jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kianoosh Sattari | Yuchao Wu | Zhenru Chen | Alireza Mahjoubnia | Changhua Su | Jian Lin
Source: ChemRxiv (preprint, 2023-11-19)
Field: Materials Science — Materials Chemistry

Physics Constrained Multi-objective Bayesian Optimization to Accelerate 3D Printing of Thermoplastics Vat photopolymerization (VPP) of thermoplastics faces an issue of unpredictable printability due to the involved competing photopolymerization and dissolution reactions. The printing outcome largely depends on physicochemical properties of monomers and their compositions in resins, which also greatly determine the material properties, e.g., strength/toughness and phase transition temperature (Tg). Thus, a methodology for optimizing the resin formulation is of paramount importance in realizing highly printable thermoplastics with balanced strength/toughness and target Tg while remaining largely underexplored. Herein, we introduce a multi-objective Bayesian optimization (MOBO) algorithm with two physics informed constraints (printability and Tg) to optimize two conflicting properties: tensile strength (σT) and toughness (UT). The two constraints are formulated as two machine learning (ML) models, which are trained with weight ratios of the six input monomers and physics informed (PI) descriptors derived from their physiochemical parameters. Dimensional reduction analysis reveals that the algorithm avoids recommendation of the monomer ratios that do not pass the two constraints. The printing failure rate is reduced from 16% in the background experiments to 3% in the recommended experiments. Within only 36 iterations (72 samples), the MOBO algorithm successfully identifies five sets of ratios leading to Pareto optimal of σT and UT. Due to the constraint in Tg they show appropriate Tg for shape memory application. The partial dependence analysis indicates that σT and UT depend on both the ratios and physiochemical features of the monomers. These results underscore capability of such a smart decision-making algorithm—with constraints that are not fully understood but can be informed by prior knowledge—in planning the experiments from the vast design space, thus holding a great promise for broader applications in materials and manufacturing. Monomer ; Photopolymer ; Toughness ; Bayesian optimization ; Constraint (computer-aided design) ; Materials science ; Ultimate tensile strength ; Set (abstract data type) ; Glass transition ; Computer science ; Bayesian probability ; Algorithm ; Composite material ; Mechanical engineering ; Polymer ; Artificial intelligence ; Engineering Machine Learning in Materials Science ; Additive Manufacturing and 3D Printing Technologies ; Advanced Multi-Objective Optimization Algorithms
Authors: Mie Andersen
Source: Nature Computational Science (article, 2023-12-14)
Field: Materials Science — Materials Chemistry

Machine learning speeds up search for surface structure Monte Carlo method ; Computer science ; Sampling (signal processing) ; Surface (topology) ; Artificial intelligence ; Machine learning ; Algorithm ; Mathematics ; Geometry ; Computer vision ; Statistics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Advanced Memory and Neural Computing
Authors: Kianoosh Sattari | Yunchao Wu | Zhenru Chen | Alireza Mahjoubnia | Changhua Su | Jian Lin
Source: ChemRxiv (preprint, 2023-11-10)
Field: Materials Science — Materials Chemistry

Physics Constrained Multi-objective Bayesian Optimization to Accelerate 3D Printing of Thermoplastics Vat photopolymerization (VPP) of thermoplastics faces an issue of unpredictable printability due to the involved competing photopolymerization and dissolution reactions. The printing outcome largely depends on physicochemical properties of monomers and their compositions in resins, which also greatly determine the material properties, e.g., strength/toughness and phase transition temperature (Tg). Thus, a methodology for optimizing the resin formulation is of paramount importance in realizing highly printable thermoplastics with balanced strength/toughness and target Tg while remaining largely underexplored. Herein, we introduce a multi-objective Bayesian optimization (MOBO) algorithm with two physics informed constraints (printability and Tg) to optimize two conflicting properties: tensile strength (σT) and toughness (UT). The two constraints are formulated as two machine learning (ML) models, which are trained with weight ratios of the six input monomers and physics informed (PI) descriptors derived from their physiochemical parameters. Dimensional reduction analysis reveals that the algorithm avoids recommendation of the monomer ratios that do not pass the two constraints. The printing failure rate is reduced from 16% in the background experiments to 3% in the recommended experiments. Within only 36 iterations (72 samples), the MOBO algorithm successfully identifies five sets of ratios leading to Pareto optimal of σT and UT. Over these iterations, the hypervolume indicator increases while the error decreases. The partial dependence analysis indicates that σT and UT depend on both the ratios and physiochemical features of the monomers. These results underscore capability of such a smart decision-making algorithm—with constraints that are not fully understood but can be informed by prior knowledge—in planning the experiments from the vast design space, thus holding a great promise for broader applications in materials and manufacturing. Monomer ; Photopolymer ; Toughness ; Bayesian optimization ; Ultimate tensile strength ; Materials science ; Set (abstract data type) ; Bayesian probability ; Computer science ; Dissolution ; Algorithm ; Composite material ; Polymer ; Artificial intelligence ; Chemical engineering ; Engineering Machine Learning in Materials Science ; Additive Manufacturing and 3D Printing Technologies ; Advanced Multi-Objective Optimization Algorithms
Authors: Jigyasa Nigam | Sergey N. Pozdnyakov | Kevin K. Huguenin-Dumittan | Michele Ceriotti
Source: arXiv (Cornell University) (preprint, 2023-02-28)
Field: Materials Science — Materials Chemistry

Completeness of Atomic Structure Representations In this paper, we address the challenge of obtaining a comprehensive and symmetric representation of point particle groups, such as atoms in a molecule, which is crucial in physics and theoretical chemistry. The problem has become even more important with the widespread adoption of machine-learning techniques in science, as it underpins the capacity of models to accurately reproduce physical relationships while being consistent with fundamental symmetries and conservation laws. However, some of the descriptors that are commonly used to represent point clouds -- most notably those based on discretized correlations of the neighbor density, that underpin most of the existing ML models of matter at the atomic scale -- are unable to distinguish between special arrangements of particles in three dimensions. This makes it impossible to machine learn their properties. Atom-density correlations are provably complete in the limit in which they simultaneously describe the mutual relationship between all atoms, which is impractical. We present a novel approach to construct descriptors of \emph{finite} correlations based on the relative arrangement of particle triplets, which can be employed to create symmetry-adapted models with universal approximation capabilities, which have the resolution of the neighbor discretization as the sole convergence parameter. Our strategy is demonstrated on a class of atomic arrangements that are specifically built to defy a broad class of conventional symmetric descriptors, showcasing its potential for addressing their limitations. Discretization ; Homogeneous space ; Representation (politics) ; Point (geometry) ; Limit (mathematics) ; Class (philosophy) ; Convergence (economics) ; Construct (python library) ; Computer science ; Theoretical physics ; Statistical physics ; Theoretical computer science ; Physics ; Mathematics ; Artificial intelligence ; Geometry ; Mathematical analysis Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Surface Chemistry and Catalysis
Authors: Dongping Chang | Pengcheng Xu | Xiaobo Ji | Minjie Li | Wencong Lu
Source: Science of Advanced Materials (article, 2023-08-01)
Field: Materials Science — Materials Chemistry

Application of Online Computational Platform of Materials Data Mining (OCPMDM) in Search for ABO<sub>3</sub> Perovskites with Multi-Properties OCPMDM is an online platform specially developed for researchers who do not have any programming basics to perform material machine learning projects, in which the processing of ABO 3 perovskite machine learning has even reached automation. In this work, we used OCPMDM to discover perovskite materials with multi-properties to demonstrate some functions of the platform, including the descriptor filling, regression, classification, pattern recognition, and virtual screening. The results of LOOCV and independent test of the constructed regression and classification models for Curie temperature and band gap show the reliable predictive ability of the models via the platform. In the pattern recognition optimization area, the occupancy rate of superior samples with high Curie temperature and suitable band gap reached 92.73% and 80%, respectively. In addition, we also screened out 8 candidates with higher Curie temperature and proper band gap for experiments. Curie temperature ; Materials science ; Artificial intelligence ; Machine learning ; Perovskite (structure) ; Regression ; Regression analysis ; Computer science ; Data mining ; Condensed matter physics ; Mathematics ; Statistics ; Engineering ; Physics Machine Learning in Materials Science ; Gas Sensing Nanomaterials and Sensors ; Machine Learning and ELM
Authors: Shaokang Du | Xin You | Hailong Yang | Jing Shang | Zhiwen Xiao | Zhihui Wu | Zhongzhi Luan | Depei Qian
Source:  (article, 2023-12-17)
Field: Materials Science — Materials Chemistry

Efficient Deep Molecular Dynamic Model Training on Heterogeneous System Molecular dynamics is a widely adopted simulation method for analyzing the movement of atoms and molecules. Traditional molecular dynamics simulation methods are computationally intensive and difficult to simulate a large number of atoms. In contrast, molecular dynamics based on deep potential models such as DeePMD can leverage deep learning techniques to improve simulation efficiency. Although DeePMD has incorporated mainstream deep learning frameworks, it still suffers from low performance and efficiency during its model training on heterogeneous systems such as CPU and GPU. Particularly, a large number of operators cannot be accelerated by GPU, resulting in low utilization of GPU computational resources. In this paper, we comprehensively analyze the computational bottlenecks and the corresponding root causes of DeePMD. We correspondingly propose several novel optimization strategies. Specifically, for preprocessing, we identify the computation redundancies and the GPU parallelization opportunities for performance optimization. For training, we propose optimization strategies such as operator fusion, redundancy elimination, and concurrent execution of multiple streams and threads in the computation process. Moreover, we apply systematical optimization of computational graphs and operators. The evaluation results show that DeePMD can achieve significant speedups in several cases after applying our proposed optimizations, resulting in a maximum overall speedup of 6.36× with acceptable accuracy. Computer science ; Training (meteorology) ; Artificial intelligence Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Advanced Memory and Neural Computing
Authors: Jacob A. Zorn | Long‐Qing Chen
Source: Journal of materials research/Pratt's guide to venture capital sources (article, 2023-02-06)
Field: Materials Science — Materials Chemistry

Machine-learning enabled construction of temperature-strain phase diagrams of ferroelectric thin films Ferroelectricity ; Materials science ; Thin film ; Microelectronics ; Phase diagram ; Substrate (aquarium) ; Stability (learning theory) ; Phase (matter) ; Strain engineering ; Field (mathematics) ; Nanotechnology ; Machine learning ; Condensed matter physics ; Optoelectronics ; Computer science ; Silicon ; Organic chemistry ; Physics ; Dielectric Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices ; Ferroelectric and Piezoelectric Materials
Authors: Maud Parrot | Hamza Tajmouati | Vinícius Barros Ribeiro da Silva | Brian Atwood | Robin Fourcade | Yann Gaston‐Mathé | Nicolas Do Huu | Quentin Perron
Source: Research Square (preprint, 2023-01-31)
Field: Materials Science — Materials Chemistry

Integrating synthetic accessibility with AI-based generative drug design Abstract Generative models are frequently used for de novo design in drug discovery projects to propose new molecules. However, the question of whether or not the generated molecules can be synthesized is not systematically taken into account during generation, even though being able to synthesize the generated molecules is a fundamental requirement for such methods to be useful in practice. Methods have been developed to estimate molecule “synthesizability”, but, so far, there is no consensus on whether or not a molecule is synthesizable. In this paper we introduce the Retro-Score (RScore), which computes a synthetic feasibility score of molecules by performing a full retrosynthetic analysis through our data-driven synthetic planning software Spaya, and its dedicated API: Spaya-API (https://spaya.ai). We start by comparing several synthetic feasibility scores to a binary ground truth of synthetic feasibility as estimated by chemists on a bench of generated molecules, as a first experimental validation that the RScore is a reliable synthetic feasibility score. We then describe a pipeline to generate molecules that validate a list of targets while still being easy to synthesize. We further this idea by performing experiments comparing molecular generator outputs across a range of constraints and conditions. We show that the RScore can be learned by a Neural Network, which leads to a new score: RSPred. We demonstrate that using the RScore or RSPred as a constraint during molecular generation enables our molecular generators to produce more synthesizable solutions, with higher diversity. The open-source Python code containing all the scores and the experiments can be found on https://github.com/iktos/generation-under- synthetic-constraint. Computer science ; Retrosynthetic analysis ; Python (programming language) ; Code generation ; Generator (circuit theory) ; Generative grammar ; Program synthesis ; Artificial intelligence ; Source code ; Programming language ; Machine learning ; Data mining ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Yong Ju Lee | Soon Wan Kweon | Jae Hyeop Kim | Eun Ji | Kwang-Ho Kang | Hyoung Jin Kim
Source: Journal of Korea Technical Association of The Pulp and Paper Industry (article, 2023-10-30)
Field: Chemistry — Analytical Chemistry

Spectral Preprocessing and Machine Learning Modeling for Discriminating Manufacturing Origins of Mulberry Bast Fiber The objective of this study was exploring the impact of spectral data preprocessing techniques on the performance of machine learning models for classifying the origin of mulberry bast fibers. The findings indicated that a selective spectral region (1800-1200 cm-1) significantly improves classification model performance. Among the classifiers tested, Partial Least Squares Discriminant Analysis (PLS-DA) and Support Vector Machines (SVM) demonstrated the highest accuracy. Additionally, A spectral preprocessing with the Norris-Williams algorithm effectively improved model performance within the same classifier for this dataset. These results suggest that applying machine learning modeling with spectral preprocessing can enable the origin classification of mulberry bast fibers and provide a chemical basis for classification rules beyond simple categorization. Bast fibre ; Preprocessor ; Artificial intelligence ; Support vector machine ; Pattern recognition (psychology) ; Classifier (UML) ; Categorization ; Linear discriminant analysis ; Data pre-processing ; Computer science ; Machine learning ; Spectral analysis ; Mathematics ; Botany ; Biology ; Physics Spectroscopy and Chemometric Analyses ; Leaf Properties and Growth Measurement ; Smart Agriculture and AI
Authors: Alvaro Ramos Peralta | Gerardo Odriozola
Source: Journal of Chemical Theory and Computation (article, 2023-06-12)
Field: Materials Science — Materials Chemistry

A Neural-Network-Optimized Hydrogen Peroxide Pairwise Additive Model for Classical Simulations We have developed an all-atom pairwise additive model for hydrogen peroxide using an optimization procedure based on artificial neural networks (ANNs). The model is based on experimental molecular geometry and includes a dihedral potential that hinders the <i>cis</i>-type configuration and allows for crossing the <i>trans</i> one, defined between the planes that have the two oxygen atoms and each hydrogen. The model's parametrization is achieved by training simple ANNs to minimize a target function that measures the differences between various thermodynamic and transport properties and the corresponding experimental values. Finally, we evaluated a range of properties for the optimized model and its mixtures with SPC/E water, including bulk-liquid properties (density, thermal expansion coefficient, adiabatic compressibility, etc.) and properties of systems at equilibrium (vapor and liquid density, vapor pressure and composition, surface tension, etc.). Overall, we obtained good agreement with experimental data. Parametrization (atmospheric modeling) ; Artificial neural network ; Thermodynamics ; Materials science ; Compressibility ; Molecular dynamics ; Adiabatic process ; Thermal expansion ; Biological system ; Chemistry ; Computer science ; Physics ; Computational chemistry Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Computational Drug Discovery Methods
Authors: Chaeyeong Kim | Takuji Oda
Source: Physical Chemistry Chemical Physics (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Temperature dependence of O solubility in liquid Na by atomistic simulation of Na(l)–Na<sub>2</sub>O(s) interfaces using corrected machine learning potential: a step towards simulating Na combustion Liquid Na combustion is a significant safety concern in sodium-cooled fast reactors. Atomistic simulations are an alternative to experiments for studying detailed mechanisms of complex combustion processes. However, accurate simulations of the interfaces involved in combustion are challenging even for density functional theory (DFT), because the systematic error between different chemical systems cannot be fully cancelled. Herein, we report the achievement of a key milestone in atomistic simulation of liquid Na combustion, which involves the development of a machine learning (ML) moment tensor potential that allows accurate simulation of interface systems between liquid Na and solid Na<sub>2</sub>O. The ML potential is trained by using supervised and active learning to ensure DFT-level accuracy. An empirical correction is then applied to achieve experimental accuracy by reducing systematic error. Consequently, the basic properties of liquid Na and solid Na<sub>2</sub>O are accurately simulated. In addition, with empirical correction, experimental O solubility data for liquid Na at 350-900 K are reproduced by using interface molecular dynamics simulations and a thermodynamic model. The temperature dependence of the enthalpy and entropy of the Na<sub>2</sub>O solution and their effect on O solubility are evaluated. The results show that, despite the increase in solution enthalpy with temperature, O solubility increases more rapidly than the linear Arrhenius plot due to the effect of solution entropy. The results of this study indicate that, with appropriate correction, ML potentials can achieve near-experimental accuracy, beyond the accuracy of DFT, in interface simulations and material properties calculations, paving the way for sodium combustion simulations in the future. Solubility ; Thermodynamics ; Combustion ; Materials science ; Molecular dynamics ; Physical chemistry ; Chemistry ; Chemical physics ; Physics ; Computational chemistry Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Thermal and Kinetic Analysis
Authors: Oshani Navodya
Source: International Research Journal of Innovations in Engineering and Technology (article, 2023-01-01)
Field: Chemistry — Analytical Chemistry

A Machine Learning Approach to Detect the Adulterants in Turmeric Powder The popular spice turmeric powder is used in many dishes and offers several health advantages.However, it is frequently contaminated with less expensive materials, producing a product of lower quality, and possibly posing health risks to consumers.The initial deficiencies after the economic crisis in Sri Lanka started a black-market for turmeric as prices were increase steeply.Fake powders also made an appearance in the Sri Lankan market.Food adulteration is a frequent crisis which has been a concern over decades now.Thus, it is vital to discover the possibility of the composition of original turmeric powder among the turmeric powders in the market.There is a great possibility of the adulteration of Turmeric in powdered forms and mixing other ingredients with powdered turmeric is easy.Distinguishing these other ingredients mixed powdered turmeric is not easy.Due to the rise in demand in customers, the manufacturers tend to keep up the production but by adulterating turmeric powders using different methods and these powders have several health effects.This study intends to construct a machine learning-based fraud detection web application that can precisely identify adulterants in turmeric powder samples using microscope pictures to solve this issue.Machine learning has emerged as a powerful tool for detecting adulterants in various food products and has shown promising results in recent years.In this research, we propose a machine learning-based fraud detection web application to detect adulterants in turmeric powder samples using microscope images.The application will utilize transfer learning, specifically the MobileV2Net, to improve the accuracy of adulterant detection. Computer science ; Artificial intelligence ; Machine learning ; Natural language processing Spectroscopy and Chemometric Analyses
Authors: Takuya Naruse | Atsuto Seko | Isao Tanaka
Source: Journal of the Ceramic Society of Japan (article, 2023-09-30)
Field: Materials Science — Materials Chemistry

Global structure optimization following imaginary phonon modes accelerated by machine learning potentials in Cu, Ag, and Au Algorithms of crystal structure prediction produce many different structures, some of which are dynamically unstable. Following the imaginary phonon modes obtained by lattice dynamics calculations, dynamically stable structures can be rationally derived from unstable structures. Following the imaginary phonon modes, however, generally requires lengthy and often prohibitively expensive calculations. In this study, we employ polynomial machine learning potentials to predict globally stable and metastable structures following the imaginary phonon modes. As a result, we discover many dynamically stable and metastable structures efficiently, and the face-centered cubic structure is the globally stable structure consistent with experimental reports for the elemental Cu, Ag, and Au. Phonon ; Metastability ; The Imaginary ; Lattice (music) ; Crystal structure ; Materials science ; Imaginary time ; Cubic crystal system ; Condensed matter physics ; Statistical physics ; Physics ; Quantum mechanics ; Crystallography ; Chemistry ; Quantum ; Quantum dynamics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods