jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Cheng-Han Li | Daniel P. Tabor
Source: The Journal of Physical Chemistry A (article, 2023-04-05)
Field: Materials Science — Materials Chemistry

Reorganization Energy Predictions with Graph Neural Networks Informed by Low-Cost Conformers A critical bottleneck for the design of high-conductivity organic materials is finding molecules with low reorganization energy. To enable high-throughput virtual screening campaigns for many types of organic electronic materials, a fast reorganization energy prediction method compared to density functional theory is needed. However, the development of low-cost machine-learning-based models for calculating the reorganization energy has proven to be challenging. In this paper, we combine a 3D graph-based neural network (GNN) recently benchmarked for drug design applications, ChIRo, with low-cost conformational features for reorganization energy predictions. By comparing the performance of ChIRo to another 3D GNN, SchNet, we find evidence that the bond-invariant property of ChIRo enables the model to learn from low-cost conformational features more efficiently. Through an ablation study with a 2D GNN, we find that using low-cost conformational features on top of 2D features informs the model for making more accurate predictions. Our results demonstrate the feasibility of reorganization energy predictions on the benchmark QM9 data set without needing DFT-optimized geometries and demonstrate the types of features needed for robust models that work on diverse chemical spaces. Furthermore, we show that ChIRo informed with low-cost conformational features achieves comparable performance with the previously reported structure-based model on π-conjugated hydrocarbon molecules. We expect this class of methods can be applied to the high-throughput screening of high-conductivity organic electronics candidates. Bottleneck ; Computer science ; Benchmark (surveying) ; Artificial neural network ; Artificial intelligence ; Conformational isomerism ; Molecule ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Stiv Llenga | Ganna Gryn’ova
Source: The Journal of Chemical Physics (article, 2023-06-02)
Field: Materials Science — Materials Chemistry

Matrix of orthogonalized atomic orbital coefficients representation for radicals and ions Chemical (molecular, quantum) machine learning relies on representing molecules in unique and informative ways. Here, we present the matrix of orthogonalized atomic orbital coefficients (MAOC) as a quantum-inspired molecular and atomic representation containing both structural (composition and geometry) and electronic (charge and spin multiplicity) information. MAOC is based on a cost-effective localization scheme that represents localized orbitals via a predefined set of atomic orbitals. The latter can be constructed from such small atom-centered basis sets as pcseg-0 and STO-3G in conjunction with guess (non-optimized) electronic configuration of the molecule. Importantly, MAOC is suitable for representing monatomic, molecular, and periodic systems and can distinguish compounds with identical compositions and geometries but distinct charges and spin multiplicities. Using principal component analysis, we constructed a more compact but equally powerful version of MAOC-PCX-MAOC. To test the performance of full and reduced MAOC and several other representations (CM, SOAP, SLATM, and SPAHM), we used a kernel ridge regression machine learning model to predict frontier molecular orbital energy levels and ground state single-point energies for chemically diverse neutral and charged, closed- and open-shell molecules from an extended QM7b dataset, as well as two new datasets, N-HPC-1 (N-heteropolycycles) and REDOX (nitroxyl and phenoxyl radicals, carbonyl, and cyano compounds). MAOC affords accuracy that is either similar or superior to other representations for a range of chemical properties and systems. Atomic orbital ; Molecular orbital ; Chemistry ; Molecular graph ; Molecule ; Quantum chemistry ; Hamiltonian matrix ; Computational chemistry ; Atom (system on chip) ; Chemical physics ; Molecular physics ; Physics ; Electron ; Quantum mechanics ; Eigenvalues and eigenvectors ; Computer science ; Symmetric matrix Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Tatiana Osheter | Salvatore Campisi‐Pinto | C. Randieri | A. Perrotta | Charles Linder | Zeev Weisman
Source: Sensors (article, 2023-02-13)
Field: Chemistry — Analytical Chemistry

Semi-Autonomic AI LF-NMR Sensor for Industrial Prediction of Edible Oil Oxidation Status The evaluation of an oil's oxidation status during industrial production is highly important for monitoring the oil's purity and nutritional value during production, transportation, storage, and cooking. The oil and food industry is seeking a real-time, non-destructive, rapid, robust, and low-cost sensor for nutritional oil's material characterization. Towards this goal, a <sup>1</sup>H LF-NMR relaxation sensor application based on the chemical and structural profiling of non-oxidized and oxidized oils was developed. This study dealt with a relatively large-scale oil oxidation database, which included crude data of a <sup>1</sup>H LF-NMR relaxation curve, and its reconstruction into T<sub>1</sub> and T<sub>2</sub> spectral fingerprints, self-diffusion coefficient D, and conventional standard chemical test results. This study used a convolutional neural network (CNN) that was trained to classify T<sub>2</sub> relaxation curves into three ordinal classes representing three different oil oxidation levels (non-oxidized, partial oxidation, and high level of oxidation). Supervised learning was used on the T<sub>2</sub> signals paired with the ground-truth labels of oxidation values as per conventional chemical lab oxidation tests. The test data results (not used for training) show a high classification accuracy (95%). The proposed AI method integrates a large training set, an LF-NMR sensor, and a machine learning program that meets the requirements of the oil and food industry and can be further developed for other applications. Convolutional neural network ; Artificial intelligence ; Computer science ; Training set ; Artificial neural network ; Test set ; Pattern recognition (psychology) ; Food industry ; Environmental science ; Chemistry ; Materials science ; Food science Spectroscopy and Chemometric Analyses ; NMR spectroscopy and applications ; Advanced Chemical Sensor Technologies
Authors: Prateek Malhotra | Subhayan Biswas | Ganesh D. Sharma
Source: ACS Applied Materials & Interfaces (article, 2023-07-25)
Field: Materials Science — Materials Chemistry

Directed Message Passing Neural Network for Predicting Power Conversion Efficiency in Organic Solar Cells Organic solar cells (OSCs) have emerged as a promising technology for renewable energy generation, and researchers are constantly exploring ways to improve their efficiency. For prediction of photovoltaic properties in OSCs, many machine learning models have been used in the past. All the models are used with fixed molecular descriptors and molecular fingerprints as input for power conversion efficiency (PCE) prediction. Recently, the graph neural network (GNN), which can model graph structures of the molecule, has received increasing attention as a method that could potentially overcome the limitations of fixed descriptors by learning the task-specific representations using graph convolutions. In this study, we have used the directed message passing neural network (D-MPNN), an emerging type of GNN for predicting PCE of organic solar cells, and the results are compared for the same train and test set with fixed descriptors and fingerprints. The excellent performance demonstrated by the D-MPNN model in this investigation highlights its potential for predicting PCE, surpassing the limitations of conventional fixed descriptors. Photovoltaic system ; Artificial neural network ; Computer science ; Organic solar cell ; Artificial intelligence ; Renewable energy ; Energy conversion efficiency ; Graph ; Deep learning ; Set (abstract data type) ; Machine learning ; Materials science ; Theoretical computer science ; Engineering ; Electrical engineering Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Conducting polymers and applications
Authors: Thomas S. Hofer | Risnita Vicky Listyarini | Emir Hajdarevic | L. MAIER | Felix R. S. Purtscher | Jakob Gamper | F. Hanser
Source: The Journal of Physical Chemistry Letters (review, 2023-06-23)
Field: Chemistry — Inorganic Chemistry

Beyond the Status Quo: Density Functional Tight Binding and Neural Network Potentials as a Versatile Simulation Strategy to Characterize Host–Guest Interactions in Metal- and Covalent Organic Frameworks In recent years, research focused on synthesis, characterization, and application of metal-organic frameworks (MOFs) has attracted increased interest, from both an experimental as well as a theoretical perspective. Self-consistent charge density functional tight binding (SCC DFTB) in conjunction with a suitable constrained molecular dynamics (MD) simulation protocol provides a versatile and flexible platform for the study of pristine MOFs as well as guest@MOF systems. Although being a semi-empirical quantum mechanical method, SCC DFTB inherently accounts for polarization and many-body contributions, which may become a limiting factor in purely force field-based simulation studies. A number of examples such as CO<sub>2</sub>, indigo, and drug molecules embedded in various MOF hosts are discussed to highlight the capabilities of the presented simulation approach. Furthermore, a promising extension of the outlined simulation strategy toward the treatment of covalent organic frameworks utilizing state-of-the-art neural network potentials providing a description at DFT accuracy and force field cost is outlined. Host (biology) ; Status quo ; Metal-organic framework ; Covalent bond ; Artificial neural network ; Nanotechnology ; Chemistry ; Computer science ; Computational chemistry ; Materials science ; Combinatorial chemistry ; Organic chemistry ; Artificial intelligence ; Adsorption ; Biology ; Ecology Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Ionic liquids properties and applications
Authors: Keda Ding | Tong Yang | Man Tai Leung | Ke Yang | Hao Cheng | Minggang Zeng | Bing Li | Ming Yang
Source: Current Opinion in Electrochemistry (article, 2023-10-04)
Field: Materials Science — Materials Chemistry

Recent advances in the data-driven development of emerging electrocatalysts Throughput ; Computer science ; Electrocatalyst ; Nanotechnology ; Biochemical engineering ; Materials science ; Wireless ; Engineering ; Chemistry ; Electrochemistry ; Telecommunications Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; CO2 Reduction Techniques and Catalysts
Authors: Andrey V. Dobrynin | Anastasia Stroujkova | Mohammad Vatankhah‐Varnosfaderani | Sergei S. Sheiko
Source: ACS Macro Letters (article, 2023-10-27)
Field: Materials Science — Surfaces, Coatings and Films

Coarse-Grained Artificial Intelligence for Design of Brush Networks The ability to synthesize elastomeric materials with programmable mechanical properties is vital for advanced soft matter applications. Due to the inherent complexity of hierarchical structure-property correlations in brush-like polymer networks, the application of conventional theory-based, so-called Human Intelligence (HI) approaches becomes increasingly difficult. Herein we developed a design strategy based on synergistic combination of HI and AI tools which allows precise encoding of mechanical properties with three architectural parameters: degrees of polymerization (DP) of network strands, <i>n</i><sub><i>x</i></sub>, side chains, <i>n</i><sub>sc</sub>, backbone spacers between side chains, <i>n</i><sub>g</sub>. Implementing a multilayer feedforward artificial neural network (ANN), we took advantage of model-predicted structure-property cross-correlations between coarse-grained system code including chemistry specific characteristics <b>S</b> = [<i>l</i>, <i>v</i>, <i>b</i>] defined by monomer projection length <i>l</i> and excluded volume <i>v</i>, Kuhn length <i>b</i> of bare backbone and side chains, and architecture <b>A</b> = [<i>n</i><sub>sc</sub>, <i>n</i><sub>g</sub>, <i>n</i><sub><i>x</i></sub>] of polymer networks and their equilibrium mechanical properties <b>P</b> = [<i>G</i>, β] including the structural shear modulus <i>G</i> and firmness parameter β. The ANN was trained by minimizing the mean-square error with Bayesian regularization to avoid overfitting using a data set of experimental stress-deformation curves of networks with brush-like strands of poly(<i>n</i>-butyl acrylate), poly(isobutylene), and poly(dimethylsiloxane) having structural modulus <i>G</i> < 50 kPa and 0.01 ≤ β ≤ 0.3. The trained ANN predicts network mechanical properties with 95% confidence. The developed ANN was implemented for synthesis of model networks with identical mechanical properties but different chemistries of network strands. Brush ; Artificial intelligence ; Computer science ; Materials science ; Composite material Polymer Surface Interaction Studies ; Advanced Surface Polishing Techniques ; Hydrogels: synthesis, properties, applications
Authors: Xinlun Cheng | Sheng Zhang | Phong Nguyen | Shahab Azarfar | Gia-Wei Chern | Stephen Baek
Source: Physical Review Research (article, 2023-09-14)
Field: Materials Science — Materials Chemistry

Convolutional neural networks for large-scale dynamical modeling of itinerant magnets Complex spin textures in itinerant electron magnets hold promises for next-generation memory and information technology. The long-ranged and often frustrated electron-mediated spin interactions in these materials give rise to intriguing localized spin structures such as skyrmions. Yet, simulations of magnetization dynamics for such itinerant magnets are computationally difficult due to the need for repeated solutions to the electronic structure problems. We present a convolutional neural network (CNN) model to accurately and efficiently predict the electron-induced magnetic torques acting on local spins. Importantly, as the convolutional operations with a fixed kernel (receptive field) size naturally take advantage of the locality principle for many-electron systems, CNNs offer a scalable machine learning approach to spin dynamics. We apply our approach to enable large-scale dynamical simulations of skyrmion phases in itinerant spin systems. By incorporating the CNN model into Landau-Lifshitz-Gilbert dynamics, our simulations successfully reproduce the relaxation process of the skyrmion phase and stabilize a skyrmion lattice in larger systems. The CNN model also allows us to compute the effective receptive fields, thus providing a systematic and unbiased method for determining the locality of the original electron models. Skyrmion ; Convolutional neural network ; Locality ; Statistical physics ; Physics ; Spins ; Magnet ; Spin (aerodynamics) ; Electron ; Computer science ; Relaxation (psychology) ; Spin ice ; Artificial intelligence ; Condensed matter physics ; Quantum mechanics ; Magnetic monopole Machine Learning in Materials Science ; Magnetic properties of thin films ; Quantum many-body systems
Authors: Zijuan Luo | Jiale Peng | Yue Mu | Li Sun | Zhihua Zhu | Zhen Liu
Source: Journal of Catalysis (article, 2023-09-15)
Field: Materials Science — Materials Chemistry

Design of Cr-PNP catalysts for ethylene tri-/tetramerization assisted by a data-driven approach Chemistry ; Selectivity ; Catalysis ; Ethylene ; Gibbs free energy ; Transition metal ; Feature selection ; Combinatorial chemistry ; Biological system ; Computational chemistry ; Biochemical engineering ; Artificial intelligence ; Thermodynamics ; Computer science ; Organic chemistry Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Chemical Synthesis and Analysis
Authors: Jackson Burns | David Rogers
Source: Journal of Chemical Information and Modeling (article, 2023-11-21)
Field: Materials Science — Materials Chemistry

QuantumScents: Quantum-Mechanical Properties for 3.5k Olfactory Molecules Quantitative structure-odor relationships are critically important for studies related to the function of olfaction. Current literature data sets contain expert-labeled molecules but lack feature data. This paper introduces QuantumScents, a quantum mechanics augmented derivative of the Leffingwell data set. QuantumScents contains 3.5k structurally and chemically diverse molecules ranging from 2 to 30 heavy atoms (CNOS) and their corresponding 3D coordinates, total PBE0 energy, molecular dipole moment, and per-atom Hirshfeld charges, dipoles, and ratios. The authors demonstrate that Hirshfeld charges and ratios contain sufficient information to perform molecular classification by training a Message Passing Neural Network with chemprop (Heid, E.; et al. <i>ChemRxiv</i>, 2023, DOI: 10.26434/chemrxiv-2023-3zcfl) to predict scent labels. The QuantumScents data set is freely available on Zenodo along with the authors' code, example models, and data set generation workflow (https://zenodo.org/doi/10.5281/zenodo.8239853). Quantum ; Molecule ; Chemistry ; Physics ; Organic chemistry ; Quantum mechanics Machine Learning in Materials Science ; Various Chemistry Research Topics ; Advanced Chemical Sensor Technologies
Authors: Myongin Oh | Gabriel C. A. da Hora | Jessica M. J. Swanson
Source: Journal of Chemical Theory and Computation (article, 2023-11-09)
Field: Chemistry — Electrochemistry

tICA-Metadynamics for Identifying Slow Dynamics in Membrane Permeation Molecular simulations are commonly used to understand the mechanism of membrane permeation of small molecules, particularly for biomedical and pharmaceutical applications. However, despite significant advances in computing power and algorithms, calculating an accurate permeation free energy profile remains elusive for many drug molecules because it can require identifying the rate-limiting degrees of freedom (i.e., appropriate reaction coordinates). To resolve this issue, researchers have developed machine learning approaches to identify slow system dynamics. In this work, we apply time-lagged independent component analysis (tICA), an unsupervised dimensionality reduction algorithm, to molecular dynamics simulations with well-tempered metadynamics to find the slowest collective degrees of freedom of the permeation process of trimethoprim through a multicomponent membrane. We show that tICA-metadynamics yields translational and orientational collective variables (CVs) that increase convergence efficiency ∼1.5 times. However, crossing the periodic boundary is shown to introduce artifacts in the translational CV that can be corrected by taking absolute values of molecular features. Additionally, we find that the convergence of the tICA CVs is reached with approximately five membrane crossings and that data reweighting is required to avoid deviations in the translational CV. Metadynamics ; Molecular dynamics ; Degrees of freedom (physics and chemistry) ; Permeation ; Curse of dimensionality ; Convergence (economics) ; Computer science ; Statistical physics ; Chemistry ; Biological system ; Chemical physics ; Membrane ; Computational chemistry ; Physics ; Artificial intelligence ; Thermodynamics Electrochemical Analysis and Applications ; Spectroscopy and Quantum Chemical Studies ; Force Microscopy Techniques and Applications
Authors: Mao-Ken Hsu | Wei Chen | Bo-Yu Huang | Li-Hsuan Shen | Chia‐Hsiang Hsu | Rong‐Yeu Chang | Chi‐Hua Yu
Source: Frontiers in Materials (article, 2023-05-05)
Field: Materials Science — Polymers and Plastics

A deep learning empowered smart representative volume element method for long fiber woven composites In response to the global trend of carbon reduction over the last few years, various industries, including the aviation and automobile industries, have gradually begun research, design, and production of carbon fiber composite materials. These have excellent mechanical properties, such as being lightweight, high strength, and of high rigidity, which provide weight reduction and energy savings in applications across many fields. When used as a load-beam structure, the weave pattern determines the primary mechanical properties of the composite material. Therefore, the production of diverse products and components can be carried out using different patterns of weaving and manufacturing according to an application’s requirements. The mechanical properties of woven fiber composites can be obtained by using simulation analysis software, which can reduce unnecessary waste during design and manufacturing. However, difficulties arise in the simulation analysis due to the complexity of the weaving method. With the continuous improvement of computer technology in recent years and the enormous amount of training data available, many research teams have begun to implement artificial intelligence (AI) technology, which has been widely used to overcome long-standing obstacles in many different fields. For example, the problems involved in the prediction of protein folding sequences and the prediction of the physics of structural materials have all been resolved by AI. We implement a convolutional neural network (CNN), a deep learning method, to establish a model that utilizes a representative volume element for the prediction of the mechanical properties of a woven fiber composite material. The predictive model significantly streamlines the computational complexity involved in analyzing woven composite materials, resulting in a substantial reduction in processing time compared to conventional methods. Unlike traditional finite element simulations, which necessitate intricate boundary conditions and interactions on a case-by-case basis, our research simplifies these complex procedures and accommodates a wide range of scenarios. This research offers substantial advantages for industrial manufacturing, particularly in the design and mass production of woven fiber composite materials. Weaving ; Computer science ; Representative elementary volume ; Finite element method ; Rigidity (electromagnetism) ; Fiber ; Convolutional neural network ; Composite number ; Fiber-reinforced composite ; Mechanical engineering ; Beam (structure) ; Structural engineering ; Composite material ; Materials science ; Artificial intelligence ; Engineering ; Algorithm Textile materials and evaluations ; Mechanical Behavior of Composites ; 3D Shape Modeling and Analysis