jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Leroy Cronin | Daniel Salley | J. Sebastin Manzano | Philip J. Kitson
Source: ChemRxiv (preprint, 2023-02-10)
Field: Materials Science — Materials Chemistry

Robotic Modules for the Programmable Chemputation of Molecules and Materials Before big data methods like machine learning and artificial intelligence methods can be used in chemistry, the digitization of chemistry and materials requires the development of a universal standard that is both affordable and broadly applicable. This has parallels with the foundations of the digital revolution which required standard architectures with a well-defined specification. Recently, we have developed automated platforms for the chemical artificial intelligence driven discovery, synthesis of molecules, materials, nanomaterials, and formulations. To avoid the use of highly expensive systems we focussed on the design and construction of standard hardware and software modules creating a road map for the digitization of chemistry across different fields. Our platforms can be divided into four different categories depending on their application: i) discovery systems for the search of chemical space and new reactivity, ii) synthesis and manufacture of fine chemicals, iii) formulation discovery and exploration, and iv) materials discovery and synthesis. We describe the evolution and convergence of these platforms in terms of, common hardware, firmware, and software along with the development of a programming language for chemical and material systems. This programming approach is not only useful for reliable synthesis, but for design of experiments, discovery, optimisation and providing new standards for collaboration. This approach is also vital for the verification of findings published in the literature, databases, to increase the reliability of experimental outcomes, and to allow collaboration across different research laboratory settings as well as the sharing of failed experiments. Computer science ; Chemical space ; Digitization ; Firmware ; Software ; Bioorthogonal chemistry ; Parallels ; Software engineering ; Programming language ; Drug discovery ; Engineering ; Chemistry ; Combinatorial chemistry ; Click chemistry Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Scientific Computing and Data Management
Authors: Fatmagul Kivrak Yildiz | Merve Sogancioglu Kalem
Source: Environmental Progress & Sustainable Energy (article, 2023-04-19)
Field: Materials Science — Biomaterials

Investigation of mechanical, thermal, and acoustic performance of insulation materials containing microwave and ultrasound pretreated coffee wastes and estimation of thermal parameters using artificial neural network Abstract As a result of increasing coffee consumption in the world, the amount of coffee waste, which is an important environmental problem, is increasing. Due to their complex organic properties, they should not be discharged to landfills. At the same time, these wastes with a porous and cellulosic structure, have high acoustic and heat insulation performances in the building material they are used. In this study, commercial coffee wastes were evaluated without pre‐treatment (CW) and with microwave (MCW) and ultrasound (UCW) pre‐treatment in insulation plaster containing boron. First, the morphological, thermal, and chemical properties of coffee wastes were determined by SEM, TGA, and FTIR analyzes. CW, MCW, and UCW wastes were replaced with insulation plaster at rates of 10%, 20%, and 30%. The physical, mechanical, thermal, and acoustic properties of insulation plasters produced from coffee waste were determined by the analysis methods given in Turkish Standards. According to the results, insulation plasters obtained from pre‐treated wastes were more efficient in terms of mechanical and thermal properties. In terms of thermal and acoustic insulation, UCW30 insulation plasters showed the best performance. Thermal conductivity values were estimated by entering dosage, density, water absorption, bond strength, compressive strength, and moisture values as input dataset to artificial neural network (ANN). The developed ANN model and the experimentally obtained data were compared and it was observed that the results were in harmony. As a result, with this study, a new generation green insulation material was produced by recycling coffee wastes. In this way, a different solution has been found for an important environmental problem. Materials science ; Compressive strength ; Composite material ; Thermal insulation ; Absorption of water ; Thermal conductivity ; Porosity ; Coffee grounds ; Waste management ; Microwave ; Moisture ; Thermal ; Engineering Advanced Cellulose Research Studies ; Natural Fiber Reinforced Composites ; Aerosol Filtration and Electrostatic Precipitation
Authors: Zheng Yuan | Yaoyun Zhang | Chuanqi Tan | Wei Wang | Fei Huang | Songfang Huang
Source: arXiv (Cornell University) (preprint, 2023-02-02)
Field: Materials Science — Materials Chemistry

Molecular Geometry-aware Transformer for accurate 3D Atomic System modeling Molecular dynamic simulations are important in computational physics, chemistry, material, and biology. Machine learning-based methods have shown strong abilities in predicting molecular energy and properties and are much faster than DFT calculations. Molecular energy is at least related to atoms, bonds, bond angles, torsion angles, and nonbonding atom pairs. Previous Transformer models only use atoms as inputs which lack explicit modeling of the aforementioned factors. To alleviate this limitation, we propose Moleformer, a novel Transformer architecture that takes nodes (atoms) and edges (bonds and nonbonding atom pairs) as inputs and models the interactions among them using rotational and translational invariant geometry-aware spatial encoding. Proposed spatial encoding calculates relative position information including distances and angles among nodes and edges. We benchmark Moleformer on OC20 and QM9 datasets, and our model achieves state-of-the-art on the initial state to relaxed energy prediction of OC20 and is very competitive in QM9 on predicting quantum chemical properties compared to other Transformer and Graph Neural Network (GNN) methods which proves the effectiveness of the proposed geometry-aware spatial encoding in Moleformer. Molecular geometry ; Transformer ; Energy minimization ; Computer science ; Quantum ; Algorithm ; Torsion (gastropod) ; Geometry ; Benchmark (surveying) ; Topology (electrical circuits) ; Statistical physics ; Physics ; Molecule ; Mathematics ; Quantum mechanics ; Combinatorics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Topic Modeling
Authors: Haiyang Yu | Meng Liu | Youzhi Luo | Alex Strasser | Xiaofeng Qian | Xiaoning Qian | Shuiwang Ji
Source: arXiv (Cornell University) (preprint, 2023-06-15)
Field: Materials Science — Materials Chemistry

QH9: A Quantum Hamiltonian Prediction Benchmark for QM9 Molecules Supervised machine learning approaches have been increasingly used in accelerating electronic structure prediction as surrogates of first-principle computational methods, such as density functional theory (DFT). While numerous quantum chemistry datasets focus on chemical properties and atomic forces, the ability to achieve accurate and efficient prediction of the Hamiltonian matrix is highly desired, as it is the most important and fundamental physical quantity that determines the quantum states of physical systems and chemical properties. In this work, we generate a new Quantum Hamiltonian dataset, named as QH9, to provide precise Hamiltonian matrices for 999 or 2998 molecular dynamics trajectories and 130,831 stable molecular geometries, based on the QM9 dataset. By designing benchmark tasks with various molecules, we show that current machine learning models have the capacity to predict Hamiltonian matrices for arbitrary molecules. Both the QH9 dataset and the baseline models are provided to the community through an open-source benchmark, which can be highly valuable for developing machine learning methods and accelerating molecular and materials design for scientific and technological applications. Our benchmark is publicly available at https://github.com/divelab/AIRS/tree/main/OpenDFT/QHBench. Benchmark (surveying) ; Hamiltonian (control theory) ; Computer science ; Quantum chemical ; Quantum ; Hamiltonian matrix ; Quantum chemistry ; Molecule ; Artificial intelligence ; Machine learning ; Statistical physics ; Physics ; Mathematics ; Quantum mechanics ; Mathematical optimization Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Frederik van Veen | Luca Ornago | Herre S. J. van der Zant | Maria El Abbassi
Source: Journal of Materials Chemistry C (article, 2023-01-01)
Field: Chemistry — Electrochemistry

A generalized neural network approach for separation of molecular breaking traces The presented generalized neural network is a reliable and efficient tool for the separation of tunneling-only traces from break-junction data sets. Separation (statistics) ; Artificial neural network ; Materials science ; Quantum tunnelling ; Biological system ; Artificial intelligence ; Computer science ; Machine learning ; Optoelectronics Electrochemical Analysis and Applications
Authors: Yinuo Yang | Shuhao Zhang | Kavindri Ranasinghe | Olexandr Isayev | Adrián E. Roitberg
Source: ChemRxiv (preprint, 2023-09-20)
Field: Materials Science — Materials Chemistry

Machine Learning of Reactive Potentials In the past two decades, machine learning potentials (MLPs) have driven significant developments in chemical, biological and material sciences. The construction and training of MLPs enables fast and accurate simulations and analysis on thermodynamic and kinetic properties. This review focuses on the applications of MLPs to reaction systems with consideration of bond breaking and formation. We review the development of MLP models, primarily with neural network and kernel-based algorithms, and recent applications of reactive MLPs (RMLPs) to systems at different scales. We will show how RMLPs are constructed and how they speed up the calculation of reactive dynamics and facilitate study of reaction trajectories, reaction rates, free energy calculations, and many other calculations. Different data sampling strategies applied in building RMLPs are also discussed with a focus on how to collect structures for rare events and how to further improve the performance with active learning. Computer science ; Artificial neural network ; Machine learning ; Kernel (algebra) ; Artificial intelligence ; Focus (optics) ; Physics ; Mathematics Machine Learning in Materials Science ; Topic Modeling
Authors: Naoya Chiba | Yuta Suzuki | Tatsunori Taniai | Ryo Igarashi | Yoshitaka Ushiku | Kotaro Saito | Kanta Ono
Source: Communications Materials (article, 2023-12-12)
Field: Materials Science — Materials Chemistry

Neural structure fields with application to crystal structure autoencoders Abstract Representing crystal structures of materials to facilitate determining them via neural networks is crucial for enabling machine-learning applications involving crystal structure estimation. Among these applications, the inverse design of materials can contribute to explore materials with desired properties without relying on luck or serendipity. Here, we propose neural structure fields (NeSF) as an accurate and practical approach for representing crystal structures using neural networks. Inspired by the concepts of vector fields in physics and implicit neural representations in computer vision, the proposed NeSF considers a crystal structure as a continuous field rather than as a discrete set of atoms. Unlike existing grid-based discretized spatial representations, the NeSF overcomes the tradeoff between spatial resolution and computational complexity and can represent any crystal structure. We propose an autoencoder of crystal structures that can recover various crystal structures, such as those of perovskite structure materials and cuprate superconductors. Extensive quantitative results demonstrate the superior performance of the NeSF compared with the existing grid-based approach. Artificial neural network ; Autoencoder ; Computer science ; Artificial intelligence ; Crystal structure ; Crystal structure prediction ; Crystal (programming language) ; Discretization ; Grid ; Algorithm ; Field (mathematics) ; Mathematics ; Geometry ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced X-ray and CT Imaging
Authors: Alessio Fallani | Leonardo Medrano Sandonas | Alexandre Tkatchenko
Source: arXiv (Cornell University) (preprint, 2023-09-01)
Field: Materials Science — Materials Chemistry

Enabling Inverse Design in Chemical Compound Space: Mapping Quantum Properties to Structures for Small Organic Molecules Computer-driven molecular design combines the principles of chemistry, physics, and artificial intelligence to identify novel chemical compounds and materials with desired properties for a specific application. In particular, quantum-mechanical (QM) methods combined with machine learning (ML) techniques have accelerated the estimation of accurate molecular properties, providing a direct mapping from 3D molecular structures to their properties. However, the development of reliable and efficient methodologies to enable \emph{inverse mapping} in chemical space is a long-standing challenge that has not been accomplished yet. Here, we address this challenge by demonstrating the possibility of parametrizing a given chemical space with a finite set of extensive and intensive QM properties. In doing so, we develop a proof-of-concept implementation that combines a Variational Auto-Encoder (VAE) trained on molecular structures with a property encoder designed to learn the latent representation from a set of QM properties. The result of this joint architecture is a common latent space representation for both structures and properties, which enables property-to-structure mapping for small drug-like molecules contained in the QM7-X dataset. We illustrate the capabilities of our approach by conditional generation of \emph{de novo} molecular structures with targeted properties, transition path interpolation for chemical reactions as well as insights into property-structure relationships. Our findings thus provide a proof-of-principle demonstration aiming to enable the inverse property-to-structure design in diverse chemical spaces. Chemical space ; Representation (politics) ; Computer science ; Property (philosophy) ; Interpolation (computer graphics) ; Set (abstract data type) ; Space (punctuation) ; Inverse ; Quantum chemical ; Encoder ; Theoretical computer science ; Algorithm ; Molecule ; Mathematics ; Artificial intelligence ; Chemistry ; Physics ; Drug discovery ; Quantum mechanics ; Geometry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Oliver Schilter | Carlo Baldassari | Teodoro Laino | Philippe Schwaller
Source: ChemRxiv (preprint, 2023-08-11)
Field: Materials Science — Materials Chemistry

Predicting solvents with the help of Artificial Intelligence The right solvent is a crucial factor in achieving environmentally friendly, selective, and highly converted chemical reactions. Artificial intelligence-based tools, often lack the ability to reliably predict reaction conditions such as the appropriate solvent. Here, we present a comprehensive investigation into the efficacy of data-driven machine-learning models for solvent prediction for a broad spectrum of single-solvent organic reactions. Remarkably, our models achieve a Top-3 accuracy of 86.88%, showcasing outstanding performance in predicting solvents from underrepresented classes. An uncertainty analysis revealed that the models' misclassifications could be explained by the fact that the reaction can be run in multiple solvents. In the experimental validation, 8 out of 11 reactions succeeded with the predicted solvent. Our work addresses a key challenge in organic synthesis and demonstrates the practical application of machine learning models in predicting reaction solvents for more efficient and sustainable chemical synthesis. Solvent ; Environmentally friendly ; Biochemical engineering ; Work (physics) ; Machine learning ; Computer science ; Organic solvent ; Artificial intelligence ; Chemistry ; Process engineering ; Organic chemistry ; Chemical engineering ; Engineering ; Mechanical engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Zhongheng Fu | Dawei Zhang
Source: Journal of Energy Chemistry (article, 2023-04-24)
Field: Materials Science — Materials Chemistry

Universal machine learning potential accelerates atomistic modeling of materials Coupling (piping) ; Charge (physics) ; Computer science ; Ionic bonding ; Motion (physics) ; Artificial intelligence ; Nanotechnology ; Statistical physics ; Materials science ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Electrocatalysts for Energy Conversion
Authors: Nada Alfryyan | Muhammad Saqib | Muhammad Arsal Farooq | Muhammad Ali | Tayyaba Mubashir | Mudassir Hussain Tahir | Z.A. Alrowaili | M.S. Al-Buriahi
Source: Chemical Physics Letters (article, 2023-06-25)
Field: Materials Science — Polymers and Plastics

Virtual mining of polymer monomers for photodetectors application and regression-aided reorganization energy prediction Computer science ; Photodetector ; Intramolecular force ; Artificial intelligence ; Organic semiconductor ; Machine learning ; Materials science ; Chemistry ; Optoelectronics Conducting polymers and applications ; Organic Electronics and Photovoltaics ; Machine Learning in Materials Science
Authors: Lingyu Kong | Jielan Li | Lixin Sun | Han Yang | Hongxia Hao | Chi Chen | Nongnuch Artrith | José Antonio Garrido Torres | Ziheng Lu | Yichi Zhou
Source: arXiv (Cornell University) (preprint, 2023-11-14)
Field: Materials Science — Materials Chemistry

Overcoming the Size Limit of First Principles Molecular Dynamics Simulations with an In-Distribution Substructure Embedding Active Learner Large-scale first principles molecular dynamics are crucial for simulating complex processes in chemical, biomedical, and materials sciences. However, the unfavorable time complexity with respect to system sizes leads to prohibitive computational costs when the simulation contains over a few hundred atoms in practice. We present an In-Distribution substructure Embedding Active Learner (IDEAL) to enable efficient simulation of large complex systems with quantum accuracy by maintaining a machine learning force field (MLFF) as an accurate surrogate to the first principles methods. By extracting high-uncertainty substructures into low-uncertainty atom environments, the active learner is allowed to concentrate on and learn from small substructures of interest rather than carrying out intractable quantum chemical computations on large structures. IDEAL is benchmarked on various systems and shows sub-linear complexity, accelerating the simulation thousands of times compared with conventional active learning and millions of times compared with pure first principles simulations. To demonstrate the capability of IDEAL in practical applications, we simulated a polycrystalline lithium system composed of one million atoms and the full ammonia formation process in a Haber-Bosch reaction on a 3-nm Iridium nanoparticle catalyst on a computing node comprising one single A100 GPU and 24 CPU cores. Molecular dynamics ; Embedding ; Computer science ; Substructure ; Computational science ; Ideal (ethics) ; Statistical physics ; Physics ; Quantum mechanics ; Artificial intelligence Machine Learning in Materials Science ; Machine Learning and Algorithms ; Microfluidic and Capillary Electrophoresis Applications
Authors: Mingze Zheng | Brandon C. Bukowski
Source: ChemRxiv (preprint, 2023-10-30)
Field: Chemistry — Inorganic Chemistry

Probing the role of acid site distribution on water structure in aluminosilicate zeolites: insights from molecular dynamics Water plays a pivotal role in numerous chemical processes, especially in the production of fuels and fine chemicals derived from bio-based feedstocks. Zeolites are porous catalysts used extensively due to their shape-selective adsorption and confinement interactions; However the kinetics of zeolite catalyzed reactions are significantly impacted by the presence of water, which may affect product selectivity and intrinsic rate constants depending on transition state polarity. In this study, we employed machine learning force fields (MLFFs) to accelerate ab initio molecular dynamics (AIMD) simulations and enhance the phase space exploration of water configurations in Brønsted acid zeolites. We interrogated the structure of adsorbed water based on the Si/Al ratio and acid site distribution to disentangle the impact of acid site density and distribution on water matrix organization as a function of water loading. We integrated adsorption thermodynamics, vibrational spectroscopy simulations, and local density maps to interrogate the spatial orientation of adsorbed water clusters and their degree of hydrogen bonding. Our analysis unveiled the intricate interplay between zeolite structure, Brønsted acid site location, and water where spatially disparate acid sites nucleate extended clusters that span siliceous regions of the zeolite. We found that the length scale of ordered water regions is directly related to the Si/Al ratio and spatial distribution of Al sites. These findings provide insights into the molecular-level structure of water in microporous aluminosilicate micropores and demonstrate how acid sites can be used to control water activity which has applications to heterogeneous catalysis and adsorptive separations. Zeolite ; Microporous material ; Aluminosilicate ; Molecular dynamics ; Catalysis ; Chemistry ; Chemical physics ; Adsorption ; Brønsted–Lowry acid–base theory ; Nucleation ; Chemical engineering ; Computational chemistry ; Materials science ; Physical chemistry ; Organic chemistry Zeolite Catalysis and Synthesis ; Spectroscopy and Quantum Chemical Studies ; Mesoporous Materials and Catalysis