jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yagyank Srivastava | Ankit Jain
Source: Journal of Applied Physics (article, 2023-12-11)
Field: Materials Science — Materials Chemistry

End-to-end material thermal conductivity prediction through machine learning We investigated the accelerated prediction of the thermal conductivity of materials through end-to-end structure-based approaches employing machine learning methods. Due to the non-availability of high-quality thermal conductivity data, we first performed high-throughput calculations based on first principles and the Boltzmann transport equation for 225 materials, effectively more than doubling the size of the existing dataset. We assessed the performance of state-of-the-art machine learning models for thermal conductivity prediction on this expanded dataset and observed that all these models suffered from overfitting. To address this issue, we introduced a different graph-based neural network model, which demonstrated more consistent and regularized performance across all evaluated datasets. Nevertheless, the best mean absolute percentage error achieved on the test dataset remained in the range of 50–60%. This suggests that while these models are valuable for expediting material screening, their current accuracy is still limited. Overfitting ; Thermal conductivity ; Expediting ; Machine learning ; Computer science ; Artificial neural network ; Artificial intelligence ; Support vector machine ; Graph ; Algorithm ; Materials science ; Engineering ; Theoretical computer science Machine Learning in Materials Science ; Thermal properties of materials ; Nuclear Materials and Properties
Authors: Shuo Zhang | Yang Liu | Lei Xie
Source: Scientific Reports (article, 2023-11-06)
Field: Materials Science — Materials Chemistry

A universal framework for accurate and efficient geometric deep learning of molecular systems Molecular sciences address a wide range of problems involving molecules of different types and sizes and their complexes. Recently, geometric deep learning, especially Graph Neural Networks, has shown promising performance in molecular science applications. However, most existing works often impose targeted inductive biases to a specific molecular system, and are inefficient when applied to macromolecules or large-scale tasks, thereby limiting their applications to many real-world problems. To address these challenges, we present PAMNet, a universal framework for accurately and efficiently learning the representations of three-dimensional (3D) molecules of varying sizes and types in any molecular system. Inspired by molecular mechanics, PAMNet induces a physics-informed bias to explicitly model local and non-local interactions and their combined effects. As a result, PAMNet can reduce expensive operations, making it time and memory efficient. In extensive benchmark studies, PAMNet outperforms state-of-the-art baselines regarding both accuracy and efficiency in three diverse learning tasks: small molecule properties, RNA 3D structures, and protein-ligand binding affinities. Our results highlight the potential for PAMNet in a broad range of molecular science applications. Computer science ; Limiting ; Benchmark (surveying) ; Range (aeronautics) ; Artificial intelligence ; Affinities ; Binding affinities ; Graph ; Deep learning ; Theoretical computer science ; Machine learning ; Chemistry ; Materials science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Atsuto Seko
Source: Journal of Applied Physics (article, 2023-01-03)
Field: Materials Science — Materials Chemistry

Tutorial: Systematic development of polynomial machine learning potentials for elemental and alloy systems Machine learning potentials (MLPs) developed from extensive datasets constructed from density functional theory calculations have become increasingly appealing to many researchers. This paper presents a framework of polynomial-based MLPs, called polynomial MLPs. The systematic development of accurate and computationally efficient polynomial MLPs for many elemental and binary alloy systems and their predictive powers for various properties are also demonstrated. Consequently, many polynomial MLPs are available in a repository website [A. Seko, Polynomial Machine Learning Potential Repository at Kyoto University, https://sekocha.github.io]. The repository will help many scientists perform accurate and efficient large-scale atomistic simulations and crystal structure searches. Computer science ; Polynomial ; Binary number ; Artificial intelligence ; Theoretical computer science ; Machine learning ; Mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallography and molecular interactions
Authors: Peter Eastman | Raimondas Galvelis | Raúl P. Peláez | Charlles R. A. Abreu | Stephen E. Farr | Emilio Gallicchio | Anton Gorenko | Michael M. Henry | Frank Hu | Jing Huang | Andreas Krämer | Julien Michel | Joshua A. Mitchell | Vijay S. Pande | João PGLM Rodrigues | Jaime Rodrı́guez-Guerra | Andrew C. Simmonett | Singh, Sukrit | Jason Swails | Turner, Philip | Wang, Yuanqing | Ivy Zhang | John D. Chodera | Gianni De Fabritiis | Thomas E. Markland
Source: arXiv (Cornell University) (preprint, 2023-10-04)
Field: Materials Science — Materials Chemistry

OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use of machine learning potentials. Arbitrary PyTorch models can be added to a simulation and used to compute forces and energy. A higher-level interface allows users to easily model their molecules of interest with general purpose, pretrained potential functions. A collection of optimized CUDA kernels and custom PyTorch operations greatly improves the speed of simulations. We demonstrate these features on simulations of cyclin-dependent kinase 8 (CDK8) and the green fluorescent protein (GFP) chromophore in water. Taken together, these features make it practical to use machine learning to improve the accuracy of simulations at only a modest increase in cost. Computer science ; Molecular dynamics ; CUDA ; Interface (matter) ; Artificial intelligence ; Computational science ; Molecular machine ; Parallel computing ; Chemistry ; Nanotechnology ; Materials science ; Computational chemistry Machine Learning in Materials Science ; Protein Structure and Dynamics ; Scientific Computing and Data Management
Authors: Ethan M. Sunshine | Muhammed Shuaibi | Zachary W. Ulissi | John R. Kitchin
Source: The Journal of Physical Chemistry C (article, 2023-11-27)
Field: Materials Science — Materials Chemistry

Chemical Properties from Graph Neural Network-Predicted Electron Densities According to density functional theory, any chemical property can be inferred from the electron density, making it the most informative attribute of an atomic structure. In this work, we demonstrate the use of established physical methods to obtain important chemical properties from model-predicted electron densities. We introduce graph neural network architectural choices that provide physically relevant and useful electron density predictions. Despite not being trained to predict atomic charges, the model is able to predict atomic charges with an error of an order of magnitude lower than that of a sum of atomic charge densities. Similarly, the model predicts dipole moments with half the error of the sum of the atomic charge densities method. We demonstrate that larger data sets lead to more useful predictions for these tasks. These results pave the way for an alternative path in atomistic machine learning where data-driven approaches and existing physical methods are used in tandem to obtain a variety of chemical properties in an explainable and self-consistent manner. Dipole ; Artificial neural network ; Electron density ; Computer science ; Statistical physics ; Graph ; Electron ; Property (philosophy) ; Charge density ; Physics ; Biological system ; Machine learning ; Theoretical computer science ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Yousef A. Alghofaili | Mohammed Alghadeer | Abdulmohsen Alsaui | Saad M. Alqahtani | Fahhad H. Alharbi
Source: The Journal of Physical Chemistry C (article, 2023-08-11)
Field: Materials Science — Materials Chemistry

Accelerating Materials Discovery through Machine Learning: Predicting Crystallographic Symmetry Groups Predicting crystal structure from the chemical composition is one of the most challenging and long-standing problems in condensed matter physics. This problem resides at the interface between materials sciences and physics. With reliable data and proper physics-guided modeling, machine learning (ML) can provide an alternative venue to undertake and reduce the problem’s complexity. In this work, very robust ML classifiers for crystallographic symmetry groups were developed and applied for ternary (AlBmCn) and binary (AlBm) materials starting only from the chemical formula. This is the first essential step toward predicting full geometry. Such a problem is highly multi-label and multi-class from an ML perspective and requires careful preprocessing due to the size imbalance of the data. The resulting predictive models are highly accurate for all symmetry groups, including crystal systems, point groups, Bravais lattices, and space groups, with weighted balanced accuracies exceeding 95%. The models were developed with only a small set of ionic and compositional features, namely, stoichiometry, ionic radii, ionization energies, and oxidation states for each element in the ternary and binary compounds. Considering such minimal feature space, the obtained high accuracies ascertain that the physics is well captured. This is even further confirmed as we demonstrate that the accuracy of our approach is limited only by the size of data by comparing the size of ternary and binary materials with the accuracy of developed models. The presented work could effectively contribute to accelerating new materials discovery and development. Ternary operation ; Binary number ; Symmetry (geometry) ; Crystal structure prediction ; Chemical space ; Computer science ; Ionic bonding ; Machine learning ; Statistical physics ; Physics ; Materials science ; Crystal structure ; Artificial intelligence ; Geometry ; Mathematics ; Chemistry ; Crystallography ; Quantum mechanics ; Ion Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Materials Characterization Techniques
Authors: Zhihao Huang | Hanxige Chen | Songbo Ye | Guotan Liu | Han Chen | Yudong Fu | Yibo Sun | Mufu Yan
Source: Materials Chemistry and Physics (article, 2023-02-25)
Field: Materials Science — Materials Chemistry

Discovery of novel low modulus Nb–Ti–Zr biomedical alloys via combined machine learning and first principles approach Ternary operation ; Materials science ; Niobium ; Modulus ; Bulk modulus ; Random forest ; Elastic modulus ; Valence (chemistry) ; Valence electron ; Shape-memory alloy ; Machine learning ; Computer science ; Electron ; Metallurgy ; Composite material ; Chemistry ; Physics Titanium Alloys Microstructure and Properties ; Machine Learning in Materials Science ; Advanced materials and composites
Authors: Zun Wang | Hong-Fei Wu | Lixin Sun | Xinheng He | Zhirong Liu | Bin Shao | Tong Wang | Tie‐Yan Liu
Source: The Journal of Chemical Physics (article, 2023-07-17)
Field: Materials Science — Materials Chemistry

Improving machine learning force fields for molecular dynamics simulations with fine-grained force metrics Machine learning force fields (MLFFs) have gained popularity in recent years as they provide a cost-effective alternative to ab initio molecular dynamics (MD) simulations. Despite a small error on the test set, MLFFs inherently suffer from generalization and robustness issues during MD simulations. To alleviate these issues, we propose global force metrics and fine-grained metrics from element and conformation aspects to systematically measure MLFFs for every atom and every conformation of molecules. We selected three state-of-the-art MLFFs (ET, NequIP, and ViSNet) and comprehensively evaluated on aspirin, Ac-Ala3-NHMe, and Chignolin MD datasets with the number of atoms ranging from 21 to 166. Driven by the trained MLFFs on these molecules, we performed MD simulations from different initial conformations, analyzed the relationship between the force metrics and the stability of simulation trajectories, and investigated the reason for collapsed simulations. Finally, the performance of MLFFs and the stability of MD simulations can be further improved guided by the proposed force metrics for model training, specifically training MLFF models with these force metrics as loss functions, fine-tuning by reweighting samples in the original dataset, and continued training by recruiting additional unexplored data. Force field (fiction) ; Molecular dynamics ; Computer science ; Robustness (evolution) ; Stability (learning theory) ; Generalization ; Set (abstract data type) ; Machine learning ; Artificial intelligence ; Computational chemistry ; Chemistry ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Kianoosh Sattari | Lucas Eddy | Jacob L. Beckham | Kevin M. Wyss | Richard Byfield | Long Qian | James M. Tour | Jian Lin
Source: Digital Discovery (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

A scientific machine learning framework to understand flash graphene synthesis The SML model was trained on both direct experimental and indirect physics-informed features to predict graphene quality synthesized from Flash Joule heating. With an R 2 of 0.81, the model performs better compared to 0.73 without indirect features. Flash (photography) ; Graphene ; Joule heating ; Joule (programming language) ; Computer science ; Quality (philosophy) ; Nanotechnology ; Artificial intelligence ; Materials science ; Physics ; Engineering ; Electrical engineering ; Quantum mechanics ; Composite material ; Optics ; Efficient energy use Graphene research and applications ; Machine Learning in Materials Science ; Thermal properties of materials
Authors: A. Flanagan | Frank G. Glavin
Source: Journal of Chemical Information and Modeling (article, 2023-10-11)
Field: Chemistry — Analytical Chemistry

A Comparative Analysis of Data Synthesis Techniques to Improve Classification Accuracy of Raman Spectroscopy Data Raman spectra are examples of high dimensional data that can often be limited in the number of samples. This is a primary concern when Deep Learning frameworks are developed for tasks such as chemical species identification, quantification, and diagnostics. Open-source data are difficult to obtain and often sparse; furthermore, the collecting and curating of new spectra require expertise and resources. Deep generative modeling utilizes Deep Learning architectures to approximate high dimensional distributions and aims to generate realistic synthetic data. The evaluation of the data and the performance of the deep models is usually conducted on a per-task basis and provides no indication of an increase to robustness, or generalization, on a wider scale. In this study, we compare the benefits and limitations of a standard statistical approach to data synthesis (<i>weighted blending</i>) with a popular deep generative model, the <i>Variational Autoencoder</i>. Two binary data sets are divided into 3-fold to simulate small, limited samples. Synthetic data distributions are created per fold using the two methods and then augmented into the training of two Deep Learning algorithms, a <i>Convolutional Neural Network</i> and a <i>Fully-Connected Neural Network</i>. The goal of this study is to observe the trends in learning as synthetic data are continually augmented to the training data in increasing batches. To determine the impact of each synthetic method, <i>Principal Component Analysis</i> and the <i>discrete Fréchet distance</i> are implemented to visualize and measure the distance between the source and synthetic distributions along with the Machine Learning metric <i>balanced accuracy</i> for evaluating performance on imbalanced data. Computer science ; Deep learning ; Artificial intelligence ; Synthetic data ; Autoencoder ; Robustness (evolution) ; Binary classification ; Artificial neural network ; Convolutional neural network ; Generative model ; Principal component analysis ; Machine learning ; Pattern recognition (psychology) ; Metric (unit) ; Data mining ; Generative grammar ; Support vector machine Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Currency Recognition and Detection
Authors: Yi Shen | Shuze Zhu
Source: Computational Materials Science (article, 2023-02-15)
Field: Materials Science — Materials Chemistry

Machine learning mechanical properties of defect-engineered hexagonal boron nitride Materials science ; Hexagonal boron nitride ; Boron nitride ; Hexagonal crystal system ; Nanotechnology ; Convolutional neural network ; Vacancy defect ; Doping ; Artificial intelligence ; Computer science ; Optoelectronics ; Chemistry ; Crystallography ; Graphene Graphene research and applications ; Boron and Carbon Nanomaterials Research ; MXene and MAX Phase Materials
Authors: Thomas Dufils | Lisanne Knijff | Yunqi Shao | Chao Zhang
Source: Journal of Chemical Theory and Computation (article, 2023-07-21)
Field: Materials Science — Materials Chemistry

PiNNwall: Heterogeneous Electrode Models from Integrating Machine Learning and Atomistic Simulation Electrochemical energy storage always involves the capacitive process. The prevailing electrode model used in the molecular simulation of polarizable electrode-electrolyte systems is the Siepmann-Sprik model developed for perfect metal electrodes. This model has been recently extended to study the metallicity in the electrode by including the Thomas-Fermi screening length. Nevertheless, a further extension to heterogeneous electrode models requires introducing chemical specificity, which does not have any analytical recipes. Here, we address this challenge by integrating the atomistic machine learning code (PiNN) for generating the base charge and response kernel and the classical molecular dynamics code (MetalWalls) dedicated to the modeling of electrochemical systems, and this leads to the development of the PiNNwall interface. Apart from the cases of chemically doped graphene and graphene oxide electrodes as shown in this study, the PiNNwall interface also allows us to probe polarized oxide surfaces in which both the proton charge and the electronic charge can coexist. Therefore, this work opens the door for modeling heterogeneous and complex electrode materials often found in energy storage systems. Electrode ; Graphene ; Computer science ; Oxide ; Materials science ; Electrolyte ; Nanotechnology ; Standard electrode potential ; Molecular dynamics ; Interface (matter) ; Chemical physics ; Chemistry ; Physical chemistry Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Electrocatalysts for Energy Conversion
Authors: Martin Šípka | Andreas Erlebach | Lukáš Grajciar
Source: Journal of Chemical Theory and Computation (article, 2023-01-25)
Field: Materials Science — Materials Chemistry

Constructing Collective Variables Using Invariant Learned Representations On the time scales accessible to atomistic numerical modeling, chemical reactions are considered rare events. Therefore, the atomistic simulations are commonly biased along a low-dimensional representation of a chemical reaction in an atomic structure space, i.e., along the collective variables. However, suitable collective variables are often complicated to guess <i>a priori</i>. We propose a novel method of collective variable discovery based on dimensionality reduction of the atomic representation vectors. These linear-scaling and invariant representations can be either fixed (untrained) or learned by supervised training of the end-to-end machine learning potential. The learned representations are expected to reflect not only the structural but also the energetic features of the system that are transferable to all of the reactive transformation covered by the machine learning potential. We demonstrate our approach on four high-barrier reactions ranging from a simple gas-phase hydrogen jump reaction to complex reactions in periodic models of industrially relevant heterogeneous catalysts. High data efficiency, automatized feature extraction, favorable scaling, and retention of inherent invariances are all properties that are expected to enable fast and largely automatic construction of suitable collective variables even in highly complex reactive scenarios such as reactive/catalytic transformations at solid-liquid interfaces. Invariant (physics) ; Computer science ; Data science ; Theoretical computer science ; Mathematics ; Mathematical physics Machine Learning in Materials Science ; Topic Modeling ; Neural Networks and Applications
Authors: Aaron Garrison | Javier Heras‐Domingo | John R. Kitchin | Gabriel dos Passos Gomes | Zachary W. Ulissi | Samuel M. Blau
Source: Journal of Chemical Information and Modeling (article, 2023-12-04)
Field: Materials Science — Materials Chemistry

Applying Large Graph Neural Networks to Predict Transition Metal Complex Energies Using the tmQM_wB97MV Data Set Machine learning (ML) methods have shown promise for discovering novel catalysts but are often restricted to specific chemical domains. Generalizable ML models require large and diverse training data sets, which exist for heterogeneous catalysis but not for homogeneous catalysis. The tmQM data set, which contains properties of 86,665 transition metal complexes calculated at the TPSSh/def2-SVP level of density functional theory (DFT), provided a promising training data set for homogeneous catalyst systems. However, we find that ML models trained on tmQM consistently underpredict the energies of a chemically distinct subset of the data. To address this, we present the tmQM_wB97MV data set, which filters out several structures in tmQM found to be missing hydrogens and recomputes the energies of all other structures at the ωB97M-V/def2-SVPD level of DFT. ML models trained on tmQM_wB97MV show no pattern of consistently incorrect predictions and much lower errors than those trained on tmQM. The ML models tested on tmQM_wB97MV were, from best to worst, GemNet-T > PaiNN ≈ SpinConv > SchNet. Performance consistently improves when using only neutral structures instead of the entire data set. However, while models saturate with only neutral structures, more data continue to improve the models when including charged species, indicating the importance of accurately capturing a range of oxidation states in future data generation and model development. Furthermore, a fine-tuning approach in which weights were initialized from models trained on OC20 led to drastic improvements in model performance, indicating transferability between ML strategies of heterogeneous and homogeneous systems. Transferability ; Homogeneous ; Computer science ; Set (abstract data type) ; Training set ; Data set ; Artificial neural network ; Density functional theory ; Graph ; Machine learning ; Biological system ; Artificial intelligence ; Chemistry ; Computational chemistry ; Theoretical computer science ; Statistical physics ; Physics Machine Learning in Materials Science ; Topic Modeling ; Catalysis and Oxidation Reactions