compute their statistical distribution to model a complex multicomponent alloy. Calculating kinosons is orders of magnitude more efficient than computing whole trajectories, and elucidates kinetic mechanisms for diffusion. The distribution of kinosons with temperature leads to new accurate analytic models for macroscale diffusivity. This combination of machine learning with diffusion theory promises insight into other complex materials. Thermal diffusivity ; Diffusion ; Statistical physics ; Distribution (mathematics) ; Computer science ; Mass diffusivity ; Thermodynamics ; Mathematics ; Physics ; Mathematical analysis Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Electron and X-Ray Spectroscopy Techniques
Authors: Tianzhixi Yin | Peiyuan Gao | Audrey Xu | Emily Saldanha
Source: Research Square (preprint, 2024-11-07)
Field: Materials Science — Materials Chemistry

Enhanced Prediction of Molecular Properties Using Transfer Learning on Sigma Profiles Sigma ; Transfer of learning ; Computer science ; Artificial intelligence ; Computational biology ; Physics ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Saurabh Tiwari | Prathamesh Satpute | Supriyo Ghosh
Source: arXiv (Cornell University) (preprint, 2024-08-22)
Field: Materials Science — Materials Chemistry

Time series forecasting of multiphase microstructure evolution using deep learning Microstructure evolution, which plays a critical role in determining materials properties, is commonly simulated by the high-fidelity but computationally expensive phase-field method. To address this, we approximate microstructure evolution as a time series forecasting problem within the domain of deep learning. Our approach involves implementing a cost-effective surrogate model that accurately predicts the spatiotemporal evolution of microstructures, taking an example of spinodal decomposition in binary and ternary mixtures. Our surrogate model combines a convolutional autoencoder to reduce the dimensional representation of these microstructures with convolutional recurrent neural networks to forecast their temporal evolution. We use different variants of recurrent neural networks to compare their efficacy in developing surrogate models for phase-field predictions. On average, our deep learning framework demonstrates excellent accuracy and speedup relative to the "ground truth" phase-field simulations. We use quantitative measures to demonstrate how surrogate model predictions can effectively replace the phase-field timesteps without compromising accuracy in predicting the long-term evolution trajectory. Additionally, by emulating a transfer learning approach, our framework performs satisfactorily in predicting new microstructures resulting from alloy composition and physics unknown to the model. Therefore, our approach offers a useful data-driven alternative and accelerator to the materials microstructure simulation workflow. Series (stratigraphy) ; Microstructure ; Artificial intelligence ; Computer science ; Econometrics ; Economics ; Geology ; Materials science ; Metallurgy ; Paleontology Machine Learning in Materials Science ; Metallurgical Processes and Thermodynamics ; Metallurgy and Material Forming
Authors: Chiwen Feng | Yanwei Liang | Jiaying Sun | Renhai Wang | Huaijun Sun | Huafeng Dong
Source: arXiv (Cornell University) (preprint, 2024-09-04)
Field: Materials Science — Materials Chemistry

Predicting Miscibility in Binary Compounds: A Machine Learning and Genetic Algorithm Study The combination of data science and materials informatics has significantly propelled the advancement of multi-component compound synthesis research. This study employs atomic-level data to predict miscibility in binary compounds using machine learning, demonstrating the feasibility of such predictions. We have integrated experimental data from the Materials Project (MP) database and the Inorganic Crystal Structure Database (ICSD), covering 2,346 binary systems. We applied a random forest classification model to train the constructed dataset and analyze the key factors affecting the miscibility of binary systems and their significance while predicting binary systems with high synthetic potential. By employing advanced genetic algorithms on the Co-Eu system, we discovered three novel thermodynamically stable phases, CoEu8, Co3Eu2, and CoEu. This research offers valuable theoretical insights to guide experimental synthesis endeavors in binary and complex material systems. Miscibility ; Binary number ; Genetic algorithm ; Algorithm ; Computer science ; Machine learning ; Artificial intelligence ; Materials science ; Mathematics ; Polymer ; Arithmetic Machine Learning in Materials Science ; Process Optimization and Integration ; History and advancements in chemistry
Authors: Vicente Domínguez-Arca
Source: Research Square (preprint, 2024-03-15)
Field: Materials Science — Materials Chemistry

Bridging the Computational Gap: Sliding Window Technique Meets GCNN for Enhanced Molecular Charge Predictions Abstract In the quest for advancing computational tools capable of accurately calculating, estimating, or predicting partial atomic charges in organic molecules, this work introduces a pioneering Machine Learning-based tool designed to transcend the limitations of traditional methods like DFT, Mulliken, and semi-empirical approaches such as MOPAC and Gaussian. Recognizing the crucial role of partial atomic charges in molecular dynamics simulations for studying solvation, protein interactions, substrate interactions, and membrane permeability, we aim to introduce a tool that not only offers enhanced computational efficiency but also extends the predictive capabilities to molecules larger than those in the QM9 dataset, traditionally analyzed using Mulliken charges. Employing a novel neural network architecture adept at learning graph properties and, by extension, the characteristics of organic molecules, this study presents a "sliding window" technique. This method segments larger molecules into smaller, manageable substructures for charge prediction, significantly reducing computational demands and processing times. Our results highlight the model's predictive accuracy for unseen molecules from the QM9 database and its successful application to the resveratrol molecule, providing insights into the hydrogen-donating capabilities of CH groups in aromatic rings—a feature not predicted by existing tools like CGenFF or ATB but supported by literature. This breakthrough not only presents a novel alternative for determining partial atomic charges in computational chemistry but also underscores the potential of convolutional neural networks to discern molecular features based on stoichiometry and geometric configuration. Such advancements hint at the future possibility of designing molecules with desired charge sequences, promising a transformative impact on drug discovery. Bridging (networking) ; Charge (physics) ; Window (computing) ; Materials science ; Computer science ; Physics ; Quantum mechanics ; Computer network ; World Wide Web Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: da Silveira, Kelly Cristine
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2024-08-24)
Field: Materials Science — Materials Chemistry

Development of sustainable technologies using machine learning, high throughput, and polymeric additives for the formation of gas hydrates Apresentação de Palestra intitulada: “Desenvolvimento de tecnologias sustentáveis com machine learning, high throughtput e aditivos poliméricos para a formação de hidratos de gás”. Palestra ministrada nos eventos: 7º Encontro Interdisciplinar Interinstitucional em Ciências e Engenharia (EI2CE) realizado presencialmente nos dias 24 e 25 de agosto de 2024, na Unidade de Desenvolvimento Tecnológico/Laboratório Patrícia Oliva Soares de Experimentação Numérica e Simulação em Transferência de calor e Massa, UDT/LEMA – Instituto Politécnico da UERJ Sustainable development ; Work (physics) ; Sustainability ; Clathrate hydrate Machine Learning in Materials Science ; Methane Hydrates and Related Phenomena ; Offshore Engineering and Technologies
Authors: Shubhang Goswami | Scott Jensen | Yubo Yang | Markus Holzmann | Carlo Pierleoni | David M. Ceperley
Source: arXiv (Cornell University) (preprint, 2024-11-23)
Field: Materials Science — Materials Chemistry

High temperature melting of dense molecular hydrogen from machine-learning interatomic potentials trained on quantum Monte Carlo We present results and discuss methods for computing the melting temperature of dense molecular hydrogen using a machine learned model trained on quantum Monte Carlo data. In this newly trained model, we emphasize the importance of accurate total energies in the training. We integrate a two phase method for estimating the melting temperature with estimates from the Clausius-Clapeyron relation to provide a more accurate melting curve from the model. We make detailed predictions of the melting temperature, solid and liquid volumes, latent heat and internal energy from 50 GPa to 180 GPa for both classical hydrogen and quantum hydrogen. At pressures of roughly 173 GPa and 1635K, we observe molecular dissociation in the liquid phase. We compare with previous simulations and experimental measurements. Monte Carlo method ; Quantum Monte Carlo ; Statistical physics ; Quantum ; Melting temperature ; Hydrogen molecule ; Condensed matter physics ; Interatomic potential ; Hydrogen ; Physics ; Materials science ; Molecular dynamics ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Quantum, superfluid, helium dynamics ; Phase Equilibria and Thermodynamics
Authors: Ignacio Migliaro | Thomas R. Cundari
Source: Journal of Chemical Theory and Computation (article, 2024-06-28)
Field: Chemistry — Organic Chemistry

Integrated Study on Methane Activation: Exploring Main Group Frustrated Lewis Pairs through Density Functional Theory, Machine Learning, and Machine-Learned Force Fields Frustrated Lewis Pairs (FLP) are an important advance in metal-free catalysis due to their ability to activate a variety of small molecules. Many studies have focused on a very limited sample of Lewis acids and bases. Herein, we disclose an automated exploration algorithm using density functional methods, artificial neural networks (ANNs), and a molecule builder that incentivizes the exploration of favorable FLP space for the activation of methane <i>via</i> two mechanisms: deprotonation and hydride abstraction. The exploration algorithm creates FLPs with different Lewis acids (LA), Lewis bases (LB), and their substituents (L<sub>A</sub>/L<sub>B</sub>), which proved successful in quickly converging in the favorable chemical space, suggesting chemically sound structures, and generating thousands of potential candidates for methane activating FLPs. By modeling thousands of reactions, an FLP database of methane activation was created, allowing one to data mine properties, <i>e.g.</i>, adduct bond length, highest occupied molecular orbital-lowest-unoccupied molecular orbital (HOMO-LUMO) gap, global electrophilicity index, favored Lewis acids/bases/substituents, and substituent steric volume. These properties not only successfully narrow the FLP chemical space but also provide meaningful insight into the chemical nature of competent methane activators. The machine learning discovery strategy disclosed here is general enough to be applicable to many chemical optimization tasks. This study also investigates the efficacy of a Machine-Learned Force Field (MLFF) in predicting the formation energies of Frustrated Lewis Pairs (FLPs). Our model, exhibiting a test error of ±10 kcal/mol, highlighted impressive computational efficiency by enabling the calculation of all possible FLP permutations within our chemical space. The MLFF demonstrated proficiency in predicting energies, providing a significant acceleration compared to quantum mechanics methods. However, challenges emerged in accurately capturing forces, necessitating recourse to classical force fields for reliable structure relaxation. The present study sheds light on the MLFF's potential as a tool for rapid energy predictions, emphasizing the need for further refinement to enhance its accuracy, particularly in force predictions, to expand its utility in chemical simulations. Density functional theory ; Frustrated Lewis pair ; Group (periodic table) ; Computer science ; Machine learning ; Artificial intelligence ; Chemistry ; Lewis acids and bases ; Computational chemistry Organoboron and organosilicon chemistry ; Machine Learning in Materials Science ; Boron Compounds in Chemistry
Authors: Mark Nichols | Christopher D. Barrett | Doyl Dickel | Mashroor S. Nitol | Saryu Fensin
Source: arXiv (Cornell University) (preprint, 2024-11-12)
Field: Materials Science — Materials Chemistry

Predicting Ti-Al Binary Phase Diagram with an Artificial Neural Network Potential The microstructure of the Ti-Al binary system is an area of great interest as it affects material properties and plasticity. Phase transformations induce microstructural changes; therefore, accurately modeling the phase transformations of the Ti-Al system is necessary to describe plasticity. Interatomic potentials can be a powerful tool to model how materials behave; however, existing potentials lack accuracy in certain aspects. While classical potentials like the Embedded Atom Method (EAM) and Modified Embedded Atom Method (MEAM) perform adequately for modeling dilute Al solute within Ti's $α$ phase, they struggle with accurately predicting plasiticity. In particular, they struggle with stacking fault energies in intermetallics and to some extent elastic properties. This hinders their effectiveness in investigating the plastic behavior of formed intermetallics in Ti-Al alloys. Classical potentials also fail to predict the $α$ to $β$ phase boundary. Existing machine learning (ML) potentials reproduce the properties of formed intermetallics with density functional theory (DFT) but do not examine the $α$ to $β$ or $α$ to D0$_{19}$ phase boundaries. This work uses a rapid artificial neural network (RANN) framework to produce a neural network potential for the Ti-Al binary system. This potential is capable of reproducing the Ti-Al binary phase diagram up to 50$\%$ Al concentration. The present interatomic potential ensures stability and allows results near the accuracy of DFT. Using Monte Carlo simulations, RANN potential accurately predicts the $α$ to $β$ and $α$ to D0$_{19}$ phase transitions. The current potential also exhibits accurate elastic constants and stacking fault energies for the L1$_0$ and D0$_{19}$ phases. Phase diagram ; Artificial neural network ; Binary number ; Phase (matter) ; Diagram ; Artificial intelligence ; Computer science ; Statistical physics ; Mathematics ; Physics ; Arithmetic ; Database Machine Learning in Materials Science ; Metal and Thin Film Mechanics ; Titanium Alloys Microstructure and Properties
Authors: Kaiwei Zhang | Yange Lin | Guangcheng Wu | Yuxiang Ren | Xuecang Zhang | Bo Wang | Xiaoyu Zhang | Weitao Du
Source: arXiv (Cornell University) (preprint, 2024-03-06)
Field: Materials Science — Materials Chemistry

Sculpting Molecules in Text-3D Space: A Flexible Substructure Aware Framework for Text-Oriented Molecular Optimization The integration of deep learning, particularly AI-Generated Content, with high-quality data derived from ab initio calculations has emerged as a promising avenue for transforming the landscape of scientific research. However, the challenge of designing molecular drugs or materials that incorporate multi-modality prior knowledge remains a critical and complex undertaking. Specifically, achieving a practical molecular design necessitates not only meeting the diversity requirements but also addressing structural and textural constraints with various symmetries outlined by domain experts. In this article, we present an innovative approach to tackle this inverse design problem by formulating it as a multi-modality guidance optimization task. Our proposed solution involves a textural-structure alignment symmetric diffusion framework for the implementation of molecular optimization tasks, namely 3DToMolo. 3DToMolo aims to harmonize diverse modalities including textual description features and graph structural features, aligning them seamlessly to produce molecular structures adhere to specified symmetric structural and textural constraints by experts in the field. Experimental trials across three guidance optimization settings have shown a superior hit optimization performance compared to state-of-the-art methodologies. Moreover, 3DToMolo demonstrates the capability to discover potential novel molecules, incorporating specified target substructures, without the need for prior knowledge. This work not only holds general significance for the advancement of deep learning methodologies but also paves the way for a transformative shift in molecular design strategies. 3DToMolo creates opportunities for a more nuanced and effective exploration of the vast chemical space, opening new frontiers in the development of molecular entities with tailored properties and functionalities. Substructure ; Computer science ; Molecule ; Nanotechnology ; Chemistry ; Materials science ; Engineering ; Structural engineering Machine Learning in Materials Science
Authors: Dennis Svatunek
Source: ChemRxiv (preprint, 2024-04-25)
Field: Chemistry — Organic Chemistry

Computational Large-Scale Screening of Bioorthogonal 1,2,4,5-Tetrazine/trans-Cyclooctene Cycloadditions Bioorthogonal reactions between 1,2,4,5-tetrazines and trans-cyclooctenes have emerged as valuable chemical tools in the fields of chemical biology and material science, and they hold significant potential for medical applications. The most critical attribute of such reactions is their rate. Experimental investigations into the reactivity of 1,2,4,5-tetrazines are time-consuming and costly. In contrast, computational screenings can rapidly identify reactants that exhibit desired reactivity. In this study, we introduce a tool for automated computational screening that assesses the reactivity of a large pool of tetrazines. This effort has produced an initial dataset of 1,288 reaction barriers, which can be utilized to develop machine learning models. Bioorthogonal chemistry ; Tetrazine ; Reactivity (psychology) ; Computer science ; Cyclooctene ; Chemistry ; Combinatorial chemistry ; National laboratory ; Biochemical engineering ; Computational chemistry ; Organic chemistry ; Click chemistry ; Engineering ; Catalysis ; Medicine ; Engineering physics Click Chemistry and Applications ; Chemical Synthesis and Analysis ; Computational Drug Discovery Methods
Authors: Martin Uhrin | Austin Zadoks | Luca Binci | Nicola Marzari | Iurii Timrov
Source: arXiv (Cornell University) (preprint, 2024-06-04)
Field: Materials Science — Materials Chemistry

Machine learning Hubbard parameters with equivariant neural networks Density-functional theory with extended Hubbard functionals (DFT+$U$+$V$) provides a robust framework to accurately describe complex materials containing transition-metal or rare-earth elements. It does so by mitigating self-interaction errors inherent to semi-local functionals which are particularly pronounced in systems with partially-filled d and f electronic states. However, achieving accuracy in this approach hinges upon the accurate determination of the on-site $U$ and inter-site $V$ Hubbard parameters. In practice, these are obtained either by semi-empirical tuning, requiring prior knowledge, or, more correctly, by using predictive but expensive first-principles calculations. Here, we present a machine learning model based on equivariant neural networks which uses atomic occupation matrices as descriptors, directly capturing the electronic structure, local chemical environment, and oxidation states of the system at hand. We target here the prediction of Hubbard parameters computed self-consistently with iterative linear-response calculations, as implemented in density-functional perturbation theory (DFPT), and structural relaxations. Remarkably, when trained on data from 12 materials spanning various crystal structures and compositions, our model achieves mean absolute relative errors of 3% and 5% for Hubbard $U$ and $V$ parameters, respectively. By circumventing computationally expensive DFT or DFPT self-consistent protocols, our model significantly expedites the prediction of Hubbard parameters with negligible computational overhead, while approaching the accuracy of DFPT. Moreover, owing to its robust transferability, the model facilitates accelerated materials discovery and design via high-throughput calculations, with relevance for various technological applications. Hubbard model ; Perturbation theory (quantum mechanics) ; Density functional theory ; Computer science ; Artificial neural network ; Electronic structure ; Overhead (engineering) ; Algorithm ; Statistical physics ; Artificial intelligence ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Electronic and Structural Properties of Oxides
Authors: Hyunwoo Ryoo | Seul Ji Song | Min Ji Jeon | Juhyun Moon | Jihye Lee | ByungHyun Hwang | Jengho Ahn | Yoon-jong Song | Hidong Kwak | Lior Neeman | Noga Meir | Jaehong Jang | Il Hwan Kim | Hyun-Kyu Kim
Source:  (article, 2024-04-10)
Field: Materials Science — Materials Chemistry

On-cell thickness monitoring of chalcogenide alloy layer using spectral interferometry, Raman spectroscopy, and hybrid machine learning Interferometry ; Chalcogenide ; Raman spectroscopy ; Alloy ; Materials science ; Spectroscopy ; Layer (electronics) ; Optoelectronics ; Optics ; Computer science ; Nanotechnology ; Composite material ; Physics Phase-change materials and chalcogenides ; Chalcogenide Semiconductor Thin Films ; Nonlinear Optical Materials Research