jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: S. Heinen | Guido Falk von Rudorff | O. Anatole von Lilienfeld
Source: arXiv (Cornell University) (preprint, 2022-05-05)
Field: Materials Science — Materials Chemistry

Geometry Relaxation and Transition State Search throughout Chemical Compound Space with Quantum Machine Learning We use energies and forces predicted within response operator based quantum machine learning (OQML) to perform geometry optimization and transition state search calculations with legacy optimizers. For randomly sampled initial coordinates of small organic query molecules we report systematic improvement of equilibrium and transition state geometry output as training set sizes increase. Out-of-sample S$_\mathrm{N}$2 reactant complexes and transition state geometries have been predicted using the LBFGS and the QST2 algorithm with an RMSD of 0.16 and 0.4 Å -- after training on up to 200 reactant complexes relaxations and transition state search trajectories from the QMrxn20 data-set, respectively. For geometry optimizations, we have also considered relaxation paths up to 5'500 constitutional isomers with sum formula C$_7$H$_{10}$O$_2$ from the QM9-database. Using the resulting OQML models with an LBFGS optimizer reproduces the minimum geometry with an RMSD of 0.14~Å. For converged equilibrium and transition state geometries subsequent vibrational normal mode frequency analysis indicates deviation from MP2 reference results by on average 14 and 26\,cm$^{-1}$, respectively. While the numerical cost for OQML predictions is negligible in comparison to DFT or MP2, the number of steps until convergence is typically larger in either case. The success rate for reaching convergence, however, improves systematically with training set size, underscoring OQML's potential for universal applicability. Relaxation (psychology) ; Geometry ; State (computer science) ; State space ; Energy minimization ; Convergence (economics) ; Quantum ; Operator (biology) ; Space (punctuation) ; Set (abstract data type) ; Molecular geometry ; Mathematics ; Statistical physics ; Physics ; Chemistry ; Algorithm ; Molecule ; Computational chemistry ; Computer science ; Quantum mechanics ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Kasra Asnaashari | Roman V. Krems
Source:  (preprint, 2022-05-24)
Field: Materials Science — Materials Chemistry

Gradient domain machine learning with composite kernels: improving the accuracy of PES and force fields for large molecules The generalization accuracy of machine learning models of potential energy surfaces (PES) and force fields (FF) for large polyatomic molecules can be improved either by increasing the number of training points or by improving the models.In order to build accurate models based on expensive ab initio calculations, much of recent work has focused on the latter.In particular, it has been shown that gradient domain machine learning (GDML) models produce accurate results for highdimensional molecular systems with a small number of ab initio calculations.The present work extends GDML to models with composite kernels built to maximize inference from a small number of molecular geometries.We illustrate that GDML models can be improved by increasing the complexity of underlying kernels through a greedy search algorithm using Bayesian information criterion as the model selection metric.We show that this requires including anisotropy into kernel functions and produces models with significantly smaller generalization errors.The results are presented for ethanol, uracil, malonaldehyde and aspirin.For aspirin, the model with composite kernels trained by forces at 1000 randomly sampled molecular geometries produces a global 57dimensional PES with the mean absolute accuracy 0.177 kcal/mol (61.9 cm -1 ) and FFs with the mean absolute error 0.457 kcal/mol Å -1 . Composite number ; Domain (mathematical analysis) ; Computer science ; Force field (fiction) ; Field (mathematics) ; Artificial intelligence ; Materials science ; Algorithm ; Mathematics ; Mathematical analysis Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Nuclear Physics and Applications
Authors: Emma Van de Reydt | Noam Maron | James Saunderson | Mario Boley | Tanja Junkers
Source: ChemRxiv (preprint, 2022-11-04)
Field: Chemistry — Organic Chemistry

Machine-Learning Based Prediction of Kinetic Rate Coefficients in Radical Polymerization Using a log-log regression, the propagation rate coefficients for radical polymerization are correlated with basic molecular properties. These are either available from literature, or from simple and non-time-consuming calculations. Parameters under consideration are molecular weights, boiling points or dipolar moments. The model brings acrylates and methacrylates with linear and branched structures, and monomers that are known to be influenced strongly by H-bonding in line with each other, allowing to fit all data in a single approach. The model also successfully correlates monomers such as styrene and acrylonitrile successfully. Both absolute rate coefficients, as well as Arrhenius activation parameters can be described with high accuracy. With the presented model it is thus possible to describe practically all monomers for which kinetic data is available simultaneously and to carry out predictions for monomers for which no experimental data exist. Arrhenius equation ; Monomer ; Acrylonitrile ; Polymerization ; Thermodynamics ; Kinetic energy ; Radical polymerization ; Methacrylate ; Materials science ; Chemistry ; Polymer ; Mathematics ; Physical chemistry ; Activation energy ; Organic chemistry ; Physics ; Copolymer Advanced Polymer Synthesis and Characterization ; Machine Learning in Materials Science ; Fuel Cells and Related Materials
Authors: Lipichanda Goswami | Manoj Deka | Mohendra Roy
Source: arXiv (Cornell University) (review, 2022-09-15)
Field: Materials Science — Materials Chemistry

Artificial Intelligence in Material Engineering: A review on applications of AI in Material Engineering The role of artificial intelligence (AI) in material science and engineering (MSE) is becoming increasingly important as AI technology advances. The development of high-performance computing has made it possible to test deep learning (DL) models with significant parameters, providing an opportunity to overcome the limitation of traditional computational methods, such as density functional theory (DFT), in property prediction. Machine learning (ML)-based methods are faster and more accurate than DFT-based methods. Furthermore, the generative adversarial networks (GANs) have facilitated the generation of chemical compositions of inorganic materials without using crystal structure information. These developments have significantly impacted material engineering (ME) and research. Some of the latest developments in AI in ME herein are reviewed. First, the development of AI in the critical areas of ME, such as in material processing, the study of structure and material property, and measuring the performance of materials in various aspects, is discussed. Then, the significant methods of AI and their uses in MSE, such as graph neural network, generative models, transfer of learning, etc. are discussed. The use of AI to analyze the results from existing analytical instruments is also discussed. Finally, AI's advantages, disadvantages, and future in ME are discussed. Generative grammar ; Artificial intelligence ; Computer science ; Artificial neural network ; Property (philosophy) ; Deep learning ; Machine learning Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; X-ray Diffraction in Crystallography
Authors: Manabu Ihara | Sergei Manzhos
Source: Preprints.org (preprint, 2022-03-01)
Field: Materials Science — Materials Chemistry

Advanced Machine Learning Methods for Learning from Sparse Data in High-Dimensional Spaces: A Perspective on Uses in The Upstream of Development of Novel Energy Technologies Machine learning (ML) has found increasing use in research on energy conversion and storage technologies, in particular, so-called sustainable technologies. While often ML is used to directly optimize the parameters or phenomena of interest in the space of features, in this perspective, we focus on using ML to construct objects and methods that help in or enable the modeling of the underlying phenomena. We highlight the need for machine learning from very sparse and unevenly distributed numeric data in multidimensional spaces in these applications. After a brief introduction of some common regression-type machine learning techniques, we will focus on more advanced ML techniques which use these known methods as building blocks of more complex schemes and thereby allow working with extremely sparse data and also allow generating insight. Specifically, we will highlight the utility of using representations with sub-dimensional functions by combining the high-dimensional model representation ansatz with machine learning methods like neural networks or Gaussian process regressions in applications ranging from heterogeneous catalysis to nuclear energy. Computer science ; Machine learning ; Artificial intelligence ; Perspective (graphical) ; Construct (python library) ; Process (computing) ; Focus (optics) Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference
Authors: Hannes Kneiding | Ruslan Lukin | David Balcells
Source: ChemRxiv (preprint, 2022-06-27)
Field: Materials Science — Materials Chemistry

Deep Learning Metal Complex Properties with Natural Quantum Graphs Deep graph learning can make a strong contribution to accelerating the discovery of transition metal complexes (TMCs). TMCs will play a key role in the development of new technologies for which there is an urgent need; e.g. the production of green hydrogen from renewable sources. Despite the recent developments in machine learning for drug discovery and organic chemistry in general, the application of these methods to TMCs remains challenged by the higher complexity of these compounds, and the limited availability of the large and complex data required. In this work, we report a new graph representation for neural networks – the natural quantum graph (NatQG), which is based on natural bond orbitals (NBOs) and their second-order perturbation analysis (SOPA). The combined use of NBOs and SOPA was used to build two different types of NatQG graphs: undirected (u-NatQG) and directed (d-NatQG) – both were informed with quantum electronic structure information included in node, edge, and whole-graph attribute vectors. These graphs were used to develop graph neural networks (GNNs) based on modifications of the gated message passing neural network (MPNN) and the multiplex molecular graph neural network (MXMNet). For a number of molecular properties, including the HOMO-LUMO gap, polarizability, and dipole moment, the NatQG models performed at a level similar or higher than that of state-of-the-art GNNs based on radius graphs. Further, we provide the transition metal quantum mechanics graph (tmQMg) dataset, including the quantum geometries, properties, and NatQG graphs of 60k TMCs, which can be used as a benchmark for graph-based machine learning models. We believe that this work makes a significant contribution to the further development of deep graph learning for TMCs. Computer science ; Theoretical computer science ; Molecular graph ; Quantum machine learning ; Artificial neural network ; Quantum ; Graph ; Quantum computer ; Artificial intelligence ; Quantum mechanics ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; History and advancements in chemistry
Authors: Miruna T. Cretu | Alessandra Toniato | Alain C. Vaucher | Amol Thakkar | Amin Debabeche | Teodoro Laino
Source: ChemRxiv (preprint, 2022-11-15)
Field: Materials Science — Materials Chemistry

Standardization of chemical compounds using language modeling With the growing amount of chemical data stored digitally, it has become crucial to represent chemical compounds consistently. Harmonized representations facilitate the extraction of insightful information from datasets, and are advantageous for machine learning applications. Compound standardization is typically accomplished using rule-based algorithms that modify undesirable descriptions of functional groups, resulting in a consistent representation throughout the dataset. Here, we present the first deep-learning model for molecular standardization. We enable custom schemes based solely on data, which also support standardization options that are difficult to encode into rules. Our model achieves >98% accuracy in learning two popular rule-based protocols. When fine-tuned on a relatively small dataset of catalysts (for which there is currently no automated standardization practice), the model predicts the expected standardized molecular format with a test accuracy of 62% on average. We show that our model learns not only the grammar and syntax of molecular representations, but also the details of atom ordering, types of bonds, and representations of charged species. In addition, we demonstrate the model's ability to reproduce a canonicalization algorithm with a 95.6% success rate. Standardization ; Computer science ; Syntax ; Representation (politics) ; Artificial intelligence ; ENCODE ; Machine learning ; Natural language processing ; Data mining ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Eric Sun
Source:  (article, 2022-09-30)
Field: Materials Science — Materials Chemistry

Demystifying Quantum Materials with Deep Learning and Angle-Resolved Photoemission Spectroscopy Modern materials often exhibit behavior that cannot be explained by current condensed matter physics. These elusive properties, like cuprate superconductivity, emerge from quantum phenomena and are yet to be fully understood. Angle-resolved photoemission spectroscopy (ARPES) can probe these properties by determining the allowable energy-momentum states. Specifically, inhomogeneities within these states often accompany emergent behavior. However, spatially-resolved ARPES generates noisy high-dimensional data difficult for human analysis. In this paper, we develop a deep autoencoder that extracts and characterizes electron spectral features, before comparing our methodology against currently used techniques. We then dissect data from the high-superconductor Bi<inf>2</inf> Sr<inf>2</inf> CaCu<inf>2</inf> O<inf>8</inf> (BSCCO) and correlated insulator TaSe<inf>2</inf>, utilizing our approach to automatically extract Fermi levels, band structures, surface inhomogeneities, and potentially electron mass renormalization kinks hidden under noise. Angle-resolved photoemission spectroscopy ; Physics ; Superconductivity ; Photoemission spectroscopy ; Autoencoder ; Topology (electrical circuits) ; Artificial intelligence ; Condensed matter physics ; Quantum mechanics ; Computer science ; Electronic structure ; Deep learning ; Combinatorics ; Mathematics ; Spectral line Electronic and Structural Properties of Oxides ; Physics of Superconductivity and Magnetism ; Advanced Condensed Matter Physics
Authors: Haitz Sáez de Ocáriz Borde | Federico Barbero
Source: arXiv (Cornell University) (preprint, 2022-09-24)
Field: Materials Science — Materials Chemistry

Graph Neural Network Expressivity and Meta-Learning for Molecular Property Regression We demonstrate the applicability of model-agnostic algorithms for meta-learning, specifically Reptile, to GNN models in molecular regression tasks. Using meta-learning we are able to learn new chemical prediction tasks with only a few model updates, as compared to using randomly initialized GNNs which require learning each regression task from scratch. We experimentally show that GNN layer expressivity is correlated to improved meta-learning. Additionally, we also experiment with GNN emsembles which yield best performance and rapid convergence for k-shot learning. Computer science ; Meta learning (computer science) ; Regression ; Artificial intelligence ; Machine learning ; Scratch ; Task (project management) ; Graph ; Property (philosophy) ; Artificial neural network ; Convergence (economics) ; Multi-task learning ; Theoretical computer science ; Statistics ; Mathematics Machine Learning in Materials Science ; Metabolomics and Mass Spectrometry Studies ; Computational Drug Discovery Methods
Authors: Henning Otto Brinkhaus | Kohulan Rajan | Jonas Schaub | Achim Zielesny | Christoph Steinbeck
Source: ChemRxiv (preprint, 2022-10-21)
Field: Materials Science — Materials Chemistry

Open data and algorithms for open science in AI-driven molecular informatics Recent years have seen a sharp increase in the development of deep learning and artificial intelligence-based molecular informatics. The success of AlphaFold led to a growing interest in applying deep learning to a number of subfields, including the digital transformation of synthetic chemistry, extraction of chemical information from the scientific literature, and AI in natural product-based drug discovery. The application of AI to molecular informatics is still constrained by the fact that most of the data used for training and testing deep learning models are not available as FAIR and open data. As open science practices continue to grow in popularity, initiatives such as the FAIR data movement, open data, and open-source software have emerged. It is becoming increasingly important for researchers in the field of molecular informatics to embrace open science and to submit data and software that support their studies. With the advent of open-source deep learning frameworks and cloud computing platforms, academic researchers are now able to deploy and test their own deep learning algorithms with ease. With the development of new and faster hardware for deep learning and the increasing number of initiatives towards digital research data management infrastructures, as well as a culture promoting open data, open source, and open science, AI-driven molecular informatics will continue to grow. This review examines the current state of open data and open algorithms in molecular informatics, as well as ways in which they could be improved in future. Computer science ; Data science ; Informatics ; Deep learning ; Big data ; Artificial intelligence ; Open data ; Cloud computing ; Open research ; Popularity ; World Wide Web ; Data mining ; Engineering ; Political science Machine Learning in Materials Science ; Scientific Computing and Data Management ; Computational Drug Discovery Methods
Authors: Yachong Guo | Marco Werner | Vladimir A. Baulin
Source: Scientific Reports (article, 2022-08-29)
Field: Materials Science — Materials Chemistry

Efficient enumeration-selection computational strategy for adaptive chemistry Design problems of finding efficient patterns, adaptation of complex molecules to external environments, affinity of molecules to specific targets, dynamic adaptive behavior of chemical systems, reconstruction of 3D structures from diffraction data are examples of difficult to solve optimal design or inverse search problems. Nature inspires evolution strategies to solve design problems that are based on selection of successful adaptations and heritable traits over generations. To exploit this strategy in the creation of new materials, a concept of adaptive chemistry was proposed to provide a route for synthesis of self-adapting molecules that can fit to their environment. We propose a computational method of an efficient exhaustive search exploiting massive parallelization on modern GPUs, which finds a solution for an inverse problem by solving repetitively a direct problem in the mean field approximation. One example is the search for a composition of a copolymer that allows the polymer to translocate through a lipid membrane at a minimal time. Another example is a search of a copolymer sequence that maximizes the polymer load in the micelle defined by the radial core-shell potentials. The length and the composition of the sequence are adjusted to fit into the restricted environment. Hydrogen bonding is another pathway of adaptation to the environment through reversible links. A linear polymer that interacts with water through hydrogen bonds adjusts the position of hydrogen bonds along the chain as a function of the concentration field around monomers. In the last example, branching of the molecules is adjusted to external fields, providing molecules with annealed topology, that can be flexibly changed by changing external conditions. The method can be generalized and applied to a broad spectrum of design problems in chemistry and physics, where adaptive behavior in multi-parameter space in response to environmental conditions lead to non-trivial patterns or molecule architectures and compositions. It can further be combined with machine learning or other optimization techniques to explore more efficiently the parameter space. Computer science ; Branching (polymer chemistry) ; Sequence (biology) ; Molecule ; Hydrogen bond ; Field (mathematics) ; Set (abstract data type) ; Polymer ; Algorithm ; Theoretical computer science ; Mathematical optimization ; Chemistry ; Mathematics Machine Learning in Materials Science ; Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods
Authors: Chuan Heng Tan | Khairiyah Mohd‐Yusof | Sharifah Rafidah Wan Alwi
Source: Jurnal Teknologi (article, 2022-09-25)
Field: Chemistry — Analytical Chemistry

QUALITY PREDICTION WITH NEURAL NETWORK TECHNIQUES FOR POLYPROPYLENE PRODUCTION VIA THE SPHERIPOL PROCESS In the polypropylene (PP) industry, melt index (MI) is the most important quality variable. Different grades of PP have their specific range of MI. Accurate prediction of MI is essential for efficient monitoring and off-grade reduction. Artificial Neural Network (ANN) models are proposed as the technique for MI estimation. It has powerful adaptive capabilities in response to nonlinear behaviour. In this research, ANN models for PP polymerization to predict the MI based on reactor parameters were developed. Three types of ANN models, the single hidden layer ANN (shallow ANN), stacked neural network (SNN) and deep learning are compared. The simulation results show that deep learning can perform better than shallow ANN and SNN by considering the accuracy of the prediction and detection of process fluctuation. All three model have proven that ANN are able to perform non-linear function approximation. Thus, ANN models are effective for supporting MI prediction such as for soft-sensors and process optimization in the polymer industry. Artificial neural network ; Nonlinear system ; Process (computing) ; Computer science ; Artificial intelligence ; Polypropylene ; Deep learning ; Machine learning ; Reduction (mathematics) ; Materials science ; Mathematics Spectroscopy and Chemometric Analyses ; Fault Detection and Control Systems ; Water Quality Monitoring and Analysis