jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hongwei Du | Jiamin Wang | Jian Hui | Lanting Zhang | Hong Wang
Source: npj Computational Materials (article, 2024-12-19)
Field: Materials Science — Materials Chemistry

DenseGNN: universal and scalable deeper graph neural networks for high-performance property prediction in crystals and molecules Modern generative models based on deep learning have made it possible to design millions of hypothetical materials. To screen these candidate materials and identify promising new materials, we need fast and accurate models to predict material properties. Graphical neural networks (GNNs) have become a current research focus due to their ability to directly act on the graphical representation of molecules and materials, enabling comprehensive capture of important information and showing excellent performance in predicting material properties. Nevertheless, GNNs still face several key problems in practical applications: First, although existing nested graph network strategies increase critical structural information such as bond angles, they significantly increase the number of trainable parameters in the model, resulting in a increase in training costs; Second, extending GNN models to broader domains such as molecules, crystalline materials, and catalysis, as well as adapting to small data sets, remains a challenge. Finally, the scalability of GNN models is limited by the over-smoothing problem. To address these issues, we propose the DenseGNN model, which combines Dense Connectivity Network (DCN), hierarchical node-edge-graph residual networks (HRN), and Local Structure Order Parameters Embedding (LOPE) strategies to create a universal, scalable, and efficient GNN model. We have achieved state-of-the-art performance (SOAT) on several datasets, including JARVIS-DFT, Materials Project, QM9, Lipop, FreeSolv, ESOL, and OC22, demonstrating the generality and scalability of our approach. By merging DCN and LOPE strategies into GNN models in computing, crystal materials, and molecules, we have improved the performance of models such as GIN, Schnet, and Hamnet on materials datasets such as Matbench. The LOPE strategy optimizes the embedding representation of atoms and allows our model to train efficiently with a minimal level of edge connections. This substantially reduces computational costs and shortens the time required to train large GNNs while maintaining accuracy. Our technique not only supports building deeper GNNs and avoids performance penalties experienced by other models, but is also applicable to a variety of applications that require large deep learning models. Furthermore, our study demonstrates that by using structural embeddings from pre-trained models, our model not only outperforms other GNNs in distinguishing crystal structures but also approaches the standard X-ray diffraction (XRD) method. Scalability ; Computer science ; Embedding ; Generality ; Artificial neural network ; Theoretical computer science ; Graph ; Artificial intelligence ; Machine learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Carmine Zuccarini | Karthikeyan Ramachandran | D.D. Jayaseelan
Source: APL Materials (article, 2024-09-01)
Field: Materials Science — Materials Chemistry

Material discovery and modeling acceleration via machine learning This paper delves into the transformative role of Machine Learning (ML) and Artificial Intelligence (AI) in materials science, spotlighting their capability to expedite the discovery and development of newer, more efficient, and stronger compounds. It underscores the shift from traditional, resource-intensive approaches toward data-driven methodologies that leverage large datasets to predict properties, identify new materials, and optimize synthesis conditions with a satisfactory level of accuracy. Highlighting various techniques, including supervised, unsupervised, and reinforcement learning, alongside deep learning potential, the chapter presents case studies and applications ranging from predicting stress points in stochastic fields to optimizing thermal protection systems for spacecraft re-entry. It also explores the challenges and future directions, emphasizing the need for integrating experimental validations and developing tailored algorithms to overcome data and computational constraints. The narrative showcases ML and AI’s promise in revolutionizing material discovery, paving the way for innovative solutions in science and engineering. Acceleration ; Materials science ; Computer science ; Nanotechnology ; Physics Machine Learning in Materials Science ; Mineral Processing and Grinding ; Computational Drug Discovery Methods
Authors: Hiroto Okuyama | Yuuki Sugawara | Takeo Yamaguchi
Source: ACS Applied Materials & Interfaces (article, 2024-05-03)
Field: Materials Science — Surfaces, Coatings and Films

Machine-Learning-Aided Understanding of Protein Adsorption on Zwitterionic Polymer Brushes Constructing antifouling surfaces is a crucial technique for optimizing the performance of devices such as water treatment membranes and medical devices in practical environments. These surfaces are achieved by modification with hydrophilic polymers. Notably, zwitterionic (ZI) polymers have attracted considerable interest because of their ability to form a robust hydration layer and inhibit the adsorption of foulants. However, the importance of the molecular weight and density of the ZI polymer on the antifouling property is partially understood, and the surface design still retains an empirical flavor. Herein, we individually assessed the influence of the molecular weight and density of the ZI polymer on protein adsorption through machine learning. The results corroborated that protein adsorption is more strongly influenced by density than by molecular weight. Furthermore, the distribution of predicted protein adsorption against molecular weight and polymer density enabled us to determine conditions that enhanced (or weaken) antifouling. The relevance of this prediction method was also demonstrated by estimating the protein adsorption over a wide range of ionic strengths. Overall, this machine-learning-based approach is expected to contribute as a tool for the optimized functionalization of materials, extending beyond the applications of ZI polymer brushes. Biofouling ; Adsorption ; Polymer ; Materials science ; Protein adsorption ; Surface modification ; Chemical engineering ; Polymer adsorption ; Membrane ; Nanotechnology ; Organic chemistry ; Chemistry ; Composite material Polymer Surface Interaction Studies ; Marine Biology and Environmental Chemistry ; Advanced Sensor and Energy Harvesting Materials
Authors: Thomas Edward Koker | Keegan Quigley | Eric Taw | Kevin Tibbetts | Lin Li
Source: npj Computational Materials (article, 2024-07-26)
Field: Materials Science — Materials Chemistry

Higher-order equivariant neural networks for charge density prediction in materials Abstract The calculation of electron density distribution using density functional theory (DFT) in materials and molecules is central to the study of their quantum and macro-scale properties, yet accurate and efficient calculation remains a long-standing challenge. We introduce ChargE3Net, an E(3)-equivariant graph neural network for predicting electron density in atomic systems. ChargE3Net enables the learning of higher-order equivariant features to achieve high predictive accuracy and model expressivity. We show that ChargE3Net exceeds the performance of prior work on diverse sets of molecules and materials. When trained on the massive dataset of over 100K materials in the Materials Project database, our model is able to capture the complexity and variability in the data, leading to a significant 26.7% reduction in self-consistent iterations when used to initialize DFT calculations on unseen materials. Furthermore, we show that non-self-consistent DFT calculations using our predicted charge densities yield near-DFT performance on electronic and thermodynamic property prediction at a fraction of the computational cost. Further analysis attributes the greater predictive accuracy to improved modeling of systems with high angular variations. These results illuminate a pathway towards a machine learning-accelerated ab initio calculations for materials discovery. Density functional theory ; Computer science ; Equivariant map ; Artificial neural network ; Ab initio ; Reduction (mathematics) ; Statistical physics ; Algorithm ; Artificial intelligence ; Topology (electrical circuits) ; Machine learning ; Physics ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Meijin Lu | Silin Rao | Hong Yue | Junjie Han | Jingtao Wang
Source: Crystal Growth & Design (article, 2024-06-06)
Field: Materials Science — Materials Chemistry

Recent Advances in the Application of Machine Learning to Crystal Behavior and Crystallization Process Control Crystals are integral to a variety of industrial applications, such as the development of pharmaceuticals and advancements in material science. To anticipate crystal behavior and pinpoint effective crystallization techniques, a thorough investigation of crystal structures, properties, and the associated processes is essential. However, conventional methods like experimental procedures and quantum mechanics calculations, while crucial, can be expensive and time-consuming. In response, machine learning has risen as an effective alternative, complementing the traditional approaches based on quantum mechanics and classical force fields. In the recent years, the deployment of machine learning in the realm of crystallization has yielded notable progress. This review offers a concise overview of the application of machine learning techniques in crystallization, focusing on the past five years. Our analysis of the literature indicates that machine learning has accelerated the prediction of crystal structures by streamlining the generation and evaluation of structures. Additionally, it has facilitated the prediction of key crystal properties such as solubility, melting point, and habit. The review further explores the role of machine learning in refining the control and optimization of crystallization processes, highlighting the restrictions of conventional algorithms and sensing technologies. The advantages of end-to-end processing for enhancing the accuracy of predictions and the combination of data-driven with mechanism-based models for robustness are also considered. In summary, this review provides insights into the current state of machine learning in the field of intelligent crystallization and suggests pathways for future research and development. Crystallization ; Process (computing) ; Materials science ; Process control ; Crystal (programming language) ; Computer science ; Nanotechnology ; Crystallography ; Process engineering ; Chemistry ; Engineering ; Chemical engineering ; Programming language Crystallization and Solubility Studies ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Sanlve Pei | Chaoneng Dai | Xiaohua Yang | Lijun Zhang | Haitao Wang | Shaolin Zhang | Yuexing Han | Qian Li | Jingfeng Wang
Source: Corrosion Science (article, 2024-07-27)
Field: Materials Science — Metals and Alloys

Quantitative prediction of Mg-RE-Ni alloy corrosion behavior by machine learning Corrosion ; Alloy ; Metallurgy ; Materials science Hydrogen embrittlement and corrosion behaviors in metals ; Corrosion Behavior and Inhibition ; Magnesium Alloys: Properties and Applications
Authors: Adarsh V. Kalikadien | A.H. Mirza | Aydin Najl Hossaini | A. Sreenithya | Evgeny A. Pidko
Source: ChemPlusChem (review, 2024-01-26)
Field: Materials Science — Materials Chemistry

Paving the road towards automated homogeneous catalyst design In the past decade, computational tools have become integral to catalyst design. They continue to offer significant support to experimental organic synthesis and catalysis researchers aiming for optimal reaction outcomes. More recently, data-driven approaches utilizing machine learning have garnered considerable attention for their expansive capabilities. This Perspective provides an overview of diverse initiatives in the realm of computational catalyst design and introduces our automated tools tailored for high-throughput in silico exploration of the chemical space. While valuable insights are gained through methods for high-throughput in silico exploration and analysis of chemical space, their degree of automation and modularity are key. We argue that the integration of data-driven, automated and modular workflows is key to enhancing homogeneous catalyst design on an unprecedented scale, contributing to the advancement of catalysis research. Workflow ; Modularity (biology) ; Modular design ; Computer science ; Chemical space ; Expansive ; Automation ; Throughput ; Biochemical engineering ; Systems engineering ; Nanotechnology ; Engineering ; Drug discovery ; Chemistry ; Materials science Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Authors: Lukas M. Sigmund | Shree Sowndarya S. | Andreas E. Albers | Philipp Erdmann | Robert S. Paton | Lutz Greb
Source: Angewandte Chemie International Edition (article, 2024-03-07)
Field: Materials Science — Materials Chemistry

Predicting Lewis Acidity: Machine Learning the Fluoride Ion Affinity of <i>p</i>‐Block‐Atom‐Based Molecules "How strong is this Lewis acid?" is a question researchers often approach by calculating its fluoride ion affinity (FIA) with quantum chemistry. Here, we present FIA49k, an extensive FIA dataset with 48,986 data points calculated at the RI-DSD-BLYP-D3(BJ)/def2-QZVPP//PBEh-3c level of theory, including 13 different p-block atoms as the fluoride accepting site. The FIA49k dataset was used to train FIA-GNN, two message-passing graph neural networks, which predict gas and solution phase FIA values of molecules excluded from training with a mean absolute error of 14 kJ mol<sup>-1</sup> (r<sup>2</sup>=0.93) from the SMILES string of the Lewis acid as the only input. The level of accuracy is notable, given the wide energetic range of 750 kJ mol<sup>-1</sup> spanned by FIA49k. The model's value was demonstrated with four case studies, including predictions for molecules extracted from the Cambridge Structural Database and by reproducing results from catalysis research available in the literature. Weaknesses of the model are evaluated and interpreted chemically. FIA-GNN and the FIA49k dataset can be reached via a free web app (www.grebgroup.de/fia-gnn). Fluoride ; Lewis acids and bases ; Chemistry ; Molecule ; Atom (system on chip) ; Ion ; Block (permutation group theory) ; Catalysis ; Computational chemistry ; Computer science ; Inorganic chemistry ; Mathematics ; Combinatorics ; Organic chemistry Machine Learning in Materials Science ; Inorganic Fluorides and Related Compounds ; Carbon dioxide utilization in catalysis
Authors: Zheyong Fan | Yang Xiao | Yanzhou Wang | Penghua Ying | Shunda Chen | Haikuan Dong
Source: Journal of Physics Condensed Matter (article, 2024-03-08)
Field: Materials Science — Materials Chemistry

Combining linear-scaling quantum transport and machine-learning molecular dynamics to study thermal and electronic transports in complex materials We propose an efficient approach for simultaneous prediction of thermal and electronic transport properties in complex materials. Firstly, a highly efficient machine-learned neuroevolution potential (NEP) is trained using reference data from quantum-mechanical density-functional theory calculations. This trained potential is then applied in large-scale molecular dynamics simulations, enabling the generation of realistic structures and accurate characterization of thermal transport properties. In addition, molecular dynamics simulations of atoms and linear-scaling quantum transport calculations of electrons are coupled to account for the electron-phonon scattering and other disorders that affect the charge carriers governing the electronic transport properties. We demonstrate the usefulness of this unified approach by studying electronic transport in pristine graphene and thermoelectric transport properties of a graphene antidot lattice, with a general-purpose NEP developed for carbon systems based on an extensive dataset. Scaling ; Molecular dynamics ; Statistical physics ; Quantum ; Graphene ; Electron ; Transport phenomena ; Multiscale modeling ; Phonon ; Electron transport chain ; Electronic structure ; Linear scale ; Materials science ; Physics ; Computer science ; Nanotechnology ; Condensed matter physics ; Chemistry ; Quantum mechanics ; Computational chemistry ; Mathematics Machine Learning in Materials Science ; Thermal properties of materials ; Fuel Cells and Related Materials
Authors: Joel Y. Y. Loh | Andrew Wang | Abhinav Mohan | Athanasios A. Tountas | Abdelaziz Gouda | Alexandra Tavasoli | Geoffrey A. Ozin
Source: Advanced Science (article, 2024-03-13)
Field: Materials Science — Materials Chemistry

Leave No Photon Behind: Artificial Intelligence in Multiscale Physics of Photocatalyst and Photoreactor Design Although solar fuels photocatalysis offers the promise of converting carbon dioxide directly with sunlight as commercially scalable solutions have remained elusive over the past few decades, despite significant advancements in photocatalysis band-gap engineering and atomic site activity. The primary challenge lies not in the discovery of new catalyst materials, which are abundant, but in overcoming the bottlenecks related to material-photoreactor synergy. These factors include achieving photogeneration and charge-carrier recombination at reactive sites, utilizing high mass transfer efficiency supports, maximizing solar collection, and achieving uniform light distribution within a reactor. Addressing this multi-dimensional problem necessitates harnessing machine learning techniques to analyze real-world data from photoreactors and material properties. In this perspective, the challenges are outlined associated with each bottleneck factor, review relevant data analysis studies, and assess the requirements for developing a comprehensive solution that can unlock the full potential of solar fuels photocatalysis technology. Physics-informed machine learning (or Physics Neural Networks) may be the key to advancing this important area from disparate data towards optimal reactor solutions. Bottleneck ; Photocatalysis ; Scalability ; Nanotechnology ; Solar fuel ; Computer science ; Materials science ; Biochemical engineering ; Process engineering ; Systems engineering ; Engineering ; Chemistry ; Catalysis Machine Learning in Materials Science ; Solar Radiation and Photovoltaics ; Machine Learning and ELM
Authors: Xi Shen | Peng Zhao | Cheng He | Wenxue Zhang
Source: Journal of Catalysis (article, 2024-06-14)
Field: Materials Science — Materials Chemistry

High-throughput screening of single-atom catalysts on 1 T-TMD for highly active and selective CO2 reduction reaction: Computational and machine learning insights Chemistry ; Catalysis ; Throughput ; Reduction (mathematics) ; Atom (system on chip) ; Selective reduction ; High-throughput screening ; Combinatorial chemistry ; Nanotechnology ; Organic chemistry ; Parallel computing ; Computer science ; Biochemistry Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Catalytic Processes in Materials Science
Authors: Xiaoxun Gong | Steven G. Louie | Wenhui Duan | Yong Xu
Source: Nature Computational Science (article, 2024-10-03)
Field: Materials Science — Materials Chemistry

Generalizing deep learning electronic structure calculation to the plane-wave basis Deep neural networks capable of representing the density functional theory (DFT) Hamiltonian as a function of material structure hold great promise for revolutionizing future electronic structure calculations. However, a notable limitation of previous neural networks is their compatibility solely with the atomic-orbital (AO) basis, excluding the widely used plane-wave (PW) basis. Here we overcome this critical limitation by proposing an accurate and efficient real-space reconstruction method for directly computing AO Hamiltonian matrices from PW DFT results. The reconstruction method is orders of magnitude faster than traditional projection-based methods to convert PW results to the AO basis, and the reconstructed Hamiltonian matrices can faithfully reproduce the PW electronic structure, thus bridging the longstanding gap between the AO basis deep learning electronic structure approach and PW DFT. Advantages of the PW methods, such as high accuracy, high flexibility and wide applicability, thus can be all integrated into deep learning electronic structure methods without sacrificing these methods' inherent benefits. This allows for the construction of large-scale and high-fidelity training datasets with the help of PW DFT results towards the development of precise and broadly applicable deep learning electronic structure models. Basis (linear algebra) ; Plane wave ; Plane (geometry) ; Physics ; Statistical physics ; Mathematics ; Geometry ; Optics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Chemical Physics Studies
Authors: Argyrios Karatrantos | Olivier Couture | Channya Hesse | Daniel F. Schmidt
Source: Polymers (review, 2024-05-11)
Field: Materials Science — Polymers and Plastics

Molecular Simulation of Covalent Adaptable Networks and Vitrimers: A Review Covalent adaptable networks and vitrimers are novel polymers with dynamic reversible bond exchange reactions for crosslinks, enabling them to modulate their properties between those of thermoplastics and thermosets. They have been gathering interest as materials for their recycling and self-healing properties. In this review, we discuss different molecular simulation efforts that have been used over the last decade to investigate and understand the nanoscale and molecular behaviors of covalent adaptable networks and vitrimers. In particular, molecular dynamics, Monte Carlo, and a hybrid of molecular dynamics and Monte Carlo approaches have been used to model the dynamic bond exchange reaction, which is the main mechanism of interest since it controls both the mechanical and rheological behaviors. The molecular simulation techniques presented yield sufficient results to investigate the structure and dynamics as well as the mechanical and rheological responses of such dynamic networks. The benefits of each method have been highlighted. The use of other tools such as theoretical models and machine learning has been included. We noticed, amongst the most prominent results, that stress relaxes as the bond exchange reaction happens, and that at temperatures higher than the glass transition temperature, the self-healing properties are better since more bond BERs are observed. The lifetime of dynamic covalent crosslinks follows, at moderate to high temperatures, an Arrhenius-like temperature dependence. We note the modeling of certain properties like the melt viscosity with glass transition temperature and the topology freezing transition temperature according to a behavior ruled by either the Williams-Landel-Ferry equation or the Arrhenius equation. Discrepancies between the behavior in dissociative and associative covalent adaptable networks are discussed. We conclude by stating which material parameters and atomistic factors, at the nanoscale, have not yet been taken into account and are lacking in the current literature. Covalent bond ; Nanotechnology ; Materials science ; Computer science ; Chemistry ; Organic chemistry Polymer composites and self-healing ; Synthetic Organic Chemistry Methods ; Fuel Cells and Related Materials