jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Valentina A. Afonina | Daniyar Mazitov | Albina T. Nurmukhametova | M. D. Shevelev | Dina A. Khasanova | Ramil Nugmanov | В. А. Бурилов | Timur Madzhidov | Alexandre Varnek
Source: International Journal of Molecular Sciences (article, 2021-12-27)
Field: Materials Science — Materials Chemistry

Prediction of Optimal Conditions of Hydrogenation Reaction Using the Likelihood Ranking Approach The selection of experimental conditions leading to a reasonable yield is an important and essential element for the automated development of a synthesis plan and the subsequent synthesis of the target compound. The classical QSPR approach, requiring one-to-one correspondence between chemical structure and a target property, can be used for optimal reaction conditions prediction only on a limited scale when only one condition component (e.g., catalyst or solvent) is considered. However, a particular reaction can proceed under several different conditions. In this paper, we describe the Likelihood Ranking Model representing an artificial neural network that outputs a list of different conditions ranked according to their suitability to a given chemical transformation. Benchmarking calculations demonstrated that our model outperformed some popular approaches to the theoretical assessment of reaction conditions, such as k Nearest Neighbors, and a recurrent artificial neural network performance prediction of condition components (reagents, solvents, catalysts, and temperature). The ability of the Likelihood Ranking model trained on a hydrogenation reactions dataset, (~42,000 reactions) from Reaxys<sup>®</sup> database, to propose conditions that led to the desired product was validated experimentally on a set of three reactions with rich selectivity issues. Ranking (information retrieval) ; Artificial neural network ; Benchmarking ; Computer science ; Quantitative structure–activity relationship ; Reaction conditions ; Yield (engineering) ; Machine learning ; Artificial intelligence ; Catalysis ; Chemistry ; Materials science ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Process Optimization and Integration
Authors: Leif D. Jacobson | James Stevenson | Farhad Ramezanghorbani | Delaram Ghoreishi | Karl Leswing | Ed Harder | Robert Abel
Source:  (preprint, 2021-08-02)
Field: Materials Science — Materials Chemistry

Transferable Neural Network Potential Energy Surfaces for Closed-Shell Organic Molecules: Extension to Ions Transferable high dimensional neural network potentials (HDNNP) have shown great promise as an avenue to increase the accuracy and domain of applicability of existing atomistic force fields for organic systems relevant to life science. We have previously reported such a potential (Schrödinger-ANI) that has broad coverage of druglike molecules. We extend that work here to cover ionic and zwitterionic druglike molecules expected to be relevant to drug discovery research activities. We report a novel HDNNP architechture, which we call QRNN, that predicts atomic charges and uses these charges as descriptors in an energy model which delivers conformational energies within chemical accuracy when measured against the reference theory it is trained to. Further, we find that delta learning based on a semi-empirical level of theory approximately halves the errors. We test the models on torsion energy profiles, relative conformational energies, geometric parameters and relative tautomer errors. Potential energy ; Artificial neural network ; Molecule ; Computer science ; Organic molecules ; Ion ; Chemistry ; Physics ; Chemical physics ; Artificial intelligence ; Atomic physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Christopher J. Bartel
Source: Patterns (article, 2021-11-01)
Field: Materials Science — Materials Chemistry

Data-centric approach to improve machine learning models for inorganic materials Pandey et al. (2021) demonstrate the importance of diversifying training data to make balanced predictions of thermodynamic properties for inorganic crystals. Scopus ; Materials informatics ; Computer science ; Code (set theory) ; Space (punctuation) ; Informatics ; Database ; Materials science ; Nanotechnology ; Chemistry ; Set (abstract data type) ; Health informatics ; MEDLINE ; Engineering Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Marcus Wieder | Josh Fass | John D. Chodera
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2021-08-26)
Field: Materials Science — Materials Chemistry

Teaching free energy calculations to learn from experimental data Abstract Alchemical free energy calculations are an important tool in the computational chemistry toolbox, enabling the efficient calculation of quantities critical for drug discovery such as ligand binding affinities, selectivities, and partition coefficients. However, modern alchemical free energy calculations suffer from three significant limitations: (1) modern molecular mechanics force fields are limited in their ability to model complex molecular interactions, (2) classical force fields are unable to treat phenomena that involve rearrangements of chemical bonds, and (3) these calculations are unable to easily learn to improve their performance if readily-available experimental data is available. Here, we show how all three limitations can be overcome through the use of quantum machine learning (QML) potentials capable of accurately modeling quantum chemical energetics even when chemical bonds are made and broken. Because these potentials are based on mathematically convenient deep learning architectures instead of traditional quantum chemical formulations, QML simulations can be run at a fraction of the cost of quantum chemical simulations using modern graphics processing units (GPUs) and machine learning frameworks. We demonstrate that alchemical free energy calculations in explicit solvent are especially simple to implement using QML potentials because these potentials lack singularities and other pathologies typical of molecular mechanics potentials, and that alchemical free energy calculations are highly effective even when bonds are broken or made. Finally, we show how a limited number of experimental free energy measurements can be used to significantly improve the accuracy of computed free energies for unrelated compounds with no significant generalization gap. We illustrate these concepts on the prediction of aqueous tautomer free energies (related to tautomer ratios), which are highly relevant to drug discovery in that more than a quarter of all approved drugs exist as a mixture of tautomers. Toolbox ; Computer science ; Quantum chemical ; Statistical physics ; Quantum ; Thermodynamic integration ; Affinities ; Graphics ; Computational science ; Computational chemistry ; Theoretical computer science ; Chemistry ; Quantum mechanics ; Molecular dynamics ; Physics ; Molecule Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: P. Álvarez-Zapatero | A. Vega | Andrés Aguado
Source: Acta Materialia (article, 2021-09-24)
Field: Materials Science — Materials Chemistry

A neural network potential for searching the atomic structures of pure and mixed nanoparticles. Application to ZnMg nanoalloys with an eye on their anticorrosive properties The accurate description of the potential energy landscape of moderate-sized nanoparticles is a formidable task, but of paramount importance if one aims to characterize, in a realistic way, their physical and chemical properties. We present here a Neural Network potential able to predict structures of pure and mixed nanoparticles with an error in energy and forces of the order of chemical accuracy as compared with the values provided by the theoretical method used in the training process, in our case the density functional theory. The neural network is integrated into a basin-hopping algorithm which dynamically feeds the training process. The main ingredients of the neural network algorithm as well as the protocol used for its implementation and training are detailed, with particular emphasis on those aspects that make it so efficient and transferable. As a first test, we have applied it to the determination of the global minimum structures of ZnMg nanoalloys with up to 52 atoms and stoichiometries corresponding to MgZn2 and Mg2Zn11, of special interest in the context of anticorrosive coatings. We present and discuss the structural properties, chemical order, stability and pertinent electronic indicators, and we extract some conclusions on fundamental aspects that may be at the roots of the good performance of ZnMg nanoalloys as protective coatings. Finally, we comment on the step forward that the presented machine learning approach constitutes, both in the fact that it allows to accurately explore the potential energy surface of systems that other methodologies can not, and that it opens new prospects for a variety of problems in Materials Science. Artificial neural network ; Context (archaeology) ; Nanoparticle ; Process (computing) ; Materials science ; Stability (learning theory) ; Computer science ; Energy (signal processing) ; Protocol (science) ; Biological system ; Nanotechnology ; Artificial intelligence ; Machine learning ; Mathematics Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Electron and X-Ray Spectroscopy Techniques
Authors: Takeshi Aoyagi
Source: MRS Advances (article, 2021-02-05)
Field: Materials Science — Materials Chemistry

High-throughput prediction of stress–strain curves of thermoplastic elastomer model block copolymers by combining hierarchical simulation and deep learning Abstract We achieved high-throughput prediction of the stress–strain (S–S) curves of thermoplastic elastomers by combining hierarchical simulation and deep learning. ABA triblock copolymer with a phase-separated structure was used as a thermoplastic elastomer model. The S–S curves of the ABA triblock copolymers were calculated from the hierarchical simulation of self-consistent field theory calculations and coarse-grained molecular dynamics simulations. Because such hierarchical simulations require considerable computational resources, we applied a deep learning technique to accelerate the prediction. Sets of phase-separated structures and the S–S curves obtained from the hierarchical simulation were used to train a 3D convolutional neural network. Using the trained network, we confirmed that the predicted S–S curves of the untrained structures accurately reproduced the simulation results. These results will enable us to design novel polymers and phase-separated structures with desired S–S curves by high-throughput screening of a wide variety of structures. Graphic abstract Materials science ; Copolymer ; Thermoplastic elastomer ; Elastomer ; Phase (matter) ; Block (permutation group theory) ; Stress–strain curve ; Stress (linguistics) ; Throughput ; Polymer ; Computer science ; Biological system ; Artificial intelligence ; Composite material ; Geometry ; Mathematics ; Physics ; Deformation (meteorology) Block Copolymer Self-Assembly ; Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices
Authors: Zhenhou Hong | Jianzong Wang | Xiaoyang Qu | Xinghua Zhu | Jie Liu | Jing Xiao
Source:  (article, 2021-07-18)
Field: Materials Science — Materials Chemistry

Quantum Convolutional Neural Network on Protein Distance Prediction Proteins are linear polymers that fold into an incredible variety of three-dimensional structures that enable sophisticated functionality for biology. Predicting protein distance with high precision remains challenging, particularly for small protein families. As deep learning achieves remarkable success in many areas, deep learning also allows scientists to predict proteins' three-dimensional structure. As convolutional neural networks have a powerful ability to learn data features at multiple levels of abstraction, we deploy CNN to predict protein distance. To accelerate the training process, we apply a quantum convolutional neural network(QCNN) to improve the protein structure prediction efficiently. For QCNN, the conventional convolutional layer is transformed to a quantum convolution or quanvolutional layer. Since the protein data has large input, we explore the large dimension of these quantum transformations. And in experiments, we compare the different number of layers in QCNN during the training phase. We found the QCNN is similar to CNN that is the deeper layer can get better performance. The simulations show the proposed method can accelerate the convergence while maintaining the performance. Convolutional neural network ; Computer science ; Deep learning ; Abstraction ; Artificial intelligence ; Convolution (computer science) ; Protein structure prediction ; Convergence (economics) ; Dimension (graph theory) ; Quantum ; Layer (electronics) ; Artificial neural network ; Machine learning ; Protein structure ; Mathematics ; Nanotechnology Machine Learning in Materials Science ; Protein Structure and Dynamics ; Machine Learning in Bioinformatics
Authors: Joonsoo Jeong | Nagyeong Lee | Yongbeom Shin | Dongil Shin
Source: Journal of the Taiwan Institute of Chemical Engineers (article, 2021-07-31)
Field: Materials Science — Materials Chemistry

Intelligent generation of optimal synthetic pathways based on knowledge graph inference and retrosynthetic predictions using reaction big data Computer science ; Artificial intelligence ; Chemical space ; Inference ; Path (computing) ; Similarity (geometry) ; Synthetic data ; Machine learning ; Data mining ; Drug discovery ; Chemistry Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Computational Drug Discovery Methods
Authors: Dylan Sérillon | Carles Bó | Xavier Barril
Source: Journal of Computer-Aided Molecular Design (article, 2021-01-19)
Field: Materials Science — Materials Chemistry

Testing automatic methods to predict free binding energy of host–guest complexes in SAMPL7 challenge The design of new host-guest complexes represents a fundamental challenge in supramolecular chemistry. At the same time, it opens new opportunities in material sciences or biotechnological applications. A computational tool capable of automatically predicting the binding free energy of any host-guest complex would be a great aid in the design of new host systems, or to identify new guest molecules for a given host. We aim to build such a platform and have used the SAMPL7 challenge to test several methods and design a specific computational pipeline. Predictions will be based on machine learning (when previous knowledge is available) or a physics-based method (otherwise). The formerly delivered predictions with an RMSE of 1.67 kcal/mol but will require further work to identify when a specific system is outside of the scope of the model. The latter is combines the semiempirical GFN2B functional, with docking, molecular mechanics, and molecular dynamics. Correct predictions (RMSE of 1.45 kcal/mol) are contingent on the identification of the correct binding mode, which can be very challenging for host-guest systems with a large number of degrees of freedom. Participation in the blind SAMPL7 challenge provided fundamental direction to the project. More advanced versions of the pipeline will be tested against future SAMPL challenges. Pipeline (software) ; Host (biology) ; Computer science ; Scope (computer science) ; Identification (biology) ; Supramolecular chemistry ; Degrees of freedom (physics and chemistry) ; Theoretical computer science ; Artificial intelligence ; Data science ; Machine learning ; Molecule ; Chemistry ; Physics ; Programming language ; Biology Enzyme Structure and Function ; Protein Structure and Dynamics ; Biochemical and Molecular Research
Authors: Robert Kozioł | Marcin Łapiński | Paweł Syty | W. Sadowski | J. E. Sienkiewicz | Bartosz Nurek | Valentin‐Adrian Maraloiu | Barbara Kościelska
Source: Applied Surface Science (article, 2021-08-02)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Experimental tuning of AuAg nanoalloy plasmon resonances assisted by machine learning method Plasmonic nanostructures based on AuAg nanoalloys were fabricated by thermal annealing of metallic films in an argon atmosphere. The nanoalloys were chosen because they can extend the wavelength range in which plasmon resonance occurs and thus allow the design of plasmonic platforms with the desired parameters. The influence of initial fabrication parameters and experimental conditions on the formation of nanostructures was investigated. For the surface morphology studies, chemical composition analysis and nanograin structure, Scanning Electron Microscopy (SEM), X-Ray Photoelectron Spectroscopy (XPS), Energy Dispersive X-Ray Spectroscopy (EDS) and High-Resolution Transmission Electron Microscopy (HR TEM) measurements were performed. The position of the resonance band was successfully tuned in the 100 nm range. The EDS together with the XPS analysis confirmed the formation of an alloy with the aspect ratio of individual metals in a single nanoisland similar to the ratio of the thicknesses of the initially sputtered layers. The experimental research was complemented by the neural network model, which enables the calculation of the absorbance peak depending on the thickness of Au and Ag layers and the annealing time. The proposed model of machine learning makes it possible to fine-tune the desired position of the plasmon resonance. X-ray photoelectron spectroscopy ; Materials science ; Plasmon ; Surface plasmon resonance ; Annealing (glass) ; Scanning electron microscope ; Nanostructure ; Transmission electron microscopy ; Spectroscopy ; Nanotechnology ; Analytical Chemistry (journal) ; Optoelectronics ; Nanoparticle ; Chemical engineering ; Chemistry ; Composite material Gold and Silver Nanoparticles Synthesis and Applications ; nanoparticles nucleation surface interactions ; Quantum Dots Synthesis And Properties
Authors: Sicong Ma | Zhi‐Pan Liu
Source: Topics in Catalysis (article, 2021-07-13)
Field: Chemistry — Inorganic Chemistry

The Role of Zeolite Framework in Zeolite Stability and Catalysis from Recent Atomic Simulation Zeolite ; Microporous material ; Catalysis ; Adsorption ; Nanotechnology ; Reactivity (psychology) ; Materials science ; Chemistry ; Molecule ; Chemical engineering ; Computational chemistry ; Organic chemistry ; Engineering Zeolite Catalysis and Synthesis ; Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Zebin Li | Jihea Lee | Fei Yao | Hongyue Sun
Source: Nanoscale (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Quantifying the CVD-grown two-dimensional materials <i>via</i> image clustering Machine learning (ML) techniques have been recently employed to facilitate the development of novel two-dimensional (2D) materials. Among various synthesis approaches, chemical vapor deposition (CVD) has demonstrated tremendous potential in producing high-quality 2D flakes with good controllability, enabling large-scale production at a relatively low cost. Traditionally, the quality of CVD-grown samples can be manually evaluated based on optical images which is labor-intensive and time-consuming. In this paper, we explored a data-driven unsupervised quality assessment strategy based on image clustering <i>via</i> integrating self-organizing map (SOM) and <i>k</i>-means methods for optical image analysis of CVD-grown 2D materials<sub>.</sub> The high matching rate between the clustering results and material experts' labels indicated a good accuracy of the proposed clustering algorithm. The proposed unsupervised ML methodology will provide materials scientists with an effective tool kit for efficient evaluation of CVD-grown materials' quality and has a broad applicability for various material systems. Cluster analysis ; Chemical vapor deposition ; Materials science ; Computer science ; Image (mathematics) ; Unsupervised learning ; Artificial intelligence ; Quality (philosophy) ; Pattern recognition (psychology) ; Nanotechnology ; Physics Graphene research and applications ; Machine Learning in Materials Science ; Advanced Neural Network Applications
Authors: Wenqi Zeng | Siqin Cao | Xuhui Huang | Yuan Yao
Source: arXiv (Cornell University) (preprint, 2021-07-14)
Field: Materials Science — Materials Chemistry

A Note on Learning Rare Events in Molecular Dynamics using LSTM and Transformer Recurrent neural networks for language models like long short-term memory (LSTM) have been utilized as a tool for modeling and predicting long term dynamics of complex stochastic molecular systems. Recently successful examples on learning slow dynamics by LSTM are given with simulation data of low dimensional reaction coordinate. However, in this report we show that the following three key factors significantly affect the performance of language model learning, namely dimensionality of reaction coordinates, temporal resolution and state partition. When applying recurrent neural networks to molecular dynamics simulation trajectories of high dimensionality, we find that rare events corresponding to the slow dynamics might be obscured by other faster dynamics of the system, and cannot be efficiently learned. Under such conditions, we find that coarse graining the conformational space into metastable states and removing recrossing events when estimating transition probabilities between states could greatly help improve the accuracy of slow dynamics learning in molecular dynamics. Moreover, we also explore other models like Transformer, which do not show superior performance than LSTM in overcoming these issues. Therefore, to learn rare events of slow molecular dynamics by LSTM and Transformer, it is critical to choose proper temporal resolution (i.e., saving intervals of MD simulation trajectories) and state partition in high resolution data, since deep neural network models might not automatically disentangle slow dynamics from fast dynamics when both are present in data influencing each other. Computer science ; Curse of dimensionality ; Artificial intelligence ; Rare events ; Metastability ; Recurrent neural network ; Molecular dynamics ; Artificial neural network ; Granularity ; Deep learning ; Transformer ; Temporal resolution ; Machine learning ; Physics ; Mathematics Machine Learning in Materials Science ; Topic Modeling ; Protein Structure and Dynamics
Authors: Gaétan Laurens | Malalatiana Rabary | Julien Lam | Daniel Peláez | Abdul-Rahman Allouche
Source: Theoretical Chemistry Accounts (article, 2021-05-20)
Field: Materials Science — Materials Chemistry

Infrared spectra of neutral polycyclic aromatic hydrocarbons based on machine learning potential energy surface and dipole mapping Transferability ; Dipole ; Potential energy surface ; Infrared spectroscopy ; Perturbation theory (quantum mechanics) ; Infrared ; Artificial neural network ; Quantum Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Synthesis and Properties of Aromatic Compounds