jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Nathan A. Seifert | Kirill Prozument | Michael W. Davis
Source: Proceedings of the 2020 International Symposium on Molecular Spectroscopy (article, 2020-06-25)
Field: Materials Science — Materials Chemistry

PREDICTION OF MOLECULAR STRUCTURES FROM ROTATIONAL CONSTANTS: A PROPOSAL FOR SOLVING THE INVERSE PROBLEM As made clear by E. Bright Wilson’s prescient thesis published in Science in 1968,\\footnote{Wilson, E. B., Science \\textbf{1968}, \\textit{162}, 3849.} applicability to analytical chemistry has long been a goal for microwave spectroscopy. This comes as no surprise, as the high-resolution nature of microwave spectroscopy leads to unparalleled sensitivity to the structures of molecular analytes. However, in the absence of isotopologues, determination of an unknown molecule's identity can be difficult by the experimental rotational constants alone. Although a structure maps trivially into its rotational constants, the reverse is not necessarily true as some structural information is lost. Yet, one can imagine a trained machine that outputs likely molecular structures by intelligent interpretation of the observed rotational spectroscopy. This is the inverse problem for rotational spectroscopy – instead of predicting constants from structures, the goal is to predict structures from constants. \n\n\tRecent developments in machine learning have led to success in decoding multidimensional chemical parameter spaces from massive, enumerated libraries of molecular structures.\\footnote{[1] Zhou, Z.; Kearnes, S.; Li, L.; Zare, R. N.; Riley, P., \\textit{Sci. Rep.} \\textbf{2019}, \\textit{9}, 10752. [2] G\\'{o}mez-Bombarelli, R.; Wei, J. N.; Duvenaud, D.; \\textit{et al.}, \\textit{ACS Cent. Sci.} \\textbf{2018}, \\textit{4}, 268.} In order to apply such methods to the inverse problem, there are critical questions that are still unanswered. For instance, is the mapping of spectrum to molecule single-valued within the uncertainty of calculated rotational constants? If not, what is the average number of reasonable molecules consistent for a random but valid set of rotational constants? Or, is there an efficient and convergent “similarity” metric that compares a target spectrum to a chemical search space, key for developing a molecular “search engine”? With these questions in mind, this talk will present an exploration of, and a proposal for, a potential solution to the inverse problem. Inverse problem ; Inverse ; Computer science ; Applied mathematics ; Mathematical optimization ; Algorithm ; Mathematics ; Mathematical analysis ; Geometry Silicone and Siloxane Chemistry
Authors: Pavan Inguva | Lachlan Mason | Indranil Pan | Miselle Hengardi | Omar K. Matar
Source: arXiv (Cornell University) (preprint, 2020-07-10)
Field: Materials Science — Materials Chemistry

Numerical simulation, clustering and prediction of multi-component\n polymer precipitation Multi-component polymer systems are of interest in organic photovoltaic and\ndrug delivery applications, among others where diverse morphologies influence\nperformance. An improved understanding of morphology classification, driven by\ncomposition-informed prediction tools, will aid polymer engineering practice.\nWe use a modified Cahn-Hilliard model to simulate polymer precipitation. Such\nphysics-based models require high-performance computations that prevent rapid\nprototyping and iteration in engineering settings. To reduce the required\ncomputational costs, we apply machine learning techniques for clustering and\nconsequent prediction of the simulated polymer blend images in conjunction with\nsimulations. Integrating ML and simulations in such a manner reduces the number\nof simulations needed to map out the morphology of polymer blends as a function\nof input parameters and also generates a data set which can be used by others\nto this end. We explore dimensionality reduction, via principal component\nanalysis and autoencoder techniques, and analyse the resulting morphology\nclusters. Supervised machine learning using Gaussian process classification was\nsubsequently used to predict morphology clusters according to species molar\nfraction and interaction parameter inputs. Manual pattern clustering yielded\nthe best results, but machine learning techniques were able to predict the\nmorphology of polymer blends with $\\geq$ 90 $\\%$ accuracy.\n Cluster analysis ; Principal component analysis ; Component (thermodynamics) ; Computer science ; Dimensionality reduction ; Autoencoder ; Artificial intelligence ; Polymer ; Machine learning ; Biological system ; Materials science ; Artificial neural network Machine Learning in Materials Science ; Scientific Computing and Data Management ; Phase Equilibria and Thermodynamics
Authors: Zachary L. Glick | Derek P. Metcalf | Alexios Koutsoukas | Steven A. Spronk | Daniel L. Cheney | C. David Sherrill
Source: ChemRxiv (preprint, 2020-05-06)
Field: Materials Science — Materials Chemistry

AP-Net: An Atomic-Pairwise Neural Network for Smooth and Transferable Interaction Potentials Intermolecular interactions are critical to many chemical phenomena, but their accurate computation using ab initio methods is often limited by computational cost. The recent emergence of machine learning (ML) potentials may be a promising alternative. Useful ML models should not only estimate accurate interaction energies, but also predict smooth and asymptotically correct potential energy surfaces. However, existing ML models are not guaranteed to obey these constraints. Indeed, systemic deficiencies are apparent in the predictions of our previous hydrogen-bond model as well as the popular ANI-1X model, which we attribute to the use of an atomic energy partition. As a solution, we propose an alternative atomic-pairwise framework specifically for intermolecular ML potentials, and we introduce AP-Net—a neural network model for interaction energies. The AP-Net model is developed using this physically motivated atomic-pairwise paradigm and also exploits the interpretability of symmetry adapted perturbation theory (SAPT). We show that in contrast to other models, AP-Net produces smooth, physically meaningful intermolecular potentials exhibiting correct asymptotic behavior. Initially trained on only a limited number of mostly hydrogen-bonded dimers, AP-Net makes accurate predictions across the chemically diverse S66x8 dataset, demonstrating significant transferability. On a test set including experimental hydrogen-bonded dimers, AP-Net predicts total interaction energies with a mean absolute error of 0.37 kcal mol−1, reducing errors by a factor of 2-5 across SAPT components from previous neural network potentials. The pairwise interaction energies of the model are physically interpretable, and an investigation of predicted electrostatic energies suggests that the model ‘learns’ the physics of hydrogen-bonded interactions. Pairwise comparison ; Artificial neural network ; Computer science ; Intermolecular force ; Computation ; Statistical physics ; Transferability ; Ab initio ; Interaction energy ; Chemistry ; Biological system ; Algorithm ; Artificial intelligence ; Machine learning ; Physics ; Molecule Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Kumiko Komizo | Noriaki Kuwahara
Source: International Journal of Advanced Computer Science and Applications (article, 2020-01-01)
Field: Materials Science — Polymers and Plastics

Using the Convolution Neural Network Attempts to Match Japanese Women’s Kimono and Obi Currently, the decline in kimono usage in Japan is serious. This has become an important problem for the kimono industry and kimono culture. The reason behind this lack of usage is that Japanese clothing has many strict rules attached to it. One of those difficult rules is that kimonos have status, and one must consider the proper kimono to wear depending on the place and type of event. At the same time, the obi (sash) also has status, and the status of the kimono and obi must match. The matching of the kimono and obi is called “obiawase” in Japanese, and it is not just a matter of the person wearing the kimono selecting a pair that she likes. Instead, the first place you wear a kimono determines its status, and the obi must match that status and kimono. In other words, the color, material, meaning behind the pattern must be matched with obi. Kimono patterns may evoke the seasons or a celebratory event. All this must be considered. The kimono was originally everyday wear, and people were taught these things in their households, but with today’s increasingly nuclear families, that person who could teach these things isn’t nearby, adding to the lack of use of kimonos. Because of this, there has been interest in using CNN (Convolution Neural Network) from the digital fashion industry. We are attempting to use machine learning to tackle the difficult task of matching an obi to a kimono, using the CNN machines drawing the most attention today. Meaning (existential) ; Clothing ; Computer science ; Task (project management) ; History ; Psychology ; Engineering ; Archaeology Textile materials and evaluations
Authors: Jiadong Dan | Xiaoxu Zhao | Shoucong Ning | Jiong Lu | Kian Ping Loh | N. Duane Loh | Stephen J. Pennycook
Source: arXiv (Cornell University) (preprint, 2020-05-23)
Field: Materials Science — Surfaces, Coatings and Films

Learning Motifs and their Hierarchies in Atomic Resolution Microscopy Progress in functional materials discovery has been accelerated by advances in high throughput materials synthesis and by the development of high-throughput computation. However, a complementary robust and high throughput structural characterization framework is still lacking. New methods and tools in the field of machine learning suggest that a highly automated high-throughput structural characterization framework based on atomic-level imaging can establish the crucial statistical link between structure and macroscopic properties. Here we develop a machine learning framework towards this goal. Our framework captures local structural features in images with Zernike polynomials, which is demonstrably noise-robust, flexible, and accurate. These features are then classified into readily interpretable structural motifs with a hierarchical active learning scheme powered by a novel unsupervised two-stage relaxed clustering scheme. We have successfully demonstrated the accuracy and efficiency of the proposed methodology by mapping a full spectrum of structural defects, including point defects, line defects, and planar defects in scanning transmission electron microscopy (STEM) images of various 2D materials, with greatly improved separability over existing methods. Our techniques can be easily and flexibly applied to other types of microscopy data with complex features, providing a solid foundation for automatic, multiscale feature analysis with high veracity. Computer science ; Throughput ; Artificial intelligence ; Cluster analysis ; Characterization (materials science) ; Computation ; Visualization ; Feature (linguistics) ; Pattern recognition (psychology) ; Machine learning ; Nanotechnology ; Algorithm ; Materials science Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Electronic and Structural Properties of Oxides
Authors: Maxim Ziatdinov | Christopher T. Nelson | Xiaohang Zhang | Rama K. Vasudevan | Eugene А. Eliseev | Anna N. Morozovska | Ichiro Takeuchi | Sergei V. Kalinin
Source: npj Computational Materials (preprint, 2020-08-21)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Causal analysis of competing atomistic mechanisms in ferroelectric materials from high-resolution scanning transmission electron microscopy data Abstract Machine learning has emerged as a powerful tool for the analysis of mesoscopic and atomically resolved images and spectroscopy in electron and scanning probe microscopy, with the applications ranging from feature extraction to information compression and elucidation of relevant order parameters to inversion of imaging data to reconstruct structural models. However, the fundamental limitation of machine learning methods is their correlative nature, leading to extreme susceptibility to confounding factors. Here, we implement the workflow for causal analysis of structural scanning transmission electron microscopy (STEM) data and explore the interplay between physical and chemical effects in a ferroelectric perovskite across the ferroelectric–antiferroelectric phase transitions. The combinatorial library of the Sm-doped BiFeO 3 is grown to cover the composition range from pure ferroelectric BFO to orthorhombic 20% Sm-doped BFO. Atomically resolved STEM images are acquired for selected compositions and are used to create a set of local compositional, structural, and polarization field descriptors. The information-geometric causal inference (IGCI) and additive noise model (ANM) analysis are used to establish the pairwise causal directions between the descriptors, ordering the data set in the causal direction. The causal chain for IGCI and ANM across the composition is compared and suggests the presence of common causal mechanisms across the composition series. Ultimately, we believe that the causal analysis of the multimodal data will allow exploring the causal links between multiple competing mechanisms that control the emergence of unique functionalities of morphotropic materials and ferroelectric relaxors. Ferroelectricity ; Scanning transmission electron microscopy ; Artificial intelligence ; Materials science ; Computer science ; Transmission electron microscopy ; Dielectric ; Nanotechnology ; Optoelectronics Multiferroics and related materials ; Machine Learning in Materials Science ; Geochemistry and Geologic Mapping
Authors: Alessandro Lunghi | Stefano Sanvito
Source: The Journal of Chemical Physics (preprint, 2020-11-05)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Multiple spin–phonon relaxation pathways in a Kramer single-ion magnet We present a first-principles investigation of spin-phonon relaxation in a molecular crystal of Co<sup>2+</sup> single-ion magnets. Our study combines electronic structure calculations with machine-learning force fields and unravels the nature of both the Orbach and the Raman relaxation channels in terms of atomistic processes. We find that although both mechanisms are mediated by the excited spin states, the low temperature spin dynamics is dominated by phonons in the THz energy range, which partially suppress the benefit of having a large magnetic anisotropy. This study also determines the importance of intra-molecular motions for both the relaxation mechanisms and paves the way to the rational design of a new generation of single-ion magnets with tailored spin-phonon coupling. Phonon ; Condensed matter physics ; Relaxation (psychology) ; Anisotropy ; Spin (aerodynamics) ; Ion ; Magnet ; Raman spectroscopy ; Spin engineering ; Excited state ; Coupling (piping) ; Materials science ; Physics ; Spin polarization ; Atomic physics ; Quantum mechanics ; Electron Magnetism in coordination complexes ; Quantum and electron transport phenomena ; Organic and Molecular Conductors Research
Authors: Nobuyuki Yoshioka | Wataru Mizukami | Franco Nori
Source: arXiv (Cornell University) (preprint, 2020-10-03)
Field: Materials Science — Materials Chemistry

Neural-Network Quantum States for the Electronic Structure of Real Solids Establishing a predictive ab initio method for solid systems is one of the fundamental goals in condensed matter physics and computational materials science. We demonstrate that artificial neural networks can simulate first-principles calculations of extended periodic materials. The stochastic optimization of a highly expressive ansatz accurately provides the ground-state energy of a one-dimensional system up to the thermodynamic limit, reaching its chemical accuracy. The correlation energy is simulated precisely also for real solids in both two and three dimensions. We further show that multiple excited states corresponding to the quasiparticle band structure can be obtained with an additional small computation exploiting a technique developed from quantum simulations. Ansatz ; Excited state ; Artificial neural network ; Statistical physics ; Quasiparticle ; Computation ; Thermodynamic limit ; Ab initio ; Quantum ; Limit (mathematics) ; Computer science ; Electronic structure ; Physics ; Quantum mechanics ; Artificial intelligence ; Mathematics ; Algorithm Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Chemical Physics Studies
Source: Indian Journal of Pure & Applied Physics (article, 2020-08-21)
Field: Materials Science — Materials Chemistry

Computational studies for the effective electrical conductivity of Copper powder filled LDPE/LLDPE composites The effective electrical conductivity (EEC) of low density polyethylene (LDPE) and linear low density polyethylene (LLDPE) polymer composites filled with copper has been studied. The nonlinear behavior has been observed for effective electrical conductivity versus filler content. Several approaches have been described to predict the electrical conductivities of polymer composites. EEC is described by artificial neural network (ANN) and it demonstrates the accurate match of experimental data for EEC with different training functions (TRAINOSS, TRAINLM, TRAINBR, TRAINSCG, TRAINBFG, and TRAINRP). The ANN approach satisfied the experimental data for EEC of polymer composites reasonably well. The complex structure encountered in LDPE/Cu and LLDPE/Cu, along with the difference in the EEC of the components, make it difficult to estimate the EEC exactly. This is the reason for which artificial neural network has been employed here. By using ANN approach experimental results indicate that EEC of polymer composites increases with increasing filler content at the same concentration. Low-density polyethylene ; Linear low-density polyethylene ; Composite material ; Materials science ; Electrical resistivity and conductivity ; Polyethylene ; Copper ; Polymer ; Conductivity ; Polymer blend ; Metallurgy ; Chemistry ; Electrical engineering High voltage insulation and dielectric phenomena ; Advanced Sensor and Energy Harvesting Materials
Authors: Rachel C. Kurchin | Eric S. Muckley | Lance Kavalsky | Vinay I. Hegde | Dhairya Gandhi | Xiaoyu Sun | Matthew Johnson | Alan Edelman | James E. Saal | Christopher Rackauckas | Bryce Meredig | Viral B. Shah | Venkatasubramanian Viswanathan
Source: arXiv (Cornell University) (preprint, 2020-11-03)
Field: Materials Science — Materials Chemistry

ACED: Accelerated Computational Electrochemical systems Discovery. Large-scale electrification is vital to addressing the climate crisis, but many engineering challenges remain to fully electrifying both the chemical industry and transportation. In both of these areas, new electrochemical materials and systems will be critical, but developing these systems currently relies heavily on computationally expensive first-principles simulations as well as human-time-intensive experimental trial and error. We propose to develop an automated workflow that accelerates these computational steps by introducing both automated error handling generating the first-principles training data as well as physics-informed machine learning surrogates to further reduce computational cost. It will also have the capacity to include automated experiments in the loop order to dramatically accelerate the overall materials discovery pipeline. Workflow ; Pipeline (software) ; Computer science ; Electrification ; Computational model ; Scale (ratio) ; Systems engineering ; Risk analysis (engineering) ; Data science ; Industrial engineering ; Artificial intelligence ; Engineering ; Electrical engineering ; Electricity Machine Learning in Materials Science ; Topic Modeling ; Electron and X-Ray Spectroscopy Techniques
Authors: Samuel Boateng | Kwang Ryeol Lee | Deepika Choudhary | Haneol Cho | Kyu Hwan Lee | Chansoo Kim
Source: Korean Journal of Metals and Materials (article, 2020-10-05)
Field: Materials Science — Materials Chemistry

KIST-NOMAD - a Repository to Manage Large Amounts of Computational Materials Science Data We introduce the Korea Institute of Science and Technology-Novel Materials Discovery (KISTNOMAD) platform, a materials data repository. We describe its functionality and novel features from an academic viewpoint. It is a data repository designed for computational material science, especially focusing on managing and sharing the results of molecular dynamics simulation results as well as quantum mechanical computations. It consists of three main components: a database, file storage, and web-based front end. The database hosts material properties, which are extracted from the computational results. The front end has a graphical user interface and an open application programming interface, which allow researchers to interact with the system more easily. KIST-NOMAD’s panel displays the searched results on a well-organized and research-oriented web page. All the open access data and files are available for downloading in comma-separated value format as well as zipped archives. This automated extraction function was developed by utilizing database parsers and JSON scripts. KISTNOMAD also has an efficient option to download simulation and computation results on a large-scale. All of the above functions are designed to satisfy academic and research demands, and make highthroughput screening available, while incorporating machine learning for computational material engineering. We finally stress that the repository platform is user-driven and user-friendly. It is clearly designed to follow the modern big-data architecture and re-use principles for scientific data, such as being findable, accessible, and interoperable. Computer science ; Scripting language ; Upload ; Interface (matter) ; Interoperability ; JSON ; Database ; World Wide Web ; User interface ; Computational science ; Operating system Machine Learning in Materials Science ; Scientific Computing and Data Management ; Research Data Management Practices
Authors: Iván Olier
Source: Data Archiving and Networked Services (DANS) (article, 2020-10-30)
Field: Chemistry — Analytical Chemistry

QSAR datasets - Meta-QSAR We extracted 2,219 protein targets from ChEMBL with a diverse number of drug-like chemical compounds, ranging from 30 to about 6,000, each target resulting in a dataset with as many examples as compounds. The datasets were originally used in (Olier et al. Meta-QSAR: a large-scale application of meta-learning to drug design and discovery. Machine Learning, 2018, 107 (1), 285-311). Chemical compounds were intrinsically described using a standard fingerprint representation (as it is the most commonly used in QSAR learning), where the presence or absence of a particular molecular substructure in a molecule (e.g. methyl group, benzene ring) is indicated by a Boolean variable. Specifically, we used the RDKit to calculate the 1024 bits FCFP4 fingerprint representation, which is one of the extended-connectivity fingerprints (Rogers and Hahn, 2010) for molecular characterisation. Each dataset consisted of 1,024 input binary variables, one for each fingerprint bit, and one floating-point output variable which represented the chemical compound activities against the target. We used IC50 values, inhibitory drug concentrations at 50%. IC50 value states the concentration of the drug compound that is required to block or inhibit 50% of the proteins. This response data has been normalised by taking the negative log of the drug concentrations that inhibited 50% of a target (pXC50). Quantitative structure–activity relationship ; Computer science ; Artificial intelligence ; Machine learning Spectroscopy and Chemometric Analyses ; Brain Tumor Detection and Classification