jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Lihang Liu | Donglong He | Xiaomin Fang | Shanzhuo Zhang | Fan Wang | Jingzhou He | Hua Wu
Source: arXiv (Cornell University) (preprint, 2022-08-11)
Field: Materials Science — Materials Chemistry

GEM-2: Next Generation Molecular Property Prediction Network by Modeling Full-range Many-body Interactions Molecular property prediction is a fundamental task in the drug and material industries. Physically, the properties of a molecule are determined by its own electronic structure, which is a quantum many-body system and can be exactly described by the Schr"odinger equation. Full-range many-body interactions between electrons have been proven effective in obtaining an accurate solution of the Schr"odinger equation by classical computational chemistry methods, although modeling such interactions consumes an expensive computational cost. Meanwhile, deep learning methods have also demonstrated their competence in molecular property prediction tasks. Inspired by the classical computational chemistry methods, we design a novel method, namely GEM-2, which comprehensively considers full-range many-body interactions in molecules. Multiple tracks are utilized to model the full-range interactions between the many-bodies with different orders, and a novel axial attention mechanism is designed to approximate the full-range interaction modeling with much lower computational cost. Extensive experiments demonstrate the overwhelming superiority of GEM-2 over multiple baseline methods in quantum chemistry and drug discovery tasks. The ablation studies also verify the effectiveness of the full-range many-body interactions. Range (aeronautics) ; Property (philosophy) ; Computer science ; Quantum ; Quantum chemistry ; Statistical physics ; Theoretical computer science ; Computational chemistry ; Molecule ; Chemistry ; Physics ; Quantum mechanics ; Aerospace engineering ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Yunsie Chung | Florence H. Vermeire | Haoyang Wu | Pierre J. Walker | Michael H. Abraham | William H. Green
Source: ChemRxiv (preprint, 2022-01-03)
Field: Materials Science — Materials Chemistry

Group Contribution and Machine Learning Approaches to Predict Abraham Solute Parameters, Solvation Free Energy, and Solvation Enthalpy We present a group contribution method (SoluteGC) and a machine learning model (SoluteML) to predict the Abraham solute parameters, as well as a machine learning model (DirectML) to predict solvation free energy and enthalpy at 298 K. The proposed group contribution method uses atom-centered functional groups with corrections for ring and polycyclic strain whilst the machine learning models adopt a directed message passing neural network. The solute parameters predicted from SoluteGC and SoluteML are used to calculate solvation energy and enthalpy via linear free energy relationships. Extensive data sets containing 8366 solute parameters, 20253 solvation free energies, and 6322 solvation enthalpies are compiled in this work to train the models. The three models are each evaluated on the same test sets using both random and substructure-based solute splits for solvation energy and enthalpy predictions. The results show that the DirectML model is superior to the SoluteML and SoluteGC models for both predictions and can provide accuracy comparable to that of advanced quantum chemistry methods. Yet, even though the DirectML model performs better in general, all three models are useful for various purposes. Uncertain predicted values can be identified by comparing the 3 models, and when the 3 models are combined together, they can provide even more accurate predictions than any one of them individually. Finally, we present our compiled solute parameter, solvation energy, and solvation enthalpy databases (SoluteDB, dGsolvDBx, dHsolvDB) and provide public access to our final prediction models through a simple web-based tool, software package, and source code. Solvation ; Enthalpy ; Implicit solvation ; Chemistry ; Thermodynamics ; Computer science ; Statistical physics ; Machine learning ; Physics ; Molecule Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Source: NanoWorld Journal (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Comparative Analysis of Prediction Models for Melt Flow Rate of C2 and C3 Polymers Synthesized using Nanocatalysts Analysis of product quality is extremely important in any industry.The polymer industry is gaining significant importance in recent years.C2 and C3 are polymers that are made up of monomeric units with two and three carbon atoms, respectively.Nanotechnology plays an important role in polymerization reactions which subsequently improves the quality and quantity of polymer products.The importance of measuring product quality in online polymerization industries poses significant difficulties.There are no online tools for measuring resin features that represent polymer quality, such as flow melting indicator (melt flow index; MFI) and congestion.MFI should invariably be tested in timeconsuming and labor-intensive lab analysis.In many processing units, MFI is measured occasionally rather than regularly during a day using manual analysis.This paper presents models to predict melt flow rate using models of Linear regression, Support Vector Regression (SVR) and Adaboost Regressor.Adaboost regressor fittest with the accuracy of 69.2%.The primary objective of this research was to identify the best machine learning (ML) algorithm that can accurately predict the melt flow rate. Nanomaterial-based catalyst ; Polymer ; Volumetric flow rate ; Flow (mathematics) ; Materials science ; Chemical engineering ; Nanotechnology ; Thermodynamics ; Composite material ; Nanoparticle ; Engineering ; Mechanics ; Physics Machine Learning in Materials Science ; Catalysis and Hydrodesulfurization Studies ; Catalysis and Oxidation Reactions
Authors: Yezhou Ni | Robert Topham | Travis Skippon | Juntian Zhang | Sean M. Hanlon | Fei Long | Catalina Anghel | E. Torres | Mark R. Daymond | Laurent Karim Béland
Source: Engineering Applications of Artificial Intelligence (article, 2022-11-25)
Field: Materials Science — Materials Chemistry

Detection of zirconium hydrides in transmission electron micrographs using deep neural networks Convolutional neural network ; Computer science ; Zirconium ; Artificial neural network ; Zirconium alloy ; Materials science ; Artificial intelligence ; Transmission electron microscopy ; Deep learning ; Micrograph ; Pattern recognition (psychology) ; Scanning electron microscope ; Nanotechnology ; Composite material ; Metallurgy Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Nuclear Physics and Applications
Authors: Samuel Genheden | Per‐Ola Norrby | Ola Engkvist
Source: ChemRxiv (preprint, 2022-11-27)
Field: Materials Science — Materials Chemistry

AiZynthTrain: robust, reproducible, and extensible pipelines for training synthesis prediction models We introduce the AiZynthTrain Python package for training synthesis models in a robust, reproducible, and extensible way. It contains two pipelines that create a template-based one-step retrosynthesis model and a RingBreaker model that can be straightforwardly integrated in retrosynthesis software. We train such models on the publicly available reaction dataset from the US Patent and Trademark Office (USPTO), and these are the first retrosynthesis models created in a completely reproducible end-to-end fashion, starting with the original reaction data source and ending with trained machine-learning models. In particular, we show that employing the pipeline greatly improves the ability of the RingBreaker model for disconnecting ring systems. Furthermore, we demonstrate the robustness of the pipeline by training on a more diverse but proprietary dataset. We envisage that this framework will be extended with other synthesis models in the future. Python (programming language) ; Computer science ; Robustness (evolution) ; Extensibility ; Pipeline (software) ; Pipeline transport ; Retrosynthetic analysis ; Training set ; Software ; Artificial intelligence ; Code refactoring ; Data mining ; Machine learning ; Software engineering ; Programming language ; Engineering Machine Learning in Materials Science ; Software Engineering Research ; Surface Chemistry and Catalysis
Authors: Zimu Li | Zihan Pengmei | Han Zheng | Erik H. Thiede | Junyu Liu | Risi Kondor
Source: arXiv (Cornell University) (preprint, 2022-11-14)
Field: Materials Science — Materials Chemistry

Unifying O(3) Equivariant Neural Networks Design with Tensor-Network Formalism Many learning tasks, including learning potential energy surfaces from ab initio calculations, involve global spatial symmetries and permutational symmetry between atoms or general particles. Equivariant graph neural networks are a standard approach to such problems, with one of the most successful methods employing tensor products between various tensors that transform under the spatial group. However, as the number of different tensors and the complexity of relationships between them increase, maintaining parsimony and equivariance becomes increasingly challenging. In this paper, we propose using fusion diagrams, a technique widely employed in simulating SU($2$)-symmetric quantum many-body problems, to design new equivariant components for equivariant neural networks. This results in a diagrammatic approach to constructing novel neural network architectures. When applied to particles within a given local neighborhood, the resulting components, which we term "fusion blocks," serve as universal approximators of any continuous equivariant function defined in the neighborhood. We incorporate a fusion block into pre-existing equivariant architectures (Cormorant and MACE), leading to improved performance with fewer parameters on a range of challenging chemical problems. Furthermore, we apply group-equivariant neural networks to study non-adiabatic molecular dynamics of stilbene cis-trans isomerization. Our approach, which combines tensor networks with equivariant neural networks, suggests a potentially fruitful direction for designing more expressive equivariant neural networks. Equivariant map ; Artificial neural network ; Computer science ; Topology (electrical circuits) ; Theoretical computer science ; Mathematics ; Pure mathematics ; Artificial intelligence ; Combinatorics Machine Learning in Materials Science ; Topic Modeling ; Computational Physics and Python Applications
Authors: Sergey N. Pozdnyakov | Michele Ceriotti
Source: arXiv (Cornell University) (preprint, 2022-01-18)
Field: Materials Science — Materials Chemistry

Incompleteness of graph neural networks for points clouds in three dimensions Graph neural networks (GNN) are very popular methods in machine learning and have been applied very successfully to the prediction of the properties of molecules and materials. First-order GNNs are well known to be incomplete, i.e., there exist graphs that are distinct but appear identical when seen through the lens of the GNN. More complicated schemes have thus been designed to increase their resolving power. Applications to molecules (and more generally, point clouds), however, add a geometric dimension to the problem. The most straightforward and prevalent approach to construct graph representation for molecules regards atoms as vertices in a graph and draws a bond between each pair of atoms within a chosen cutoff. Bonds can be decorated with the distance between atoms, and the resulting "distance graph NNs" (dGNN) have empirically demonstrated excellent resolving power and are widely used in chemical ML, with all known indistinguishable configurations being resolved in the fully-connected limit, which is equivalent to infinite or sufficiently large cutoff. Here we present a counterexample that proves that dGNNs are not complete even for the restricted case of fully-connected graphs induced by 3D atom clouds. We construct pairs of distinct point clouds whose associated graphs are, for any cutoff radius, equivalent based on a first-order Weisfeiler-Lehman test. This class of degenerate structures includes chemically-plausible configurations, both for isolated structures and for infinite structures that are periodic in 1, 2, and 3 dimensions. The existence of indistinguishable configurations sets an ultimate limit to the expressive power of some of the well-established GNN architectures for atomistic machine learning. Models that explicitly use angular or directional information in the description of atomic environments can resolve this class of degeneracies. Cutoff ; Counterexample ; Degenerate energy levels ; Vertex (graph theory) ; Point cloud ; Graph ; Artificial neural network ; Mathematics ; Computer science ; Topology (electrical circuits) ; Discrete mathematics ; Combinatorics ; Physics ; Quantum mechanics ; Artificial intelligence Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Teddy Koker | Keegan Quigley | Will Spaeth | Nathan C. Frey | Lin Li
Source: arXiv (Cornell University) (preprint, 2022-11-24)
Field: Materials Science — Materials Chemistry

Graph Contrastive Learning for Materials Recent work has shown the potential of graph neural networks to efficiently predict material properties, enabling high-throughput screening of materials. Training these models, however, often requires large quantities of labelled data, obtained via costly methods such as ab initio calculations or experimental evaluation. By leveraging a series of material-specific transformations, we introduce CrystalCLR, a framework for constrastive learning of representations with crystal graph neural networks. With the addition of a novel loss function, our framework is able to learn representations competitive with engineered fingerprinting methods. We also demonstrate that via model finetuning, contrastive pretraining can improve the performance of graph neural networks for prediction of material properties and significantly outperform traditional ML models that use engineered fingerprints. Lastly, we observe that CrystalCLR produces material representations that form clusters by compound class. Computer science ; Graph ; Artificial neural network ; Theoretical computer science ; Artificial intelligence ; Machine learning Machine Learning in Materials Science ; Domain Adaptation and Few-Shot Learning ; Non-Destructive Testing Techniques
Authors: Xiangbo Yang | Xia Bai | Zhenkai Zhang | Xiaoming Yu
Source: Advances in Materials Science and Engineering (article, 2022-07-08)
Field: Materials Science — Materials Chemistry

Metal Oxide Nanomaterials as Rice Transgenic Carriers For the development of nanotechnology, nanomaterials are in an important position. Due to their special structure and a series of unique properties, nano-metal oxide materials are widely used in nanoelectronics, catalysis, environmental monitoring, electrochemical sensors, and many other fields. Rice, as the most important food crop in the world, has a wide range of planting areas. Therefore, in this paper, the research on the construction of rice transgenic vectors is based on nano-metal oxide materials. In this paper, particle swarm optimization is used to optimize the weights of rice-growing convolutional neural networks. Secondly, it used rice as the test plant to study the effects of seven metal oxide nanomaterials on rice seed germination and root elongation. This sheds light on the toxic effects of metal oxide nanomaterials on rice growth and discusses the factors that affect the phytotoxicity of nanomaterials. The experimental results show that before optimization, the accuracy of the small batch sample test is 87.0456%, the accuracy of the large batch sample test is 93.5876%, and the accuracy of the test is increased by 6.542%. After optimization, the accuracy of the small batch sample test is 93.5876%, the accuracy of the large batch sample test is 99.0133%, and the accuracy of the test is increased by 5.4466%. The inhibition rates of nano-CuO and ZnO on rice root length were 97.28% and 66.75%. Nanomaterials ; Materials science ; Oxide ; Germination ; Nanotechnology ; Agronomy ; Metallurgy ; Biology Nanoparticles: synthesis and applications
Authors: Yaroslava Pushkarova | Галина Зайцева | Mustafa Al Saker
Source: 2022 12th International Conference on Advanced Computer Information Technologies (ACIT) (article, 2022-09-26)
Field: Chemistry — Analytical Chemistry

Prediction of Toxicity of Phenols Using Artificial Neural Networks In studies of various aspects of experimental determination of toxicity, it becomes very important to apply calculation methods to predict these indicators, which allows to assess in advance the possible risk of using chemicals without additional experiments. The present work shows the results of using artificial neural networks for prediction of toxicity of phenols based on the set of molecular descriptors. Applicability of the feed-forward neural network, cascade neural network and radial basis function neural networks was investigated. The optimal parameters (number of hidden neurons, spread value) for the used artificial neural networks have been determined. It is shown that the efficiency of toxicity prediction by means of feed-forward and cascade neural network algorithms exceeds the efficiency of prediction by means of commonly used methods. In comparison radial basis function neural networks are inapplicable for prediction of phenols toxicity. Artificial neural network ; Cascade ; Computer science ; Artificial intelligence ; Set (abstract data type) ; Function (biology) ; Machine learning ; Phenols ; Engineering ; Chemistry ; Biology Spectroscopy and Chemometric Analyses ; Statistical and Computational Modeling ; Computational Drug Discovery Methods
Authors: Gino Cassella | Halvard Sutterud | Sam Azadi | N. D. Drummond | David Pfau | James S. Spencer | W. M. C. Foulkes
Source: Lancaster EPrints (Lancaster University) (preprint, 2022-02-10)
Field: Materials Science — Materials Chemistry

Discovering Quantum Phase Transitions with Fermionic Neural Networks Deep neural networks have been extremely successful as highly accurate wave function ansätze for variational Monte Carlo calculations of molecular ground states. We present an extension of one such ansatz, FermiNet, to calculations of the ground states of periodic Hamiltonians, and study the homogeneous electron gas. FermiNet calculations of the ground-state energies of small electron gas systems are in excellent agreement with previous initiator full configuration interaction quantum Monte Carlo and diffusion Monte Carlo calculations. We investigate the spin-polarized homogeneous electron gas and demonstrate that the same neural network architecture is capable of accurately representing both the delocalized Fermi liquid state and the localized Wigner crystal state. The network is given no \emph{a priori} knowledge that a phase transition exists, but converges on the translationally invariant ground state at high density and spontaneously breaks the symmetry to produce the crystalline ground state at low density. Quantum Monte Carlo ; Ansatz ; Ground state ; Diffusion Monte Carlo ; Wave function ; Wigner crystal ; Physics ; Delocalized electron ; Fermi gas ; Variational Monte Carlo ; Monte Carlo method ; Quantum phase transition ; Condensed matter physics ; Statistical physics ; Electron ; Quantum mechanics ; Phase transition ; Monte Carlo molecular modeling ; Mathematics Machine Learning in Materials Science ; Quantum many-body systems ; Quantum, superfluid, helium dynamics
Authors: Anqi Zhang | Chandan S. Kadur | Charu Ramakrishnan | Zhenan Bao | Karl Deisseroth
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2022-12-27)
Field: Materials Science — Materials Chemistry

Genetically-encoded photosensitizers enable light-controlled polymerization on living neuronal membranes Abstract The ability to record, stimulate, and modify brains of living animals would unlock numerous research opportunities and create potential clinical interventions, but it is difficult to interface with a living neural network without damaging it. We previously reported a novel approach to building neural interfaces, namely: genetically programming cells to build artificial structures to modify the electrical properties of neurons in situ , which opens up the possibility of modifying neural circuits in living animals without surgery. However, the spatiotemporal resolution, efficiency, and biocompatibility of this approach were still limited and lacked selectivity on cell membrane. Here, we demonstrate an approach using genetically-targeted photosensitizers to instruct living cells to synthesize functional materials directly on the plasma membrane under the control of light. Polymers synthesized by this approach were selectively deposited on the membrane of targeted live neurons. This platform can be readily extended to incorporate a broad range of light-controlled reactions onto specific cells, which may enable researchers to grow seamless, dynamic interfaces directly in living animals. Biocompatibility ; Membrane ; Nanotechnology ; Living cell ; Living systems ; Biological neural network ; Neuroscience ; Materials science ; Computer science ; Chemistry ; Biology ; Cell biology ; Artificial intelligence ; Biochemistry Photochromic and Fluorescence Chemistry ; Photoreceptor and optogenetics research ; Nanoplatforms for cancer theranostics
Authors: Ao Chen | Hua Tong | Chengwei Wu | Guofeng Xie | Zhong-Xiang Xie | Chang-Qing Xiang | Wu‐Xing Zhou
Source: Chinese Physics B (article, 2022-12-09)
Field: Materials Science — Materials Chemistry

Stress effect on lattice thermal conductivity of anode material NiNb<sub>2</sub>O<sub>6</sub> for lithium-ion batteries The thermal transport properties of NiNb 2 O 6 as anode material for lithium-ion battery and the effect of strain were studied by machine learning interatomic potential combined with Boltzmann transport equation. The results show that the lattice thermal conductivity of NiNb 2 O 6 along the three crystal directions [100], [010], and [001] are 0.947 W⋅m −1 ⋅K −1 , 0.727 W⋅m −1 ⋅K −1 , and 0.465 W⋅m −1 ⋅K −1 , respectively, indicating the anisotropy of the lattice thermal conductivity of NiNb 2 O 6 . This anisotropy of the lattice thermal conductivity stems from the significant difference of phonon group velocities in different crystal directions of NiNb 2 O 6 . When the tensile strain is applied along the [001] crystal direction, the lattice thermal conductivity in all three directions decreases. However, when the compressive strain is applied, the lattice thermal conductivity in the [100] and [010] crystal directions is increased, while the lattice thermal conductivity in the [001] crystal direction is abnormally reduced due to the significant inhibition of compressive strain on the group velocity. These indicate that the anisotropy of thermal conductivity of NiNb 2 O 6 can be enhanced by the compressive strain, and reduced by the tensile strain. Materials science ; Thermal conductivity ; Anisotropy ; Ion ; Thermal conduction ; Crystal (programming language) ; Condensed matter physics ; Phonon ; Composite material ; Chemistry ; Physics ; Optics Thermal properties of materials ; Advancements in Battery Materials ; Transition Metal Oxide Nanomaterials