jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Garima Tiwari | Saibalendu Sarkar | Balaji R. Jagirdar
Source: ChemCatChem (article, 2024-07-10)
Field: Chemistry — Inorganic Chemistry

Bimetallic Catalysts in Acetylene Semi‐Hydrogenation: Conceptual Advances and Challenges Abstract The hydrogenation of acetylene to ethylene is of great industrial interest. Achieving both high selectivity and high conversion of acetylene to ethylene via semi‐hydrogenation is quite challenging. Bimetallic catalysts offer opportunities to selectively control reaction pathways by altering the electronic and geometric properties of active sites on the catalyst surface. Recent advancements in the synthesis of bimetallic catalysts enabled the creation of random alloys, intermetallics, segregated structures, or single‐site alloys with precision. These catalysts could be tailored for semi‐hydrogenation of acetylene by tuning the features including adsorption energy, active site isolation, and the d‐band center. Modern machine learning tools are also helpful for catalyst design. In this article, an overview of the conceptual advances in the design aspects of bimetallic catalysts, advances and challenges involved in the acetylene semi‐hydrogenation, has been presented. Bimetallic strip ; Acetylene ; Catalysis ; Heterogeneous catalysis ; Chemistry ; Nanotechnology ; Materials science ; Organic chemistry Asymmetric Hydrogenation and Catalysis ; Catalysis and Hydrodesulfurization Studies ; Catalytic Processes in Materials Science
Authors: Bai‐Qian Wang | Tian-Yu Zhao | H. Ding | Yuting Liu | Nian-Ke Chen | Meng Niu | Xiaodong Li | Ming Xu | Hong‐Bo Sun | Shengbai Zhang | Xianbin Li
Source: Acta Materialia (article, 2024-06-20)
Field: Materials Science — Materials Chemistry

Partial melting nature of phase-change memory Ge-Sb-Te superlattice uncovered by large-scale machine learning interatomic potential molecular dynamics Materials science ; Superlattice ; Molecular dynamics ; Scale (ratio) ; Interatomic potential ; Phase (matter) ; Atomic units ; Chemical physics ; Condensed matter physics ; Optoelectronics ; Physics ; Quantum mechanics Phase-change materials and chalcogenides ; Machine Learning in Materials Science ; Advanced Memory and Neural Computing
Authors: Zachary Fox | Ayana Ghosh
Source: Machine Learning Science and Technology (article, 2024-08-15)
Field: Materials Science — Materials Chemistry

Active causal learning for decoding chemical complexities with targeted interventions Abstract Predicting and enhancing inherent properties based on molecular structures is paramount to design tasks in medicine, materials science, and environmental management. Most of the current machine learning and deep learning approaches have become standard for predictions, but they face challenges when applied across different datasets due to reliance on correlations between molecular representation and target properties. These approaches typically depend on large datasets to capture the diversity within the chemical space, facilitating a more accurate approximation, interpolation, or extrapolation of the chemical behavior of molecules. In our research, we introduce an active learning approach that discerns underlying cause-effect relationships through strategic sampling with the use of a graph loss function. This method identifies the smallest subset of the dataset capable of encoding the most information representative of a much larger chemical space. The identified causal relations are then leveraged to conduct systematic interventions, optimizing the design task within a chemical space that the models have not encountered previously. While our implementation focused on the QM9 quantum-chemical dataset for a specific design task—finding molecules with a large dipole moment—our active causal learning approach, driven by intelligent sampling and interventions, holds potential for broader applications in molecular, materials design and discovery. Chemical space ; Computer science ; Machine learning ; Artificial intelligence ; Data science ; Drug discovery ; Bioinformatics ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Johannes Voss
Source: Journal of Computational Chemistry (article, 2024-04-26)
Field: Materials Science — Materials Chemistry

Machine learning for accuracy in density functional approximations Machine learning techniques have found their way into computational chemistry as indispensable tools to accelerate atomistic simulations and materials design. In addition, machine learning approaches hold the potential to boost the predictive power of computationally efficient electronic structure methods, such as density functional theory, to chemical accuracy and to correct for fundamental errors in density functional approaches. Here, recent progress in applying machine learning to improve the accuracy of density functional and related approximations is reviewed. Promises and challenges in devising machine learning models transferable between different chemistries and materials classes are discussed with the help of examples applying promising models to systems far outside their training sets. Computer science ; Machine learning ; Artificial intelligence ; Density functional theory ; Computational learning theory ; Active learning (machine learning) ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Adrián Domínguez-Castro
Source: Theoretical Chemistry Accounts (article, 2024-06-01)
Field: Materials Science — Materials Chemistry

DFT and machine learning for predicting hydrogen adsorption energies on rocksalt complex oxides Adsorption ; Hydrogen ; Density functional theory ; Materials science ; Chemistry ; Physical chemistry ; Computational chemistry ; Organic chemistry Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Catalysis and Hydrodesulfurization Studies
Authors: Zhanfeng Wang | Wenhao Zhang | Minghong Jiang | Yicheng Chen | Zhenyu Zhu | Wenjie Yan | Jianming Wu | Xin Xu
Source: The Journal of Physical Chemistry Letters (article, 2024-12-13)
Field: Materials Science — Materials Chemistry

X2-GNN: A Physical Message Passing Neural Network with Natural Generalization Ability to Large and Complex Molecules Neural network models excel in molecular property predictions but often struggle with generalizing from smaller to larger molecules due to increased structural diversity and complex interactions. To address this, we introduce an E(3) invariant (and equivariant capable) message passing graph neural network (GNN), namely, X2-GNN, that integrates physical insights via atomic orbital overlap integrals and core Hamiltonians. These features provide essential information about bond strength, electron delocalization, and many-body interactions, enhanced by an attention mechanism for improved learning efficiency. Benchmarked against mainstream GNNs on diverse data sets, X2-GNN trained solely on the QM9 data set (up to nine heavy atoms) effectively generalizes to larger molecules with tens of heavy atoms, achieving credible per-atom error rates. It also excels in potential energy surface modeling and accurately predicts the bond dissociation energy within subseconds. These results highlight X2-GNN's scalability and broad applicability, emphasizing the importance of integrating data-driven approaches with basic knowledge from electronic structure theory. Computer science ; Artificial neural network ; Scalability ; Theoretical computer science ; Invariant (physics) ; Transferability ; Equivariant map ; Atom (system on chip) ; Topology (electrical circuits) ; Artificial intelligence ; Machine learning ; Physics ; Mathematics ; Quantum mechanics ; Pure mathematics ; Database Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Shokirbek Shermukhamedov | Dilorom Mamurjonova | Thana Maihom | Michael Probst
Source: Journal of Chemical Information and Modeling (article, 2024-07-15)
Field: Materials Science — Materials Chemistry

Structure to Property: Chemical Element Embeddings for Predicting Electronic Properties of Crystals We present a new general-purpose machine learning model that is able to predict a variety of crystal properties, including Fermi level energy and band gap, as well as spectral ones such as electronic densities of states. The model is based on atomic representations that enable it to effectively capture complex information about each atom and its surrounding environment in a crystal. The accuracy achieved for band gaps exceeds results previously published. By design, our model is not restricted to the electronic properties discussed here but can be extended to fit diverse chemical descriptors. Its advantages are (a) its low computational requirements, making it an efficient tool for high-throughput screening of materials; and (b) the simplicity and flexibility of its architecture, facilitating implementation and interpretation, especially for researchers in the field of computational chemistry. Flexibility (engineering) ; Computer science ; Simplicity ; Property (philosophy) ; Crystal (programming language) ; Variety (cybernetics) ; Field (mathematics) ; Atom (system on chip) ; Electronic band ; Band gap ; Element (criminal law) ; Electronic structure ; Chemical physics ; Computational science ; Statistical physics ; Materials science ; Artificial intelligence ; Chemistry ; Physics ; Computational chemistry ; Mathematics ; Quantum mechanics ; Optoelectronics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Jinyan Liu | Guanghao Zhang | Jianyong Wang | Hong Zhang | Han Ye
Source: Computational Materials Science (article, 2024-10-14)
Field: Materials Science — Materials Chemistry

Research on Cu-Sn machine learning interatomic potential with active learning strategy Interatomic potential ; Active learning (machine learning) ; Materials science ; Computer science ; Chemistry ; Artificial intelligence ; Molecular dynamics ; Computational chemistry Machine Learning in Materials Science ; Force Microscopy Techniques and Applications ; Image Processing Techniques and Applications
Authors: Marcel Ruth | Tobias Gensch | Peter R. Schreiner
Source: Angewandte Chemie International Edition (review, 2024-10-15)
Field: Materials Science — Materials Chemistry

Contrasting Historical and Physical Perspectives in Asymmetric Catalysis: ΔΔ<i>G</i><sup>≠</sup> versus Enantiomeric Excess With the rise of machine learning (ML), the modeling of chemical systems has reached a new era and has the potential to revolutionize how we understand and predict chemical reactions. Here, we probe the historic dependence on utilizing enantiomeric excess (ee) as a target variable and discuss the benefits of using relative Gibbs free activation energies (ΔΔG<sup>≠</sup>), grounded firmly in transition-state theory, emphasizing practical benefits for chemists. This perspective is intended to discuss best practices that enhance modeling efforts especially for chemists with an experimental background in asymmetric catalysis that wish to explore modelling of their data. We outline the enhanced modeling performance using ΔΔG<sup>≠</sup>, escaping physical limitations, addressing temperature effects, managing non-linear error propagation, adjusting for data distributions and how to deal with unphysical predictions,in order to streamline modeling for the practical chemist and provide simple guidelines to strong statistical tools. For this endeavor, we gathered ten datasets from the literature covering very different reaction types. We evaluated the datasets using fingerprint-, descriptor-, and graph neural network-based models. Our results highlight the distinction in performance among varying model complexities with respect to the target representation, emphasizing practical benefits for chemists. Representation (politics) ; Computer science ; Graph ; Perspective (graphical) ; Machine learning ; Artificial intelligence ; Biochemical engineering ; Chemistry ; Theoretical computer science ; Engineering Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Computational Drug Discovery Methods
Authors: Vishu Gupta | Youjia Li | Alec Peltekian | Muhammed Nur Talha Kilic | Wei‐keng Liao | Alok Choudhary | Ankit Agrawal
Source: Journal of Cheminformatics (article, 2024-02-16)
Field: Materials Science — Materials Chemistry

Simultaneously improving accuracy and computational cost under parametric constraints in materials property prediction tasks Modern data mining techniques using machine learning (ML) and deep learning (DL) algorithms have been shown to excel in the regression-based task of materials property prediction using various materials representations. In an attempt to improve the predictive performance of the deep neural network model, researchers have tried to add more layers as well as develop new architectural components to create sophisticated and deep neural network models that can aid in the training process and improve the predictive ability of the final model. However, usually, these modifications require a lot of computational resources, thereby further increasing the already large model training time, which is often not feasible, thereby limiting usage for most researchers. In this paper, we study and propose a deep neural network framework for regression-based problems comprising of fully connected layers that can work with any numerical vector-based materials representations as model input. We present a novel deep regression neural network, iBRNet, with branched skip connections and multiple schedulers, which can reduce the number of parameters used to construct the model, improve the accuracy, and decrease the training time of the predictive model. We perform the model training using composition-based numerical vectors representing the elemental fractions of the respective materials and compare their performance against other traditional ML and several known DL architectures. Using multiple datasets with varying data sizes for training and testing, We show that the proposed iBRNet models outperform the state-of-the-art ML and DL models for all data sizes. We also show that the branched structure and usage of multiple schedulers lead to fewer parameters and faster model training time with better convergence than other neural networks. Scientific contribution: The combination of multiple callback functions in deep neural networks minimizes training time and maximizes accuracy in a controlled computational environment with parametric constraints for the task of materials property prediction. Computer science ; Artificial neural network ; Artificial intelligence ; Property (philosophy) ; Machine learning ; Deep learning ; Parametric statistics ; Task (project management) ; Construct (python library) ; Regression ; Support vector machine ; Process (computing) ; Data mining ; Predictive modelling Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Mahboubeh Motadayen | Nehru Devabharathi | Shweta Agarwala
Source: International Journal of AI for Materials and Design (article, 2024-07-03)
Field: Materials Science — Materials Chemistry

Advancing sustainability: Biodegradable electronics and materials discovery through artificial intelligence The pressing need for sustainable materials and devices stems from growing environmental concerns and the imperative to mitigate climate change. Traditional materials and devices often rely on non-renewable resources and generate significant waste and pollution throughout their lifecycle. By prioritizing sustainability in material and device design, we can foster innovation, promote circular economies, and build a greener future for generations to come. Artificial intelligence (AI) and machine learning (ML) can analyze vast datasets to identify novel materials with desirable properties by reducing the experimental workload. In this paper, we explore the synergistic relationship between sustainable materials discovery and ML models. By leveraging advanced algorithms, researchers can efficiently explore vast chemical spaces to identify environmentally friendly materials with tailored properties. ML techniques, including predictive modeling and generative models, facilitate the rapid discovery and optimization of sustainable materials for various applications, ranging from renewable energy technologies to eco-friendly consumer products. We present a landscape view of the field with a focus on the most recent developments, focusing mainly on transitory materials such as metals, polymers, and semiconducting materials. Furthermore, classification and regression techniques to model the degradation behavior of polymers have been addressed, pointing to key challenges and proposing solutions for enhanced ML applications. The paper discusses the challenges of scaling up data-driven technologies from small molecules to polymers, underscoring AI&amp;rsquo;s role in discovering new molecular designs and optimizing existing ones for novel applications. It emphasizes the importance of defining and standardizing polymer systems to enable ML models to create a unified data collection system for AI and automation enhancements. Furthermore, it stresses the necessity of refining ML methods to harness the benefits of data-driven polymer chemistry fully, emphasizing the importance of reliable and diverse datasets for predictive models in polymer synthesis. Sustainability ; Computer science ; Environmentally friendly ; Emerging technologies ; Renewable energy ; Data science ; Biochemical engineering ; Management science ; Systems engineering ; Risk analysis (engineering) ; Artificial intelligence ; Engineering ; Business Machine Learning in Materials Science
Authors: Arvind Ganesan | Sheng Dai
Source: Chemistry of Materials (article, 2024-10-07)
Field: Materials Science — Materials Chemistry

Porous Liquids: Versatile Fusion of Porosity and Fluidity Porous liquids and fluids with permanent porosity have been explored over the past decade as transformative functional materials for several sustainable chemical processes. This perspective tracks the evolution of porous liquids and summarizes recent advances in the development of porous liquids for several applications, such as sorptive separations, catalysis, etc. The latter section discusses the prospects of porous liquids in several novel applications (bio applications, gas storage, heat insulation) and machine learning guided pathways for task-specific design and synthesis of porous liquids. Porosity ; Materials science ; Porous medium ; Nanotechnology ; Chemical engineering ; Composite material ; Engineering Covalent Organic Framework Applications ; Mesoporous Materials and Catalysis ; Catalytic Processes in Materials Science
Authors: Fang Fang | Jie Lin | Jiajia Li | Yu Zhang | Qiuyi Fu | Quan‐Quan Zhou | Wei Li | Guobing Zhou | Zhen Yang
Source: Inorganic Chemistry Frontiers (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Molecular dynamics simulations of liquid gallium alloy Ga–X (X = Pt, Pd, Rh) <i>via</i> machine learning potentials Machine learning-based moment tensor potential (MTP) was developed to simulate the coordination and diffusion properties of transition metal atoms in liquid gallium. Gallium ; Molecular dynamics ; Alloy ; Liquid metal ; Diffusion ; Materials science ; Transition metal ; Moment (physics) ; Dynamics (music) ; Chemical physics ; Crystallography ; Physical chemistry ; Chemistry ; Computational chemistry ; Thermodynamics ; Physics ; Metallurgy ; Catalysis ; Organic chemistry ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Nuclear Materials and Properties
Authors: Rongxing Lu | Yue Han | Junbao Hu | Di Xu | Zhixuan Zhong | Heng Zhou | Tong Zhao | Jian Jiang
Source: Chemical Engineering Journal (article, 2024-08-13)
Field: Materials Science — Materials Chemistry

Deep learning model for precise prediction and design of low-melting point phthalonitrile monomers Phthalonitrile ; Monomer ; Melting point ; Point (geometry) ; Materials science ; Polymer chemistry ; Computer science ; Biological system ; Nanotechnology ; Mathematics ; Polymer ; Composite material ; Phthalocyanine Machine Learning in Materials Science ; Epoxy Resin Curing Processes ; Injection Molding Process and Properties
Authors: Elena Bandini | Rodrigo Castellano Ontiveros | Ardiana Kajtazi | Hamed Eghbali | Frédéric Lynen
Source: Journal of Cheminformatics (article, 2024-06-21)
Field: Chemistry — Spectroscopy

Physicochemical modelling of the retention mechanism of temperature-responsive polymeric columns for HPLC through machine learning algorithms Temperature-responsive liquid chromatography (TRLC) offers a promising alternative to reversed-phase liquid chromatography (RPLC) for environmentally friendly analytical techniques by utilizing pure water as a mobile phase, eliminating the need for harmful organic solvents. TRLC columns, packed with temperature-responsive polymers coupled to silica particles, exhibit a unique retention mechanism influenced by temperature-induced polymer hydration. An investigation of the physicochemical parameters driving separation at high and low temperatures is crucial for better column manufacturing and selectivity control. Assessment of predictability using a dataset of 139 molecules analyzed at different temperatures elucidated the molecular descriptors (MDs) relevant to retention mechanisms. Linear regression, support vector regression (SVR), and tree-based ensemble models were evaluated, with no standout performer. The precision, accuracy, and robustness of models were validated through metrics, such as r and mean absolute error (MAE), and statistical analysis. At <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>45</mml:mn> <mml:mmultiscripts><mml:mspace></mml:mspace> <mml:mrow></mml:mrow> <mml:mo>∘</mml:mo></mml:mmultiscripts> <mml:mtext>C</mml:mtext></mml:mrow> </mml:math> , logP predominantly influenced retention, akin to reversed-phase columns, while at <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow><mml:msup><mml:mn>5</mml:mn> <mml:mo>∘</mml:mo></mml:msup> <mml:mtext>C</mml:mtext></mml:mrow> </mml:math> , complex interactions with lipophilic and negative MDs, along with specific functional groups, dictated retention. These findings provide deeper insights into TRLC mechanisms, facilitating method development and maximizing column potential. Computer science ; Mechanism (biology) ; High-performance liquid chromatography ; Algorithm ; Chemistry ; Chromatography ; Machine learning ; Artificial intelligence Analytical Chemistry and Chromatography ; Metabolomics and Mass Spectrometry Studies ; Computational Drug Discovery Methods
Authors: Antonio Mirarchi | Raúl P. Peláez | Guillem Simeon | Gianni De Fabritiis
Source: Journal of Chemical Theory and Computation (article, 2024-11-08)
Field: Materials Science — Materials Chemistry

AMARO: All Heavy-Atom Transferable Neural Network Potentials of Protein Thermodynamics All-atom molecular simulations offer detailed insights into macromolecular phenomena, but their substantial computational cost hinders the exploration of complex biological processes. We introduce Advanced Machine-learning Atomic Representation Omni-force-field (AMARO), a new neural network potential (NNP) that combines an O(3)-equivariant message-passing neural network architecture, TensorNet, with a coarse-graining map that excludes hydrogen atoms. AMARO demonstrates the feasibility of training coarser NNP, without prior energy terms, to run stable protein dynamics with scalability and generalization capabilities. Scalability ; Artificial neural network ; Computer science ; Granularity ; Atom (system on chip) ; Generalization ; Representation (politics) ; Field (mathematics) ; Molecular dynamics ; Force field (fiction) ; Artificial intelligence ; Chemistry ; Computational chemistry ; Mathematics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods