jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ziyao Li | Shu-Wen Yang | Guojie Song | Lingsheng Cai
Source:  (article, 2021-05-03)
Field: Materials Science — Materials Chemistry

Conformation-Guided Molecular Representation with Hamiltonian Neural Networks Well-designed molecular representations (fingerprints) are vital to combine medical chemistry and deep learning. Whereas incorporating 3D geometry of molecules (i.e. conformations) in their representations seems beneficial, current 3D algorithms are still in infancy. In this paper, we propose a novel molecular representation algorithm which preserves 3D conformations of molecules with a Molecular Hamiltonian Network (HamNet). In HamNet, implicit positions and momentums of atoms in a molecule interact in the Hamiltonian Engine following the discretized Hamiltonian equations. These implicit coordinations are supervised with real conformations with translation- & rotation-invariant losses, and further used as inputs to the Fingerprint Generator, a message-passing neural network. Experiments show that the Hamiltonian Engine can well preserve molecular conformations, and that the fingerprints generated by HamNet achieve state-of-the-art performances on MoleculeNet, a standard molecular machine learning benchmark. Hamiltonian (control theory) ; Artificial neural network ; Discretization ; Computer science ; Invariant (physics) ; Molecule ; Artificial intelligence ; Physics ; Mathematics ; Quantum mechanics ; Mathematical analysis ; Mathematical optimization Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Yangjie Qi | Chang Liu
Source: ES Materials & Manufacturing (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Deep Learning for Medical Materials: Review and Perspective Deep learning and similar computational research approaches have cooperated with materials research for years. Especially in the last few years, with the fast evolution of machine learning and deep learning algorithms, a novel branch for material research is presented to be recognized, learned, practiced, adapted and perfected. Different conventional computational modeling methods, the deep learning approach assists material science and engineering from the aspect of data processing and analysis, rather than simulate the reality. However, mistakes always exist in all less explored concepts and developments. Here, in order to offer a better instruction of deep learning for materials scientists, several recent works on deep learning based material research were reviewed. The application of deep learning in material research is introduced and discussed. As an example of both general material and advanced material, medical material was discussed for the impact and future of deep learning methods. At last, we provided several insights on future work for both deep learning scientists, data scientists and materials scientists. Deep learning ; Computer science ; Artificial intelligence ; Perspective (graphical) ; Data science ; Learning sciences ; Educational technology ; Mathematics education ; Psychology Machine Learning in Materials Science ; Mineral Processing and Grinding ; Computational Drug Discovery Methods
Authors: Dmitry Viatkin | Begonya García-Zapirain | Amaia Méndez Zorrilla | М.А. Захаров
Source: Frontiers in Materials (article, 2021-10-27)
Field: Materials Science — Materials Chemistry

Deep Learning Approach for Prediction of Critical Temperature of Superconductor Materials Described by Chemical Formulas This paper proposes a novel neural network architecture and its ensembles to predict the critical superconductivity temperature of materials based on their chemical formula. The research describes the methods and processes of extracting data from the chemical formula and preparing these extracted data for use in neural network training using TensorFlow. In our approach, recurrent neural networks are used including long short-term memory layers and neural networks based on one-dimensional convolution layers for data analysis. The proposed model is an ensemble of pre-trained neural network architectures for the prediction of the critical temperature of superconductors based on their chemical formula. The architecture of seven pre-trained neural networks is based on the long short-term memory layers and convolution layers. In the final ensemble, six neural networks are used: one network based on LSTM and four based on convolutional neural networks, and one embedding ensemble of convolution neural networks. LSTM neural network and convolution neural network were trained in 300 epochs. Ensembles of models were trained in 20 epochs. All neural networks are trained in two stages. At both stages, the optimizer Adam was used. In the first stage, training was carried out by the function of losses Mean Absolute Error (MAE) with the value of optimizer learning rate equal to 0.001. In the second stage, the previously trained model was trained by the function of losses Mean Squared Error (MSE) with a learning rate equal to 0.0001. The final ensemble is trained with a learning rate equal to 0.00001. The final ensemble model has the following accuracy values: MAE is 4.068, MSE is 67.272, and the coefficient of determination (R2) is 0.923. The final model can predict the critical temperature for the chemistry formula with an accuracy of 4.068°. Artificial neural network ; Convolutional neural network ; Deep learning ; Computer science ; Artificial intelligence ; Convolution (computer science) ; Mean squared error ; Recurrent neural network ; Pattern recognition (psychology) ; Function (biology) ; Algorithm ; Machine learning ; Mathematics ; Statistics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Superconductivity in MgB2 and Alloys
Authors: D. Yu. Kirsanova | Mikhail A. Soldatov | Zaira Gadzhimagomedova | D. M. Pashkov | А. В. Чернов | Maria A. Butakova | А. В. Солдатов
Source: Journal of Surface Investigation X-ray Synchrotron and Neutron Techniques (article, 2021-05-01)
Field: Materials Science — Materials Chemistry

On the Technologies of Artificial Intelligence and Machine Learning for 2D Materials Nowadays, an important area of research in the field of two-dimensional (2D) materials and their surface characteristics is acceleration of the process of searching for synthesis parameters for new structures with unique properties. The achieved level of development of artificial intelligence and especially machine learning makes it possible to use these techniques to solve a wide range of problems, including in the field of 2D-materials science. This article describes the current state of technologies of artificial intelligence and its subset, machine learning. The presented literature review describes the capabilities of machine-learning technologies for solving problems in the field of 2D-nanomaterials both at the stages of computer design and chemical synthesis and diagnostics of the obtained 2D-nanostructures and their surfaces. Much attention is given to the application of machine-learning technologies to find new 2D materials with specified characteristics that can be successfully used in a number of promising areas of application. Artificial intelligence ; Computer science ; Field (mathematics) ; Machine learning ; Process (computing) ; Hyper-heuristic ; Robot learning ; Mathematics ; Robot Machine Learning in Materials Science ; MXene and MAX Phase Materials ; 2D Materials and Applications
Authors: Lujie Jin | Yujin Ji | Hongshuai Wang | Lifeng Ding | Youyong Li
Source: Physical Chemistry Chemical Physics (review, 2021-01-01)
Field: Materials Science — Materials Chemistry

First-principles materials simulation and design for alkali and alkaline metal ion batteries accelerated by machine learning The challenge of regeneration of batteries requires a performance improvement in the alkali/alkaline metal ion battery (AMIB) materials, whereas the traditional research paradigm fully based on experiments and theoretical simulations needs massive research and development investment. During the last decade, machine learning (ML) has made breakthroughs in many complex disciplines, which testifies to their high processing speed and ability to capture relationships. Inspired by these achievements, ML has also been introduced to bring a new paradigm for shortening the development of AMIB materials. In this Perspective, the focus will be on how this new ML technology solves the key problems of redox potentials, ionic conductivity and stability parameters in first-principles materials' simulation and design for AMIBs. It is found that ML not only accelerates the property prediction, but also gives physicochemical insights into AMIB materials' design. In addition, the final part of this paper summarizes current achievements and looks forward to the progress of a novel paradigm in direct/inverse design with the increasing number of databases, skills, and ML technologies for AMIBs. Alkali metal ; Perspective (graphical) ; Ion ; Materials science ; Computer science ; Alkaline battery ; Process engineering ; Nanotechnology ; Inorganic chemistry ; Chemistry ; Engineering ; Artificial intelligence ; Electrolyte ; Organic chemistry ; Physical chemistry ; Electrode Machine Learning in Materials Science ; Advancements in Battery Materials ; Electron and X-Ray Spectroscopy Techniques
Authors: Rahul Singh | Preeti Singh | Ashok Sharma | Onur Rauf Bingöl | Aditya Balu | Ganesh Balasubramanian | Adarsh Krishnamurthy | Shahariar Sarkar | D. D. Johnson
Source: Computational Materials Science (article, 2021-07-09)
Field: Materials Science — Materials Chemistry

Neural-network model for force prediction in multi-principal-element alloys Convolutional neural network ; Computer science ; Artificial neural network ; Bounding overwatch ; Density functional theory ; Voxel ; Statistical physics ; Artificial intelligence ; Algorithm ; Computational science ; Physics ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; High Entropy Alloys Studies
Authors: Edward Elson Kosasih | Joaquín Cabezas | Xavier Sumba | Piotr Bielak | Kamil Tagowski | Kelvin Idanwekhai | Benedict Aaron Tjandra | Arian R. Jamasb
Source: arXiv (Cornell University) (preprint, 2021-06-29)
Field: Materials Science — Materials Chemistry

On Graph Neural Network Ensembles for Large-Scale Molecular Property\n Prediction In order to advance large-scale graph machine learning, the Open Graph\nBenchmark Large Scale Challenge (OGB-LSC) was proposed at the KDD Cup 2021. The\nPCQM4M-LSC dataset defines a molecular HOMO-LUMO property prediction task on\nabout 3.8M graphs. In this short paper, we show our current work-in-progress\nsolution which builds an ensemble of three graph neural networks models based\non GIN, Bayesian Neural Networks and DiffPool. Our approach outperforms the\nprovided baseline by 7.6%. Moreover, using uncertainty in our ensemble's\nprediction, we can identify molecules whose HOMO-LUMO gaps are harder to\npredict (with Pearson's correlation of 0.5181). We anticipate that this will\nfacilitate active learning.\n Benchmark (surveying) ; Computer science ; Artificial intelligence ; Machine learning ; Artificial neural network ; Graph ; Property (philosophy) ; Ensemble learning ; Theoretical computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; History and advancements in chemistry
Authors: Nesar Ahmed Titu | Matt Baucum | Timothy No | Mitchell Trotsky | Jaydeep Karandikar | Tony L. Schmitz | Anahita Khojandi
Source: Procedia Manufacturing (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Estimating Johnson-Cook Material Parameters using Neural Networks The five-parameter Johnson-Cook (J-C) material model represents the behavior of a material under extreme mechanical loading, including high temperatures, strains, and strain rates. The goal of this study is to estimate five J-C material parameters and chip thickness jointly for a given set of force components, power, and temperature. The approach uses two neural network models on a dataset simulated by finite element analysis for orthogonal cutting of aluminum 6061-T6. The first model develops a function approximator to predict the force components, power, and temperature using a given set of J-C parameters and chip thickness for aluminum 6061-T6. The second model searches the input space of the first model to estimate the J-C parameter values and chip thickness, given a set of targeted force components, power, and temperature of interest. The performance of both neural network models is evaluated using mean absolute percentage error. The results suggest that the developed neural networks-based approach is capable of estimating multiple J-C parameters and chip thickness that will result in a targeted force components, power, and temperatures of interest, given starting ‘educated guesses’ about these values. Artificial neural network ; Chip ; Power (physics) ; Set (abstract data type) ; Function (biology) ; Parameter space ; Finite element method ; Algorithm ; Computer science ; Mathematics ; Structural engineering ; Mechanical engineering ; Engineering ; Statistics ; Artificial intelligence ; Physics ; Electrical engineering ; Thermodynamics High-Velocity Impact and Material Behavior ; Advanced machining processes and optimization ; Metallurgy and Material Forming
Authors: Luca Jovine
Source: Molecular Reproduction and Development (article, 2021-09-29)
Field: Materials Science — Materials Chemistry

Using machine learning to study protein–protein interactions: From the uromodulin polymer to egg zona pellucida filaments Neural network-based models for protein structure prediction have recently reached near-experimental accuracy and are fast becoming a powerful tool in the arsenal of biologists. As suggested by initial studies using RoseTTAFold or the ColabFold implementation of AlphaFold2, a particularly interesting future development will be the optimization of these computational methods to also routinely yield high-confidence predictions of protein-protein interactions. Here I use AlphaFold2 and ColabFold to investigate the activation and polymerization of uromodulin (UMOD)/Tamm-Horsfall protein, a zona pellucida (ZP) module-containing protein whose precursor and filamentous structures have been previously determined experimentally by X-ray crystallography and cryo-EM, respectively. Despite having no knowledge of the UMOD polymer structure (coordinates for which were neither used for model training nor as template), AlphaFold2/ColabFold are able to recapitulate a crucial conformational change underlying UMOD polymerization, as well as the general organization of protein subunits within the resulting filament. This surprising result is achieved by simply deleting from the input sequence a stretch of residues that correspond to a polymerization-inhibiting C-terminal propeptide. By mimicking in silico the activating effect of propeptide dissociation triggered by site-specific proteolysis of the protein precursor, this example has implications for the assembly of egg coat proteins and the many other molecules that also contain a ZP module. Most importantly, it shows the potential of exploiting machine learning not only to accurately predict the structures of individual proteins or complexes, but also to carry out computational experiments replicating specific molecular events. Zona pellucida ; Tamm–Horsfall protein ; Biology ; In silico ; Computational biology ; Protein filament ; Protein structure ; Polymerization ; Biophysics ; Cell biology ; Biochemistry ; Polymer ; Chemistry ; Gene ; Oocyte ; Embryo Enzyme Structure and Function ; Protein Structure and Dynamics ; Mass Spectrometry Techniques and Applications
Authors: Anders S. Christensen | Sai Krishna Sirumalla | Zhuoran Qiao | Michael B. O’Connor | Daniel G. A. Smith | Feizhi Ding | Peter J. Bygrave | Animashree Anandkumar | Matthew Welborn | Frederick R. Manby | Thomas F. Miller
Source: The Journal of Chemical Physics (preprint, 2021-10-28)
Field: Materials Science — Materials Chemistry

OrbNet Denali: A machine learning potential for biological and organic chemistry with semi-empirical cost and DFT accuracy We present OrbNet Denali, a machine learning model for an electronic structure that is designed as a drop-in replacement for ground-state density functional theory (DFT) energy calculations. The model is a message-passing graph neural network that uses symmetry-adapted atomic orbital features from a low-cost quantum calculation to predict the energy of a molecule. OrbNet Denali is trained on a vast dataset of 2.3 × 10<sup>6</sup> DFT calculations on molecules and geometries. This dataset covers the most common elements in biochemistry and organic chemistry (H, Li, B, C, N, O, F, Na, Mg, Si, P, S, Cl, K, Ca, Br, and I) and charged molecules. OrbNet Denali is demonstrated on several well-established benchmark datasets, and we find that it provides accuracy that is on par with modern DFT methods while offering a speedup of up to three orders of magnitude. For the GMTKN55 benchmark set, OrbNet Denali achieves WTMAD-1 and WTMAD-2 scores of 7.19 and 9.84, on par with modern DFT functionals. For several GMTKN55 subsets, which contain chemical problems that are not present in the training set, OrbNet Denali produces a mean absolute error comparable to those of DFT methods. For the Hutchison conformer benchmark set, OrbNet Denali has a median correlation coefficient of R<sup>2</sup> = 0.90 compared to the reference DLPNO-CCSD(T) calculation and R<sup>2</sup> = 0.97 compared to the method used to generate the training data (ωB97X-D3/def2-TZVP), exceeding the performance of any other method with a similar cost. Similarly, the model reaches chemical accuracy for non-covalent interactions in the S66x10 dataset. For torsional profiles, OrbNet Denali reproduces the torsion profiles of ωB97X-D3/def2-TZVP with an average mean absolute error of 0.12 kcal/mol for the potential energy surfaces of the diverse fragments in the TorsionNet500 dataset. Benchmark (surveying) ; Density functional theory ; Ground state ; Molecule ; Basis set ; Set (abstract data type) ; Algorithm ; Computer science ; Physics ; Chemistry ; Computational chemistry ; Artificial intelligence ; Atomic physics ; Quantum mechanics ; Cartography ; Geography Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; History and advancements in chemistry
Authors: Shichen Lin | Daoling Peng | Weitao Yang | Feng Long Gu | Zhenggang Lan
Source: The Journal of Chemical Physics (article, 2021-11-10)
Field: Materials Science — Materials Chemistry

Theoretical studies on triplet-state driven dissociation of formaldehyde by quasi-classical molecular dynamics simulation on machine-learning potential energy surface The H-atom dissociation of formaldehyde on the lowest triplet state (T<sub>1</sub>) is studied by quasi-classical molecular dynamic simulations on the high-dimensional machine-learning potential energy surface (PES) model. An atomic-energy based deep-learning neural network (NN) is used to represent the PES function, and the weighted atom-centered symmetry functions are employed as inputs of the NN model to satisfy the translational, rotational, and permutational symmetries, and to capture the geometry features of each atom and its individual chemical environment. Several standard technical tricks are used in the construction of NN-PES, which includes the application of clustering algorithm in the formation of the training dataset, the examination of the reliability of the NN-PES model by different fitted NN models, and the detection of the out-of-confidence region by the confidence interval of the training dataset. The accuracy of the full-dimensional NN-PES model is examined by two benchmark calculations with respect to ab initio data. Both the NN and electronic-structure calculations give a similar H-atom dissociation reaction pathway on the T<sub>1</sub> state in the intrinsic reaction coordinate analysis. The small-scaled trial dynamics simulations based on NN-PES and ab initio PES give highly consistent results. After confirming the accuracy of the NN-PES, a large number of trajectories are calculated in the quasi-classical dynamics, which allows us to get a better understanding of the T<sub>1</sub>-driven H-atom dissociation dynamics efficiently. Particularly, the dynamics simulations from different initial conditions can be easily simulated with a rather low computational cost. The influence of the mode-specific vibrational excitations on the H-atom dissociation dynamics driven by the T<sub>1</sub> state is explored. The results show that the vibrational excitations on symmetric C-H stretching, asymmetric C-H stretching, and C=O stretching motions always enhance the H-atom dissociation probability obviously. Potential energy surface ; Dissociation (chemistry) ; Molecular dynamics ; Ab initio ; Atom (system on chip) ; Statistical physics ; Cluster analysis ; Potential energy ; Chemistry ; Computer science ; Computational chemistry ; Physics ; Atomic physics ; Artificial intelligence ; Physical chemistry ; Quantum mechanics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Advanced Chemical Physics Studies
Authors: Zhenyu Jiang | Tao Liu | Yan Huo | Ji Fan
Source: 2021 China Automation Congress (CAC) (article, 2021-10-22)
Field: Materials Science — Materials Chemistry

Image Analysis of Crystal Size Distribution and Agglomeration for β form L-Glutamic Acid Crystallization based on YOLOv4 Deep Learning In order to monitor the growth quality of crystal during the crystallization process of β-form L-glutamic acid (LGA), an image analysis method based on YOLOv4 deep learning is proposed for on-line measurement of crystal size distribution (CSD) and detection of crystal agglomeration. Firstly, different shapes of β-form LGA crystal are classified and the corresponding strategies for measuring crystal length are given, respectively. Then, an image segmentation algorithm based on YOLOv4 deep learning is proposed for real-time analysis of in-situ captured images. Meanwhile, YOLOv4 deep learning is adopted to determine the long axis of each segmented crystal image by using a specified threshold and a gradient algorithm, such that the measurement accuracy on crystal length could be effectively improved. By introducing an index named overlapping ratio of bounding box (ORBB), detection of crystal agglomeration is conducted based on classifying the segmented crystal images. Experimental results on β-form LGA crystallization are shown to demonstrate the effectiveness and advantage of the proposed image analysis method. Crystal (programming language) ; Crystallization ; Artificial intelligence ; Image segmentation ; Computer science ; Economies of agglomeration ; Deep learning ; Image (mathematics) ; Minimum bounding box ; Materials science ; Pattern recognition (psychology) ; Chemistry ; Engineering Crystallization and Solubility Studies ; Enzyme Structure and Function