jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Lei Geng | Feng Yue | Y. R. Niu | Fang Zhang | Hua-Qing Yin
Source: Journal of Chemical Information and Modeling (article, 2024-12-19)
Field: Materials Science — Materials Chemistry

MGT: Machine Learning Accelerates Performance Prediction of Alloy Catalytic Materials The application of deep learning technology in the field of materials science provides a new method for predicting the adsorption energy of high-performance alloy catalysts in hydrogen evolution reactions and material discovery. The activity and selectivity of catalytic materials are mainly influenced by the properties and positions of active sites and adsorption sites. However, current deep learning models have not sufficiently focused on the importance of active atoms and adsorbates, instead placing more emphasis on the overall structure of the catalytic materials. In this paper, the overall molecular graph and a masked graph, which ignores fixed atoms, are separately input into the Masked Graph Transformer (MGT) network to enhance the model's ability to recognize key sites in catalytic reactions. Second, we introduce a nonlinear message-passing mechanism to improve the dot-product attention in the Transformer and capture the directional information on the relative positions of nodes by integrating molecular geometric information through deep tensor products. Subsequently, we constructed the NLMP-TransNet framework, which combines MPNN and Transformer and optimizes the model's learning and prediction capabilities through weight sharing and residual connections. The MGT achieves an error rate of 0.5447 eV on the small data set OC20-Ni, surpassing existing technologies. Ablation studies confirm the necessity of focusing on site features for accurate adsorption energy prediction. Code is available at https://github.com/KristinSun/OCP-MGT.git. Computer science ; Residual ; Graph ; Transformer ; Adsorption ; Deep learning ; Artificial intelligence ; Machine learning ; Materials science ; Theoretical computer science ; Algorithm ; Chemistry ; Electrical engineering ; Engineering Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Computational Drug Discovery Methods
Authors: Likun Wang | Na Li | Mengyao Cao | Yun Zhu | Xiewei Xiong | Li Li | Tong Zhu | Hao Pei
Source: ChemRxiv (preprint, 2024-03-13)
Field: Materials Science — Materials Chemistry

Predicting DNA Reactions with a Quantum Chemistry-Based Deep Learning Model DNA reactions are crucial in biology, synthetic biology, and DNA computing. Accurate prediction of thermodynamic and kinetic parameters is vital for understanding molecular interactions and designing functional DNA-based systems. Existing models have limitations due to simplifications and approximations that may deviate from experimental measurements. In this study, we propose a quantum chemistry-based deep learning model to enhance accuracy and efficiency in predicting DNA reaction parameters. The model integrates quantum chemistry calculations, new designed descriptor matrices, and deep learning algorithms. It comprehensively describes energy variations by expanding stacks and considering relevant factors. To address limited labeled data, an active learning method selects informative samples iteratively, optimizing data utilization. The results demonstrate the superior predictive capabilities of our model in accurately determining DNA hybridization free energies and strand displacement rate constants. This integration of quantum chemistry and deep learning improves our understanding of DNA reactions and facilitates precise design and optimization of DNA-based systems. Quantum chemistry ; Chemistry ; DNA ; Quantum ; Computer science ; Computational biology ; Biophysics ; Physics ; Biology ; Quantum mechanics ; Biochemistry ; Reaction mechanism ; Catalysis Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Eduardo Aguilar-Bejarano | Grazziela P. Figueredo | Simon Woodward | Hon Wai Lam | Ender Özcan | Raja K. Rit
Source: ChemRxiv (preprint, 2024-02-22)
Field: Materials Science — Materials Chemistry

HCat-GNet: An Interpretable Graph Neural Network for Catalysis Optimization Homogeneous catalysts enable faster conversions of molecules with higher selectivities (stereo- and regioselectivity) in chemical reactions. Traditionally, catalyst improvements are made through empirical trials, where the catalyst is functionalised by adding, removing or modifying groups within its structure and, subsequently, reevaluating the new catalytic activity. This procedure is not efficient and leads to unsuccessful trials that waste resources. Machine learning (ML) approaches have been proposed to accelerate homogeneous asymmetric catalyst optimization. However, these often lack a general descriptor generation procedure to allow encoding of molecules from a broad region of chemical space. To overcome this, we propose a homogeneous catalyst graph neural network (HCat-GNet) for the prediction of selectivity of catalysts given the SMILES of participant molecules. We demonstrate its use in rhodium-catalyzed asymmetric 1,4-addition (RhCAA), a reaction of major importance in organic synthesis. We benchmark HCat-GNet against traditional ML methods for its ability to predict RhCAA stereoselectivity from two chiral diene ligand two datasets; one for learning and one for final testing. For the learning dataset, both traditional ML and HCat-GNet methods give comparable results. However, when presented with the new unseen test dataset, traditional ML models perform poorly, while HCat-GNet retains a general ability to accurately predict product absolute stereochemistry and reaction stereoselectivity. Furthermore, HCat-GNet allows model interpretability, permitting analysis of the effect of ligand substituents in determining reaction selectivity. HCat-GNet shows greater potential for catalyst optimization than traditional ML, as it allows the use of a non-fixed number of participant molecules to train the model, only requiring the SMILES of the molecules to create graph representations. HCat-GNet allows more general models that accurately extrapolate into unseen regions of chemical space. Graph ; Artificial neural network ; Computer science ; Artificial intelligence ; Theoretical computer science Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Topic Modeling
Authors: Eun Ho Kim | Jun Hyeong Gu | Jun Ho Lee | Seong Hun Kim | Jaeseon Kim | Hyo Gyeong Shin | Shin Hyun Kim | Donghwa Lee
Source: ACS Applied Materials & Interfaces (article, 2024-08-09)
Field: Materials Science — Materials Chemistry

Boosting–Crystal Graph Convolutional Neural Network for Predicting Highly Imbalanced Data: A Case Study for Metal–Insulator Transition Materials Applying machine-learning techniques for imbalanced data sets presents a significant challenge in materials science since the underrepresented characteristics of minority classes are often buried by the abundance of unrelated characteristics in majority of classes. Existing approaches to address this focus on balancing the counts of each class using oversampling or synthetic data generation techniques. However, these methods can lead to loss of valuable information or overfitting. Here, we introduce a deep learning framework to predict minority-class materials, specifically within the realm of metal-insulator transition (MIT) materials. The proposed approach, termed boosting-CGCNN, combines the crystal graph convolutional neural network (CGCNN) model with a gradient-boosting algorithm. The model effectively handled extreme class imbalances in MIT material data by sequentially building a deeper neural network. The comparative evaluations demonstrated the superior performance of the proposed model compared to other approaches. Our approach is a promising solution for handling imbalanced data sets in materials science. Overfitting ; Boosting (machine learning) ; Oversampling ; Machine learning ; Computer science ; Artificial intelligence ; Convolutional neural network ; Artificial neural network ; Graph ; Gradient boosting ; Materials science ; Theoretical computer science Machine Learning in Materials Science ; Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Xiang‐He Meng
Source: Advances in Engineering Technology Research (article, 2024-01-25)
Field: Materials Science — Polymers and Plastics

Efficient Prediction of Polymer Glass Transition Temperatures through Machine Learning Methods The glass transition temperature (Tg) plays a crucial role in defining polymer properties. Despite the widespread use of machine learning for material design and property prediction, there are still challenges concerning the interpretability and model performance when predicting Tg. In this study, Simplified Molecular Input Line Entry System strings are utilised to encode the polymer structure, which are then transformed into molecular descriptors for analytical training and prediction of Tg using Artificial Neural Network and Random Forest models. Meticulous hyperparameter tuning of the Random Forest model was performed, resulting in reasonable Tg predictions. This methodology forges a connection between polymer structure and Tg, opening up new avenues for research in the field of polymers. Glass transition ; Polymer ; Transition (genetics) ; Materials science ; Computer science ; Machine learning ; Artificial intelligence ; Chemistry ; Composite material Textile materials and evaluations ; Color Science and Applications
Authors: Chi Chen | Hao Wang | Houzhao Wan | Dan Sun
Source: Advanced Electronic Materials (article, 2024-11-04)
Field: Materials Science — Materials Chemistry

Machine Learning Enabled High‐Throughput Screening of 2D Ultrawide Bandgap Semiconductors for Flexible Resistive Materials Abstract The 2D ultrawide bandgap (UWBG) semiconductors have attracted great attentions for the next generation of electronics and optoelectronics, owing to their superiority on material flexibility, device stability, and power consumption. However, few 2D UWBG semiconductors have been discovered, impeding their prosperous developments and widespread applications. Here, a high‐throughput workflow is constructed to screen 2D UWBG semiconductors assisted by machine learning, and 507 potential candidates are obtained. Moreover, by learning, predicting, and screening Young's modulus and Poisson's ratio, 31 flexible 2D UWBG semiconductors are identified. Then the generation and the diffusion of anion vacancies, as well as the corresponding electronic properties are investigated by using the first‐principles calculations, and 3 of them are demonstrated as the most promising candidates for the flexible resistive materials. The facile interface tunneling and the increased material conductance caused by the anion vacancies will contribute to the transition from high resistive state to low resistive state. This work provides an efficient high‐throughput screening protocol to enrich the family of 2D UWBG semiconductors and is expected to foster their practical applications. Materials science ; Semiconductor ; Resistive touchscreen ; Throughput ; Optoelectronics ; Band gap ; Nanotechnology ; High-throughput screening ; Wide-bandgap semiconductor ; Wireless ; Computer science ; Electrical engineering ; Telecommunications ; Engineering ; Bioinformatics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Ferroelectric and Negative Capacitance Devices
Authors: Hengrui Zhang | Ruishu Huang | Jie Chen | James M. Rondinelli | Wei Chen
Source: arXiv (Cornell University) (preprint, 2024-08-29)
Field: Materials Science — Materials Chemistry

Do Graph Neural Networks Work for High Entropy Alloys? Graph neural networks (GNNs) have excelled in predictive modeling for both crystals and molecules, owing to the expressiveness of graph representations. High-entropy alloys (HEAs), however, lack chemical long-range order, limiting the applicability of current graph representations. To overcome this challenge, we propose a representation of HEAs as a collection of local environment (LE) graphs. Based on this representation, we introduce the LESets machine learning model, an accurate, interpretable GNN for HEA property prediction. We demonstrate the accuracy of LESets in modeling the mechanical properties of quaternary HEAs. Through analyses and interpretation, we further extract insights into the modeling and design of HEAs. In a broader sense, LESets extends the potential applicability of GNNs to disordered materials with combinatorial complexity formed by diverse constituents and their flexible configurations. Artificial neural network ; Computer science ; Graph ; Artificial intelligence ; Theoretical computer science Machine Learning in Materials Science ; Advanced Materials Characterization Techniques
Authors: C. Rajalakshmi | Vivek Vijay | Abhirami Vijayakumar | Siti Salwah Salim | Sherin Susan Cherian | Parvathi Santhoshkumar | John B Kottooran | Ann Miriam Abraham | Krishnaveni Govindaraj | C. S. Anjanakutty | Binuja Varghese | Vibin Ipe Thomas
Source: Theoretical Chemistry Accounts (article, 2024-12-03)
Field: Materials Science — Materials Chemistry

Machine learning-based yield prediction for transition metal-catalyzed cross-coupling reactions Yield (engineering) ; Machine learning ; Artificial intelligence ; Computer science ; Artificial neural network ; Predictive modelling ; Coupling (piping) ; Transition metal ; Catalysis ; Chemistry ; Materials science ; Organic chemistry Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: G. Min | Wenbo Wei | Qingyang Fan | Teng Wan | Ming Ye | Sining Yun
Source: Materials Science in Semiconductor Processing (article, 2024-11-29)
Field: Materials Science — Materials Chemistry

High-throughput exploration of stable semiconductors using deep learning and density functional theory Materials science ; Density functional theory ; Throughput ; Semiconductor ; Nanotechnology ; Engineering physics ; Artificial intelligence ; Optoelectronics ; Computational chemistry ; Computer science ; Engineering Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Semiconductor materials and devices
Authors: M Ayyappa Chakravarthi | Sonu Sharma | Amit Kumar Sahu | T.S.R. Murthy | T. S. Arulananth | Smriti Jain
Source:  (article, 2024-10-25)
Field: Materials Science — Materials Chemistry

Machine Learning Algorithms for Automated Synthesis of Biocompatible Nanomaterials This study introduces a novel approach, the “Machine Learning Algorithms for Automated Synthesis of Biocompatible Nanomaterials,” aimed at revolutionizing the synthesis processes in nanotechnology. The proposed method leverages three powerful machine learning algorithms —Random Forest, Bayesian Optimization, and Deep Neural Networks—to optimize reaction conditions and predict the properties of biocompatible nanomaterials. In the pursuit of automated synthesis, the Random Forest algorithm acts as an ensemble learning technique, combining multiple decision trees to make robust predictions. Bayesian Optimization, a sequential model-based optimization approach, efficiently navigates the parameter space to identify optimal synthesis conditions. Deep Neural Networks, capable of capturing intricate relationships within data, are employed to model complex patterns in the synthesis process. To validate the effectiveness of the proposed method, we conducted comprehensive experiments and compared its performance with six traditional methods: Linear Regression, Polynomial Regression, Support Vector Machine, Decision Tree, K-Nearest Neighbors, and Principal Component Regression. Key performance metrics, including Mean Squared Error (MSE), R-squared ($\mathbf{R}^{2}$), and Mean Absolute Error (MAE), were employed for the evaluation. Our results demonstrate that the proposed method consistently outperforms traditional methods across all metrics. It exhibits lower MSE and MAE values and a higher $\mathbf{R}^{2}$ value, indicating superior predictive accuracy Biocompatible material ; Computer science ; Nanomaterials ; Artificial intelligence ; Algorithm ; Nanotechnology ; Materials science ; Engineering ; Biomedical engineering Machine Learning in Materials Science
Authors: Jérémie Zaffran | Minyang Jiao | Raphaël Wischert | Stéphane Streiff | Sébastien Paul
Source: The Journal of Physical Chemistry C (article, 2024-03-13)
Field: Materials Science — Materials Chemistry

Upgrading the Density Functional Theory with Machine Learning for the Fast Prediction of Polyaromatic Reactivity at Bimetallic Catalysts Polyaromatic molecules are compounds of major importance in chemistry. However, simulating their reactivity at the solid catalyst surface with density functional theory (DFT) is very challenging. Indeed, such species require large slab models for their adsorption, hence resulting in a considerable number of atoms and thus significant computational time. In the recent context of increasing use in machine learning (ML), it is clear that such tools are of first interest to speed-up DFT calculations. Considering anthraquinone (AQ) hydrogenation on the surface of metal-doped Pd-based supported catalysts as a model reaction and focusing on the main reaction products, we propose here a method aiming at predicting the energy of the determining states from several descriptors related to a small molecular fragment, benzoquinone (BZQ) adsorbed at different surfaces. We were able to identify two distinct models, both performing with a high efficiency and based on different kinds of descriptors. While the first one involving a single thermodynamic descriptor is more accurate, the second one including a combination of electronic and geometric parameters is still relevant to predict reliable qualitative trends. Interestingly, we showed that simple linear regression tools can compete with other complex ML techniques, providing very accurate models with remarkable stability. Such an approach can be applied to easily assess the effective barriers of formation of several species on catalysts presenting different bimetallic compositions, hence enabling the screening of the catalytic activity and selectivity of various surfaces in a record time. While heavy DFT computations are generally required to optimize each intermediate and transition state, our strategy relies on a single adsorbate relaxation, hence, resulting in a tremendous gain of time. Therefore, our method is crucial for the accelerated computational design of solid catalysts and may have applications in various fields of the chemical industry. Bimetallic strip ; Density functional theory ; Catalysis ; Reactivity (psychology) ; Context (archaeology) ; Adsorption ; Chemistry ; Molecule ; Computational chemistry ; Computer science ; Materials science ; Physical chemistry ; Organic chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysis and Hydrodesulfurization Studies
Authors: Vuong Van Pham | Temoor Muther | Amirmasoud Kalantari Dahaghi
Source: Machine Learning Science and Technology (article, 2024-12-01)
Field: Materials Science — Materials Chemistry

Neuromorphic, physics-informed spiking neural network for molecular dynamics Abstract Molecular dynamics (MD) simulations are used across many fields from chemical science to engineering. In recent years, Scientific Machine Learning (Sci-ML) in MD attracted significant attention and has become a new direction of scientific research. However, effectively integrating Sci-ML with MD simulations remains challenging. Compliance with the physical principles, comparable performance to a numerical method, and integration of start-of-the-art ML architectures are top-concerned examples of those gaps. This work addresses these challenges by introducing, for the first time, the neuromorphic physics-informed spiking neural network (NP-SNN) architecture to solve Newton’s equations of motion for MD systems. Unlike conventional Sci-ML methods that heavily rely on prior training data, NP-SNN performs without needing pre-existing data by embedding MD fundamentals directly into its learning process. It also leverages the enhanced representation of real biological neural systems through spiking neural network integration with molecular dynamic physical principles, offering greater efficiency compared to conventional AI algorithms. NP-SNN integrates three core components: (1) embedding MD principles directly into the training, (2) employing best practices for training physics-informed ML systems, and (3) utilizing a highly advanced and efficient SNN architecture. By integrating these core components, this proposed architecture proves its efficacy through testing across various molecular dynamics systems. In contrast to traditional MD numerical methods, NP-SNN is trained and deployed within a continuous time framework, effectively mitigating common issues related to time step stability. The results indicate that NP-SNN provides a robust Sci-ML framework that can make accurate predictions across diverse scientific molecular applications. This architecture accelerates and enhances molecular simulations, facilitating deeper insights into interactions and system dynamics at the molecular level. The proposed NP-SNN paves the way for foundational advancements across various domains of chemical and material sciences especially in energy, environment, and sustainability fields. Neuromorphic engineering ; Neuroscience ; Artificial neural network ; Spiking neural network ; Dynamics (music) ; Computer science ; Cognitive science ; Physics ; Artificial intelligence ; Psychology Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Neural Networks and Reservoir Computing
Authors: Amin Alibakhshi | Julien Steffen
Source: ChemRxiv (preprint, 2024-07-26)
Field: Materials Science — Materials Chemistry

Hydrogen diffusion on Ni(100): A Combined Machine-Learning, Ring Polymer Molecular Dynamics, and Kinetic Monte Carlo Study We introduce a methodological framework coupling machine-learning potentials, kinetic Monte Carlo (kMC), and ring polymer molecular dynamics (RPMD) to draw a comprehensive physical picture of the collective diffusion of hydrogen atoms on metal surfaces. For the benchmark case of hydrogen diffusion on a Ni(100) surface, the hydrogen adsorption and diffusion energetics and its dependence on the local coverage is described via a neural-network potential, where the training data is computed via periodic DFT and include all relevant optimized diffusion and desorption paths, sampled by nudged elastic band optimizations and molecular dynamics simulations. Nuclear quantum effects, being crucial for processes involving hydrogen at low temperatures, are treated by RPM. The diffusion rate constants are calculated with a combination of umbrella samplings employed to map the free energy profile and separate samplings of recrossing trajectories to obtain the transmission coefficient. The calculated diffusion rates for different temperatures and local environments are then combined and fitted into a kMC model allowing to access larger time and length scales. Our results demonstrate an outstanding performance for the trained neural network potential in reproducing reference DFT energies and forces. We report the effective diffusion rates for different temperatures and hydrogen surface coverages obtained via this recipe in good agreement with the experimental results. The method combination proposed in this study can be instrumental for a wide range of applications in materials science. Kinetic Monte Carlo ; Diffusion ; Molecular dynamics ; Monte Carlo method ; Diffusion Monte Carlo ; Hydrogen ; Kinetic energy ; Statistical physics ; Chemical physics ; Materials science ; Chemistry ; Thermodynamics ; Computational chemistry ; Physics ; Hybrid Monte Carlo ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Nuclear Materials and Properties