jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Indranil Saha | Andreas Erlebach | Petr Nachtigall | Christopher J. Heard | Lukáš Grajciar
Source: ChemRxiv (preprint, 2022-06-29)
Field: Chemistry — Inorganic Chemistry

Reactive Neural Network Potential for Aluminosilicate Zeolites and Water: Quantifying the effect of Si/Al ratio on proton solvation and water diffusion in H-FAU Acidic zeolites are one of the most important catalysts. In many of their catalytic applications, the mode of interaction with water heavily influences their activity, efficiency, and durability as a catalyst. Despite the recent (first principles) computational efforts to understand the mechanistic underpinning of the water-zeolite interactions, it is still prohibitively expensive to carry out comprehensive studies employing realistic zeolitic models. Therefore, we developed a reactive neural network-based potential for aluminosilicate zeolites in the protonic form including their interaction with the aqueous solution that has a capacity to accelerate simulation by orders of magnitude while retaining the reference level of accuracy. To showcase its potential, we used it to determine how multiple factors (aluminum content, water loading and temperature) influence the proton solvation and water dynamics in one of the industrially most important acidic zeolites, the faujasite (FAU). We found that Si/Al ratio is a significant determinant of the water diffusivity, water capacity to solvate protons in the nanopores, and the zeolite hydrolytic stability. We expect that many of these findings are readily extendable to other acidic zeolites in interaction with water. Zeolite ; Aluminosilicate ; Faujasite ; Solvation ; Catalysis ; Aqueous solution ; Chemistry ; Chemical engineering ; Diffusion ; Thermal diffusivity ; Proton ; Inorganic chemistry ; Materials science ; Computational chemistry ; Solvent ; Thermodynamics ; Organic chemistry Zeolite Catalysis and Synthesis ; Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies
Authors: Hibiki Yoshida | Katsuyoshi Sakamoto | Naoya Miyashita | Koichi Yamaguchi | Qing Shen | Yoshitaka Okada | Tomah Sogabe
Source: AIP Advances (article, 2022-11-01)
Field: Materials Science — Materials Chemistry

Ultrafast inverse design of quantum dot optical spectra via a joint TD-DFT learning scheme and deep reinforcement learning Here, we report a case study on inverse design of quantum dot optical spectra using a deep reinforcement learning algorithm for the desired target optical property of semiconductor CdxSeyTex−y quantum dots. Machine learning models were trained to predict the optical absorption and emission spectra by using the training dataset by time dependent density functional theory simulation. We show that the trained deep deterministic policy gradient inverse design agent can infer the molecular structure with an accuracy of less than 1 Å at a fixed computational time of milliseconds and up to 100–1000 times faster than the conventional heuristic particle swam optimization method. Most of the effective inverse design problems based on the surrogate machine learning and reinforcement learning model have been focused on the field of nano-photonics. Few attempts have been made in the field of quantum optical system in a similar manner. For the first time, our results, to our knowledge, provide concrete evidence that for computationally challenging tasks, a well-trained deep reinforcement learning agent can replace the existing quantum simulation and heuristics optimization tool, enabling fast and scalable simulations of the optical property of nanometer sized semiconductor quantum dots. Reinforcement learning ; Quantum dot ; Computer science ; Heuristics ; Photonics ; Inverse problem ; Deep learning ; Heuristic ; Artificial intelligence ; Physics ; Quantum mechanics ; Mathematics Quantum Dots Synthesis And Properties ; Machine Learning in Materials Science ; Semiconductor Quantum Structures and Devices
Authors: Andrew Lee | Suchismita Sarker | James E. Saal | Logan Ward | Christopher K. H. Borg | Apurva Mehta | Chris Wolverton
Source: Research Square (preprint, 2022-03-11)
Field: Materials Science — Materials Chemistry

Machine Learned Synthesizability Predictions Aided by Density Functional Theory Abstract A grand challenge of materials science is the computational prediction of synthesis pathways for novel compounds. Data-driven and machine learning (ML) approaches have made significant progress in addressing a portion of this problem, namely, predicting whether a compound is synthesizable or not. However, some recent attempts to do so have not incorporated energetic or phase stability information. Here, we combine thermodynamic stability calculated using density functional theory (DFT) with composition-based features to train a ML model that predicts a material's synthesizability. We focus on compositions with ABC stoichiometry and predict synthesizability in the half-heusler (HH) structure. Our model is trained on experimentally reported ABC compositions, and achieves a cross-validated precision of 0.83 and recall of 0.85. Our model shows improvement in identifying compositions that do not form HH when compared to a similar HH synthesizability model from a previous study. Our model identifies 163 synthesizable candidates out of 4141 unreported ABC compositions. More notably, 38 stable compositions are predicted unsynthesizable while 103 unstable compositions are predicted synthesizable; these findings otherwise cannot be made using DFT stability alone. This study presents a new approach for accurately predicting synthesizability, and identifies newly synthesizable candidate HH compositions for further experimental exploration. Density functional theory ; Computer science ; Physics ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Catalysis and Oxidation Reactions
Authors: Dmitry Yu. Zubarev | Carlos Raoni Mendes | Emílio Vital Brazil | Renato Cerqueira | Kristin Schmidt | Vinícius Segura | Juliana Jansen Ferreira | Debbie Sanders
Source: arXiv (Cornell University) (preprint, 2022-11-05)
Field: Materials Science — Materials Chemistry

Toward Human-AI Co-creation to Accelerate Material Discovery There is an increasing need in our society to achieve faster advances in Science to tackle urgent problems, such as climate changes, environmental hazards, sustainable energy systems, pandemics, among others. In certain domains like chemistry, scientific discovery carries the extra burden of assessing risks of the proposed novel solutions before moving to the experimental stage. Despite several recent advances in Machine Learning and AI to address some of these challenges, there is still a gap in technologies to support end-to-end discovery applications, integrating the myriad of available technologies into a coherent, orchestrated, yet flexible discovery process. Such applications need to handle complex knowledge management at scale, enabling knowledge consumption and production in a timely and efficient way for subject matter experts (SMEs). Furthermore, the discovery of novel functional materials strongly relies on the development of exploration strategies in the chemical space. For instance, generative models have gained attention within the scientific community due to their ability to generate enormous volumes of novel molecules across material domains. These models exhibit extreme creativity that often translates in low viability of the generated candidates. In this work, we propose a workbench framework that aims at enabling the human-AI co-creation to reduce the time until the first discovery and the opportunity costs involved. This framework relies on a knowledge base with domain and process knowledge, and user-interaction components to acquire knowledge and advise the SMEs. Currently,the framework supports four main activities: generative modeling, dataset triage, molecule adjudication, and risk assessment. Data science ; Computer science ; Process (computing) ; Subject-matter expert ; Knowledge extraction ; Risk analysis (engineering) ; Domain knowledge ; Knowledge management ; Generative grammar ; Artificial intelligence Machine Learning in Materials Science ; Scientific Computing and Data Management
Authors: L. A. Avakyan | D. Tolchina | V. Barkovski | С. В. Беленов | А. А. Алексеенко | A. Shaginyan | V. V. Srabionyan | В. Е. Гутерман | Л. А. Бугаев
Source: Computational Materials Science (article, 2022-03-16)
Field: Materials Science — Materials Chemistry

Ultimate sensitivity of radial distribution functions to architecture of PtCu bimetallic nanoparticles Bimetallic strip ; Platinum ; Nanoparticle ; Platinum nanoparticles ; Materials science ; Extended X-ray absorption fine structure ; Catalysis ; Nanotechnology ; Chemical physics ; Metal ; Chemistry ; Absorption spectroscopy ; Organic chemistry ; Physics ; Metallurgy ; Optics Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Computational Drug Discovery Methods
Authors: Jing Wu | E Zhou | Zhenzhen Qin | Xiaoliang Zhang | Guangzhao Qin
Source: Nanotechnology (article, 2022-03-11)
Field: Materials Science — Materials Chemistry

Accessing negative Poisson’s ratio of graphene by machine learning interatomic potentials The negative Poisson's ratio (NPR) is a novel property of materials, which enhances the mechanical feature and creates a wide range of application prospects in lots of fields, such as aerospace, electronics, medicine, etc. Fundamental understanding on the mechanism underlying NPR plays an important role in designing advanced mechanical functional materials. However, with different methods used, the origin of NPR is found different and conflicting with each other, for instance, in the representative graphene. In this study, based on machine learning technique, we constructed a moment tensor potential for molecular dynamics (MD) simulations of graphene. By analyzing the evolution of key geometries, the increase of bond angle is found to be responsible for the NPR of graphene instead of bond length. The results on the origin of NPR are well consistent with the start-of-art first-principles, which amend the results from MD simulations using classic empirical potentials. Our study facilitates the understanding on the origin of NPR of graphene and paves the way to improve the accuracy of MD simulations being comparable to first-principle calculations. Our study would also promote the applications of machine learning interatomic potentials in multiscale simulations of functional materials. Graphene ; Materials science ; Molecular dynamics ; Aerospace ; Nanotechnology ; Chemical physics ; Range (aeronautics) ; Interatomic potential ; Statistical physics ; Artificial intelligence ; Computational chemistry ; Computer science ; Physics ; Aerospace engineering ; Composite material Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Graphene research and applications
Authors: Qi Lu | Jiayi Wu | Shilong Liu | Shiqi Zhang | Xiaorong Cai | Wei Li | Jun Jiang | Xuejun Jin
Source: Ultramicroscopy (article, 2022-03-29)
Field: Materials Science — Metals and Alloys

Revealing geometrically necessary dislocation density from electron backscatter patterns via multi-modal deep learning Electron backscatter diffraction ; Convolutional neural network ; Computer science ; Dislocation ; Deep learning ; Artificial intelligence ; Pattern recognition (psychology) ; Noise (video) ; Property (philosophy) ; Materials science ; Diffraction ; Optics ; Physics ; Image (mathematics) ; Composite material Hydrogen embrittlement and corrosion behaviors in metals ; Microstructure and mechanical properties ; Machine Learning in Materials Science
Authors: Benjamin B. Hoar | Weitong Zhang | Shuangning Xu | Rana Deeba | Cyrille Costentin | Quanquan Gu | Chong Liu
Source: ChemRxiv (preprint, 2022-06-09)
Field: Chemistry — Electrochemistry

Electrochemical mechanistic analysis from cyclic voltammograms based on deep learning For decades, employing cyclic voltammetry for mechanistic investigation demands manual inspection of voltammograms. Here we report a deep-learning-based algorithm that automatically analyzes cyclic voltammograms and designates a electrochemical probable mechanism among five of the most common ones in homogenous molecular electrochemistry. The reported algorithm will aid researchers’ mechanistic analysis, utilize otherwise elusive features in voltammograms, and experimentally observe the gradual mechanism transitions encountered in electrochemistry. An automated voltammogram analysis will aid the analysis of complex electrochemical systems and promise autonomous high-throughput research in electrochemistry with minimal human interference. Electrochemistry ; Cyclic voltammetry ; Mechanism (biology) ; Computer science ; Throughput ; Biological system ; Nanotechnology ; Chemistry ; Materials science ; Electrode ; Physics ; Physical chemistry ; Telecommunications Electrochemical Analysis and Applications ; Fuel Cells and Related Materials ; Electrochemical sensors and biosensors
Authors: Anshul Jain | Sushobhan Maji | Khyati Shukla | Akanksha Kumari | Shivani Garg | Ramesh K. Metre | Sudipta Bhattacharyya | Nirmal K. Rana
Source: Organic & Biomolecular Chemistry (article, 2022-01-01)
Field: Chemistry — Organic Chemistry

Stereoselective synthesis of tri-substituted tetrahydrothiophenes and their <i>in silico</i> binding against mycobacterial protein tyrosine phosphatase B A facile approach to tri-substituted tetrahydrothiophenes <i>via</i> thia-Michael/aldol has been developed. The cascade reaction was carried out in the presence of 5 mol% of DABCO in ethyl acetate to afford diversely functionalized tetrahydrothiophenes (THTs) with excellent diastereoselectivity. The present methodology has broad substrate tolerance. Gram-scale reaction proceeds with equal efficiency. Functional group transformations further highlight the synthetic potential of the THTs. An asymmetric version of the cascade reaction has also been investigated and a maximum of 72% ee was observed with cinchonidine derived squaramide. Moreover, <i>in silico</i> based molecular docking followed by deep learning based affinity prediction and molecular dynamics simulation analysis indicate the synthesized THT derivatives can act as potent competitive inhibitors of MptpB at low micromolar to nanomolar concentrations. <i>In silico</i> ADME analysis further suggests the plausibility of these compounds to act as future anti-mycobacterial therapeutic leads. Chemistry ; In silico ; Aldol reaction ; Stereoselectivity ; Squaramide ; Combinatorial chemistry ; Cascade reaction ; DABCO ; Stereochemistry ; Docking (animal) ; Enantioselective synthesis ; Biochemistry ; Organocatalysis ; Catalysis Click Chemistry and Applications ; Synthesis and biological activity ; Synthesis and Catalytic Reactions
Authors: Maximilian Edich | David C. Briggs | Oliver Kippes | Yunyun Gao | Andrea Thorn
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2022-04-08)
Field: Materials Science — Materials Chemistry

The impact of AlphaFold on experimental structure solution Abstract AlphaFold2 is a machine-learning based program that predicts a protein structure based on the amino acid sequence. In this article, we report on the current usages of this new tool and give examples from our work in the Coronavirus Structural Task Force. With its unprecedented accuracy, it can be utilized for the design of expression constructs, de novo protein design and the interpretation of Cryo-EM data with an atomic model. However, these methods are limited by their training data and are of limited use to predict conformational variability and fold flexibility; they also lack co-factors, posttranslational modifications and multimeric complexes with oligonucleotides. They also are not always perfect in terms of chemical geometry. Nevertheless, machine learning based fold prediction are a game changer for structural bioinformatics and experimentalists alike, with exciting developments ahead. Computer science ; Flexibility (engineering) ; Interpretation (philosophy) ; Sequence (biology) ; Oligonucleotide ; Protein structure ; Task (project management) ; Artificial intelligence ; Machine learning ; Computational biology ; Chemistry ; Biology ; Engineering ; DNA ; Biochemistry ; Systems engineering ; Mathematics Enzyme Structure and Function ; RNA and protein synthesis mechanisms ; Protein Structure and Dynamics
Authors: Christopher L. Hanselman | Xiangyu Yin | David C. Miller | Chrysanthos E. Gounaris
Source: Journal of Chemical Information and Modeling (article, 2022-01-13)
Field: Materials Science — Materials Chemistry

MatOpt: A Python Package for Nanomaterials Design Using Discrete Optimization Novel materials are being enabled by advances in synthesis techniques that achieve ever better control over the atomic-scale structure of materials. The pace of materials development has been further increased by high-throughput computational experiments guided by informatics and machine learning. We have previously demonstrated complementary approaches using mathematical optimization models to search through highly combinatorial design spaces of atomic arrangements, guiding the design of nanostructured materials. In this paper, we highlight the common features of materials optimization problems that can be efficiently modeled via mixed-integer linear optimization models. To take advantage of these commonalities, we have created <i>MatOpt</i>, a Python package that formalizes the process of representing the design space and formulating optimization models for the on-demand design of nanostructured materials. This tool serves to bridge the gap between practitioners with expertise in materials science and those with expertise in formulating and solving mathematical optimization models, effectively lowering the barriers for applying rigorous numerical optimization capabilities during nanostructured materials development. Python (programming language) ; Computer science ; Discrete optimization ; Optimization problem ; Mathematical optimization ; Algorithm ; Mathematics ; Programming language Machine Learning in Materials Science ; Molecular Junctions and Nanostructures ; Surface Chemistry and Catalysis
Authors: Madhu Bala Priyadarshi | Anu Sharma | K.K. Chaturvedi | Rakesh Bhardwaj | S.B. Lal | Mohid Farooqi | Sanjeev Kumar | D.C. Mishra | Mohar Singh
Source: Legume Research - An International Journal (article, 2022-11-02)
Field: Chemistry — Analytical Chemistry

Comparing Various Machine Learning Algorithms for Sugar Prediction in Chickpea using Near-infrared Spectroscopy Background: Chickpea is the third major pulse produced globally, with 11.6 million tonnes produced per annum (Merga and Haji, 2019). Sugar alcohols, inulin, starch are all prebiotic carbohydrates found in chickpeas (Johnson et al., 2020). Near-Infrared (NIR) spectroscopy is a non-destructive, versatile and powerful analytical technique. Methods: Spectral data obtained from NIR spectroscopy requires application of various techniques to extract useful information from spectral data which is further used for building various models for prediction of physical or chemical components presents in agricultural crops. The main aim of this study is to apply various machine learning algorithms especially effective in predicting sugar concentration in chickpea. Sugar prediction models are developed using Linear Regression (LR), Artificial Neural Network (ANN), Random Forest (RF), Support Vector Regression (SVR) and Decision Tree Regression (DTR) algorithms. Performance of the models is evaluated using measures namely, Root Mean Square Error (RMSE), Residual Standard Error (RSE), Coefficient of Determination (R2) and Adjusted Coefficient of Determination (adjusted R2). Result: It was observed that, RF outperformed all other models in terms of accuracy for predicting sugar component from preprocessed spectra, with RMSE, RSE, R2 and adjusted R2 values of 0.054, 0.062, 0.954 and 0.937, respectively. The accuracy of the ANN model is similar to that of the RF, with minor differences in RMSE, RSE, R2 and adjusted R2 , values of 0.057, 0.067, 0.952 and 0.935. Mean squared error ; Coefficient of determination ; Support vector machine ; Sugar ; Random forest ; Mathematics ; Artificial neural network ; Linear regression ; Regression ; Decision tree ; Artificial intelligence ; Machine learning ; Correlation coefficient ; Algorithm ; Statistics ; Computer science ; Food science ; Chemistry Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; GABA and Rice Research
Authors: Seyyed Sajad Mousavi Nejad Souq | Faramarz Ashenai Ghasemi | Mir Masoud Seyyed Fakhrabadi
Source: Computational Materials Science (article, 2022-09-19)
Field: Materials Science — Materials Chemistry

Performance of different traditional and machine learning-based atomistic potential functions in the simulation of mechanical behavior of Fe nanowires Molecular dynamics ; Nanowire ; Necking ; Materials science ; Experimental data ; Work (physics) ; Function (biology) ; Characterization (materials science) ; Statistical physics ; Nanoscopic scale ; Nanotechnology ; Biological system ; Thermodynamics ; Computational chemistry ; Chemistry ; Physics ; Composite material ; Mathematics Microstructure and mechanical properties ; Force Microscopy Techniques and Applications ; Microstructure and Mechanical Properties of Steels
Authors: Yusuke Oba | Yuji Umeda | Daiki Ishii
Source: Japanese Journal of Applied Physics (article, 2022-06-30)
Field: Materials Science — Materials Chemistry

Materials informatics for dielectric loss tangent in the millimeter wave region Abstract For the prediction of the dielectric loss tangent in the millimeter wave region, machine-learning approaches based on the first-principles calculations were carried out. The data set was prepared by the first-principles calculations considering the anharmonicity of lattice vibrations. The two-phonon density of states, which is correlated with the dielectric loss tangent, was calculated and confirmed the connection with the difference in crystal structures. Machine-learning models to predict the dielectric loss tangent were created considering both atomic compositions and crystal structures as descriptors. In addition, transfer-learning models, in which a pretrained model for the two-phonon density of states was used as the new descriptor, were compared with models from scratch. The transfer-learning model showed 25% higher prediction accuracy than the scratch model. Dielectric ; Tangent ; Dissipation factor ; Scratch ; Anharmonicity ; Phonon ; Dielectric loss ; Tangent space ; Materials science ; Extremely high frequency ; Condensed matter physics ; Computational physics ; Physics ; Optics ; Mathematics ; Mathematical analysis ; Geometry ; Optoelectronics ; Composite material Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Semiconductor materials and devices