jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Lamaire, Aran | Bocus, Massimo | Goeminne, Ruben | Vandenhaute, Sander | Cools-Ceuppens, Maarten | Verstraelen, Toon | Van Speybroeck, Veronique
Source: Ghent University Academic Bibliography (Ghent University) (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Nuclear quantum effects in proton transfer reactions Proton transfer reactions are omnipresent in chemical processes. Due to the light mass of the proton, an adequate description of nuclear quantum effects (NQEs) is indispensable to model these reactions. Starting from three typical molecular proton transfer reactions, this talk demonstrates the impact of a proper description of the quantum free energy profile [1], which can differ significantly from the semi-classical free energy profile of the ring polymer centroid in path integral molecular dynamics (PIMD) simulations. Through the use of neural network machine learning potentials (MLPs), free energy calculations involving enhanced sampling can be easily performed for a wide range of temperatures with a systematic inclusion of NQEs. This concept is further elaborated by broadening the scope from molecular systems to a nanoporous material, to study proton hopping in the chabazite zeolite. Besides the influence of NQEs on the free energy of the proton hopping, also the hopping kinetics are studied, as well as the kinetic isotope effect on the hopping. By relying on an efficient approach to generate the data required to train an MLP, the use of MLPs in combination with NQEs and enhanced sampling is steadily becoming a standard practice, even for large nanoporous materials. [3] [1]A. Lamaire, M. Cools-Ceuppens, M. Bocus, T. Verstraelen, V. Van Speybroeck, J. Chem. Theory Comput., 19, 18-24 (2022) [2]M. Bocus, R. Goeminne, A. Lamaire, M. Cools-Ceuppens, T. Verstraelen, V. Van Speybroeck, Nat. Commun., 14, 1008 (2023) [3]S. Vandenhaute, M. Cools-Ceuppens, S. DeKeyser, T. Verstraelen, V. Van Speybroeck, npj. Comput. Mater., 9, 19 (2023) Proton ; Kinetic energy ; Nanoporous ; Quantum ; Molecular dynamics ; Kinetic isotope effect ; Range (aeronautics) ; Path integral formulation Machine Learning in Materials Science ; Advanced NMR Techniques and Applications ; Advanced Physical and Chemical Molecular Interactions
Authors: Akshansh Mishra
Source: Preprints.org (preprint, 2023-08-07)
Field: Materials Science — Materials Chemistry

Transport Equation based Physics Informed Neural Network to predict the Yield Strength of Architected Materials In this research, the application of the Physics-Informed Neural Network (PINN) model is explored to solve transport equation-based Partial Differential Equations (PDEs). The primary objective is to analyze the impact of different activation functions incorporated within the PINN model on its predictive performance, specifically assessing the Mean Squared Error (MSE) and Mean Absolute Error (MAE). The dataset used in the study consists of a varied set of input parameters related to strut diameter, unit cell size, and the corresponding yield stress values. Through this investigation, the aim is to understand the effectiveness of the PINN model and the significance of choosing appropriate activation functions for solving complex PDEs in real-world applications. The outcomes suggest that the choice of activation function may have minimal influence on the model&amp;#039;s predictive accuracy for this particular problem. The PINN model showcases exceptional generalization capabilities, indicating its capacity to avoid overfitting with the provided dataset. The research underscores the importance of striking a balance between performance and computational efficiency while selecting an activation function for specific real-world applications. These valuable findings contribute to advancing the understanding and potential adoption of PINN as an effective tool for solving challenging PDEs in diverse scientific and engineering domains. Overfitting ; Activation function ; Artificial neural network ; Function (biology) ; Structural equation modeling ; Computer science ; Generalization ; Mean squared error ; Partial differential equation ; Set (abstract data type) ; Mathematical optimization ; Applied mathematics ; Machine learning ; Artificial intelligence ; Mathematics ; Statistics Material Properties and Failure Mechanisms ; Model Reduction and Neural Networks ; Neural Networks and Applications
Authors: Jonathan P. Mailoa | Xin Li | Jiezhong Qiu | Shengyu Zhang
Source: arXiv (Cornell University) (preprint, 2023-01-12)
Field: Materials Science — Materials Chemistry

Multi-Constraint Molecular Generation using Sparsely Labelled Training Data for Localized High-Concentration Electrolyte Diluent Screening Recently, machine learning methods have been used to propose molecules with desired properties, which is especially useful for exploring large chemical spaces efficiently. However, these methods rely on fully labelled training data, and are not practical in situations where molecules with multiple property constraints are required. There is often insufficient training data for all those properties from publicly available databases, especially when ab-initio simulation or experimental property data is also desired for training the conditional molecular generative model. In this work, we show how to modify a semi-supervised variational auto-encoder (SSVAE) model which only works with fully labelled and fully unlabelled molecular property training data into the ConGen model, which also works on training data that have sparsely populated labels. We evaluate ConGen's performance in generating molecules with multiple constraints when trained on a dataset combined from multiple publicly available molecule property databases, and demonstrate an example application of building the virtual chemical space for potential Lithium-ion battery localized high-concentration electrolyte (LHCE) diluents. Computer science ; Property (philosophy) ; Chemical space ; Constraint (computer-aided design) ; Training (meteorology) ; Encoder ; Training set ; Embedding ; Experimental data ; Electrolyte ; Generative model ; Artificial intelligence ; Data mining ; Machine learning ; Generative grammar ; Chemistry ; Physics ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fuel Cells and Related Materials
Authors: Fadil Can Malay | Ozge Keremer | Pınar Kırcı | Perin Unal | Bilgin Umut Deveci
Source: Bursa Uludag University - AVESIS (article, 2023-08-15)
Field: Materials Science — Materials Chemistry

Optimisation of a Chemical Process by Using Machine Learning Algorithms with Surrogate Modeling Process (computing) ; Computational learning theory ; Support vector machine ; Feature selection ; Process modeling Machine Learning in Materials Science ; Process Optimization and Integration ; Gaussian Processes and Bayesian Inference
Authors: Alvaro Carbonero | Alexandre Duval | Victor Schmidt | Santiago Miret | Álex Hernández-García | Yoshua Bengio | David Rolnick
Source: arXiv (Cornell University) (preprint, 2023-10-10)
Field: Materials Science — Materials Chemistry

On the importance of catalyst-adsorbate 3D interactions for relaxed energy predictions The use of machine learning for material property prediction and discovery has traditionally centered on graph neural networks that incorporate the geometric configuration of all atoms. However, in practice not all this information may be readily available, e.g.~when evaluating the potentially unknown binding of adsorbates to catalyst. In this paper, we investigate whether it is possible to predict a system's relaxed energy in the OC20 dataset while ignoring the relative position of the adsorbate with respect to the electro-catalyst. We consider SchNet, DimeNet++ and FAENet as base architectures and measure the impact of four modifications on model performance: removing edges in the input graph, pooling independent representations, not sharing the backbone weights and using an attention mechanism to propagate non-geometric relative information. We find that while removing binding site information impairs accuracy as expected, modified models are able to predict relaxed energies with remarkably decent MAE. Our work suggests future research directions in accelerated materials discovery where information on reactant configurations can be reduced or altogether omitted. Pooling ; Measure (data warehouse) ; Computer science ; Catalysis ; Graph ; Binding energy ; Position (finance) ; Work (physics) ; Artificial neural network ; Chemistry ; Theoretical computer science ; Artificial intelligence ; Data mining ; Physics Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Topic Modeling
Authors: Albert Thie | Maximilian F. S. J. Menger | Shirin Faraji
Source: Molecular Physics (article, 2023-02-02)
Field: Materials Science — Materials Chemistry

HOAX: a hyperparameter optimisation algorithm explorer for neural networks Computational chemistry has become an important tool to predict and understand molecular properties and reactions. Even though recent years have seen a significant growth in new algorithms and computational methods that speed up quantum chemical calculations, the bottleneck for trajectory-based methods to study photo-induced processes is still the huge number of electronic structure calculations. In this work, we present an innovative solution, in which the amount of electronic structure calculations is drastically reduced, by employing machine learning algorithms and methods borrowed from the realm of artificial intelligence. However, applying these algorithms effectively requires finding optimal hyperparameters, which remains a challenge itself. Here we present an automated user-friendly framework, HOAX, to perform the hyperparameter optimisation for neural networks, which bypasses the need for a lengthy manual process. The neural network-generated potential energy surfaces (PESs) reduce the computational costs compared to the ab initio-based PESs. We perform a comparative investigation on the performance of different hyperparameter optimiziation algorithms, namely grid search, simulated annealing, genetic algorithm, and Bayesian optimizer in finding the optimal hyperparameters necessary for constructing the well-performing neural network in order to fit the PESs of small organic molecules. Our results show that this automated toolkit not only facilitate a straightforward way to perform the hyperparameter optimisation but also the resulting neural networks-based generated PESs are in reasonable agreement with the ab initio-based PESs. Hyperparameter optimization ; Hyperparameter ; Computer science ; Artificial neural network ; Machine learning ; Artificial intelligence ; Bottleneck ; Algorithm Machine Learning in Materials Science ; Machine Learning and Data Classification ; Reservoir Engineering and Simulation Methods
Authors: Mikołaj Sacha | Michał Sadowski | Piotr Kozakowski | Ruard van Workum | Stanisław Jastrzȩbski
Source: arXiv (Cornell University) (preprint, 2023-10-11)
Field: Materials Science — Materials Chemistry

Molecule-Edit Templates for Efficient and Accurate Retrosynthesis Prediction Retrosynthesis involves determining a sequence of reactions to synthesize complex molecules from simpler precursors. As this poses a challenge in organic chemistry, machine learning has offered solutions, particularly for predicting possible reaction substrates for a given target molecule. These solutions mainly fall into template-based and template-free categories. The former is efficient but relies on a vast set of predefined reaction patterns, while the latter, though more flexible, can be computationally intensive and less interpretable. To address these issues, we introduce METRO (Molecule-Edit Templates for RetrOsynthesis), a machine-learning model that predicts reactions using minimal templates - simplified reaction patterns capturing only essential molecular changes - reducing computational overhead and achieving state-of-the-art results on standard benchmarks. Retrosynthetic analysis ; Template ; Computer science ; Set (abstract data type) ; Overhead (engineering) ; Artificial intelligence ; Sequence (biology) ; Theoretical computer science ; Programming language ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Vincent Martinetto | Karan Shah | Attila Cangi | Aurora Pribram−Jones
Source: arXiv (Cornell University) (preprint, 2023-12-23)
Field: Materials Science — Materials Chemistry

Inverting the Kohn-Sham equations with physics-informed machine learning Electronic structure theory calculations offer an understanding of matter at the quantum level, complementing experimental studies in materials science and chemistry. One of the most widely used methods, density functional theory (DFT), maps a set of real interacting electrons to a set of fictitious non-interacting electrons that share the same probability density. Ensuring that the density remains the same depends on the exchange-correlation (XC) energy and, by a derivative, the XC potential. Inversions provide a method to obtain exact XC potentials from target electronic densities, in hopes of gaining insights into accuracy-boosting approximations. Neural networks provide a new avenue to perform inversions by learning the mapping from density to potential. In this work, we learn this mapping using physics-informed machine learning (PIML) methods, namely physics informed neural networks (PINNs) and Fourier neural operators (FNOs). We demonstrate the capabilities of these two methods on a dataset of one-dimensional atomic and molecular models. The capabilities of each approach are discussed in conjunction with this proof-of-concept presentation. The primary finding of our investigation is that the combination of both approaches has the greatest potential for inverting the Kohn-Sham equations at scale. Artificial neural network ; Kohn–Sham equations ; Electronic density ; Density functional theory ; Boosting (machine learning) ; Statistical physics ; Set (abstract data type) ; Physics ; Electron ; Artificial intelligence ; Machine learning ; Computer science ; Theoretical physics ; Quantum mechanics Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Quantum many-body systems
Authors: Bhupesh Bishnoi
Source: arXiv (Cornell University) (preprint, 2023-05-05)
Field: Materials Science — Materials Chemistry

Materials Informatics: An Algorithmic Design Rule Materials informatics, data-enabled investigation, is a "fourth paradigm" in materials science research after the conventional empirical approach, theoretical science, and computational research. Materials informatics has two essential ingredients: fingerprinting materials proprieties and the theory of statistical inference and learning. We have researched the organic semiconductor's enigmas through the materials informatics approach. By applying diverse neural network topologies, logical axiom, and inferencing information science, we have developed data-driven procedures for novel organic semiconductor discovery for the semiconductor industry and knowledge extraction for the materials science community. We have reviewed and corresponded with various algorithms for the neural network design topology for the materials informatics dataset. Materials informatics ; Informatics ; Computer science ; Axiom ; Inference ; Data science ; Engineering informatics ; Artificial neural network ; Health informatics ; Artificial intelligence ; Data mining ; Engineering ; Mathematics Machine Learning in Materials Science
Authors: Cheng-Han Li | Daniel P. Tabor
Source: ChemRxiv (preprint, 2023-06-30)
Field: Materials Science — Materials Chemistry

Generative Organic Electronic Molecular Design via Reinforcement Learning Integration with Quantum Chemistry: Tuning Singlet and Triplet Energy Energy Levels Generative molecular design strategies have emerged as promising alternatives to trial-and-error approaches for exploring and optimizing within large chemical spaces. To date, generative models with reinforcement learning approaches have frequently used low-cost methods to evaluate the quality of the generated molecules, enabling many loops through the generative model. However, for functional molecular materials tasks, such low-cost methods are either not available or would require the generation of large amounts of training data to train surrogate machine learning models. In this work, we develop a framework that connects the REINVENT reinforcement learning framework with excited state quantum chemistry calculations to discover molecules with specified molecular excited state energy levels, specifically molecules with excited state landscapes that would serve as promising singlet fission or triplet-triplet annihilation materials. We employ a two-step curriculum strategy to find a set of diverse promising molecules, then exploit a more focused chemical space with anthracene derivatives. Under this protocol, we show that the framework can find desired molecules and improve Pareto fronts for targeted properties versus synthesizability. Moreover, from this framework, we are able to find several different design principles used by chemists for the design of singlet fission and triplet-triplet annihilation molecules. Reinforcement learning ; Computer science ; Singlet fission ; Chemical space ; Excited state ; Chemistry ; Singlet state ; Artificial intelligence ; Theoretical computer science ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Computational Drug Discovery Methods
Authors: Baozhu Qi | Haohua Qin | Yanan Li | Mingjia Wang
Source: Ferroelectrics (article, 2023-05-19)
Field: Materials Science — Materials Chemistry

Dynamic crystal monitoring method for crystallization process based on deep learning The real-time monitoring of the crystallization process plays an important role in the optimal control of the crystallization process. Because of the characteristics of multiple concurrences and irregular morphology in the crystallization process, this paper starts from the two aspects of target detection and image segmentation to explore the actual and effective dynamic monitoring method in the crystallization process. By using YOLOV5s target detection algorithm to detect the crystal position in the image to determine the target position. In addition, the adaptive threshold segmentation method and Sobel edge feature extraction algorithm are used to achieve accurate positioning and segmentation of crystal target, to achieve the position detection of crystal in the image and accurate segmentation of edge information. Crystallization ; Segmentation ; Process (computing) ; Artificial intelligence ; Materials science ; Position (finance) ; Computer science ; Enhanced Data Rates for GSM Evolution ; Sobel operator ; Edge detection ; Image segmentation ; Computer vision ; Crystal (programming language) ; Pattern recognition (psychology) ; Image (mathematics) ; Image processing Crystallization and Solubility Studies ; Minerals Flotation and Separation Techniques ; Geochemistry and Geologic Mapping
Authors: Jason I. Brown | Bohan Chen | Harris Hardiman-Mostow | Adrien Weihs | Andrea L. Bertozzi | Jocelyn Chanussot
Source:  (article, 2023-10-31)
Field: Chemistry — Analytical Chemistry

Material Identification in Complex Environments: Neural Network Approaches to Hyperspectral Image Analysis Hyperspectral imagery is often used in chemometric studies for quality sorting and recycling due to its ability to produce rich spectroscopy data. In this paper, we study plastics detection in a complex environment. In particular, we analyze hyperspectral images of three scenes with spectra in the near-infrared and visible wavelength ranges; our task is to detect plastic within the scenes. The images contain materials with high intraclass variability and significant mixing. Our novel contribution compares various methods for hyperspectral pixel classification in these complicated, real-world environments, specifically deep methods such as contrastive learning and autoencoders, as well as comparing the viability of the hyperspectral cameras’ light spectrum for the application of plastic detection. Hyperspectral imaging ; Identification (biology) ; Computer science ; Artificial neural network ; Artificial intelligence ; Pattern recognition (psychology) ; Image (mathematics) ; Computer vision ; Biology Spectroscopy and Chemometric Analyses ; Cultural Heritage Materials Analysis ; Remote-Sensing Image Classification
Authors: 敦之 石川 | Atsushi Ishikawa
Source: Institutional Repositories DataBase (IRDB) (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Self-Supervised Learning with Atom Replacement for Catalyst Energy Prediction by Graph Neural Networks Artificial neural network ; Graph ; Atom (system on chip) ; Energy (signal processing) ; Catalysis Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; CO2 Reduction Techniques and Catalysts
Authors: A. Rosen
Source: Faculty of 1000 Research Ltd (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Referee report. For: scikit-matter : A Suite of Generalisable Machine Learning Methods Born out of Chemistry and Materials Science [version 1; peer review: 1 approved] Suite ; Feature (linguistics) ; Product (mathematics) Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference ; Advanced Electron Microscopy Techniques and Applications
Authors: Xiliang Lian | Mathieu Salanne
Source: ChemRxiv (preprint, 2023-09-17)
Field: Materials Science — Materials Chemistry

Capturing the interactions in the BaSnF4 ionic conductor: comparison between a machine-learning potential and a polarizable force field BaSnF4 is a prospective solid state electrolyte for fluoride ion batteries. However, the diffusion mechanism of the fluoride ions remains difficult to study, both in experiments and in simulations. In principle, ab initio molecular dynamics could allow to fill this gap, but this method remains very costly from the computational point of view. Using machine learning potentials is a promising method that can potentially address the the accuracy issues of classical empirical potentials while maintaining high efficiency. In this work, we fitted a dipole polarizable ion model and trained machine learning potential for BaSnF4 and made comprehensive comparisons on the ease of training, accuracy and efficiency. We also compared the results with the case of a simpler ionic system (NaF). We show that contrarily to the latter, for BaSnF4 the machine learning potential offers much higher versatility. The current work lays foundations for the investigation of fluoride ion mobility in BaSnF4 and provides insight on the choice of methods for atomistic simulations. Polarizability ; Dipole ; Ion ; Ionic bonding ; Computer science ; Conductor ; Ab initio ; Work (physics) ; Point (geometry) ; Force field (fiction) ; Machine learning ; Artificial intelligence ; Chemical physics ; Chemistry ; Materials science ; Physics ; Thermodynamics ; Mathematics ; Molecule Machine Learning in Materials Science ; Inorganic Fluorides and Related Compounds ; Inorganic Chemistry and Materials
Authors: 亮 高橋 | Akira Takahashi | 史康 大場 | Fumiyasu Oba
Source: Institutional Repositories DataBase (IRDB) (article, 2023-10-01)
Field: Materials Science — Materials Chemistry

Autonomous Materials Screening Using High-Throughput Theoretical Calculation and Machine Learning Noise (video) ; Feature (linguistics) ; Active learning (machine learning) ; Computational learning theory ; Automation Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Advanced Neural Network Applications