jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: I. Miyaguchi | Miwa Sato | Akiko Kashima | Hiroyuki Nakagawa | Yuichi Kokabu | Biao Ma | Shigeyuki Matsumoto | Atsushi Tokuhisa | Masateru Ohta | Mitsunori Ikeguchi
Source: Scientific Reports (article, 2021-12-08)
Field: Materials Science — Materials Chemistry

Machine learning to estimate the local quality of protein crystal structures Low-resolution electron density maps can pose a major obstacle in the determination and use of protein structures. Herein, we describe a novel method, called quality assessment based on an electron density map (QAEmap), which evaluates local protein structures determined by X-ray crystallography and could be applied to correct structural errors using low-resolution maps. QAEmap uses a three-dimensional deep convolutional neural network with electron density maps and their corresponding coordinates as input and predicts the correlation between the local structure and putative high-resolution experimental electron density map. This correlation could be used as a metric to modify the structure. Further, we propose that this method may be applied to evaluate ligand binding, which can be difficult to determine at low resolution. Metric (unit) ; Resolution (logic) ; Computer science ; Low resolution ; Electron density ; Convolutional neural network ; Artificial intelligence ; Correlation ; Pattern recognition (psychology) ; Protein structure ; High resolution ; Electron ; Physics ; Mathematics ; Geometry ; Nuclear magnetic resonance ; Geography Enzyme Structure and Function ; Protein Structure and Dynamics ; Machine Learning in Materials Science
Authors: Md Mahmudul Hasan | Arulmurugan Senthilnathan | Pınar Acar
Source: AIAA Scitech 2021 Forum (article, 2021-01-04)
Field: Materials Science — Materials Chemistry

Machine Learning Reinforced Multi-Scale Modeling of Microstructures under Uncertainties View Video Presentation: https://doi.org/10.2514/6.2021-1140.vid This work addresses machine learning (ML) reinforced robust modeling of metallic microstructures under the effects of the uncertainties. The presented methodology is applied to explore the elasto-plastic deformation behavior of Titanium-7wt%Aluminum (Ti-7Al) alloy. Although it is a candidate aerospace material owing to the outstanding mechanical performance under high stresses, there is still no consensus on its crystal plasticity modeling parameters. As a result, understanding the large deformation behavior of the alloy requires more investigations. Additionally, the-state-of-the-art studies are only focused on deterministic models, by neglecting the effects of the material uncertainties. In this work, our goal is to build a high-fidelity crystal plasticity surrogate model of the Ti-7Al alloy using an artificial neural network (ANN) framework with the consideration of the effects of the material uncertainties. The first solution step determines the lower and upper bounds of the crystal plasticity parameters by modeling the uncertainty in the macro-scale stress-strain data. Next, an ANN model is developed to predict the microstructural texture as a function of the crystal plasticity parameters. The optimum crystal plasticity parameters of the alloy are obtained by solving an inverse problem to minimize the difference between the experimental texture data and the ANN prediction. Materials science ; Plasticity ; Texture (cosmology) ; Microstructure ; Deformation (meteorology) ; Crystal (programming language) ; Artificial neural network ; Machine learning ; Computer science ; Artificial intelligence ; Mechanical engineering ; Metallurgy ; Composite material ; Engineering Machine Learning in Materials Science ; Titanium Alloys Microstructure and Properties ; Metallurgy and Material Forming
Authors: Leif D. Jacobson | James Stevenson | Farhad Ramezanghorbani | Delaram Ghoreishi | Karl Leswing | Ed Harder | Robert Abel
Source: ChemRxiv (preprint, 2021-12-09)
Field: Materials Science — Materials Chemistry

Transferable Neural Network Potential Energy Surfaces for Closed-Shell Organic Molecules: Extension to Ions Transferable high dimensional neural network potentials (HDNNP) have shown great promise as an avenue to increase the accuracy and domain of applicability of existing atomistic force fields for organic systems relevant to life science. We have previously reported such a potential (Schrödinger-ANI) that has broad coverage of druglike molecules. We extend that work here to cover ionic and zwitterionic druglike molecules expected to be relevant to drug discovery research activities. We report a novel HDNNP architechture, which we call QRNN, that predicts atomic charges and uses these charges as descriptors in an energy model which delivers conformational energies within chemical accuracy when measured against the reference theory it is trained to. Further, we find that delta learning based on a semi-empirical level of theory approximately halves the errors. We test the models on torsion energy profiles, relative conformational energies, geometric parameters and relative tautomer errors. Artificial neural network ; Molecule ; Potential energy ; Ion ; Torsion (gastropod) ; Organic molecules ; Ionic bonding ; Extension (predicate logic) ; Work (physics) ; Computer science ; Energy (signal processing) ; Chemistry ; Chemical physics ; Physics ; Statistical physics ; Artificial intelligence ; Atomic physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Zhengkai Tu | Connor W. Coley
Source: arXiv (Cornell University) (preprint, 2021-10-19)
Field: Materials Science — Materials Chemistry

Permutation invariant graph-to-sequence model for template-free retrosynthesis and reaction prediction Synthesis planning and reaction outcome prediction are two fundamental problems in computer-aided organic chemistry for which a variety of data-driven approaches have emerged. Natural language approaches that model each problem as a SMILES-to-SMILES translation lead to a simple end-to-end formulation, reduce the need for data preprocessing, and enable the use of well-optimized machine translation model architectures. However, SMILES representations are not an efficient representation for capturing information about molecular structures, as evidenced by the success of SMILES augmentation to boost empirical performance. Here, we describe a novel Graph2SMILES model that combines the power of Transformer models for text generation with the permutation invariance of molecular graph encoders that mitigates the need for input data augmentation. As an end-to-end architecture, Graph2SMILES can be used as a drop-in replacement for the Transformer in any task involving molecule(s)-to-molecule(s) transformations. In our encoder, an attention-augmented directed message passing neural network (D-MPNN) captures local chemical environments, and the global attention encoder allows for long-range and intermolecular interactions, enhanced by graph-aware positional embedding. Graph2SMILES improves the top-1 accuracy of the Transformer baselines by $1.7\%$ and $1.9\%$ for reaction outcome prediction on USPTO_480k and USPTO_STEREO datasets respectively, and by $9.8\%$ for one-step retrosynthesis on the USPTO_50k dataset. Computer science ; Transformer ; Encoder ; Artificial intelligence ; Retrosynthetic analysis ; Theoretical computer science ; Machine translation ; Machine learning ; Natural language processing ; Chemistry ; Engineering Machine Learning in Materials Science ; Topic Modeling ; Asymmetric Hydrogenation and Catalysis
Authors: Wenjing Wang | Run Luo | Qiannan Duan | Yunjin Feng | Jiayuan Chen | Yicai Huang | Sifan Bi | Fenli Liu | Jianchao Lee
Source: ChemistrySelect (article, 2021-04-14)
Field: Chemistry — Analytical Chemistry

Direct Quantification of Mixed Organic Acids Based on Spectral Image with Deep Learning Abstract Identification of organic acid is crucial indicator for field of environment, healthcare and industry, which involves the time‐consuming and labor‐intensive progress. The simultaneous detection for complex mixture becomes a new target when large‐scale instruments is the mainstream measurement approach. With the burst development of machine learning in solving complex problem, the progress is being made for quick detection of mixtures. The next challenge is to provide sufficient data to satisfy the need of algorithm. In this work, we proposed the modified spectrometer technology combined with the deep learning to quantify the mixed organic acids. Organic acids interact with light in the various background to map the information in the form of images. Deep learning could establish the relationship between images and concentrations. According to the result, trained convolutional neural network could achieve the goal of simultaneous measurement of organic acids, which speeds up the implementation in the parallel detection and suggests new twists in the field of chemistry. Image (mathematics) ; Artificial intelligence ; Deep learning ; Chemistry ; Computer science ; Environmental science ; Environmental chemistry ; Pattern recognition (psychology) Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: Geemi P. Wellawatte | Aditi Seshadri | Andrew Dickson White
Source: ChemRxiv (preprint, 2021-08-16)
Field: Materials Science — Materials Chemistry

Model agnostic generation of counterfactual explanations for molecules An outstanding challenge in deep learning is its lack of interpretability. The inability of both designers and users of neural networks to explain why a prediction is made is a major drawback. This not only dissuades chemists from using deep learning predictions, but also has led to neural networks learning spurious correlations that are difficult to notice. Counterfactuals are a category of prediction explanations that provide a rationale behind the prediction value. Counterfactuals have satisfying properties like being in the same space as the features and require no deep learning expertise to understand. Counterfactuals are generated via optimization, which makes them complex to use when the features are chemical structures. Here we present an algorithm built on the "Superfast Traversal, Optimization, Novelty, Exploration and Discovery" method (Nigam et. al, 2021) that is efficient and model-agnostic for generating counterfactuals. We show this method is applicable in random forest models, sequence models, and graph neural networks in both classification and regression. Our method requires no additional training, data, or model gradients. Counterfactual conditional ; Interpretability ; Computer science ; Artificial intelligence ; Counterfactual thinking ; Spurious relationship ; Machine learning ; Deep learning ; Artificial neural network ; Novelty ; Tree traversal ; Ambiguity ; Algorithm ; Psychology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Joydeep Munshi | Alexander Rakowski | Benjamin H. Savitzky | Colin Ophus | Matthew Henderson | Shreyas Cholia | Maria K. Y. Chan
Source: Microscopy and Microanalysis (article, 2021-07-30)
Field: Materials Science — Materials Chemistry

4D &gt;Crystal: Deep Learning Crystallographic Information From Electron Diffraction Images Ongoing development in electron detector technology, in the recent years, has enabled a new age of electron microscopy which facilitated easy accessibility of multi-dimensional data. The 4D Camera at Lawrence Berkeley National Laboratory [1], for example, is capable of generating enormous amounts of data (estimated ~ 5 PBs of data each year) due to the development of high-speed electron detector technology. To make the data analysis of 4D Camera easy and accessible, tools such as the robust open-source python-based 4D-STEM (4D-Scanning Transmission Electron Microscope) analysis package (py4DSTEM) have been recently developed Despite the comprehensive and analysis strategy in many instances, there is a need for image analysis using cutting-edge machine learning (AI/ML) techniques to complement the existing relatively slow, complex and hyperparameter-dependent pipeline. To this end, we propose the design of a fully automated AI/ML python-based pipeline, which is fast, automated, and robust against experimental error and background noise. Crystallography ; Electron diffraction ; Materials science ; Diffraction ; Crystal (programming language) ; Electron ; Electron crystallography ; Computer science ; Optics ; Chemistry ; Physics ; Nuclear physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Enzyme Structure and Function
Authors: William Borrelli | Joshua Schrier
Source: ChemRxiv (preprint, 2021-06-29)
Field: Materials Science — Materials Chemistry

Evaluating the Performance of a Transformer-based Organic Reaction Prediction Model Forward and retrosynthetic organic reaction prediction are challenging applications of artificial intelligence (AI) research in chemistry. IBM’s freely available RXN for Chemistry (https://rxn.res.ibm.com) treats reaction prediction as a translation problem, by using transformer-based machine learning models trained on patent data to convert reactant SMILES string sequences into product strings. Here we characterize the performance of transformer models on 100 undergraduate text-book problems to expose reaction classes where the fundamentals of organic chemistry are violated. The forward prediction model is generally successful in predicting outcomes for substitution reactions and unsuccessful for elimination and organocopper reactions. For the retrosynthesis model, we found characteristic examples of a lack of atom conservation and nonsensical chemical transformations. We also compared the dif- ferences in molecular complexity and synthetic accessibility between predicted and literature reactions to probe how AI plans reactions compared to humans. Forward predictions replicated a similar distribution of differences in molecular complexity as the human reactions from the literature, whereas retrosynthetic predictions resulted in both positive and negative deviations from literature complexity. Finally, we analyzed the atom mapping in test reactions to expose errors in how the model identifies reactive atoms and species. Retrosynthetic analysis ; Transformer ; IBM ; Computer science ; Artificial intelligence ; Machine translation ; Chemistry ; Machine learning ; Biochemical engineering ; Nanotechnology ; Engineering ; Materials science ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Software Engineering Research
Authors: Yuanpeng Deng | Tao Du | Hui Li
Source: Journal of the American Ceramic Society (article, 2021-03-12)
Field: Materials Science — Ceramics and Composites

Relationship of structure and mechanical property of silica with enhanced sampling and machine learning Abstract As one of the most abundant materials on Earth, silica has been widely studied in various crystal structures and glassy states. However, in terms of molecular structure and the corresponding mechanical properties between those of ordered and disordered states, partially ordered silica states are not well‐explored in the relevant literature owing to a low probability of appearance in experiments and simulations. The lack of this knowledge significantly hinders the understanding of the inherent mechanism of mechanical properties and limits the applications in many engineering fields. In this study, we present an exploration of the complex interdependent relations of the structural properties of silica over a wide range of free energy surfaces and establish a machine learning‐based prediction model via high‐throughput molecular dynamics simulations coupling with an enhanced sampling method. Each scale of structure information of samples with varying crystallinity is analyzed. First, descriptors of silica structures were identified and selected as inputs to a deep neural network (DNN). The results indicate that our DNN‐based approach can provide an accurate prediction of bulk modulus, shear modulus, and tensile strength of silica samples. Furthermore, the generalizability of the machine learning model is verified on the prediction tasks for much larger silica systems, as well as silica quenched at varying cooling rates. Overall, the enhanced sampling method can reliably accelerate the exploration of free energy surfaces and collection of training samples, and machine learning methods are effective in generating accurate and reliable predictions of mechanical properties of materials over the free energy surface. Materials science ; Artificial neural network ; Crystallinity ; Artificial intelligence ; Sampling (signal processing) ; Microscale chemistry ; Ultimate tensile strength ; Machine learning ; Biological system ; Computer science ; Composite material ; Mathematics ; Computer vision Glass properties and applications ; Theoretical and Computational Physics ; Material Dynamics and Properties
Authors: Samuel Kim | Peter Y. Lu | Charlotte Loh | Jamie Smith | Jasper Snoek | Marin Soljačić
Source: arXiv (Cornell University) (preprint, 2021-04-23)
Field: Materials Science — Materials Chemistry

Scalable and Flexible Deep Bayesian Optimization with Auxiliary Information for Scientific Problems. Bayesian optimization (BO) is a popular paradigm for global optimization of expensive black-box functions, but there are many domains where the function is not completely black-box. The data may have some known structure, e.g. symmetries, and the data generation process can yield useful intermediate or auxiliary information in addition to the value of the optimization objective. However, surrogate models traditionally employed in BO, such as Gaussian Processes (GPs), scale poorly with dataset size and struggle to incorporate known structure or auxiliary information. Instead, we propose performing BO on complex, structured problems by using Bayesian Neural Networks (BNNs), a class of scalable surrogate models that have the representation power and flexibility to handle structured data and exploit auxiliary information. We demonstrate BO on a number of realistic problems in physics and chemistry, including topology optimization of photonic crystal materials using convolutional neural networks, and chemical property optimization of molecules using graph neural networks. On these complex tasks, we show that BNNs often outperform GPs as surrogate models for BO in terms of both sampling efficiency and computational cost. Bayesian optimization ; Computer science ; Gaussian process ; Scalability ; Artificial intelligence ; Optimization problem ; Theoretical computer science ; Machine learning ; Mathematical optimization ; Gaussian ; Algorithm ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning and Data Classification
Authors: Jing Dong | Xuechun Mu | Zelin Zhang | Yuqing Zhang | Xiaohui Yang | Puyu Zhang
Source: match Communications in Mathematical and in Computer Chemistry (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Intelligent predicting reaction performance in multi-dimensional chemical space using quantile regression forest Buchwald-Hartwig amination reaction is widely applied in synthetic organic chemistry, which faces tedious and complex experimental process. In 2018, an interesting yield prediction technique is proposed via machine learning (random forest) in Science. However, the method is based on point prediction with many feature descriptors. For tackling these problems, complements and improvements have been made from the perspectives of machine learning and statistics, including feature dimensionality reduction, distributed prediction and visualization, so as to provide accurate and reliable decision information. Random forest ; Curse of dimensionality ; Artificial intelligence ; Dimensionality reduction ; Feature (linguistics) ; Computer science ; Machine learning ; Process (computing) ; Quantile regression ; Visualization ; Data mining Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Masahiro Sato
Source: 2021 IEEE Conference on Electrical Insulation and Dielectric Phenomena (CEIDP) (article, 2021-12-12)
Field: Materials Science — Materials Chemistry

First-Principles Modeling in the Context of Dielectric Materials Science and Design First-principles calculation and data-centric approach have become powerful tools for materials design. In this study, we introduce our latest research outcomes that are related to computational dielectric materials science. The main contents are as follows: (1) first-principles-based multiscale modeling of electronic charge transfer and molecular dynamics simulation of ionic carrier transfer in polymer dielectrics, (2) first-principles modeling of the inorganic filler/polymer interface and charge injection from the (metal) electrode to polymer dielectrics, and (3) a combined first-principles and machine learning approach for predicting dielectric properties of various materials. The recent advances in the atomistic understanding of the electrical properties of dielectric materials is highlighted. In addition, we show that, with this knowledge of the underlying physics, one can develop a machine learning model that can accurately predict the physical properties, albeit with only a small dataset. Dielectric ; Context (archaeology) ; Materials science ; Computer science ; Ionic bonding ; Charge (physics) ; Molecular dynamics ; Nanotechnology ; Engineering physics ; Physics ; Ion ; Optoelectronics ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Ionic liquids properties and applications ; Electrochemical Analysis and Applications
Authors: Abdul Gani Abdul Jameel
Source:  (article, 2021-06-07)
Field: Chemistry — Spectroscopy

A Comprehensive Model for Cetane Number Prediction Using Machine Learning Abstract Machine learning based predictive models are being extensively applied for predicting combustion properties like derived cetane number (DCN), which is the measure of a fuels ignition quality. In the present work, a comprehensive model was developed using artificial neural networks (ANN) that can predict the DCN of fuels containing a large number of chemical classes like paraffins, iso-paraffins, olefins, naphthenes, aromatics, alcohols, ethers, aldehydes, ketones and esters. Experimental DCN’s of 275 fuels was used as a dataset and the composition of the fuels expressed in the form of twelve functional groups and two structural parameters namely, branching index (BI) and molecular weight were used as the input features for the model. A feed forward neural network with two hidden layers with 40 neurons in each layer was developed using Levenberg-Marquardt algorithm. The developed model was validated with 15% of the data points that were randomly generated and kept aside for validation. A regression coefficient (R2) of 0.99 was observed between the predicted and the experimental values along with an average absolute error of 1.1. The results showed that the developed model was successful in predicting the DCN of fuels and can be applied to pure compounds, blends and real fuels containing diverse chemical functionalities. Cetane number ; Artificial neural network ; Combustion ; Refinery ; Computer science ; Work (physics) ; Biological system ; Machine learning ; Chemistry ; Organic chemistry ; Engineering ; Biodiesel Analytical Chemistry and Chromatography ; Chemical Thermodynamics and Molecular Structure ; Computational Drug Discovery Methods