jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kaveh Safavigerdini | Koundinya Nouduri | Ramakrishna Surya | Andrew Reinhard | Zach Quinlan | Filiz Bunyak | Matthew R. Maschmann | Kannappan Palaniappan
Source: arXiv (Cornell University) (preprint, 2023-07-16)
Field: Materials Science — Materials Chemistry

Predicting mechanical properties of Carbon Nanotube (CNT) images Using Multi-Layer Synthetic Finite Element Model Simulations We present a pipeline for predicting mechanical properties of vertically-oriented carbon nanotube (CNT) forest images using a deep learning model for artificial intelligence (AI)-based materials discovery. Our approach incorporates an innovative data augmentation technique that involves the use of multi-layer synthetic (MLS) or quasi-2.5D images which are generated by blending 2D synthetic images. The MLS images more closely resemble 3D synthetic and real scanning electron microscopy (SEM) images of CNTs but without the computational cost of performing expensive 3D simulations or experiments. Mechanical properties such as stiffness and buckling load for the MLS images are estimated using a physics-based model. The proposed deep learning architecture, CNTNeXt, builds upon our previous CNTNet neural network, using a ResNeXt feature representation followed by random forest regression estimator. Our machine learning approach for predicting CNT physical properties by utilizing a blended set of synthetic images is expected to outperform single synthetic image-based learning when it comes to predicting mechanical properties of real scanning electron microscopy images. This has the potential to accelerate understanding and control of CNT forest self-assembly for diverse applications. Carbon nanotube ; Artificial intelligence ; Artificial neural network ; Materials science ; Computer science ; Pipeline (software) ; Finite element method ; Deep learning ; Feature (linguistics) ; Random forest ; Stiffness ; Nanotechnology ; Composite material ; Structural engineering ; Engineering Carbon Nanotubes in Composites ; Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications
Authors: Daniel M. Packwood
Source:  (preprint, 2023-02-15)
Field: Materials Science — Materials Chemistry

Data science for novel molecular materials: Applications to supramolecular chemistry and stem cell science Over the last several years, machine learning and other data science techniques have made a profound impact on computational chemistry. On the one hand, data science techniques have significantly broadened the scope of molecular simulation , allowing us to study more complex materials over longer time scales. On the other, these techniques have allowed us to extract obscure structure function correlations from molecule databases, which can subsequently be used to design new functional materials. In this presentation, I will illustrate these points by introducing two topics from our group's research. The first topic concerns the formation of supramolecular clusters via on surface molecular self assembly. By utilizing machine learned intermolecular potentials, we have developed new methodology for simulating the molecular self assembly process and predicting what kinds of supra molecular clusters will form. This simulation enables one to screen different types of molecules for the purposes of designing new supramolecular materials with novel functionality. The second topic concerns the generation of cardiac tissue from human stem cells. We have developed a machine learned model which can predict whether a particular organic compound will induce cardiac differentiation when applied to a stem cell, on the basis of molecule shape and hydrophilicity information extracted from a small chemical database. This talk is intended for general audiences who wish to know something about how data science can be applied in chemistry. Supramolecular chemistry ; Presentation (obstetrics) ; Computer science ; Nanotechnology ; Intermolecular force ; Scope (computer science) ; Function (biology) ; Chemistry ; Data science ; Molecule ; Materials science ; Biology ; Organic chemistry Machine Learning in Materials Science ; Supramolecular Self-Assembly in Materials ; Fullerene Chemistry and Applications
Authors: Arsalan Hahsemi | Reza Khakpour | Amir Mahdian | Michael Busch | Pekka Peljo | Kari Laasonen
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2023-05-20)
Field: Chemistry — Electrochemistry

Density Functional Theory and Machine Learning for Electrochemical Square-Scheme Prediction: An Application to Quinone-type Molecules Relevant to Redox Flow Batteries The uploaded data contains (i) "<strong>01_Data</strong>" optimized molecular structure in XYZ format and the primary attributes and SMILES, (ii) "<strong>02_Datasets</strong>" datasets used in the publication, and (iv) "<strong>03_pynb_script</strong>" a Jupyter-Notebook. The <strong>01_Data </strong>directory contains more than 8000 subdirectories. Each is for a molecule that undergoes a two-proton two-electron transfer reaction. In each subdirectory, one finds the following files: (1) directories named corresponding to the ones in Figure 1 of the paper. Inside each, there are geometries and properties in XYZ and CSV format, respectively. (2)<strong> "freeEnergy.dat" </strong>contains the free energy of different states. (3) <strong>"schemesquare.dat" </strong>has the parameters of the electrochemical scheme of square representation. ├── A<br> │ ├── info.csv<br> │ └── pos.xyz<br> ├── A1-<br> │ ├── info.csv<br> │ └── pos.xyz<br> ├── A2-<br> │ ├── info.csv<br> │ └── pos.xyz<br> ├── AH<br> │ ├── info.csv<br> │ └── pos.xyz<br> ├── AH1+<br> │ ├── info.csv<br> │ └── pos.xyz<br> ├── AH1-<br> │ ├── info.csv<br> │ └── pos.xyz<br> ├── AH2<br> │ ├── info.csv<br> │ └── pos.xyz<br> ├── AH21+<br> │ ├── info.csv<br> │ └── pos.xyz<br> ├── AH22+<br> │ ├── info.csv<br> │ └── pos.xyz<br> ├── <strong>freeEnergy.dat</strong><br> └── <strong>schemesquare.dat</strong><br> ************************************ <em><strong>In the new version (v. 1.1), the following updates can be found:</strong></em><br> i) The DFT calculations workflow in the "<strong>04_workflow_of_DFT</strong>" folder ii) The Python scripts for DB extraction in the "<strong>05_how_to_extracted_data</strong>" folder iii) Explanation for Pourbaix diagram computation in the "<strong>06_how_to_compute_Pourbaix_diagram</strong>" folder By the way, all of the changes were implemented in accordance with the Digital Discovery Journal Referee. We'd like to thank him/her here. Quinone ; Density functional theory ; Redox ; Electrochemistry ; Scheme (mathematics) ; Molecule ; Flow (mathematics) ; Computer science ; Square (algebra) ; Chemistry ; Materials science ; Computational chemistry ; Chemical physics ; Mechanics ; Mathematics ; Physics ; Electrode ; Inorganic chemistry ; Organic chemistry ; Mathematical analysis ; Physical chemistry ; Geometry Electrochemical Analysis and Applications ; Fuel Cells and Related Materials ; Electrocatalysts for Energy Conversion
Authors: Zihao Wang | Yongqiang Chen | Yang Duan | Weijiang Li | Bo Han | James Cheng | Hanghang Tong
Source: arXiv (Cornell University) (preprint, 2023-10-04)
Field: Materials Science — Materials Chemistry

Towards out-of-distribution generalizable predictions of chemical kinetics properties Machine Learning (ML) techniques have found applications in estimating chemical kinetic properties. With the accumulated drug molecules identified through "AI4drug discovery", the next imperative lies in AI-driven design for high-throughput chemical synthesis processes, with the estimation of properties of unseen reactions with unexplored molecules. To this end, the existing ML approaches for kinetics property prediction are required to be Out-Of-Distribution (OOD) generalizable. In this paper, we categorize the OOD kinetic property prediction into three levels (structure, condition, and mechanism), revealing unique aspects of such problems. Under this framework, we create comprehensive datasets to benchmark (1) the state-of-the-art ML approaches for reaction prediction in the OOD setting and (2) the state-of-the-art graph OOD methods in kinetics property prediction problems. Our results demonstrated the challenges and opportunities in OOD kinetics property prediction. Our datasets and benchmarks can further support research in this direction. Categorization ; Property (philosophy) ; Computer science ; Benchmark (surveying) ; Kinetics ; Graph ; Artificial intelligence ; Machine learning ; Biological system ; Theoretical computer science ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Chenglong Qin | Jinde Liu | Shiyin Ma | Jiguang Du | Gang Jiang | Liang Zhao
Source: arXiv (Cornell University) (preprint, 2023-11-20)
Field: Materials Science — Materials Chemistry

Enhancing crystal structure prediction by combining computational and experimental data via graph networks Crystal structure prediction (CSP) stands as a powerful tool in materials science, driving the discovery and design of innovative materials. However, existing CSP methods heavily rely on formation enthalpies derived from density functional theory (DFT) calculations, often overlooking differences between DFT and experimental values. Moreover, material synthesis is intricately influenced by factors such as kinetics and experimental conditions. To overcome these limitations, a novel collaborative approach was proposed for CSP that combines DFT with experimental data, utilizing advanced deep learning models and optimization algorithms. We illustrate the capability to predict formation enthalpies that closely align with actual experimental observations through the transfer learning on experimental data. By incorporating experimental synthesizable information of crystals, our model is capable of reverse engineering crystal structures that can be synthesized in experiments. Applying the model to 17 representative compounds, the results indicate that the model can accurately identify experimentally synthesized structures with high precision. Moreover, the obtained formation enthalpies and lattice constants closely align with experimental values, underscoring the model's effectiveness. The synergistic approach between theoretical and experimental data bridges the longstanding disparities between theoretical predictions and experimental results, thereby alleviating the demand for extensive and costly experimental trials. Experimental data ; Computer science ; Biological system ; Algorithm ; Materials science ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Edirisuriya M. Dilanga Siriwardane | Yong Zhao | Jianjun Hu
Source: ChemRxiv (preprint, 2023-02-21)
Field: Materials Science — Materials Chemistry

Data-Driven Deep Generative Design of Stable Spintronic Materials Discovering novel magnetic materials is essential for advancing the spintronic technology with significant applications in data communication, data storage, quantum computing, and etc. While Density functional theory (DFT) has been widely used for designing materials, its high computational demand for estimating the magnetic ground states of even a single material limits its ability to explore the vast chemical design space for finding the right materials for spintronic applications. In this work, we developed a computational framework combining generative adversarial networks (GAN), machine learning (ML) classifiers, and DFT for de novo magnetic material discovery. We used the CubicGAN generative crystal structure design model for creating new ternary cubic structures. Machine learning classifiers were developed with around 90% accuracy to screen candidate ternary magnetic materials, which are then subject to DFT based stability validation. Our calculations discovered and confirmed that Na6TcO6, K6TcO6, and BaCuF6 are stable ferromagnetic compounds, while Rb6IrO6 is a stable antiferromagnetic material. Moreover, Na6TcO6 and BaCuF6 are found to be half metals that are highly favorable for spintronic applications. Due to the structural differences, A6MO6 materials have a higher thermal capacity (Cv) compared to BaCuF6. At 300 K temperature, Cv of A6MO6 materials is around 1100 J/K.mol and that of BaCuF6 is about 176 J/K.mol. This work demonstrates the promising potential of deep generative design for discovering novel functional materials. Spintronics ; Ternary operation ; Antiferromagnetism ; Ferromagnetism ; Density functional theory ; Materials science ; Generative grammar ; Artificial intelligence ; Computer science ; Machine learning ; Nanotechnology ; Condensed matter physics ; Physics ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Magnetic properties of thin films
Authors: Pandu Wisesa | Meng Li | Matthew T. Curnan | Jeong Woo Han | Judith C. Yang | Wissam A. Saidi
Source: arXiv (Cornell University) (preprint, 2023-08-23)
Field: Materials Science — Materials Chemistry

Atomic scale understanding of initial Cu-Ni oxidation from machine-learning accelerated first-principles simulations and in situ TEM experiments The development of accurate methods for determining how alloy surfaces spontaneously restructure under reactive and corrosive environments is a key, long-standing, grand challenge in materials science. Current oxidation models, such as Cabrera-Mott, are based on macroscopic empirical knowledge that lacks fundamental insight at the atomic level. Using machine learning-accelerated density functional theory with in situ environmental transmission electron microscopy (ETEM), we examine the interplay between surface reconstructions and preferential segregation tendencies of CuNi(100) surfaces under oxidation conditions. Our modeling approach based on molecular dynamics and grand canonical Monte Carlo simulations shows that oxygen-induced Ni segregation in CuNi alloy favors Cu(100)-O c(2x2) reconstruction and destabilizes the Cu(100)-O missing row reconstruction. The underpinnings of these stabilization tendencies are rationalized based on the similar atomic coordination and bond lengths in NiO rock salt and Cu(100)-O c(2x2) structures. In situ ETEM experiments show Ni segregation followed by NiO nucleation and growth in regions without MRR, with secondary nucleation and growth of Cu2O in MRR regions. This further corroborates the simulated surface oxidation and segregation modelling outcomes. Our findings are general and are expected to extend to other alloy systems. Nucleation ; Non-blocking I/O ; Atomic units ; Materials science ; Alloy ; In situ ; Chemical physics ; Transmission electron microscopy ; Kinetic Monte Carlo ; Molecular dynamics ; Monte Carlo method ; Nanotechnology ; Metallurgy ; Chemistry ; Computational chemistry ; Physics ; Thermodynamics ; Catalysis Copper-based nanomaterials and applications ; Nanoporous metals and alloys ; Electron and X-Ray Spectroscopy Techniques
Authors: Vahe Gharakhanyan | José Antonio Garrido Torres | Alexander Urban
Source: ECS Meeting Abstracts (article, 2023-08-28)
Field: Materials Science — Materials Chemistry

Towards Predicting Metal Extraction from Oxides with Machine-Learning Augmented First-Principles Calculations The experimental characterization of high-temperature (electro)chemistry for the extraction of metals from their ores or spent electronics is challenging and requires specialized equipment. CALPHAD simulations can be an alternative but require assessed phase diagrams for the system of interest. On the other hand, first-principles calculations can provide robust estimates of many materials properties at zero Kelvin without experimental input, but simulating processes at high temperatures is computationally demanding and often too approximate. Here, we discuss the initial steps toward the efficient computational prediction of chemical reactions and process conditions using machine-learning augmented first-principles calculations. We show that a combination of zero-Kelvin density-functional theory (DFT) calculations and machine-learning models trained on temperature-dependent reaction-free energies allows predicting reduction temperatures of metal oxides (Garrido Torres et al., Nat. Commun. 12 , 7012, 2021). An application to crystalline binary and ternary oxides demonstrates that the temperature dependence of the free energy can be cross-learned, removing the limitation to compounds that have previously been thermodynamically assessed. We also show recent progress in extending this approach to the prediction of melting temperatures. Ternary operation ; CALPHAD ; Density functional theory ; Binary number ; Process (computing) ; Phase diagram ; Materials science ; Computer science ; Thermodynamics ; Phase (matter) ; Machine learning ; Chemistry ; Computational chemistry ; Physics ; Mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Kasper Tolborg | Aron Walsh
Source: ChemRxiv (preprint, 2023-11-09)
Field: Materials Science — Materials Chemistry

Low-cost Vibrational Free Energies in Solid Solutions with Machine Learning Force Fields The rational design of alloys and solid solutions relies on accurate computational predictions of phase diagrams. The cluster expansion method with effective cluster interactions fitted to energies from first principles calculations has proven to be a valuable tool for studying disordered crystals. However, the effects of vibrational entropy are commonly neglected due to their additional computational cost. Here, we devise a method for including vibrational free energy in cluster expansions at a very low computational cost by fitting a machine learning force field (MLFF) to the relaxation trajectories already available from the cluster expansion construction. We demonstrate our method for two (pseudo)binary systems, Na1-xKxCl and Ag1-xPd_x, for which accurate phonon dispersions and vibrational free energies are derived from the MLFF. For both systems, inclusion of vibrational effects results in significantly better agreement with miscibility gaps in experimental phase diagrams. This methodology can allow routine inclusion of vibrational effects in calculated compositional phase diagrams, and thus more accurate predictions of properties and stability for materials mixtures. Statistical physics ; Cluster expansion ; Phase diagram ; Cluster (spacecraft) ; Phonon ; Vibrational energy relaxation ; Materials science ; Phase (matter) ; Computer science ; Physics ; Thermodynamics ; Condensed matter physics ; Molecule ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; nanoparticles nucleation surface interactions
Authors: Mao Su | Shufei Zhang | Taoyong Cui | Chenyu Tang | Yuqiang Li | Yuhan Dong | Xin-Gao Gong | Wanli Ouyang | Lei Bai
Source: Research Square (preprint, 2023-10-23)
Field: Materials Science — Materials Chemistry

GPIP: Geometry-enhanced Pre-training on Interatomic Potentials Abstract Machine learning interatomic potentials (MLIPs) enables molecular dynamics (MD) simulations with ab initio accuracy and has been applied to various fields of physical science. However, the performance and transferability of MLIPs are limited by insufficient labeled training data, which require expensive ab initio calculations to obtain the labels, especially for complex molecular systems. To address this challenge, we design a novel geometric structure learning paradigm that consists of two stages. We first generate a large quantity of 3D configurations of target molecular system with classical molecular dynamics simulations. Then, we propose geometry-enhanced self-supervised learning consisting of masking, denoising, and contrastive learning to better capture the topology and 3D geometric information from the unlabeled 3D configurations. We evaluate our method on various benchmarks ranging from small molecule datasets to complex periodic molecular systems with more types of elements. The experimental results show that the proposed pre-training method can greatly enhance the accuracy of MLIPs with few extra computational costs and works well with different invariant or equivariant graph neural network architectures. Our method improves the generalization capability of MLIPs and helps to realize accurate MD simulations for complex molecular systems. Computer science ; Molecular dynamics ; Invariant (physics) ; Transferability ; Generalization ; Ab initio ; Artificial neural network ; Artificial intelligence ; Ranging ; Molecular graph ; Topology (electrical circuits) ; Graph ; Algorithm ; Theoretical computer science ; Machine learning ; Physics ; Computational chemistry ; Mathematics ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Pablo Calvo-Barlés | Sergio G. Rodrigo | Eduardo Sánchez-Burillo | L. Martı́n-Moreno
Source: arXiv (Cornell University) (preprint, 2023-07-25)
Field: Chemistry — Spectroscopy

Finding discrete symmetry groups via Machine Learning We introduce a machine-learning approach (denoted Symmetry Seeker Neural Network) capable of automatically discovering discrete symmetry groups in physical systems. This method identifies the finite set of parameter transformations that preserve the system's physical properties. Remarkably, the method accomplishes this without prior knowledge of the system's symmetry or the mathematical relationships between parameters and properties. Demonstrating its versatility, we showcase examples from mathematics, nanophotonics, and quantum chemistry. Symmetry (geometry) ; Artificial neural network ; Set (abstract data type) ; Symmetry group ; Discrete symmetry ; Computer science ; Quantum ; Symmetry operation ; Physical system ; Nanophotonics ; Artificial intelligence ; Homogeneous space ; Algebra over a field ; Theoretical physics ; Topology (electrical circuits) ; Theoretical computer science ; Mathematics ; Pure mathematics ; Physics ; Quantum mechanics ; Geometry ; Combinatorics ; Optics Molecular spectroscopy and chirality ; Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Paul J. Atzberger
Source: The Journal of Open Source Software (article, 2023-09-14)
Field: Materials Science — Materials Chemistry

MLMOD: Machine Learning Methods for Data-Driven Modeling in LAMMPS MLMOD is a software package for incorporating machine learning approaches and models into simulations of microscale mechanics and molecular dynamics in LAMMPS. Recent machine learning approaches provide promising data-driven approaches for learning representations for system behaviors from experimental data and high fidelity simulations. The package facilitates learning and using data-driven models for (i) dynamics of the system at larger spatial-temporal scales (ii) interactions between system components, (iii) features yielding coarser degrees of freedom, and (iv) features for new quantities of interest characterizing system behaviors. MLMOD provides hooks in LAMMPS for (i) modeling dynamics and time-step integration, (ii) modeling interactions, and (iii) computing quantities of interest characterizing system states. The package allows for use of machine learning methods with general model classes including Neural Networks, Gaussian Process Regression, Kernel Models, and other approaches. Here we discuss our prototype C++/Python package, aims, and example usage. For related papers, examples, updates, and additional information see https://github.com/atzberg/mlmod and http://atzberger.org/. Python (programming language) ; Computer science ; Artificial intelligence ; Machine learning ; Programming language Machine Learning in Materials Science ; Protein Structure and Dynamics ; Gaussian Processes and Bayesian Inference