jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ana Molina-Taborda | Pilar Cossio | Olga Lopez‐Acevedo | Marylou Gabrié
Source: arXiv (Cornell University) (preprint, 2024-01-29)
Field: Materials Science — Materials Chemistry

Active learning of Boltzmann samplers and potential energies with quantum mechanical accuracy Extracting consistent statistics between relevant free-energy minima of a molecular system is essential for physics, chemistry and biology. Molecular dynamics (MD) simulations can aid in this task but are computationally expensive, especially for systems that require quantum accuracy. To overcome this challenge, we develop an approach combining enhanced sampling with deep generative models and active learning of a machine learning potential (MLP). We introduce an adaptive Markov chain Monte Carlo framework that enables the training of one Normalizing Flow (NF) and one MLP per state, achieving rapid convergence towards the Boltzmann distribution. Leveraging the trained NF and MLP models, we compute thermodynamic observables such as free-energy differences or optical spectra. We apply this method to study the isomerization of an ultrasmall silver nanocluster, belonging to a set of systems with diverse applications in the fields of medicine and catalysis. Boltzmann constant ; Quantum ; Statistical physics ; Physics ; Environmental science ; Quantum mechanics Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Advanced Electron Microscopy Techniques and Applications
Authors: Karina Ruzaeva | Dieter Weber | Jonas Werner | Stefan Sandfeld
Source: BIO Web of Conferences (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Unsupervised Machine Learning-based STEM diffraction pattern denoising for enhanced grain visualization in phase change materials Phase change materials (PCM) are an emerging class of materials in whichdifferent phases of the same material may have different optical, electric, ormagnetic properties and can be used as a phase change memory [1]. Phase-change memory materials, exemplified by (Ag, In)-doped Sb2Te (AIST) in thisresearch, have several advantages, including high-speed read and writeoperations, non-volatility, and a long lifespan [2]. PCMs are able to switchbetween amorphous and crystalline phases when subjected to heat orelectrical current. However, the full understanding of PCMs depends heavilyon accurate characterization, often through techniques such as scanningtransmission electron microscopy (STEM). Visualization ; Unsupervised learning ; Phase change ; Materials science ; Phase (matter) ; Artificial intelligence ; Pattern recognition (psychology) ; Computer science ; Engineering ; Chemistry ; Engineering physics Machine Learning in Materials Science ; Phase-change materials and chalcogenides ; X-ray Diffraction in Crystallography
Authors: Tiago F. T. Cerqueira | Wang, Haichen | Silvana Botti | Miguel A. L. Marques
Source: arXiv (Cornell University) (preprint, 2024-06-28)
Field: Chemistry — Physical and Theoretical Chemistry

A non-orthogonal representation of the chemical space We present a novel approach to generate a fingerprint for crystalline materials that balances efficiency for machine processing and human interpretability, allowing its application in both machine learning inference and understanding of structure-property relationships. Our proposed material encoding has two components: one representing the crystal structure and the other characterizing the chemical composition, that we call Pettifor embedding. For the latter we construct a non-orthogonal space where each axis represents a chemical element and where the angle between the axes quantifies a measure of the similarity between them. The chemical composition is then defined by the point on the unit sphere in this non-orthogonal space. We show that the Pettifor embeddings systematically outperform other commonly used elemental embeddings in compositional machine learning models. Using the Pettifor embeddings to define a distance metric and applying dimension reduction techniques, we construct a two-dimensional global map of the space of thermodynamically stable crystalline compounds. Despite their simplicity, such maps succeed in providing a physical separation of material classes according to basic physical properties. Representation (politics) ; Space (punctuation) ; Chemical space ; Mathematics ; Algebra over a field ; Computer science ; Pure mathematics ; Chemistry ; Political science History and advancements in chemistry ; Various Chemistry Research Topics
Authors: Alexandra Boltasseva
Source:  (article, 2024-10-02)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Plasmonics for sustainable technologies and green energy: from advancing materials to machine-learning assisted designs Plasmon ; Sustainable design ; Computer science ; Sustainable energy ; Nanotechnology ; Engineering physics ; Engineering ; Materials science ; Architectural engineering ; Sustainability ; Renewable energy ; Optoelectronics ; Electrical engineering Gold and Silver Nanoparticles Synthesis and Applications ; Plasmonic and Surface Plasmon Research
Authors: Wenqi Guo | Yiyang Du | Mohamed Shehata
Source: ChemRxiv (preprint, 2024-10-14)
Field: Materials Science — Materials Chemistry

3D2SMILES: Translating Physical Molecular Models into Digital DeepSMILES Notations Using Deep Learning Physical molecular models are widely used in educational settings for teaching organic and other branches of chemistry, offering an intuitive understanding of molecular structures. Conversely, while less intuitive, virtual models provide additional functionalities, such as retrieving molecular names and other properties. Currently, to the best of our knowledge, there is a gap between 3D molecular models and their digital counterparts. This paper introduces a computer vision model designed to bridge this gap by converting images of physical molecular models into their digital DeepSMILES representations. This conversion facilitates further information retrieval, enhancing educational utility. We developed synthetic and real datasets to train our model and evaluated its performance across various dataset combinations. Additionally, we attempted to improve the model’s accuracy by multi-image input and beam search. We achieved 62.0% top1 accuracy and 80.3% top-3 accuracy with beam search and multi-image input on our validation set. We also explored the model’s characteristics, such as explainability by saliency maps, and examined its calibration. We also discussed the model’s limitations and directions for future research. Computer science ; Notation ; Deep learning ; Artificial intelligence ; Natural language processing ; Linguistics ; Philosophy Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Electron and X-Ray Spectroscopy Techniques
Authors: Brian DeCost
Source: Faculty of 1000 Research Ltd (article, 2024-01-01)
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 2; peer review: 2 approved, 1 approved with reservations] Suite ; Feature (linguistics) Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference ; Advanced Electron Microscopy Techniques and Applications
Authors: Raj Kumar Gupta | S. Vidhya | Mahendra Pratap Singh | Manasi Vyankatesh Ghamande | Sudhir Kumar | Pappu Kumar
Source:  (article, 2024-11-14)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Optimizing Synthesis Parameters of BiFeO<sub>3</sub> Nanoparticles with Bayesian Neural Networks The research focuses on improving synthesis parameters for BiFeO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</inf> nanoparticles, a material with substantial promise in many applications. Conventional approaches frequently fail to precisely forecast the most favorable circumstances for synthesis due to the intricate nature of the process. This article introduces a BNN model as a solution to these restrictions. The BNN model underwent training and validation using datasets obtained from Kaggle and the Materials Project. It achieved a training accuracy of 98.5% and a validation accuracy of 97.8%. The model achieved an F1 score of 0.975 and a recall of 0.980, surpassing the performance of current technology in the sector. The findings presented here showcase the efficacy of the suggested model in delivering precise and dependable predictions, rendering it a helpful instrument for academics and practitioners in the realm of material synthesis. Artificial neural network ; Nanoparticle ; Materials science ; Bayesian probability ; Computer science ; Nanotechnology ; Artificial intelligence Multiferroics and related materials
Authors: Wenhao He | Zhibin Lu
Source: arXiv (Cornell University) (preprint, 2024-12-19)
Field: Chemistry — Physical and Theoretical Chemistry

A Theoretical Framework for the Most Probable Distribution of Meta-structures in Materials Inspired by the principle of equal probability proposed by Boltzmann in the 1870s, we establish a theoretical framework for the most probable distribution of meta-structures in materials. Furthermore, we validate the reliability of this theoretical framework based on statistical results of these meta-structures within randomly generated binary alloys. Finally, combining this theoretical framework, the high-throughput first-principles computations and machine learning, we determine the atomic chemical potentials in the binary FeCr alloy, thereby providing a demonstrative application. This theoretical framework will open a new research area and lay a foundation for the atomic-scale design of targeted properties. Distribution (mathematics) ; Statistical physics ; Computer science ; Mathematics ; Physics ; Mathematical analysis Advanced Physical and Chemical Molecular Interactions
Authors: Mohamad Hossein Nargesi | Kamran Kheiralipour | jafar amiriparian | Hossein Bagherpour
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Detection of Different Adulteration in Cinnamon Powder Using Chemical Imaging and Artificial Neural Network Method Artificial neural network ; Artificial intelligence ; Process engineering ; Materials science ; Food science ; Chromatography ; Computer science ; Chemistry ; Engineering Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Stephanie Helmling | Lars Nieradzik | Ina Jördis Sieburg-Rockel | Thomas Weibel | Stephanie Wrage | Petra Gospodnetić | Janis Keuper | Henrike Stephani | Markus Rauhut | Andrea Olbrich
Source: OpenAgrar (article, 2024-01-01)
Field: Chemistry — Organic Chemistry

Automated wood species identification in microscopic images of fibrous materials using machine learning / AI Identification (biology) ; Pattern recognition (psychology) ; Wood industry ; Species identification ; Training set ; Random forest Wood and Agarwood Research ; Wood Treatment and Properties ; Industrial Vision Systems and Defect Detection
Authors: Olga Klimanova | Nikita Rybin | Alexander V. Shapeev
Source: arXiv (Cornell University) (preprint, 2024-12-26)
Field: Materials Science — Materials Chemistry

Accelerating global search of adsorbate molecule position using machine-learning interatomic potentials with active learning We present an algorithm for accelerating the search of molecule's adsorption site based on global optimization of surface adsorbate geometries. Our approach uses a machine-learning interatomic potential (moment tensor potential) to approximate the potential energy surface and an active learning algorithm for the automatic construction of an optimal training dataset. To validate our methodology, we compare the results across various well-known catalytic systems with surfaces of different crystallographic orientations and adsorbate geometries, including CO/Pd(111), NO/Pd(100), NH$_3$/Cu(100), C$_6$H$_6$/Ag(111), and CH$_2$CO/Rh(211). In the all cases, we observed an agreement of our results with the literature. Position (finance) ; Computer science ; Molecule ; Active learning (machine learning) ; Artificial intelligence ; Physics ; Quantum mechanics ; Economics Machine Learning in Materials Science ; Various Chemistry Research Topics ; Computational Drug Discovery Methods
Authors: Wenqi Guo | Yiyang Du | Mohamed Shehata
Source: ChemRxiv (preprint, 2024-08-05)
Field: Materials Science — Materials Chemistry

3D2SMILES: Translating Physical Molecular Models into Digital DeepSMILES Notations Using Deep Learning Physical molecular models are widely used in educational settings for teaching organic and other branches of chemistry, offering an intuitive way of understanding molecular structures. Conversely, virtual models, while less intuitive, provide additional functionalities such as the ability to retrieve molecular names and other properties. Currently, to the best of our knowledge, there is a gap between 3D molecular models and their digital counterparts. This paper introduces a computer vision model designed to bridge this gap by converting images of physical molecular models into their digital DeepSMILES representations. This conversion facilitates further information retrieval, enhancing educational utility. We developed both synthetic and real datasets to train our model and evaluated its performance across various dataset combinations, model architectures, and dataset sizes. Additionally, we attempted to improve the model's accuracy by multi-image input and beam search. We achieved 62.0\% top-1 accuracy and 80.3\% top-3 accuracy with beam search and multi-image input on our validation set. We also explored the model's characteristics, such as explainability by saliency maps, error analysis, and examined its calibration. We also discussed the model's limitations and directions for future research. Computer science ; Bridge (graph theory) ; Set (abstract data type) ; Artificial intelligence ; Deep learning ; Calibration ; Notation ; Image (mathematics) ; Machine learning ; Data mining ; Information retrieval Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Cell Image Analysis Techniques
Authors: Xiao Zhu | Srinivasan S. Iyengar
Source: arXiv (Cornell University) (preprint, 2024-12-05)
Field: Materials Science — Materials Chemistry

A large language model-type architecture for high-dimensional molecular potential energy surfaces Computing high-dimensional potential energy surfaces for molecular systems and materials is considered to be a great challenge in computational chemistry with potential impact in a range of areas including the fundamental prediction of reaction rates. In this paper, we design and discuss an algorithm that has similarities to large language models in generative AI and natural language processing. Specifically, we represent a molecular system as a graph which contains a set of nodes, edges, faces, etc. Interactions between these sets, which represent molecular subsystems in our case, are used to construct the potential energy surface for a reasonably sized chemical system with 51 nuclear dimensions. For this purpose, a family of neural networks that pertain to the graph-theoretically obtained subsystems get the job done for this 51 nuclear dimensional system. We then ask if this same family of lower-dimensional graph-based neural networks can be transformed to provide accurate predictions for a 186-dimensional potential energy surface. We find that our algorithm does provide accurate results for this larger-dimensional problem with sub-kcal/mol accuracy for the higher-dimensional potential energy surface problem. Indeed, as a result of these developments, here we produce the first efforts towards a full-dimensional potential energy surface for the protonated 21-water cluster (186 nuclear dimensions) at CCSD level accuracy. Architecture ; Type (biology) ; Computer science ; Geology ; Geography ; Paleontology Machine Learning in Materials Science
Authors: Yaroslava Pushkarova | І. М. Tymchenko
Source: Modern medicine pharmacy and psychological health (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

PREDICTION OF ORAL DRUG BIOAVAILABILITY BASED ON CHEMICAL STRUCTURE Prediction of bioavailability is a crucial aspect of drug development and formulation design. Aim. To investigate the possibility of predicting the bioavailability of oral drug compounds based on molecular descriptors by means of feedforward neural network. Materials and methods. Software ChemOffice 2020 was used for calculation molecular descriptors from the drug structures. Software Matlab R2022b was used for building the multivariate regression between molecular descriptors and bioavailability as well as feedforward neural network. For each of the 145 drug molecules 16 molecular descriptors were calculated. Of the 145 compounds, 10 were randomly selected for use as a test subset, 5 compounds – as a validation subset. The remaining 130 compounds were used to train feedforward neural network. Results and discussion. It is established that there are 7 the most informativeness molecular descriptors for oral drug bioavailability prediction: molecular weight, hydrogen bond acceptors, hydrogen bond donors, partition coefficient, Balaban index, molecular topological index, Wiener index (multiple coefficient of determination is equal to 0,3701). Optimal number of hidden neurons for effective realization feedforward neural network was found by experimental way and its equal to 16. Trained feedforward neural network with 16 hidden neurons was used for predicting bioavailability values for test and validation subsets. Conclusions. Feedforward neural network is the effective tool for prediction of oral drug bioavailability based on chemical structure. This is evidenced by high values of determination coefficient between predicted bioavailability values and experimental bioavailability values for test and validation subsets (0,7084 and 0,8432, correspondingly). Obtained results can be useful at the stage of experiment planning or drug design. Bioavailability ; Drug ; Chemistry ; Pharmacology ; Medicine Analytical Methods in Pharmaceuticals ; Computational Drug Discovery Methods ; Analytical Chemistry and Chromatography
Authors: Sam Holton
Source:  (preprint, 2024-05-14)
Field: Materials Science — Materials Chemistry

Roadmapping Report: Focused Research Organizations in Atomistic Simulation The machine learning revolution hasdemonstrated the power of leveraging hugeamounts of data and computation to solveproblems. Computational chemistry seemspoised to create a similar revolution in drugdesign, catalysis, and materials science.However, the field of atomistic simulationhas existed for a long time, with decades ofresearch in academia and industry. The hugeopportunities in this space have drawn theattention of tech companies, pharmaceuticalcompanies, and venture capital whilespurring the creation of dedicated softwareplatforms for molecular discovery.Care must be taken when finding scalableopportunities in such a sought-after field.After briefly reviewing the state-of-the-art, Imake some general points to guide thediscussion of opportunities in atomisticsimulation. I suggest a few unique avenuesfor starting focused research organizationsin the field, centered around creating highquality datasets to spark a revolution inFrictionless Reproducibility and thedevelopment of autonomous agents foratomistic simulation. Process management ; Computer science ; Business ; Knowledge management Machine Learning in Materials Science ; Scientific Computing and Data Management ; Advanced Materials Characterization Techniques
Authors: Srishti Gupta | Ajin Rajan | Edvin Fako | Tiago J. Goncalves | Imke Mueller | Jithin John Varghese | Sandip De
Source: ChemRxiv (preprint, 2024-04-24)
Field: Materials Science — Materials Chemistry

Exploring the Intricacies of Glycerol Hydrodeoxygenation to Propanediol on Cu Catalysts: A Comprehensive Investigation with the Aid of Machine Learning Forcefield Utilization of biomass to feedstock chemical relies on transforming hydroxyl containing molecules, as the hydroxyl group is found on the backbone of bio-molecules. For example, glycerol can undergo a hydro-deoxygenation reaction to produce propanediol, a valuable chemical precursor. This reaction captures the complexity and challenges of modelling surface-reactivity of flexible organic molecules in heterogenous catalysis, where surface intermediates can have many configurations. High computational costs of Density Functional Theory(DFT) restrict exhaustive exploration of the factorial reaction space, leading to having limited insights of the hydrodeoxygenation mechanism and hindering rational catalyst design. We deploy a Machine-Learned-Force-Field (MLFF) driven approach to elucidate the complex reaction network involved in the hydro-deoxygenation of glycerol on Cu(111). We present the bond cleavage activity ranking order for glycerol, and other intermediates and identify reaction pathways resulting in 1,2-PDO formation while highlighting indications of its higher selectivity over 1,3-PDO. This investigation delivers a comprehensive exploration of the transformation process from glycerol to propanediol, addressing the existing knowledge deficit through an advanced active-learning based MLFF approach. Notably, following a mere four iterations, our trained MLFF model accurately discerns 26 transition-states reconfirmed with DFT with root-mean-squared-error of 0.056 eV (0.74 meV/atom total-energy) embedded within network of seven competitive pathways. Hydrodeoxygenation ; Glycerol ; Catalysis ; Propanediol ; 1,3-Propanediol ; Chemistry ; Organic chemistry Machine Learning in Materials Science
Authors: Zilong Yuan | Zechen Tang | Honggeng Tao | Xiaoxun Gong | Zezhou Chen | Yuxiang Wang | Li He | Yang Li | Zhiming Xu | Minghui Sun | Boheng Zhao | Chong Wang | Wenhui Duan | Yong Xu
Source: arXiv (Cornell University) (preprint, 2024-07-19)
Field: Materials Science — Materials Chemistry

Deep learning density functional theory Hamiltonian in real space Deep learning electronic structures from ab initio calculations holds great potential to revolutionize computational materials studies. While existing methods proved success in deep-learning density functional theory (DFT) Hamiltonian matrices, they are limited to DFT programs using localized atomic-like bases and heavily depend on the form of the bases. Here, we propose the DeepH-r method for deep-learning DFT Hamiltonians in real space, facilitating the prediction of DFT Hamiltonian in a basis-independent manner. An equivariant neural network architecture for modeling the real-space DFT potential is developed, targeting a more fundamental quantity in DFT. The real-space potential exhibits simplified principles of equivariance and enhanced nearsightedness, further boosting the performance of deep learning. When applied to evaluate the Hamiltonian matrix, this method significantly improved in accuracy, as exemplified in multiple case studies. Given the abundance of data in the real-space potential, this work may pave a novel pathway for establishing a ``large materials model" with increased accuracy. Hamiltonian (control theory) ; Space (punctuation) ; Computer science ; Artificial intelligence ; Psychology ; Mathematics ; Mathematical optimization Machine Learning in Materials Science
Authors: Hai Bi | zhaoming he | Baoyan Liang | Zhiqiang Li | Zhang He-Ming | Yue Wang
Source: Research Square (preprint, 2024-07-22)
Field: Materials Science — Materials Chemistry

Frontier Molecular Orbital Weighted Model Based Networks for Revealing Organic Delayed Fluorescence Efficiency Frontier ; Fluorescence ; Molecular orbital ; Biological system ; Chemistry ; Computer science ; Physics ; Geography ; Optics ; Organic chemistry ; Biology ; Molecule ; Archaeology Luminescence and Fluorescent Materials ; Organic Light-Emitting Diodes Research ; Conducting polymers and applications