jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Takuma Wakiya | Yoshinobu Kamakura | Hiroki Shibahara | Kazuyoshi Ogasawara | Akinori Saeki | Ryosuke Nishikubo | Akihiro Inokuchi | Hirofumi Yoshikawa | Daisuke Tanaka
Source: Angewandte Chemie (article, 2021-08-25)
Field: Chemistry — Inorganic Chemistry

Machine‐Learning‐Assisted Selective Synthesis of a Semiconductive Silver Thiolate Coordination Polymer with Segregated Paths for Holes and Electrons Abstract Coordination polymers (CPs) with infinite metal–sulfur bond networks have unique electrical conductivities and optical properties. However, the development of new (‐M‐S‐) n ‐structured CPs is hindered by difficulties with their crystallization. Herein, we describe the use of machine learning to optimize the synthesis of trithiocyanuric acid (H 3 ttc)‐based semiconductive CPs with infinite Ag−S bond networks, report three CP crystal structures, and reveal that isomer selectivity is mainly determined by proton concentration in the reaction medium. One of the CPs, [Ag 2 Httc] n , features a 3D‐extended infinite Ag−S bond network with 1D columns of stacked triazine rings, which, according to first‐principle calculations, provide separate paths for holes and electrons. Time‐resolved microwave conductivity experiments show that [Ag 2 Httc] n is highly photoconductive ( φ Σ μ max =1.6×10 −4 cm 2 V −1 s −1 ). Thus, our method promotes the discovery of novel CPs with selective topologies that are difficult to crystallize. Crystallization ; Materials science ; Electron ; Proton ; Topology (electrical circuits) ; Metal ; Polymer ; Selectivity ; Chemical physics ; Chemistry ; Composite material ; Physics ; Organic chemistry ; Catalysis Metal-Organic Frameworks: Synthesis and Applications ; Organic and Molecular Conductors Research ; Covalent Organic Framework Applications
Authors: Hui Shao | Jiansu Pu | Yanlin Zhu | Boyang Gao | Zhengguo Zhu | Yunbo Rao
Source:  (article, 2021-04-01)
Field: Materials Science — Materials Chemistry

Visual Analysis on Machine Learning Assisted Prediction of Ionic Conductivity for Solid-State Electrolytes Lithium ion batteries (LIBs) are widely used as the important energy sources in our daily life such as mobile phones, electric vehicles, and drones etc. Due to the potential safety risks caused by liquid electrolytes, the experts have tried to replace liquid electrolytes with solid ones. However, it is very difficult to find suitable alternatives materials in traditional ways for its incredible high cost in searching. Machine learning (ML) based methods are currently introduced and used for material prediction. But there is rarely an assisting learning tools designed for domain experts for institutive performance comparison and analysis of ML model. In this case, we propose an interactive visualization system for experts to select suitable ML models, understand and explore the predication results comprehensively. Our system employs a multi-faceted visualization scheme designed to support analysis from the perspective of feature composition, data similarity, model performance, and results presentation. A case study with real experiments in lab has been taken by the expert and the results of confirmed the effectiveness and helpfulness of our system. Computer science ; Visualization ; Helpfulness ; Domain (mathematical analysis) ; Feature (linguistics) ; Artificial intelligence ; Human–computer interaction ; Machine learning Machine Learning in Materials Science ; Advanced Battery Technologies Research ; Green IT and Sustainability
Authors: Takayuki Kurogi | Mayumi Etou | Rei Hamada | Shingo Sakai
Source: Journal of the Japan Petroleum Institute (article, 2021-08-31)
Field: Materials Science — Materials Chemistry

Catalyst Design by Machine Learning and Multiobjective Optimization The computer technologies of machine learning and multiobjective optimization were introduced to develop the catalyst for fluid catalytic cracking (FCC). Response surface methodology was applied for a training set consisting of 1000 data points with varied catalyst compositions which consist of a variety of catalysts compositions, feedstock properties, pseudo-equilibrium conditions, cracking performance test conditions as input parameters and the cracking test results as outputs. At first, response surface model (RSM) was obtained with four approximation methods, among which the radial basis function (RBF) method was found to give the highest score accurate RSM with the smallest average error and the highest coefficient of determination among them. Then the virtual experiments were carried out with the RSM applied with multiobjective genetic algorithm (MOGA) to optimize the catalyst design considering the multiobjective; to yield less bottoms, less coke, more gasoline and less gas. After 5000 virtual experiments with RSM were carried out, we found that the pareto front was obtained. Finally, the optimum catalyst design was selected from the designs on the pareto front. As a result, the selected catalyst design showed 2.7 % higher gasoline yield and was confirmed to show the excellent performance over conventional FCC catalyst. Response surface methodology ; Fluid catalytic cracking ; Multi-objective optimization ; Yield (engineering) ; Pareto principle ; Catalysis ; Cracking ; Process engineering ; Raw material ; Coke ; Gasoline ; Materials science ; Computer science ; Mathematics ; Engineering ; Chemistry ; Composite material ; Mathematical optimization ; Machine learning ; Metallurgy ; Waste management ; Organic chemistry Machine Learning in Materials Science ; Advanced Multi-Objective Optimization Algorithms ; Evolutionary Algorithms and Applications
Authors: Nirvana Delgado Otalvaro | Pembe Gül Bilir | Karla Herrera Delgado | Stephan Pitter | Jörg Sauer
Source: Chemie Ingenieur Technik (article, 2021-03-01)
Field: Materials Science — Materials Chemistry

Modeling the Direct Synthesis of Dimethyl Ether using Artificial Neural Networks Abstract Artificial neural networks (ANNs) are designed and implemented to model the direct synthesis of dimethyl ether (DME) from syngas over a commercial catalyst system. The predictive power of the ANNs is assessed by comparison with the predictions of a lumped model parameterized to fit the same data used for ANN training. The ANN training converges much faster than the parameter estimation of the lumped model, and the predictions show a higher degree of accuracy under all conditions. Furthermore, the simulations show that the ANN predictions are also accurate even at some conditions beyond the validity range. Dimethyl ether ; Artificial neural network ; Syngas ; Parameterized complexity ; Range (aeronautics) ; Training set ; Power (physics) ; Computer science ; Engineering ; Biological system ; Catalysis ; Artificial intelligence ; Algorithm ; Chemistry ; Thermodynamics ; Organic chemistry ; Physics Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Catalysts for Methane Reforming
Authors: Ramin Ansari | Amirata Ghorbani
Source: arXiv (Cornell University) (preprint, 2021-04-15)
Field: Materials Science — Materials Chemistry

Accurate Prediction of Free Solvation Energy of Organic Molecules via Graph Attention Network and Message Passing Neural Network from Pairwise Atomistic Interactions Deep learning based methods have been widely applied to predict various kinds of molecular properties in the pharmaceutical industry with increasingly more success. Solvation free energy is an important index in the field of organic synthesis, medicinal chemistry, drug delivery, and biological processes. However, accurate solvation free energy determination is a time-consuming experimental process. Furthermore, it could be useful to assess solvation free energy in the absence of a physical sample. In this study, we propose two novel models for the problem of free solvation energy predictions, based on the Graph Neural Network (GNN) architectures: Message Passing Neural Network (MPNN) and Graph Attention Network (GAT). GNNs are capable of summarizing the predictive information of a molecule as low-dimensional features directly from its graph structure without relying on an extensive amount of intra-molecular descriptors. As a result, these models are capable of making accurate predictions of the molecular properties without the time consuming process of running an experiment on each molecule. We show that our proposed models outperform all quantum mechanical and molecular dynamics methods in addition to existing alternative machine learning based approaches in the task of solvation free energy prediction. We believe such promising predictive models will be applicable to enhancing the efficiency of the screening of drug molecules and be a useful tool to promote the development of molecular pharmaceutics. Solvation ; Pairwise comparison ; Artificial neural network ; Message passing ; Computer science ; Graph ; Organic molecules ; Deep neural networks ; Molecule ; Theoretical computer science ; Artificial intelligence ; Computational chemistry ; Chemical physics ; Chemistry ; Distributed computing Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Kakeru Kikumasa | Shin Kiyohara | Kiyou Shibata | Teruyasu Mizoguchi
Source: Advanced Intelligent Systems (article, 2021-10-13)
Field: Materials Science — Materials Chemistry

Quantification of the Properties of Organic Molecules Using Core‐Loss Spectra as Neural Network Descriptors Artificial neural networks are applied to quantify the properties of organic molecules by introducing a new descriptor, a core‐loss spectrum, which is typically observed experimentally using electron or X‐ray spectroscopy. Using the calculated C K‐edge core‐loss spectra of organic molecules as the descriptor, the neural network models quantitatively predict both intensive and extensive properties, such as the gap between highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) (HOMO–LUMO gap) and internal energy. The prediction accuracy estimated by the mean absolute errors for the HOMO–LUMO gap and internal energy is 0.205 and 97.3 eV, respectively, which are comparable with those of previously reported chemical descriptors. This study indicates that the neural network approach using the core‐loss spectra as the descriptor has the potential to deconvolute the abundant information available in core‐loss spectra for both prediction and experimental characterization of many physical properties. The study shows the practical potential of machine‐learning‐based material property measurements taking advantage of experimental core‐loss spectra, which can be measured with high sensitivity, high spatial resolution, and high temporal resolution. HOMO/LUMO ; Spectral line ; Artificial neural network ; Spectroscopy ; Molecule ; Core (optical fiber) ; Resolution (logic) ; Electron energy loss spectroscopy ; Characterization (materials science) ; Chemistry ; Materials science ; Biological system ; Chemical physics ; Physics ; Artificial intelligence ; Nanotechnology ; Optics ; Computer science ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Claudio Sánchez Pérez de Amézaga | Víctor M. García‐Suárez | Juan Luis Fernández‐Martínez
Source: Applied Mathematics and Computation (article, 2021-08-22)
Field: Materials Science — Materials Chemistry

Classification and prediction of bulk densities of states and chemical attributes with machine learning techniques Curse of dimensionality ; Basis (linear algebra) ; Computer science ; Scandium ; Lattice (music) ; Artificial intelligence ; Machine learning ; Data mining ; Statistical physics ; Algorithm ; Mathematics ; Materials science ; Physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Alain Tchagang | Julio J. Valdés
Source:  (article, 2021-09-12)
Field: Materials Science — Materials Chemistry

Time Frequency Representations and Deep Convolutional Neural Networks: A Recipe for Molecular Properties Prediction In recent years, Quantum Mechanics (QM) has been combined with Machine Learning (ML) algorithms to speed up the design of molecules, drugs and materials. These paradigms known as QM↔ML have been successful in providing the precision of QM at the speed of ML. In this work, we show that by integrating well-known signal processing (SP) techniques in the QM↔ML pipeline, we obtain a powerful methodology (QM↔SP↔ML) that can be used for representation, visualization and molecular properties predictions. Tested on the benchmark QM9 dataset, the new QM↔SP↔ML framework is able to predict the properties of molecules with a mean absolute error below acceptable chemical accuracy, and yield better or similar results compared to other ML state-of-the-art techniques described in the literature. Benchmark (surveying) ; Computer science ; Convolutional neural network ; Pipeline (software) ; Representation (politics) ; Artificial intelligence ; Artificial neural network ; Recipe ; Visualization ; Algorithm ; Mean absolute error ; Machine learning ; Mean squared error ; Chemistry ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Simon Johansson | Hampus Gummesson Svensson | Esben Jannik Bjerrum | Alexander Schliep | Morteza Haghir Chehreghani | Christian Tyrchan | Ola Engkvist
Source:  (preprint, 2021-06-30)
Field: Materials Science — Materials Chemistry

Using Active Learning to Develop Machine Learning Models for Reaction Yield Prediction Computer aided synthesis planning is a rapidly growing field for suggesting synthetic routes for molecules of interest. The methods used are usually dependent on access to large datasets for training, but with a finite experimental budget there are limitations on how much data can be obtained from experiments. Active learning, which has been used in recent studies with success, is a strategy to identify which data points impact model accuracy the most. However, little has been done to explore the robustness of the methods predicting reaction yield. This study aims to investigate the influence of machine learning algorithms and the number of initial data points on reaction yield prediction for two public high-throughput experimentation datasets. Our results show that active learning based on output margin reached a pre-defined accuracy (AUROC) faster than using passive learning. Feature importance analysis of the trained machine learning models suggested active learning had larger influence on the model accuracy when only a few features were important for the model prediction. Machine learning ; Computer science ; Artificial intelligence ; Active learning (machine learning) ; Margin (machine learning) ; Robustness (evolution) ; Support vector machine ; Chemistry Machine Learning in Materials Science ; Machine Learning and Algorithms ; Computational Drug Discovery Methods
Authors: Guangwei Huang | Zhiyuan Li | Zun-Gang Wang | Wurui Song | Kun Wu | Lin-Xiang Li | Chunzhi Zhou | Yiyun Zhang | Zhiqiang Liu | Xiaoyan Yi | Jinmin Li
Source: Advanced Photonics Research (article, 2021-10-09)
Field: Materials Science — Materials Chemistry

High‐Performance Single‐Crystal Diamond Detector for Accurate Pulse Shape Discrimination Based on Self‐Organizing Map Neural Networks Herein, a high‐performance single‐crystal diamond (SCD) detector (4.5 × 4.5 ×0.3 mm 3 ) to achieve accurate pulse shape discrimination, which is critical for source tracking in harsh and complex radiation conditions, is demonstrated. Enabled by a deep learning algorithm based on self‐organizing map (SOM) neural networks, and using the transient current technique (TCT) for sampling the detector's response to γ, α, and neutron radiation fields, the SCD detector achieves high recognition accuracy of 97.51%. The SCD detector exhibits a low leakage current of 0.75 pA mm −2 under an electric field of 0.51 V μm −1 , and its response to 238 Pu α‐rays shows that the charge collection efficiency for electrons and holes is as high as 99.2 and 98.8% respectively, with an energy resolution as low as 1.42%. The results indicate that the high‐performance SCD detector assisted by the machine learning algorithm can effectively distinguish α‐particles and γ‐rays with a potential application in separating the neutron and γ events as well. Detector ; Diamond ; Particle detector ; Materials science ; Radiation ; Physics ; Artificial neural network ; Optics ; Electron ; Tracking (education) ; Neutron ; Optoelectronics ; Computer science ; Artificial intelligence ; Nuclear physics Diamond and Carbon-based Materials Research ; High-pressure geophysics and materials ; Ion-surface interactions and analysis
Authors: Pankaj Rajak | Anikeya Aditya | Shogo Fukushima | Rajiv K. Kalia | Thomas Linker | Kuang Liu | Ye Luo | Aiichiro Nakano | Ken‐ichi Nomura | Kohei Shimamura | Fuyuki Shimojo | Priya Vashishta
Source:  (article, 2021-06-01)
Field: Materials Science — Materials Chemistry

Ex-NNQMD: Extreme-Scale Neural Network Quantum Molecular Dynamics Deep learning is revolutionizing countless scientific and engineering fields. In particular, SC20 Gordon Bell award represented a breakthrough in molecular simulation, i.e., 100-million-atom simulation with quantum-mechanical accuracy on the Summit supercomputer at ORNL, using deep potential molecular dynamics (MD). Moving forward, while these simulations were performed only in gentle equilibrium conditions, far-from-equilibrium MD simulation involving light-induced electronic excited states finds numerous scientific and engineering applications. However, it remains a challenge to perform such far-from-equilibrium simulations at larger spatiotemporal scales, where growing number of unphysical predictions of interatomic force prohibits simulations involving larger numbers of atoms for longer times. In this paper, we propose a physically-based inductive bias, maximally-preserved Maxwell-Boltzmann (MPMB), to overcome this fidelity-scaling problem. Along with hybrid divide-and-conquer parallelization and single-node level optimization using multithreading and data parallel SIMD, the resulting Ex-NNQMD (extreme-scale neural network quantum molecular dynamics) algorithm has achieved unprecedented scales of far-from-equilibrium simulations: 1) 5.1-billion atom system with a parallel efficiency of 0.94, and 2) a sustained performance of 6.4 nanoseconds/day for 10-million atom system both on 262,144 cores of the Theta supercomputer at Argonne Leadership Computing Facility. Extended fidelity scaling and efficient parallelization have allowed us for the first time to study light-induced ferroelectric switching under extreme electronic excitation at experimentally relevant spatiotemporal scales with accuracy. Supercomputer ; Computer science ; Massively parallel ; Molecular dynamics ; Quantum computer ; Parallel computing ; Statistical physics ; Scale (ratio) ; Qubit ; Computational science ; Quantum ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices ; Electronic and Structural Properties of Oxides
Authors: Haruka Sakanoue | Yuki Hayashi | Kenji Katayama
Source: Scientific Reports (article, 2021-04-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Inference of molecular orientation/ordering change nearby topological defects by the neural network function from the microscopic color information Topological defects in liquid crystals (LCs) dominate molecular alignment/motion in many cases. Here, the neural network (NN) function has been introduced to predict the LC orientation condition (orientation angle and order parameter) at local positions around topological defects from the phase/polarization microscopic color images. The NN function was trained in advance by using the color information of an LC in a planar alignment cell for different orientation angles and temperatures. The photo-induced changes of LC molecules around topological defects observed by the time-resolved measurement was converted into the image sequences of the orientation angle and the order parameter change. We found that each pair of brushes with different colors around topological defects showed different orientation angle and ordering changes. The photo-induced change was triggered by the photoisomerization reaction of molecules, and one pair of brushes increased in its order parameter just after light irradiation, causing gradual rotation in the brush. The molecules in the other pair of brushes were disordered and rotated by the effect of the initially affected region. This combination approach of the time-resolved phase/polarization microscopy and the NN function can provide detailed information on the molecular alignment dynamics around the topological defects. Topology (electrical circuits) ; Orientation (vector space) ; Polarization (electrochemistry) ; Planar ; Topological defect ; Materials science ; Microscopy ; Molecule ; Function (biology) ; Optics ; Molecular physics ; Biological system ; Physics ; Condensed matter physics ; Computer science ; Chemistry ; Geometry ; Mathematics ; Biology Liquid Crystal Research Advancements ; Molecular spectroscopy and chirality ; Spectroscopy and Quantum Chemical Studies
Authors: Hexin Bai | Peng Chu | Jeng-Yuan Tsai | Nathan M. Wilson | Xiaofeng Qian | Qimin Yan | Haibin Ling
Source: Neural Computing and Applications (article, 2021-11-13)
Field: Materials Science — Materials Chemistry

Graph neural network for Hamiltonian-based material property prediction Hamiltonian (control theory) ; Computer science ; Atomic orbital ; Hamiltonian matrix ; Computation ; Computational Science and Engineering ; Electronic structure ; Artificial neural network ; Graph ; Topology (electrical circuits) ; Theoretical computer science ; Algorithm ; Artificial intelligence ; Physics ; Machine learning ; Quantum mechanics ; Mathematics ; Electron ; Symmetric matrix ; Eigenvalues and eigenvectors Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Catalysis and Oxidation Reactions
Authors: Elizabeth S. Thrall | Seung Eun Lee | Joshua Schrier | Yijun Zhao
Source: ChemRxiv (preprint, 2021-06-21)
Field: Chemistry — Physical and Theoretical Chemistry

Machine learning for functional group identification in vibrational spectroscopy: A pedagogical lab for undergraduate chemistry students Here we report a computational activity that introduces undergraduate physical chemistry students to machine learning (ML) in the context of vibrational spectroscopy. In the first part of the activity, students use ML binary classification algorithms to distinguish between carbonyl-containing and non-carbonyl-containing molecules on the basis of their infrared absorption spectra and test modifications to this basic analysis. In a further extension of the activity, students implement a multiclass classification to predict whether carbonyl-containing molecules contain a ketone, a carboxylic acid, or another carbonyl group. This activity is designed to introduce students both to the basic workflow of a ML classification analysis and to some of the ways in which machine learning analyses can fail. We provide a comprehensive handout for the activity, including theoretical background and a detailed protocol, as well as datasets and code to implement the exercise in Python or Mathematica. Workflow ; Python (programming language) ; Context (archaeology) ; Chemistry ; Infrared spectroscopy ; Computer science ; Molecule ; Artificial intelligence ; Mathematics education ; Organic chemistry ; Programming language ; Mathematics ; Database Various Chemistry Research Topics ; Metabolomics and Mass Spectrometry Studies ; Advanced Chemical Sensor Technologies