jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hikaru Ibayashi | Taufeq Mohammed Razakh | Liqiu Yang | Thomas Linker | Marco Olguin | Shinnosuke Hattori | Ye Luo | Rajiv K. Kalia | Aiichiro Nakano | Ken‐ichi Nomura | Priya Vashishta
Source: arXiv (Cornell University) (preprint, 2023-03-14)
Field: Materials Science — Materials Chemistry

Allegro-Legato: Scalable, Fast, and Robust Neural-Network Quantum Molecular Dynamics via Sharpness-Aware Minimization Neural-network quantum molecular dynamics (NNQMD) simulations based on machine learning are revolutionizing atomistic simulations of materials by providing quantum-mechanical accuracy but orders-of-magnitude faster, illustrated by ACM Gordon Bell prize (2020) and finalist (2021). State-of-the-art (SOTA) NNQMD model founded on group theory featuring rotational equivariance and local descriptors has provided much higher accuracy and speed than those models, thus named Allegro (meaning fast). On massively parallel supercomputers, however, it suffers a fidelity-scaling problem, where growing number of unphysical predictions of interatomic forces prohibits simulations involving larger numbers of atoms for longer times. Here, we solve this problem by combining the Allegro model with sharpness aware minimization (SAM) for enhancing the robustness of model through improved smoothness of the loss landscape. The resulting Allegro-Legato (meaning fast and "smooth") model was shown to elongate the time-to-failure $t_\textrm{failure}$, without sacrificing computational speed or accuracy. Specifically, Allegro-Legato exhibits much weaker dependence of timei-to-failure on the problem size, $t_{\textrm{failure}} \propto N^{-0.14}$ ($N$ is the number of atoms) compared to the SOTA Allegro model $\left(t_{\textrm{failure}} \propto N^{-0.29}\right)$, i.e., systematically delayed time-to-failure, thus allowing much larger and longer NNQMD simulations without failure. The model also exhibits excellent computational scalability and GPU acceleration on the Polaris supercomputer at Argonne Leadership Computing Facility. Such scalable, accurate, fast and robust NNQMD models will likely find broad applications in NNQMD simulations on emerging exaflop/s computers, with a specific example of accounting for nuclear quantum effects in the dynamics of ammonia. Scalability ; Quantum ; Molecular dynamics ; Massively parallel ; Robustness (evolution) ; Computer science ; Speedup ; Parallel computing ; Artificial neural network ; Minification ; Scaling ; Statistical physics ; Algorithm ; Computational science ; Physics ; Mathematics ; Quantum mechanics ; Artificial intelligence ; Chemistry ; Geometry Machine Learning in Materials Science ; Nuclear Physics and Applications ; Topic Modeling
Authors: Amirhossein Kardoost | Robert Schönherr | Carsten Deiter | Lars Redecke | Kristina Lorenzen | Joachim Schulz | Iñaki de Diego
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2023-03-29)
Field: Materials Science — Materials Chemistry

Convolutional neural network approach for the automated identification of <i>in cellulo</i> crystals In cellulo crystallization is a rarely occurring event in nature. Recent advances, making use of heterologous overexpression, can promote the intracellular formation of protein crystals, but new tools are required to detect and to characterize these targets in the complex cell environment. In the present work we make use of Mask R-CNN, a Convolutional Neural Network (CNN) based instance segmentation method, for the identification of either single or multi-shaped crystals growing in living insect cells, using conventional bright field images. The algorithm can be rapidly adapted to recognize different targets, with the aim to extract relevant information to support a semi-automated screening pipeline, with the purpose to aid in the development of the intracellular protein crystallization approach. Convolutional neural network ; Pipeline (software) ; Identification (biology) ; Computer science ; Crystallization ; Segmentation ; Protein crystallization ; Artificial intelligence ; Field (mathematics) ; Pattern recognition (psychology) ; Artificial neural network ; Chemistry ; Biology ; Ecology ; Mathematics Enzyme Structure and Function ; Cell Image Analysis Techniques
Authors: Zeyan Li | Xiaoping Wang
Source: Textile Research Journal (article, 2023-04-06)
Field: Materials Science — Polymers and Plastics

Optimizing process parameters for the production of intercalated melt-blown nonwoven materials for face masks based on machine learning algorithms Currently a new type of coronavirus is raging around the world, and many countries have relaxed the control of the epidemic. Wearing a mask has become the best self-protection measure for people to travel. Intercalated melt-blown nonwoven materials are in short supply as filter layers for daily-worn masks. This paper studies the relationship between the process parameters and structural variables of intercalated melt-blown nonwoven materials, and creatively uses machine learning-related algorithms to solve its nonlinear relationship. The optimized back propagation neural network model is the most suitable in this field, and the goodness of fit can reach more than 99.99%. Based on various limitations of actual industrial production, this model is used to traverse the process parameters, and the intercalated melt-blown nonwoven material is obtained. The best process parameters, in which the receiving distance is 27 cm, and the hot air velocity is 890 r/min, in this case, the thickness and porosity of the material produced are very low, while the compression resilience is very high, considering the filtration efficiency of the mask and comfort. Traverse ; Process (computing) ; Filtration (mathematics) ; Materials science ; Porosity ; Production (economics) ; Algorithm ; Artificial neural network ; Field (mathematics) ; Mechanical engineering ; Computer science ; Process engineering ; Composite material ; Artificial intelligence ; Engineering ; Mathematics Textile materials and evaluations ; Industrial Vision Systems and Defect Detection ; Infection Control and Ventilation
Authors: Ademola Soyemi | Shubham K. Pandey | Shane A. Vaara | Tibor Szilvási
Source: Liquid Crystals (article, 2023-11-03)
Field: Chemistry — Spectroscopy

Predicting the melting point of liquid crystals with directed message passing neural networks Liquid crystal (LC) phases must exist in an appropriate temperature range for practical applications and thus the melting point (MP) is a critical design target of LCs. In this work, we have evaluated the performance of directed message passing neural networks trained on a large database of organic molecules (27,000+) in the prediction of a structurally diverse set of 780 LC and LC-like molecules including cyano-, azo-, ester, cyclohexyl- and halogen-containing compounds. Our results show that the model can provide accurate MP predictions for LC and LC-like molecules with an overall Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) of 23°C and 30°C, respectively. Our model provides similar levels of performance with MAEs of 20°C, 20°C, 23°C, 25°C and 24°C for azo, cyclohexyl, ester, halogen and nitrile compounds, respectively, and is most accurate for molecules with MP between 50 and 150°C (MAE = 21°C). We have developed an online tool called LCMelt (v1.0) (lcmelt.streamlit.app) where our model can be used to predict the MP of potential LC candidates before synthesis free of charge. Nitrile ; Melting point ; Molecule ; Mean squared error ; Liquid crystal ; Materials science ; Artificial neural network ; Halogen ; Root mean square ; Range (aeronautics) ; Mean squared prediction error ; Computer science ; Organic chemistry ; Algorithm ; Chemistry ; Physics ; Optoelectronics ; Mathematics ; Machine learning Molecular spectroscopy and chirality ; Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods
Authors: Hannes Kneiding | David Balcells
Source: Nordic Machine Intelligence (article, 2023-12-21)
Field: Materials Science — Materials Chemistry

Deep Graph Learning for Molecules and Materials Machine learning approaches have become an important tool in chemistry and materials science for the accurate and efficient prediction of physical properties. Most notably among them are graph neural networks that leverage the inherent graph structure of molecules and materials in order to achieve state-of-the-art accuracy. In this perspective we give a brief introduction to the theoretical foundations of graph neural networks for molecular structures and their specific applications in chemistry and materials science. We conclude with a short outlook discussing remaining research questions as well as opportunities for further developments of the field. Leverage (statistics) ; Computer science ; Graph ; Artificial neural network ; Data science ; Artificial intelligence ; Molecular graph ; Theoretical computer science ; Machine learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Takuya Taniguchi | Mayuko Hosokawa | Toru Asahi
Source: ChemRxiv (preprint, 2023-07-18)
Field: Materials Science — Materials Chemistry

Graph comparison of molecular crystals in band gap prediction using neural networks In materials informatics, the representation of the material structure is fundamentally essential to obtain better prediction results, and graph representation has attracted much attention in recent years. Molecular crystals can be graphically represented in molecular and crystal representations, but the comparison of which representation is more effective has not been examined. In this study, we compared the prediction accuracy between molecular and crystal graphs for band gap prediction. The results showed that the prediction accuracies using crystal graphs were better than those using molecular graphs. While this result is not surprising, error analysis quantitatively evaluated that the crystal graph reduces the error 0.4 times with medium correlation than the molecular graph. The novelty of this study lies in the comparison of molecular crystal representations and in the quantitative evaluation of the contribution of intermolecular interactions in the band gap. Crystal (programming language) ; Representation (politics) ; Graph ; Novelty ; Molecular graph ; Band gap ; Computer science ; Crystal structure prediction ; Materials science ; Theoretical computer science ; Crystal structure ; Chemistry ; Crystallography ; Optoelectronics ; Psychology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Eshwar S Ramanathan | Chandra Chowdhury
Source: ChemPhysChem (article, 2023-08-17)
Field: Materials Science — Materials Chemistry

Structural and Electronic Properties of Two‐Dimensional Materials: A Machine‐Learning‐Guided Prediction The growing number of studies and interest in two-dimensional (2D) materials has not yet resulted in a wide range of material applications. This is a result of difficulties in getting the properties, which are often determined through numerical experiments or through first-principles predictions, both of which require lots of time and resources. Here we provide a general machine learning (ML) model that works incredibly well as a predictor for a variety of electronic and structural properties such as band gap, fermi level, work function, total energy and area of unit cell for a wide range of 2D materials derived from the Computational 2D Materials Database (C2DB). Our predicted model for classification of samples works extraordinarily well and gives an accuracy of around 99 %. We are able to successfully decrease the number of studied features by employing a strict permutation-based feature selection method along with the sure independence screening and sparsifying operator (SISSO), which further supports the design recommendations for the identification of novel 2D materials with the desired properties. Computer science ; Range (aeronautics) ; Operator (biology) ; Permutation (music) ; Independence (probability theory) ; Variety (cybernetics) ; Identification (biology) ; Function (biology) ; Electronic structure ; Feature (linguistics) ; Feature selection ; Work (physics) ; Machine learning ; Artificial intelligence ; Statistical physics ; Materials science ; Mathematics ; Mechanical engineering ; Physics ; Chemistry ; Condensed matter physics ; Engineering ; Statistics Machine Learning in Materials Science ; Graphene research and applications ; 2D Materials and Applications
Authors: Gabriel Monteiro‐de‐Castro | Júlio César Duarte | Itamar Borges
Source: ChemRxiv (preprint, 2023-02-23)
Field: Chemistry — Organic Chemistry

Machine learning determination of new Hammett's constants for meta- and para-substituted benzoic acid derivatives employing quantum chemical atomic charge methods Hammett's constants quantify the electron or electron acceptor power of a chemical group bonded to an aromatic ring. Their experimental values have been successfully used in a large variety of applications, but some of them may have inconsistent values or were not measured. For this reason, developing an accurate and consistent set of Hammett's values is paramount. In this work, we employed the machine learning (ML) regression algorithms Decision Tree Regressor, the neural network Multilayer Perceptron Regressor, and Lasso Lars IC in a cross-validation (CV) approach combined with quantum chemical calculations of atomic charges to estimate theoretically the new Hammett's constants for 90 chemical donor or acceptor groups by employing different types of quantum chemical atomic charges of the groups as input properties. New 219 sigma values, including previously unknown ones, are proposed for 90 chemical donor or acceptor groups by employing different types of quantum chemical atomic charges of the groups as input properties. The different substituent groups were bonded to benzene and meta- and para-substituted benzoic acid derivatives. Among the investigated atomic charge methods (Mulliken, Lwdin, Hirshfeld, and ChelpG), Hirshfeld's method showed the best regressions for most of the different kinds of sigma values. For each type of Hammett constant, linear expressions depending only on the atomic charges of the group were obtained. Correlation coefficients as high as 0.945, mean squared errors (MSE) as low as 0.004, and root mean square errors (RMSE) as low 0.062, were found. The ML approach, in most cases, showed very close predictions to the original experimental values, with the values from meta- and para-substituted benzoic acid derivatives showing the most accurate values. A new consistent set of Hammetts constants is presented, as well as simple equations for predicting new values for groups not included in the original set of 90. Chemistry ; Hammett equation ; Acceptor ; Substituent ; Computational chemistry ; Extrapolation ; Electron affinity (data page) ; Physical chemistry ; Molecule ; Mathematics ; Quantum mechanics ; Stereochemistry ; Organic chemistry ; Physics ; Reaction rate constant ; Statistics Free Radicals and Antioxidants ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Roy T. Forestano | K. Matchev | Katia Matcheva | Eyup B. Unlu
Source: The Astrophysical Journal (article, 2023-11-16)
Field: Chemistry — Analytical Chemistry

Searching for Novel Chemistry in Exoplanetary Atmospheres Using Machine Learning for Anomaly Detection Abstract The next generation of telescopes will yield a substantial increase in the availability of high-quality spectroscopic data for thousands of exoplanets. The sheer volume of data and number of planets to be analyzed greatly motivate the development of new, fast, and efficient methods for flagging interesting planets for reobservation and detailed analysis. We advocate the application of machine learning (ML) techniques for anomaly (novelty) detection to exoplanet transit spectra, with the goal of identifying planets with unusual chemical composition and even searching for unknown biosignatures. We successfully demonstrate the feasibility of two popular anomaly detection methods (local outlier factor and one-class support vector machine) on a large public database of synthetic spectra. We consider several test cases, each with different levels of instrumental noise. In each case, we use receiver operating characteristic curves to quantify and compare the performance of the two ML techniques. Exoplanet ; Planet ; Physics ; Anomaly detection ; Flagging ; Outlier ; Anomaly (physics) ; Transit (satellite) ; Novelty detection ; Astronomy ; Astrobiology ; Computer science ; Artificial intelligence ; Novelty Spectroscopy and Chemometric Analyses ; Spectroscopy and Laser Applications ; Advanced Statistical Methods and Models
Authors: Jiahui Liu | Jesper Byggmästar | Zheyong Fan | Ping Qian | Yanjing Su
Source: arXiv (Cornell University) (preprint, 2023-05-14)
Field: Materials Science — Materials Chemistry

Large-scale machine-learning molecular dynamics simulation of primary radiation damage in tungsten Simulating collision cascades and radiation damage poses a long-standing challenge for existing interatomic potentials, both in terms of accuracy and efficiency. Machine-learning based interatomic potentials have shown sufficiently high accuracy for radiation damage simulations, but most existing ones are still not efficient enough to model high-energy collision cascades with sufficiently large space and time scales. To this end, we here extend the highly efficient neuroevolution potential (NEP) framework by combining it with the Ziegler-Biersack-Littmark (ZBL) screened nuclear repulsion potential, obtaining a NEP-ZBL framework. We train a NEP-ZBL model for tungsten and demonstrate its accuracy in terms of the elastic properties, melting point, and various energetics of defects that are relevant for radiation damage. We then perform large-scale molecular dynamics simulations with the NEP-ZBL model with up to 8.1 million atoms and 240 ps (using a single 40-GB A100 GPU) to study the difference of primary radiation damage in both bulk and thin-foil tungsten. While our findings for bulk tungsten are consistent with existing results simulated by embedded atom method (EAM) models, the radiation damage differs significantly in foils and shows that larger and more vacancy clusters as well as smaller and fewer interstitial clusters are produced due to the presence of a free surface. Tungsten ; Molecular dynamics ; Embedded atom model ; Collision ; Radiation ; Interatomic potential ; Radiation damage ; Atom (system on chip) ; Vacancy defect ; Physics ; Scale (ratio) ; Atomic physics ; Materials science ; Statistical physics ; Computational physics ; Nuclear physics ; Computer science ; Parallel computing ; Condensed matter physics ; Quantum mechanics Nuclear Materials and Properties ; Nuclear Physics and Applications ; Fusion materials and technologies
Authors: Gabe Guo | Judah Goldfeder | Ling Lan | Aniv Ray | Albert Hanming Yang | Boyuan Chen | Simon J. L. Billinge | Hod Lipson
Source: arXiv (Cornell University) (preprint, 2023-12-23)
Field: Materials Science — Materials Chemistry

Towards End-to-End Structure Solutions from Information-Compromised Diffraction Data via Generative Deep Learning The revolution in materials in the past century was built on a knowledge of the atomic arrangements and the structure-property relationship. The sine qua non for obtaining quantitative structural information is single crystal crystallography. However, increasingly we need to solve structures in cases where the information content in our input signal is significantly degraded, for example, due to orientational averaging of grains, finite size effects due to nanostructure, and mixed signals due to sample heterogeneity. Understanding the structure property relationships in such situations is, if anything, more important and insightful, yet we do not have robust approaches for accomplishing it. In principle, machine learning (ML) and deep learning (DL) are promising approaches since they augment information in the degraded input signal with prior knowledge learned from large databases of already known structures. Here we present a novel ML approach, a variational query-based multi-branch deep neural network that has the promise to be a robust but general tool to address this problem end-to-end. We demonstrate the approach on computed powder x-ray diffraction (PXRD), along with partial chemical composition information, as input. We choose as a structural representation a modified electron density we call the Cartesian mapped electron density (CMED), that straightforwardly allows our ML model to learn material structures across different chemistries, symmetries and crystal systems. When evaluated on theoretically simulated data for the cubic and trigonal crystal systems, the system achieves up to $93.4\%$ average similarity with the ground truth on unseen materials, both with known and partially-known chemical composition information, showing great promise for successful structure solution even from degraded and incomplete input data. Artificial intelligence ; Computer science ; Artificial neural network ; Cartesian coordinate system ; Representation (politics) ; Crystal structure ; Powder diffraction ; Diffraction ; Algorithm ; Materials science ; Crystallography ; Chemistry ; Mathematics ; Physics ; Geometry ; Optics X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Crystallography and molecular interactions
Authors: Alex M. Maldonado | Igor Poltavsky | Valentín Vassilev-Galindo | Alexandre Tkatchenko | John A. Keith
Source: ChemRxiv (preprint, 2023-01-11)
Field: Materials Science — Materials Chemistry

Modeling molecular ensembles with gradient-domain machine learning force fields Gradient-domain machine learning (GDML) force fields have shown excellent accuracy, data efficiency, and applicability for molecules with hundreds of atoms, but the employed global descriptor limits transferability to ensembles of molecules. Many-body expansions (MBEs) should provide a rigorous procedure for size-transferable GDML by training models on fundamental n-body interactions. We developed many-body GDML (mbGDML) force fields for water, acetonitrile, and methanol by training 1-, 2-, and 3-body models on only 1000 MP2/def2-TZVP calculations each. Our mbGDML force field includes intramolecular flexibility and intermolecular interactions, providing that the reference data properly describes these effects. We also compare this mbGDML approach to GAP, SchNet, and NequIP potentials. Energy and force predictions of clusters containing up to 20 molecules are within 0.38 kcal/mol per monomer of reference supersystem calculations. This deviation partially arises from the restriction of the mbGDML model to 3-body interactions. Given these approximations, our automated mbGDML training schemes also resulted in fair agreement with reference radial distribution functions (RDFs) of bulk solvents. These results highlight mbGDML as valuable for modeling explicitly solvated systems with quantum-mechanical accuracy. Force field (fiction) ; Intermolecular force ; Intramolecular force ; Flexibility (engineering) ; Domain (mathematical analysis) ; Transferability ; Statistical physics ; Computer science ; Field (mathematics) ; Quantum ; Molecular dynamics ; Molecule ; Chemical physics ; Physics ; Computational chemistry ; Chemistry ; Artificial intelligence ; Machine learning ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Advanced Chemical Physics Studies