jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Cosmin Safta | Reese E. Jones
Source:  (article, 2022-08-01)
Field: Materials Science — Materials Chemistry

Graph Convolutional Neural Networks for Modeling Materials with Microstructure. Motivationconstitutive models are frequently the weakness of simulation Goal efficient accurate surrogate models of material processes Which one is the ML prediction? Computer science ; Convolutional neural network ; Graph ; Artificial intelligence ; Microstructure ; Theoretical computer science ; Materials science ; Metallurgy Machine Learning in Materials Science
Authors: Vaidish Sumaria | Luan Nguyen | Franklin Tao | Philippe Sautet
Source: ChemRxiv (preprint, 2022-08-03)
Field: Materials Science — Materials Chemistry

Atomic scale mechanism of Pt catalyst restructuring under a pressure of gas Heterogeneous catalysis is key for chemical transformations. Understanding how catalyst active sites dynamically evolve at the atomic scale under reaction conditions is a prerequisite for accurate determination of catalytic mechanisms and predictably developing catalysts. We combine in-situ observation and Machine Learning accelerated first-principle atomistic simulations to uncover the mechanism of restructuring for Pt catalysts under a pressure of carbon monoxide CO. We show that a high CO coverage at a Pt step edge triggers the formation of atomic protrusions of low-coordination Pt atoms, that then detach from the step edge to create sub-nano-islands on the terraces, where undercoordinated sites are stabilized by the CO adsorbates. These studies open an avenue to achieve an atom-scale understanding of structural dynamics of more complex metal nanoparticles under reaction. Catalysis ; Atomic units ; Restructuring ; Carbon monoxide ; Chemical physics ; Atom (system on chip) ; Nanotechnology ; Nanoscopic scale ; Heterogeneous catalysis ; Chemical engineering ; Molecular dynamics ; Enhanced Data Rates for GSM Evolution ; Materials science ; Chemistry ; Computational chemistry ; Computer science ; Physics Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; nanoparticles nucleation surface interactions
Authors: Grigorii V. Smirnov-Pinchukov | Tamara Molyarova | D. Semenov | Vitaly Akimkin | Sierk van Terwisga | Riccardo Francheschi | Thomas Henning
Source: arXiv (Cornell University) (preprint, 2022-09-27)
Field: Chemistry — Organic Chemistry

Machine learning-accelerated chemistry modeling of protoplanetary disks Aims. With the large amount of molecular emission data from (sub)millimeter observatories and incoming James Webb Space Telescope infrared spectroscopy, access to fast forward models of the chemical composition of protoplanetary disks is of paramount importance. Methods. We used a thermo-chemical modeling code to generate a diverse population of protoplanetary disk models. We trained a K-nearest neighbors (KNN) regressor to instantly predict the chemistry of other disk models. Results. We show that it is possible to accurately reproduce chemistry using just a small subset of physical conditions, thanks to correlations between the local physical conditions in adopted protoplanetary disk models. We discuss the uncertainties and limitations of this method. Conclusions. The proposed method can be used for Bayesian fitting of the line emission data to retrieve disk properties from observations. We present a pipeline for reproducing the same approach on other disk chemical model sets. Protoplanetary disk ; Pipeline (software) ; Population ; Debris disk ; Astrophysics ; Computer science ; Millimeter ; Physics ; Planet ; Planetary system Chemical Thermodynamics and Molecular Structure ; Analytical Chemistry and Chromatography ; Molecular Spectroscopy and Structure
Authors: Xinyao Liu | Kasra Amini | Aurelien Sanchez | Blanca Belsa | Tobias Steinle | Katharina Chirvi | Jens Biegert
Source: The International Conference on Ultrafast Phenomena (UP) 2022 (article, 2022-01-01)
Field: Chemistry — Spectroscopy

Atomic-resolution imaging of complex chiral molecular structures by machine learning based laser-induced electron diffraction We show how laser-induced electron diffraction (LIED) techniques combined with convolutional neural networks (CNNs) enable atomic-resolution imaging of the complex chiral molecule Fenchone (C 10 H 16 O). Electron diffraction ; Diffraction ; Resolution (logic) ; Convolutional neural network ; Laser ; Electron ; Materials science ; Optics ; Molecule ; Computer science ; Physics ; Artificial intelligence Mass Spectrometry Techniques and Applications ; Advanced Chemical Physics Studies ; Laser-Matter Interactions and Applications
Authors: special to C EN Fernando Gomollón-Bel
Source: Chemical & Engineering News (article, 2022-05-20)
Field: Materials Science — Materials Chemistry

Machine learning spots metals in single-atom catalysts Catalysis ; Spots ; Atom (system on chip) ; Nanotechnology ; Chemistry ; Materials science ; Computer science ; Organic chemistry ; Physical chemistry ; Operating system Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Materials Characterization Techniques
Authors: Tomoki SHIONOYA | Iori Shimada
Source: Journal of Computer Chemistry Japan (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Data-driven Reaction Pathway Estimation with Efficient Use of Small Number of Data Chemical reaction neural network (CRNN) is a machine learning model that enables a data-driven search of chemical reaction mechanisms by incorporating reaction kinetics theory into the neural network architecture. Conventionally, 103 order data were required for searching a simple reaction system, but the reduction in the number of data required for CRNN is expected to enable its application to various experimental systems. In this study, we investigated the number of data required for prediction in the CRNN. The result showed that prediction of the reaction is possible with as few as 180 data by avoiding falling into local optimal solutions. We also confirmed that incorporating the concept of material balance into loss function has effect of reducing the computational complexity. Computer science ; Artificial neural network ; Reduction (mathematics) ; Simple (philosophy) ; Function (biology) ; Machine learning ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning in Bioinformatics
Authors: M. Sakai | Mitsunori KANESHIGE | Koji Yasuda
Source: Journal of Computer Chemistry Japan (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Learning Organotransition Metal Reactions Using Graph Neural Networks 深層学習による反応予測は，人が識別パターンを設計する必要がないため最近注目されている．しかし有機遷移金属反応は一見複雑で，深層学習の適応例は殆どなかった．我々は，論文や特許などから集めた数万件の実験反応を，素反応に分解したデータベースを構築し，グラフニューラルネットワークを用いて学習させた．最高で97.1%の精度で反応が予測できた． Metal ; Artificial neural network ; Graph ; Computer science ; Artificial intelligence ; Chemistry ; Theoretical computer science ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Zhenwei Li | Junjie Wang
Source: Science and Technology of Advanced Materials Methods (article, 2022-09-01)
Field: Materials Science — Materials Chemistry

Machine learning prediction on intermetallic compounds with implemented virtual-center-atom structural descriptor A virtual-center-atom (VCA) structural descriptor was proposed to construct machine learning potentials which can be applied in studying alloy materials. The descriptor performs by describing the spatial features of atomic environments centered on a defined virtual atom within crystalline structure. Computational algorithms were developed to determine the position of virtual center atom. To verify the generality of this descriptor, crystal structures from both molecular dynamics trajectory and online accessible databases are utilized to check their machine learning (ML) performances. The challenge of the ML lies in increasing the transferability of the derived weight parameters to cover a broad range of structure distribution. Forces are found difficult to train the ML model under simple optimization methods. We thus suggest deep learning techniques for improving the applicability of the VCA potential. The descriptor is simple compared to other reported schemes and has promising applications in accelerating material discovery. Transferability ; Computer science ; Atom (system on chip) ; Generality ; Simple (philosophy) ; Artificial intelligence ; Center (category theory) ; Position (finance) ; Intermetallic ; Range (aeronautics) ; Construct (python library) ; Algorithm ; Machine learning ; Materials science ; Alloy ; Chemistry ; Crystallography Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Zhihao Huang | Yudong Fu | Guotan Liu | Han Chen | Kang Wang | Yibo Sun
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Supplementary Data of ‘Discovering Novel Low Young's Modulus Nb–Ti–Zr Biomedical Alloys Via Combined Machine Learning and First Principles Approach’ Materials science ; Modulus ; Composite material Titanium Alloys Microstructure and Properties ; Orthopaedic implants and arthroplasty ; Advanced Materials Characterization Techniques
Authors: Anoop Kini | Amit Kumar Choudhary | Dominic Hohs | Andreas Jansche | Hermann Baumgartl | Ricardo Buettner | Timo Bernthaler | D. Goll | Gerhard Schneider
Source: arXiv (Cornell University) (preprint, 2022-07-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine learning-based mass density model for hard magnetic 14:2:1 phases using chemical composition-based features The Fe14Nd2B-based permanent magnets are technologically sought-after for energy conversion due to their unparalleled high energy product (520 kJ/m3). For such 14:2:1 phases of different compositions, determining the magnetization from the measured magnetic moment is often bottlenecked by lack of mass density. We present a machine learning (ML) mass density model for 14:2:1 phases using chemical composition-based features (representing 33 elements) and optionally lattice parameter (LP) features. The datasets for training and testing contain 190 phases (177 compositionally different) with their literature reported densities and LP. With an ML model with merely compositional features, we achieved a low mean-absolute-error of 0.51% on an unseen test-dataset. Magnet ; Magnetization ; Chemical composition ; Lattice (music) ; Composition (language) ; Materials science ; Condensed matter physics ; Physics ; Thermodynamics ; Magnetic field Magnetic Properties of Alloys ; Machine Learning in Materials Science ; Magnetic Properties and Applications
Authors: Chi Chen | Shyue Ping Ong
Source: arXiv (Cornell University) (preprint, 2022-02-05)
Field: Materials Science — Materials Chemistry

A Universal Graph Deep Learning Interatomic Potential for the Periodic Table Interatomic potentials (IAPs), which describe the potential energy surface of atoms, are a fundamental input for atomistic simulations. However, existing IAPs are either fitted to narrow chemistries or too inaccurate for general applications. Here, we report a universal IAP for materials based on graph neural networks with three-body interactions (M3GNet). The M3GNet IAP was trained on the massive database of structural relaxations performed by the Materials Project over the past 10 years and has broad applications in structural relaxation, dynamic simulations and property prediction of materials across diverse chemical spaces. About 1.8 million materials were identified from a screening of 31 million hypothetical crystal structures to be potentially stable against existing Materials Project crystals based on M3GNet energies. Of the top 2000 materials with the lowest energies above hull, 1578 were verified to be stable using DFT calculations. These results demonstrate a machine learning-accelerated pathway to the discovery of synthesizable materials with exceptional properties. Computer science ; Graph ; Periodic table ; Molecular dynamics ; Interatomic potential ; Relaxation (psychology) ; Computational science ; Statistical physics ; Theoretical computer science ; Computational chemistry ; Chemistry ; Physics ; Quantum mechanics Machine Learning in Materials Science ; History and advancements in chemistry ; X-ray Diffraction in Crystallography
Authors: 顕章 鞆田
Source: Institutional Repositories DataBase (IRDB) (article, 2022-10-31)
Field: Materials Science — Materials Chemistry

深層学習を用いたM2052合金の原子間ポテンシャルの検討 Neural network interatomic potential (NNP) based on deep neural network (DNN) is useful for classical molecular dynamics simulation to obtain accurate vibration damping capacity of high damping alloys. Training data set is generated by ab initio molecular dynamics simulations using pseudo potential and generalized gradient approximation. The NNP for M2052 alloy is generated by the DeePMD-kit. The accuracy of the NNP depends on the number of training steps for deep learning. Artificial neural network ; Set (abstract data type) ; Molecular dynamics ; Ab initio ; Data set ; Dynamics (music) ; Training set Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Advanced Physical and Chemical Molecular Interactions
Authors: Paul L. Houston | Apurba Nandi | Joel M. Bowman
Source: arXiv (Cornell University) (preprint, 2022-06-24)
Field: Materials Science — Materials Chemistry

A Machine Learning Approach for Rate Constants III: Application to the Cl($^2$P) + CH$_4$ $\rightarrow$ CH$_3$ + HCl Reaction The temperature dependence of the thermal rate constant for the reaction Cl($^2$P) + CH$_4$ $\rightarrow$ CH$_3$ + HCl is calculated using a Gaussian Process machine learning (ML) approach to train on and predict thermal rate constants over a large temperature range. Following procedures developed in two previous reports, we use a training dataset of approximately 40 reaction/potential surface combinations, each of which is used to calculate the corresponding data base of rate constant at approximately eight temperatures. For the current application, we train on the entire dataset and then predict the temperature dependence of the title reaction employing a "split" dataset for correction at low and high temperatures to capture both tunneling and recrossing. The results are an improvement on recent RPMD calculations compared to accurate quantum ones, using the same high-level ab initio potential energy surface. Both tunneling at low temperatures and recrossing at high temperatures are observed to influence the rate constants. Recrossing effects, which are not described by TST and even sophisticated tunneling corrections, do appear in experiment at temperatures above around 600 K. The ML results describe these effects and in fact, merge at 600 K with RPMD results (which can describe recrossing), and both are close to experiment at the highest experimental temperatures. Reaction rate constant ; Quantum tunnelling ; Merge (version control) ; Ab initio ; Chemistry ; Thermal ; Gaussian ; Potential energy surface ; Thermodynamics ; Computational chemistry ; Physics ; Quantum mechanics ; Computer science ; Kinetics Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference ; Advanced Chemical Physics Studies
Authors: Cheng-Han Li | Daniel P. Tabor
Source: ChemRxiv (preprint, 2022-01-31)
Field: Materials Science — Materials Chemistry

Discovery of lead low-potential radical candidates for organic radical polymer batteries with machine-learning-assisted virtual screening The discovery and development of new low reduction potential molecules that also have fast charge transfer kinetics is necessary for the further development of organic redox-active polymers in practical battery applications. Theoretical methods can aid in finding the lead radical candidates in large initial screening spaces, but low-cost (yet accurate) methods are needed to predict the functional properties of the materials. In this paper, we conduct a two-objective (potential and dimer electronic coupling) virtual screening campaign to identify lead low potential candidate molecules in an initial space of 660 candidate molecules. The screening is accelerated by employing a trained Gaussian Process regression model for the voltage screening task. The model takes a combination of core-group chemical fingerprints and low-cost semi-empirical quantum chemistry calculations as the features for the model. The top-10%-predicted lowest reduction potential molecules of the initial space are then screened further to identify the candidates with the highest predicted electronic coupling. From the screening campaign, a set of promising redox-active molecules and two Pareto-optimal molecules (both N-methylphthalimides) are identified. Chemical space ; Virtual screening ; Molecule ; Computer science ; Pareto principle ; Quantum chemistry ; Materials science ; Chemistry ; Chemical physics ; Biochemical engineering ; Nanotechnology ; Computational chemistry ; Drug discovery ; Mathematical optimization ; Mathematics ; Organic chemistry ; Engineering Machine Learning in Materials Science ; Conducting polymers and applications ; Green IT and Sustainability
Authors: Simone Manti | Mark Kamper Svendsen | Nikolaj R. Knøsgaard | Peder M. Lyngby | Kristian S. Thygesen
Source: arXiv (Cornell University) (preprint, 2022-01-20)
Field: Materials Science — Materials Chemistry

Exploring and machine learning structural instabilities in 2D materials We address the problem of predicting the zero-temperature dynamical stability (DS) of a periodic crystal without computing its full phonon band structure. Here we report the evidence that DS can be inferred with good reliability from the phonon frequencies at the center and boundary of the Brillouin zone (BZ). This analysis represents a validation of the DS test employed by the Computational 2D Materials Database (C2DB). For 137 dynamically unstable 2D crystals, we displace the atoms along an unstable mode and relax the structure. This procedure yields a dynamically stable crystal in 49 cases. The elementary properties of these new structures are characterised using the C2DB workflow, and it is found that their properties can differ significantly from those of the original unstable crystals, e.g. band gaps are opened by 0.3 eV on average. All the crystal structures and properties are available in the C2DB. Finally, we train a classification model on the DS data for 3295 2D materials in the C2DB using a representation encoding the electronic structure of the crystal. We obtain an excellent receiver operating characteristic (ROC) curve with an area under the curve (AUC) of 0.90, showing that the classification model can drastically reduce computational efforts in high-throughput studies. Brillouin zone ; Phonon ; Crystal (programming language) ; Stability (learning theory) ; Representation (politics) ; Crystal structure ; Workflow ; Computer science ; Boundary (topology) ; Algorithm ; Artificial intelligence ; Physics ; Materials science ; Mathematics ; Machine learning ; Condensed matter physics ; Mathematical analysis ; Crystallography ; Chemistry ; Database Machine Learning in Materials Science ; Thermal properties of materials
Authors: Sheng Li | Xinqiao Wang | Yejian Wu | Hongliang Duan | Lan Tang
Source: ChemRxiv (preprint, 2022-05-25)
Field: Materials Science — Materials Chemistry

Generation of novel Diels-Alder reactions using a generative adversarial network Deep learning has enormous potential in the chemical and pharmaceutical fields. Among these, Generative Adversarial Network, as an excellent generative model, has shown its remarkable performance in the field of molecular generation, but it has few applications in organic chemistry. Therefore, we attempt to apply GAN as a generative model for the task of reaction generation to expand the application of GAN in chemistry. In this work, we used the MaskGAN model trained with 14092 Diels-Alder reactions, and we finally generated 1441 novel Diels-Alder reactions that learn reaction rules in-depth, which demonstrates that reaction generation can be used in the field of chemistry, and helps chemists explore novel reactions. Generative grammar ; Generative adversarial network ; Adversarial system ; Field (mathematics) ; Computer science ; Deep learning ; Diels–Alder reaction ; Chemistry ; Biochemical engineering ; Task (project management) ; Artificial intelligence ; Organic chemistry ; Systems engineering ; Engineering ; Mathematics Machine Learning in Materials Science
Authors: Brian DeCost | Howie Joress | Suchismita Sarker | Apurva Mehta | Jason Hattrick‐Simpers
Source: arXiv (Cornell University) (preprint, 2022-03-31)
Field: Materials Science — Materials Chemistry

Towards automated design of corrosion resistant alloy coatings with an autonomous scanning droplet cell We present an autonomous scanning droplet cell platform designed for on-demand alloy electrodeposition and real-time electrochemical characterization for investigating the corrosion-resistance properties of multicomponent alloys. Automation and machine learning are currently driving rapid innovation in high throughput and autonomous materials design and discovery. We present two alloy design case studies: one focusing on a multi-objective corrosion resistant alloy optimization, and a case study highlighting the complexity of the multimodal characterization needed to provide insight into the underlying structural and chemical factors that drive observed material behavior. This motivates a close coupling between autonomous research platforms and scientific machine learning methodology that blends mechanistic physical models and black box machine learning models. This emerging research area presents new opportunities to accelerate materials synthesis, evaluation, and hence discovery and design. Corrosion ; Characterization (materials science) ; Automation ; Alloy ; Black box ; Throughput ; Materials science ; Computer science ; Nanotechnology ; Systems engineering ; Coupling (piping) ; Process engineering ; Mechanical engineering ; Artificial intelligence ; Engineering ; Metallurgy Machine Learning in Materials Science ; Corrosion Behavior and Inhibition ; Electrocatalysts for Energy Conversion