jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jonathan A. Semelak | Ignacio Pickering | Kate Huddleston | Justo Olmos | Juan Santiago Grassano | Camila M. Clemente | Salvador I. Drusin | Marcelo A. Martí | Mariano C. González Lebrero | Adrián E. Roitberg | Darío A. Estrı́n
Source: ChemRxiv (preprint, 2024-12-30)
Field: Materials Science — Materials Chemistry

Advancing Multiscale Molecular Modeling with Machine Learning-Derived Electrostatics We introduce an innovative machine learning (ML)-based framework for multiscale molecular modeling, in which the ML subsystem is treated as an electrostatic entity interacting with its molecular mechanics (MM) environment through classical electrostatics. The integration of ML accuracy with multiscale modeling is accomplished by leveraging the capabilities of the ANI neural networks to predict geometry-dependent atomic partial charges at the Minimal Basis Iterative Stockholder (MBIS) level, going beyond static mechanical embedding. This ML/MM approach can closely approximate state-of-the-art multi-scale quantum-classical (QM/MM) methods while significantly lowering computational requirements, thereby facilitating more efficient and precise simulations in computational chemistry. The method requires no additional training beyond the initial model setup and is integrated into Amber, one of the most widely used software suites for molecular modeling, ensuring accessibility to the broader community. We validate its performance across a variety of challenging applications, including solvation structure, vibrational spectra, torsion free energy profiles, and protein-ligand interactions, achieving excellent agreement with QM/MM benchmarks. This framework not only advances the frontiers of multiscale modeling but also showcases the potential of machine learning to achieve quantum-level accuracy with exceptional efficiency for complex chemical systems. Electrostatics ; Computer science ; Embedding ; Multiscale modeling ; Software ; Quantum ; Computational science ; Solvation ; QM/MM ; Artificial intelligence ; Molecular dynamics ; Statistical physics ; Physics ; Chemistry ; Computational chemistry ; Ion ; Quantum mechanics Machine Learning in Materials Science ; Advanced NMR Techniques and Applications ; Zeolite Catalysis and Synthesis
Authors: H. M. Johnson | Filipp Gusev | Jordan T. Dull | Yejoon Seo | Rodney D. Priestley | Olexandr Isayev | Barry P. Rand
Source: ChemRxiv (preprint, 2024-06-25)
Field: Materials Science — Materials Chemistry

Discovery of Crystallizable Organic Semiconductors with Machine Learning Crystalline organic semiconductors are known to have improved charge carrier mobility and exciton diffusion length in comparison to their amorphous counterparts. Certain organic molecular thin films can be transitioned from initially prepared amorphous layers to large-scale crystalline films via abrupt thermal annealing. Ideally, these films crystallize as platelets with long-range-ordered domains on the scale of tens to hundreds of microns. However, other organic molecular thin films may instead crystallize as spherulites or resist crystallization entirely. Organic molecules that have the capability of transforming into a platelet morphology feature both high melting point (Tm) and crystallization driving force (ΔGc). In this work, we employed machine learning (ML) to identify candidate organic materials with the potential to crystallize into platelets by estimating the aforementioned thermal properties. Six organic molecules identified by the ML algorithm were experimentally evaluated; three crystallized as platelets, one crystallized as a spherulite, and two resisted thin film crystallization. These results demonstrate a successful application of ML in the scope of predicting thermal properties of organic molecules and reinforce the principles of Tm and ΔGc as metrics that govern the crystallization of organic thin films. Organic semiconductor ; Semiconductor ; Materials science ; Engineering physics ; Computer science ; Optoelectronics ; Engineering Machine Learning in Materials Science
Authors: Hong Wang | Hongwei Du | Jian Hui | Lanting Zhang
Source: Research Square (preprint, 2024-04-15)
Field: Materials Science — Materials Chemistry

DenseGNN: universal and scalable deeper graph neural networks for high-performance property prediction in crystals and molecules Scalability ; Computer science ; Embedding ; Theoretical computer science ; Artificial neural network ; Graph ; Artificial intelligence ; Generality ; Machine learning ; Distributed computing Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Hyuntae Lim | YounJoon Jung
Source: Research Square (preprint, 2024-04-11)
Field: Chemistry — Analytical Chemistry

Synergistic Modeling of Liquid Properties: Integrating Neural Network-Derived Molecular Features with Modified Kernel Models <title>Abstract</title> A significant challenge in applying machine learning to computational chemistry, particularly considering the growing complexity of contemporary machine learning models, is the scarcity of available experimental data. To address this issue, we introduce an approach that derives molecular features from an intricate neural network-based model and applies them to a simpler conventional machine learning model that is robust to overfitting. This method can be applied to predict various properties of a liquid system, including viscosity or surface tension, based on molecular features drawn from the ab initio-calculated free energy of solvation. Furthermore, we propose a modified kernel model that includes Arrhenius temperature dependence to incorporate theoretical principles and diminish extreme nonlinearity in the model. The modified kernel model demonstrated significant improvements in certain scenarios and possible extensions to various theoretical concepts of molecular systems. Artificial neural network ; Kernel (algebra) ; Computer science ; Artificial intelligence ; Biological system ; Mathematics ; Biology Spectroscopy and Chemometric Analyses
Authors: Changxi Yang | Chenyu Wu | Wenbo Xie | Daiqian Xie | P. Hu
Source: ChemRxiv (preprint, 2024-10-13)
Field: Materials Science — Materials Chemistry

Developing General Reactive Element-Based Machine Learning Potentials as the Main Computational Engine for Heterogeneous Catalysis Machine learning potentials (MLPs) have emerged as a promising technique to significantly enhance efficiency by replacing computationally expensive quantum mechanical calculations. However, developing truly universal MLPs remains challenging, as the consensus is that MLPs can only be used for similar structures that they have been trained on, while the vast and diverse chemical space is difficult to fully sample using the common system-dependent sampling methods. Here, our approach leverages a unique random exploration via imaginary chemicals optimization (REICO) strategy, which enables unbiased exploration of chemical space by focusing on atomic interactions. The resulting EMLP is inherently general and reactive, capable of accurately predicting elementary reactions without explicit structural or reaction pathway inputs. Benchmarked across various representative calculations of heterogeneous catalysis, our EMLP achieves quantitative agreement with density functional theory (DFT) calculations. This demonstrates the potential of EMLP as a powerful, general, and user-friendly tool for modeling complex chemical systems, paving the way to replace DFT calculations for large and intricate systems. Our approach is also applicable to broader fields such as materials science and molecular biology, representing a paradigm shift in MLPs-related research. Element (criminal law) ; Catalysis ; Computer science ; Reactive intermediate ; Chemistry ; Organic chemistry ; Political science Machine Learning in Materials Science
Authors: Sergei Tretiak
Source: ECS Meeting Abstracts (article, 2024-08-09)
Field: Materials Science — Materials Chemistry

(Invited) Machine Learning in Chemistry: Reactive Force Fields for Carbon Structure Formation Machine learning (ML) became a premier tool for modeling chemical processes and materials properties. ML interatomic potentials have become an efficient alternative to computationally expensive quantum chemistry simulations. In the case of reactive chemistry designing high-quality training data sets is crucial to overall model accuracy. To address this challenge, we develop a general reactive ML interatomic potential through unbiased active learning with an atomic configuration sampler inspired by nanoreactor molecular dynamics. The resulting model is then applied to study five distinct condensed-phase reactive chemistry systems: carbon solid-phase nucleation, graphene ring formation from acetylene, biofuel additives, combustion of methane and the spontaneous formation of glycine from early-earth small molecules. In all cases, the results closely match experiment and/or previous studies using traditional model chemistry methods. Importantly, the model does not need to be refit for each application, enabling high throughput in silico reactive chemistry experimentation. Nanoreactor ; Chemistry ; Reactive intermediate ; Molecular dynamics ; Quantum chemistry ; Nucleation ; Nanotechnology ; Molecule ; Computational chemistry ; Nanoparticle ; Materials science ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Ting Bao | Runzhang Xu | He Li | Xiaoxun Gong | Zechen Tang | Jingheng Fu | Wenhui Duan | Yong Xu
Source: arXiv (Cornell University) (preprint, 2024-04-09)
Field: Materials Science — Materials Chemistry

Deep-Learning Database of Density Functional Theory Hamiltonians for Twisted Materials Moiré-twisted materials have garnered significant research interest due to their distinctive properties and intriguing physics. However, conducting first-principles studies on such materials faces challenges, notably the formidable computational cost associated with simulating ultra-large twisted structures. This obstacle impedes the construction of a twisted materials database crucial for datadriven materials discovery. Here, by using high-throughput calculations and state-of-the-art neural network methods, we construct a Deep-learning Database of density functional theory (DFT) Hamiltonians for Twisted materials named DDHT. The DDHT database comprises trained neural-network models of over a hundred homo-bilayer and hetero-bilayer moiré-twisted materials. These models enable accurate prediction of the DFT Hamiltonian for these materials across arbitrary twist angles, with an averaged mean absolute error of approximately 1.0 meV or lower. The database facilitates the exploration of flat bands and correlated materials platforms within ultra-large twisted structures. Density functional theory ; Computer science ; Artificial intelligence ; Physics ; Quantum mechanics Machine Learning in Materials Science
Authors: Hong-Fei Wu | Lijun Wu | Guoqing Liu | Zhirong Liu | Bin Shao | Zun Wang
Source: arXiv (Cornell University) (preprint, 2024-05-26)
Field: Materials Science — Materials Chemistry

SE3Set: Harnessing equivariant hypergraph neural networks for molecular representation learning In this paper, we develop SE3Set, an SE(3) equivariant hypergraph neural network architecture tailored for advanced molecular representation learning. Hypergraphs are not merely an extension of traditional graphs; they are pivotal for modeling high-order relationships, a capability that conventional equivariant graph-based methods lack due to their inherent limitations in representing intricate many-body interactions. To achieve this, we first construct hypergraphs via proposing a new fragmentation method that considers both chemical and three-dimensional spatial information of molecular system. We then design SE3Set, which incorporates equivariance into the hypergragh neural network. This ensures that the learned molecular representations are invariant to spatial transformations, thereby providing robustness essential for accurate prediction of molecular properties. SE3Set has shown performance on par with state-of-the-art (SOTA) models for small molecule datasets like QM9 and MD17. It excels on the MD22 dataset, achieving a notable improvement of approximately 20% in accuracy across all molecules, which highlights the prevalence of complex many-body interactions in larger molecules. This exceptional performance of SE3Set across diverse molecular structures underscores its transformative potential in computational chemistry, offering a route to more accurate and physically nuanced modeling. Equivariant map ; Hypergraph ; Representation (politics) ; Artificial neural network ; Computer science ; Mathematics ; Theoretical computer science ; Artificial intelligence ; Pure mathematics ; Discrete mathematics ; Political science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Gen IHA | Hiroki Nagashima
Source: Keisan Rikigaku Koenkai koen ronbunshu/Keisan Rikigaku Kouenkai kouen rombunshuu (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Exploration of Solid Oxide Materials with High Ionic Conductivity using Molecular Dynamics and Machine Learning This study explored solid oxide materials exhibiting high oxygen ion conduction properties by using molecular dynamics (MD) and machine learning. Predicting model of ionic conductivity was constructed based on the machine learning in which ionic conductivity data measured experimentally and by MD simulation were used. Ionic conductivity data in various compositions of solid oxide material were applied to the construction of the model. MD simulations were performed to compensate the lack of experimental data of ionic conductivity. As results, it was found that the temperature and the molecular concentration of iron have large effect on the Ionic conductivity. Ionic conductivity ; Ionic bonding ; Oxide ; Molecular dynamics ; Materials science ; Conductivity ; Computer science ; Nanotechnology ; Chemical physics ; Chemical engineering ; Artificial intelligence ; Chemistry ; Physical chemistry ; Ion ; Computational chemistry ; Engineering ; Metallurgy ; Organic chemistry Machine Learning in Materials Science
Authors: Kelly Cristine da Silveira | Thomas Hille | Matheus Moraes Gago | Josiele da Silva Teixeira | Guilherme Leite | Jonathan N. Gois | Antônio José da Silva Neto
Source: Ciência e Natura (article, 2024-11-04)
Field: Materials Science — Polymers and Plastics

Structural optimization of PNIPAM-derived thermoresponsive polymers: a computational approach employing artificial neural networks and genetic algorithms In this study, artificial neural networks (ANNs) and genetic algorithms (GAs) are employed together to design optimized polymeric structures with superior cloud points. The database from a previous study of polymer synthesis with thermoresponsive polymers was used to create ANN-based models, which enabled the formulation and solution of the inverse problem using the GA. The regressors, with an average RMSE of less than 0.7 ºC, were used in the polymer evolution process over 20 generations. Mutation and selection operations led to the creation of 10 novel hybrid macromolecules with an average cloud point of 80 ºC. Furthermore, the special roles of some chemical groups are recognized and favor the structural mapping of PNIPAM-based materials. The computational approach presented here demonstrates that it is a promising tool in the development of new materials. Artificial neural network ; Computer science ; Genetic algorithm ; Polymer ; Artificial intelligence ; Algorithm ; Materials science ; Machine learning ; Composite material Polymer composites and self-healing ; Advanced Sensor and Energy Harvesting Materials ; Polymer Nanocomposites and Properties
Authors: Jaka Fajar Fatriansyah | Hanif Lanang Mahardi | Muhammad Ali Yafi Rizky | R. Janani Surya | Andreas Federico | Siti Norasmah Surip
Source:  (article, 2024-09-21)
Field: Materials Science — Materials Chemistry

Predicting The Melting Temperature Of Polymers Using Machine Learning One crucial factor in polymer material fabrication is its melting temperature, which is key to determining the manufacturing process. However, difficulties have been encountered in finding correlations between the melting temperature and influencing factors. Therefore, machine learning is employed to discern the relationship between polymer structure and its melting temperature. The models used include KNN, SVR, and XGB, as well as deep learning with ANN and NLP. The ability of SMILES and NLP descriptors in describing polymer structures is also examined. The input length of the models varied between 50 and 200. All models produce the most accurate predictions with an input length of 200. KNN and SVR generate relatively stable but consistently low R-squared values, with MAE values above 40. ANN and NLP exhibit high R-squared values in some trials but with low stability and relatively high MAE values. XGBoost consistently produces higher and more stable R2 values. The best-performing XGBoost model is validated using SMILES strings outside the dataset, demonstrating its predictive ability with an MAE of 24.09, deemed sufficient for predicting melting temperatures. Polymer ; Melting temperature ; Materials science ; Computer science ; Machine learning ; Artificial intelligence ; Composite material Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Injection Molding Process and Properties
Authors: Haoke Qiu | Zhao‐Yan Sun
Source: ChemRxiv (preprint, 2024-10-14)
Field: Materials Science — Materials Chemistry

Is BigSMILES the Friend of Polymer Machine Learning? Computational methods, exemplified by machine learning (ML), have provided theoretical guidance and solutions for the development of sustainable polymers, accelerating advancements in materials for societal needs such as equipment, environment, health, and green energy. In previous polymer ML workflows, the Simplified Molecular-Input Line-Entry System (SMILES) notation has consistently served as the primary representation of polymer structures, though the inherent randomness of polymers has long posed challenges for SMILES in the representation learning of polymer ML. Recently, BigSMILES and its extensions have paved the way for more versatile and concise representation of polymer structures. However, whether BigSMILES outperforms SMILES in polymer ML workflows has yet to be systematically explored and demonstrated. To fill this scientific gap, we conducted extensive experiments investigating this question, encompassing a variety of polymer property prediction and inverse design tasks based on both image and text inputs. Our findings reveal that in 11 tasks involving homopolymer systems, BigSMILES-based ML workflows exhibit performance comparable to or even exceeding that of SMILES, underscoring the efficacy of BigSMILES in representing polymer structures. Furthermore, BigSMILES offers a more compact textual representation compared to SMILES, significantly reducing the computational cost of model training, particularly for large language models. Through these comprehensive experiments, we for the first time demonstrate that BigSMILES can achieve performance on par with SMILES, while also facilitating faster model training and reducing energy consumption, which could have a substantial impact on a wide range of polymer tasks in the future, including property prediction (and classification) and polymer generation across various polymer types. Computer science ; Artificial intelligence ; Polymer science ; Psychology ; Materials science Machine Learning in Materials Science
Authors: Lung-Yi Chen | Yi‐Pei Li
Source: ChemRxiv (preprint, 2024-07-04)
Field: Materials Science — Materials Chemistry

Machine Learning-Guided Strategies for Reaction Condition Design and Optimization This review surveys the recent advances and challenges in predicting and optimizing reaction conditions using machine learning techniques. The paper emphasizes the importance of acquiring and processing large and diverse datasets of chemical reactions, and the use of both global and local models to guide the design of synthetic processes. Global models exploit the information from comprehensive databases to suggest general reaction conditions for new reactions, while local models fine-tune the specific parameters for a given reaction family to improve yield and selectivity. The paper also identifies the current limitations and opportunities in this field, such as the data quality and availability, and the integration of high-throughput experimentation. The paper demonstrates how the combination of chemical engineering, data science, and ML algorithms can enhance the efficiency and effectiveness of reaction condition design, and enable novel discoveries in synthetic chemistry. Computer science ; Artificial intelligence ; Machine learning ; Mathematical optimization ; Mathematics Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Catalysis and Oxidation Reactions
Authors: Poi Tamrakar | Abha Pathak | Pallavi Thorat | Mily Lal | Akanksha Goel | Manisha Bhende | Swati Sharma
Source: AIP conference proceedings (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Deep learning in chemical reaction prediction and synthesis planning Computer science ; Artificial intelligence ; Deep learning ; Machine learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Parastoo Semnani | Mihail Bogojeski | Florian Bley | Zizheng Zhang | Qiong Wu | Thomas Kneib | Jan Herrmann | Christoph Weisser | F. Patcas | Klaus‐Robert Müller
Source: arXiv (Cornell University) (preprint, 2024-07-10)
Field: Materials Science — Materials Chemistry

A Machine Learning and Explainable AI Framework Tailored for Unbalanced Experimental Catalyst Discovery The successful application of machine learning (ML) in catalyst design relies on high-quality and diverse data to ensure effective generalization to novel compositions, thereby aiding in catalyst discovery. However, due to complex interactions, catalyst design has long relied on trial-and-error, a costly and labor-intensive process leading to scarce data that is heavily biased towards undesired, low-yield catalysts. Despite the rise of ML in this field, most efforts have not focused on dealing with the challenges presented by such experimental data. To address these challenges, we introduce a robust machine learning and explainable AI (XAI) framework to accurately classify the catalytic yield of various compositions and identify the contributions of individual components. This framework combines a series of ML practices designed to handle the scarcity and imbalance of catalyst data. We apply the framework to classify the yield of various catalyst compositions in oxidative methane coupling, and use it to evaluate the performance of a range of ML models: tree-based models, logistic regression, support vector machines, and neural networks. These experiments demonstrate that the methods used in our framework lead to a significant improvement in the performance of all but one of the evaluated models. Additionally, the decision-making process of each ML model is analyzed by identifying the most important features for predicting catalyst performance using XAI methods. Our analysis found that XAI methods, providing class-aware explanations, such as Layer-wise Relevance Propagation, identified key components that contribute specifically to high-yield catalysts. These findings align with chemical intuition and existing literature, reinforcing their validity. We believe that such insights can assist chemists in the development and identification of novel catalysts with superior performance. Catalysis ; Computer science ; Artificial intelligence ; Machine learning ; Chemistry ; Organic chemistry Machine Learning in Materials Science ; Data Quality and Management