jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ruth M. Muthoka | Jinyoung Park | Hyeonsuk Yoo | Min-ho Suh | Yongjin Lee
Source: ACS Sustainable Chemistry & Engineering (article, 2024-04-30)
Field: Materials Science — Polymers and Plastics

Comprehensive Framework for the Design of High-Performing and Sustainable Polyamides Developing high-performance materials with tailored properties is a key challenge that requires innovative molecular design methods. This study presents a machine learning-based approach, integrating Monte Carlo tree search and a recurrent neural network for the molecular design of polyamide systems, and we evaluate their properties with molecular dynamics simulations. By benchmarking against the performance of polyimides, we guide our algorithm to design sustainable polyamides with superior properties. A key consideration in our design approach is the incorporation of biodegradability as a critical design factor, in line with the growing demand for sustainable materials. We use an objective function targeting an optimal profile of low octanol–water partition coefficient (log P), high thermal stability (TSef), and substantial thermal conductivity (k). With 20 independent runs, which resulted in an evaluation of 133,655 monomers for design compatibility and generation of 5255 polyamide systems, our approach successfully designed promising 157 polyamide monomers, satisfying the criteria of log P < 0, TSef ≥ 2, and k ≥ 0.5 W/m K. These selections were validated by additional molecular dynamics (MD) calculations, revealing our objective function leads to sustainable and high-performance materials. Furthermore, we performed a detailed analysis of the structure–property relationship. Our algorithm successfully demonstrates the effectiveness of this integrated computational approach in guiding the design of novel polyamides. Polyamide ; Material Design ; Computer science ; Monte Carlo method ; Molecular dynamics ; Partition coefficient ; Monomer ; Materials science ; Process engineering ; Polymer ; Chemistry ; Mathematics ; Engineering ; Organic chemistry ; Computational chemistry ; Polymer chemistry ; Composite material Polymer crystallization and properties ; Environmental Impact and Sustainability ; Polymer composites and self-healing
Authors: Xinyuan Xu | Li‐Cheng Xu | Li‐Gao Liu | Ping Lü | Shuo‐Qing Zhang | Xin Hong
Source: ChemRxiv (preprint, 2024-03-04)
Field: Chemistry — Organic Chemistry

Data-Driven Phosphine Ligand Design of Ni-Catalyzed Enantioselective Suzuki–Miyaura Cross-Coupling Reaction for the Synthesis of Biaryl Atropisomers: standing on the shoulder of Pd catalysis giants The Suzuki-Miyaura cross-coupling reaction is a cornerstone in organic synthesis, enabling the formation of carbon–carbon bonds with high efficiency and selectivity. This study represents a groundbreaking advancement in the field by pioneering the first enantioselective Ni-catalyzed Suzuki–Miyaura cross-coupling reactions for the synthesis of biaryl atropisomers. Employing a data-driven approach, we have crafted a novel N-protected Xiao-Phos ligand, which, in conjunction with commercially available Ni(COD)2, delivered unparalleled enantioselectivity and reactivity under mild conditions. The ligand design was meticulously guided by an extensive examination of the existing literature on Pd-catalyzed asymmetric Suzuki–Miyaura cross-coupling reactions, directing the virtual screening and subsequent experimental verification of the synthesized ligands in a single iteration. The innovative ligand N-Bn-Xiao-Phos exhibited impressive enantioselectivities coupled with exceptional yields, showcasing versatility with a diverse array of functional groups and aryl boronic acids, and accomplishing successful gram-scale synthesis. DFT computational studies provided profound insights into the reaction mechanism and the roots of enantioselectivity, elucidating the dynamic coordination modes of the chiral ligand and the steric induction. This breakthrough not only broadens the horizons of Ni-catalyzed cross-coupling reactions but also highlights the immense potential of machine learning in the judicious design of chiral ligands for asymmetric synthesis. Atropisomer ; Enantioselective synthesis ; Catalysis ; Ligand (biochemistry) ; Phosphine ; Coupling reaction ; Combinatorial chemistry ; Chemistry ; Coupling (piping) ; Stereochemistry ; Organic chemistry ; Materials science ; Receptor ; Biochemistry Axial and Atropisomeric Chirality Synthesis ; Molecular spectroscopy and chirality
Authors: Shuo Wang | Chenchen Li | Chong Liu | Wei Zhuang
Source: ChemCatChem (article, 2024-09-16)
Field: Materials Science — Materials Chemistry

On the Heterogeneity of Iron‐Oxo Species in Zeolites for the Oxidation of Methane to Methanol by Nitrous Oxide: A Theoretical Perspective Abstract The conversion of methane to methanol (MTM) represents a pivotal objective in the C1 chemical industry. Transition metals, such as iron, exchanged zeolites are one category of the most active catalysts for direct conversion of MTM. One important topic in understanding the mechanism of Fe‐zeolite catalyzed MTM is how the heterogeneity of catalytic (Fe) sites influences the system stability and reactivity. Employing DFT calculations and machine learning method, we herein studied the stability–reactivity relationship of a MTM catalytic cycle with N 2 O as the oxidant over Fe‐exchanged zeolites. The Fe heterogeneity was introduced by using CHA and FER zeolites and looking at a number of related Fe species (Fe II , FeO, and FeOH). A strong correlation was observed between the stability of such Fe species, which is primarily determined by the formation energy of Fe II , and such a stability trend remains consistent throughout the MTM catalytic cycle. The reactivity analysis then demonstrated that less stable Fe species may exhibit higher reactivities when situated in specific sites. Further machine learning analysis validated the significant relevance of activation barriers with reaction energies in the N 2 O decomposition step that is not sufficiently captured by the traditional one‐dimensional Brønsted–Evans–Polanyi (BEP) relationship. Methane ; Methanol ; Nitrous oxide ; Anaerobic oxidation of methane ; Oxide ; Chemistry ; Inorganic chemistry ; Iron oxide ; Environmental chemistry ; Chemical engineering ; Organic chemistry Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Catalysts for Methane Reforming
Authors: Ming Cheng | Wen Li | Wei Qi | Yuchao Dun | Victor W.-C. Chang | Zeyu Liu | Lei Jin | Guoke Wei
Source: Materials Today Communications (article, 2024-11-15)
Field: Materials Science — Materials Chemistry

Explainable machine-learning optimization of h-BN nanosheets with surface defects for enhanced hydrophobicity Materials science ; Surface (topology) ; Chemical engineering ; Surface modification ; Nanotechnology ; Composite material High voltage insulation and dielectric phenomena ; Membrane Separation Technologies ; Ferroelectric and Negative Capacitance Devices
Authors: Cunming Shu | Ghulam Mustafa | Mudassir Hussain Tahir | Mohamed A. El-Tayeb | Mahmoud A. A. Ibrahim
Source: Dyes and Pigments (article, 2024-08-05)
Field: Materials Science — Materials Chemistry

Machine learning using fingerprints and dye design in the search of lower hole reorganization energy Similarity (geometry) ; Computer science ; Artificial intelligence ; Random forest ; Energy (signal processing) ; Organic solar cell ; Machine learning ; Process engineering ; Pattern recognition (psychology) ; Biological system ; Chemistry ; Organic chemistry ; Mathematics ; Engineering ; Polymer Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Conducting polymers and applications
Authors: Mahboobeh Babaei | Ali Sadeghi
Source: Scientific Reports (article, 2024-12-28)
Field: Materials Science — Materials Chemistry

On machine learnability of local contributions to interatomic potentials from density functional theory calculations Machine learning interatomic potentials, as a modern generation of classical force fields, take atomic environments as input and predict the corresponding atomic energies and forces. We challenge the commonly accepted assumption that the contribution of an atom can be learned from the short-range local environment of that atom. We employ density functional theory calculations to quantify the decay of the induced electron density and electrostatic potential in response to local perturbations throughout insulating, semiconducting and metallic samples of different dimensionalities. Molecules and thin layers are shown to fail keeping such disturbances localized. Therefore, the learnability of local atomic contributions, which guarantees scalability and transferability of a machine learning interatomic potential, is questionable in the case of molecules and low-dimensional samples. Similarly, the induced electrostatic effects due to substituted impurities or vacancy sites in a crystalline bulk are weakly damped and remain significant beyond several interatomic distances. However, geometric deformations in bulks are practically local within the first neighbors and induce a Yukawa-type electrostatic potential that exponentially vanishes. The practical importance of this finding is that it limits the application of the machine learning interatomic potentials to conformational search or thermal properties of bulk materials and so on, where only purely geometrical deformations are involved. Once chemically impactful defects like aliovalent impurities or vacancies are present, the interatomic potentials trained on local environments need to be corrected for long-range effects. Learnability ; Atom (system on chip) ; Density functional theory ; Interatomic potential ; Range (aeronautics) ; Yukawa potential ; Electrostatics ; Physics ; Chemical physics ; Molecular physics ; Atomic physics ; Statistical physics ; Materials science ; Molecular dynamics ; Computer science ; Quantum mechanics ; Machine learning Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Dhruv Menon | David Fairen‐Jiménez
Source: ChemRxiv (preprint, 2024-06-27)
Field: Chemistry — Inorganic Chemistry

Guiding the rational design of biocompatible metal-organic frameworks for drug delivery In drug delivery, metal-organic frameworks (MOFs) have emerged as an interesting paradigm owing to their tuneable porosity, structural diversity, and ease of external surface functionalization. Compared to other drug delivery systems, state-of-the-art MOFs can encapsulate up to 50 times higher quantities of drugs per unit of mass while showing very long release times. Nevertheless, for their translation into clinical applications, concerns regarding MOF biocompatibility necessitate comprehensive mitigation. Unfortunately, experiments are resource and time-intensive, while modeling approaches fail to capture the behavior of MOFs in intricate biological systems. Herein, we report a novel computational pipeline guided by machine learning (ML) for probing the biocompatibility of MOFs. The pipeline is designed to expedite the assessment of potential MOF toxicity based on the chemical properties of their precursors. Interpretable ML models were built on a database of over 35,000 organic molecules, predicting the potential toxicity of MOF linkers with 83% accuracy. Additionally, we established a comprehensive database cataloging the toxicity of MOF metallic centers. Leveraging these, we have screened the Cambridge Structural Database (CSD) containing 86,000 non-disordered MOFs, identifying existing and future promising candidates with minimal toxicity profiles for drug delivery applications. Beyond high-throughput screening, the developed models shed light on the chemical landscape associated with high biocompatibility precursor molecules, enabling the derivation of guidelines for the rational design of biocompatible MOFs. This framework thus expedites the identification of biocompatible materials, while facilitating deeper insights into their underlying chemistry. Biocompatibility ; Drug delivery ; Biocompatible material ; Nanotechnology ; Metal-organic framework ; Rational design ; Computer science ; Pipeline (software) ; Materials science ; Biochemical engineering ; Chemistry ; Engineering ; Biomedical engineering ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; X-ray Diffraction in Crystallography ; Dendrimers and Hyperbranched Polymers
Authors: Shiang Fang | Mario Geiger | J. G. Checkelsky | Tess Smidt
Source: arXiv (Cornell University) (preprint, 2024-03-17)
Field: Materials Science — Materials Chemistry

Phonon predictions with E(3)-equivariant graph neural networks We present an equivariant neural network for predicting vibrational and phonon modes of molecules and periodic crystals, respectively. These predictions are made by evaluating the second derivative Hessian matrices of the learned energy model that is trained with the energy and force data. Using this method, we are able to efficiently predict phonon dispersion and the density of states for inorganic crystal materials. For molecules, we also derive the symmetry constraints for IR/Raman active modes by analyzing the phonon mode irreducible representations. Additionally, we demonstrate that using Hessian as a new type of higher-order training data improves energy models beyond models that only use lower-order energy and force data. With this second derivative approach, one can directly relate the energy models to the experimental observations for the vibrational properties. This approach further connects to a broader class of physical observables with a generalized energy model that includes external fields. Phonon ; Graph ; Artificial neural network ; Computer science ; Statistical physics ; Artificial intelligence ; Theoretical computer science ; Physics ; Condensed matter physics Thermal properties of materials ; Machine Learning in Materials Science
Authors: Yongyin Xu | Wei Deng | Jiaxin Zheng
Source: AIP Advances (article, 2024-11-01)
Field: Materials Science — Materials Chemistry

Study of crystal property prediction based on dual attention mechanism and transfer learning To avoid the step of manual feature engineering when predicting crystal properties, a graph convolutional neural network based on the dual attention mechanism, named DA-CGCNN, is proposed. It fuses both the channel attention mechanism and self-attention mechanism, named the dual attention mechanism, benefiting from capturing the complex features of each atom and dependencies between atomic nodes better. It is found to have comparable or superior performance to other advanced graph neural network (GNN) models by predicting five properties of the crystal: formation energy, total energy, bandgap, Fermi energy, and density. In addition, cross-property transfer learning is conducted on the computed properties from four small-sample crystal materials. The results show better performance on transferring prediction from these four samples. The proposed model in this study significantly improves the accuracy of crystal property prediction and demonstrates excellent prediction performance by incorporating transfer learning techniques. In summary, this work is important in accelerating the prediction of crystalline material properties and the discovery and design of crystalline materials. Dual (grammatical number) ; Mechanism (biology) ; Property (philosophy) ; Computer science ; Transfer of learning ; Materials science ; Chemistry ; Artificial intelligence ; Physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallization and Solubility Studies
Authors: Lin Shu | Guoqiang Zhang | Kaiwen Li | Kai Pang | Yushu Li | Jing Wan | Huasong Qin | Yilun Liu
Source: Extreme Mechanics Letters (article, 2024-06-25)
Field: Materials Science — Materials Chemistry

Strength prediction and design of defective graphene based on machine learning approach Graphene ; Artificial intelligence ; Nanotechnology ; Computer science ; Machine learning ; Materials science ; Engineering Graphene research and applications ; Graphene and Nanomaterials Applications ; Thermal properties of materials
Authors: Carlos Nieto‐Draghi | Benoît Creton | Xavier Martín | Johan Chaniot | Maxime Moreaud
Source: ACS Applied Engineering Materials (article, 2024-02-02)
Field: Materials Science — Materials Chemistry

Potential Energy Surface-Based Descriptors for Nanoporous Materials and its Applications to Classification and CO<sub>2</sub> Gas Adsorption into Zeolites The generalization of high-throughput synthesis has recently allowed the discovery of thousands of new porous materials, generating a large amount of information, with the development of specialized databases. Widespread access to databases enabled an increase in algorithms and models for property prediction and in silico design of materials. The structural information on materials still needs to be rationalized by the inclusion of descriptors to ease the characterization of solids. This is essential for in silico screening to potential applications based on machine learning (ML) approaches. Indeed, at the forefront of a real revolution in the selection and design of porous materials for many industrial applications, the use of appropriate descriptors to encode solid material properties (topology, porosity, and surface chemistry) is one of the fundamental aspects of the development of ML-based models. Our analysis of the literature reveals a lack of descriptors based on the potential energy surface (PES) of crystalline materials embedding crucial information such as the porosity, the topology, and the surface chemistry. In this work, we introduce new PES-based descriptors including the surface probability distribution of the local mean curvature (KH), the electrostatic-PES distribution (σe), as well as the local electrostatic-potential gradient surface probability distribution (∇σe). Our descriptors allow the classification of zeolites as well as its characterization by self-containing standard morphological and topological information (pore diameter, tortuosity, surface chemistry, etc.). We illustrate their usage to generate accurate ML-based models of the isosteric heat of adsorption of CO2 on purely siliceous zeolites of the IZA database and ion-exchanged zeolites in the function of the Si/Al ratio for the case of LTA topology. Nanoporous ; Tortuosity ; Characterization (materials science) ; Adsorption ; Surface (topology) ; Computer science ; Topology (electrical circuits) ; Porosity ; Materials science ; Embedding ; Machine learning ; Chemistry ; Biological system ; Nanotechnology ; Artificial intelligence ; Mathematics ; Geometry ; Organic chemistry Machine Learning in Materials Science ; Zeolite Catalysis and Synthesis ; Catalysis and Oxidation Reactions
Authors: Madhubanti Mukherjee | Harikrishna Sahu | Mark D. Losego | Will R. Gutekunst | Rampi Ramprasad
Source: ACS Applied Materials & Interfaces (article, 2024-02-17)
Field: Materials Science — Materials Chemistry

Informatics-Driven Design of Superhard B–C–O Compounds Materials containing B, C, and O, due to the advantages of forming strong covalent bonds, may lead to materials that are superhard, i.e., those with a Vicker's hardness larger than 40 GPa. However, the exploration of this vast chemical, compositional, and configurational space is nontrivial. Here, we leverage a combination of machine learning (ML) and first-principles calculations to enable and accelerate such a targeted search. The ML models first screen for potentially superhard B-C-O compositions from a large hypothetical B-C-O candidate space. Atomic-level structure search using density functional theory (DFT) within those identified compositions, followed by further detailed analyses, unravels on four potentially superhard B-C-O phases exhibiting thermodynamic, mechanical, and dynamic stability. Materials science ; Informatics ; Nanotechnology ; Materials informatics ; Engineering ethics ; Engineering physics ; Systems engineering ; Health informatics ; Engineering ; Nursing ; Electrical engineering Boron and Carbon Nanomaterials Research ; Advanced ceramic materials synthesis ; Advanced materials and composites
Authors: Marc Lehner | Paul Katzberger | Niels Maeder | Gregory A. Landrum | Sereina Riniker
Source: The Journal of Chemical Physics (article, 2024-08-15)
Field: Materials Science — Materials Chemistry

DASH properties: Estimating atomic and molecular properties from a dynamic attention-based substructure hierarchy Recently, we presented a method to assign atomic partial charges based on the DASH (dynamic attention-based substructure hierarchy) tree with high efficiency and quantum mechanical (QM)-like accuracy. In addition, the approach can be considered "rule based"-where the rules are derived from the attention values of a graph neural network-and thus, each assignment is fully explainable by visualizing the underlying molecular substructures. In this work, we demonstrate that these hierarchically sorted substructures capture the key features of the local environment of an atom and allow us to predict different atomic properties with high accuracy without building a new DASH tree for each property. The fast prediction of atomic properties in molecules with the DASH tree can, for example, be used as an efficient way to generate feature vectors for machine learning without the need for expensive QM calculations. The final DASH tree with the different atomic properties as well as the complete dataset with wave functions is made freely available. Substructure ; Dash ; Computer science ; Hierarchy ; Tree (set theory) ; Key (lock) ; Graph ; Computation ; Quantum ; Theoretical computer science ; Property (philosophy) ; Artificial intelligence ; Algorithm ; Data mining ; Topology (electrical circuits) ; Physics ; Mathematics ; Quantum mechanics ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Massimiliano Lupo Pasini | Mariia Karabin | Markus Eisenbach
Source: Machine Learning Science and Technology (article, 2024-04-10)
Field: Materials Science — Materials Chemistry

Transferring predictions of formation energy across lattices of increasing size* Abstract In this study, we show the transferability of graph convolutional neural network (GCNN) predictions of the formation energy of the nickel-platinum solid solution alloy across atomic structures of increasing sizes. The original dataset was generated with the large-scale atomic/molecular massively parallel simulator using the second nearest-neighbor modified embedded-atom method empirical interatomic potential. Geometry optimization was performed on the initially randomly generated face centered cubic crystal structures and the formation energy has been calculated at each step of the geometry optimization, with configurations spanning the whole compositional range. Using data from various steps of the geometry optimization, we first trained our open-source, scalable implementation of GCNN called HydraGNN on a lattice of 256 atoms, which accounts well for the short-range interactions. Using this data, we predicted the formation energy for lattices of 864 atoms and 2048 atoms, which resulted in lower-than-expected accuracy due to the long-range interactions present in these larger lattices. We accounted for the long-range interactions by including a small amount of training data representative for those two larger sizes, whereupon the predictions of HydraGNN scaled linearly with the size of the lattice. Therefore, our strategy ensured scalability while reducing significantly the computational cost of training on larger lattice sizes. Transferability ; Lattice (music) ; Scalability ; Energy minimization ; Computer science ; Atom (system on chip) ; Range (aeronautics) ; Massively parallel ; Crystal structure ; Interatomic potential ; Materials science ; Computational science ; Molecular dynamics ; Statistical physics ; Molecular physics ; Physics ; Chemistry ; Crystallography ; Parallel computing ; Computational chemistry ; Machine learning Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Computational Drug Discovery Methods