jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zhilong Song | Qionghua Zhou | Shuaihua Lu | Sae Dieb | Chongyi Ling | Jinlan Wang
Source: The Journal of Physical Chemistry Letters (article, 2023-04-06)
Field: Materials Science — Materials Chemistry

Adaptive Design of Alloys for CO<sub>2</sub> Activation and Methanation via Reinforcement Learning Monte Carlo Tree Search Algorithm Data-driven machine learning (ML) has earned remarkable achievements in accelerating materials design, while it heavily relies on high-quality data acquisition. In this work, we develop an adaptive design framework for searching for optimal materials starting from zero data and with as few DFT calculations as possible. This framework integrates automatic density functional theory (DFT) calculations with an improved Monte Carlo tree search via reinforcement learning algorithm (MCTS-PG). As a successful example, we apply it to rapidly identify the desired alloy catalysts for CO<sub>2</sub> activation and methanation within 200 MCTS-PG steps. To this end, seven alloy surfaces with high theoretical activity and selectivity for CO<sub>2</sub> methanation are screened out and further validated by comprehensive free energy calculations. Our adaptive design framework enables the fast computational exploration of materials with desired properties via minimal DFT calculations. Methanation ; Reinforcement learning ; Density functional theory ; Computer science ; Monte Carlo method ; Tree (set theory) ; Monte Carlo tree search ; Algorithm ; Materials science ; Artificial intelligence ; Chemistry ; Catalysis ; Computational chemistry ; Mathematics Machine Learning in Materials Science ; Carbon dioxide utilization in catalysis ; CO2 Reduction Techniques and Catalysts
Authors: Qilei Liu | Kun Tang | Lei Zhang | Jian Du | Qingwei Meng
Source: AIChE Journal (article, 2023-04-02)
Field: Materials Science — Materials Chemistry

Computer‐assisted synthetic planning considering reaction kinetics based on transition state automated generation method Abstract Organic synthesis facilitates the conversion of raw materials into high‐value chemicals. Computer‐assisted synthetic planning plays a vital role in designing synthetic pathways, which are usually evaluated by the reaction probability using deep learning models. However, this criterion is generally hard to describe real reaction behaviors such as reaction kinetics. Therefore, this article aims to establish a reaction kinetics‐based retrosynthesis planning framework to design synthetic pathways with well‐performed reaction kinetics. The key contribution of this work is developing a method for the GENeration of initial guesses of Transition States based on Reactive Sites (GENiniTS‐RS) to automatically and fast generate the initial guesses of transition states for the transition state theory‐based reaction kinetic model without sampling the minimum energy path from reactants to products. Finally, two case studies involving the design of synthetic pathways for aspirin and ibuprofen are presented to demonstrate the feasibility and effectiveness of the proposed framework. Retrosynthetic analysis ; Kinetics ; Chemical kinetics ; Transition state theory ; Biochemical engineering ; Computer science ; Chemistry ; Biological system ; Engineering ; Reaction rate constant ; Organic chemistry ; Total synthesis ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Mirko Agarla | Paolo Napoletano | Raimondo Schettini
Source: Sensors (article, 2023-09-14)
Field: Chemistry — Analytical Chemistry

Quasi Real-Time Apple Defect Segmentation Using Deep Learning Defect segmentation of apples is an important task in the agriculture industry for quality control and food safety. In this paper, we propose a deep learning approach for the automated segmentation of apple defects using convolutional neural networks (CNNs) based on a U-shaped architecture with skip-connections only within the noise reduction block. An ad-hoc data synthesis technique has been designed to increase the number of samples and at the same time to reduce neural network overfitting. We evaluate our model on a dataset of multi-spectral apple images with pixel-wise annotations for several types of defects. In this paper, we show that our proposal outperforms in terms of segmentation accuracy general-purpose deep learning architectures commonly used for segmentation tasks. From the application point of view, we improve the previous methods for apple defect segmentation. A measure of the computational cost shows that our proposal can be employed in real-time (about 100 frame-per-second on GPU) and in quasi-real-time (about 7/8 frame-per-second on CPU) visual-based apple inspection. To further improve the applicability of the method, we investigate the potential of using only RGB images instead of multi-spectral images as input images. The results prove that the accuracy in this case is almost comparable with the multi-spectral case. Computer science ; Overfitting ; Segmentation ; Artificial intelligence ; Convolutional neural network ; Deep learning ; Frame (networking) ; RGB color model ; Block (permutation group theory) ; Frame rate ; Pattern recognition (psychology) ; Pixel ; Computer vision ; Artificial neural network Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Industrial Vision Systems and Defect Detection
Authors: Giulia Fischetti | Nicolas Schmid | Simon Bruderer | Guido Caldarelli | Alessandro Scarso | Andreas Henrici | D. Wilhelm
Source: Frontiers in Artificial Intelligence (article, 2023-01-11)
Field: Chemistry — Spectroscopy

Automatic classification of signal regions in 1H Nuclear Magnetic Resonance spectra The identification and characterization of signal regions in Nuclear Magnetic Resonance (NMR) spectra is a challenging but crucial phase in the analysis and determination of complex chemical compounds. Here, we present a novel supervised deep learning approach to perform automatic detection and classification of multiplets in <sup>1</sup>H NMR spectra. Our deep neural network was trained on a large number of synthetic spectra, with complete control over the features represented in the samples. We show that our model can detect signal regions effectively and minimize classification errors between different types of resonance patterns. We demonstrate that the network generalizes remarkably well on real experimental <sup>1</sup>H NMR spectra. Spectral line ; SIGNAL (programming language) ; Nuclear magnetic resonance ; Artificial intelligence ; Pattern recognition (psychology) ; Identification (biology) ; Computer science ; Characterization (materials science) ; Artificial neural network ; Biological system ; Physics ; Materials science ; Biology ; Nanotechnology Molecular spectroscopy and chirality ; Metabolomics and Mass Spectrometry Studies ; Spectroscopy and Chemometric Analyses
Authors: Adela Habib | Nicholas Lubbers | Sergei Tretiak | Benjamin Nebgen
Source: The Journal of Physical Chemistry A (article, 2023-04-20)
Field: Materials Science — Materials Chemistry

Machine Learning Models Capture Plasmon Dynamics in Ag Nanoparticles Highly energetic electron-hole pairs (hot carriers) formed from plasmon decay in metallic nanostructures promise sustainable pathways for energy-harvesting devices. However, efficient collection before thermalization remains an obstacle for realization of their full energy generating potential. Addressing this challenge requires detailed understanding of physical processes from plasmon excitation in the metal to their collection in a molecule or a semiconductor, where atomistic theoretical investigation may be particularly beneficial. Unfortunately, first-principles theoretical modeling of these processes is extremely costly, preventing a detailed analysis over a large number of potential nanostructures and limiting the analysis to systems with a few 100s of atoms. Recent advances in machine learned interatomic potentials suggest that dynamics can be accelerated with surrogate models which replace the full solution of the Schrödinger Equation. Here, we modify an existing neural network, Hierarchically Interacting Particle Neural Network (HIP-NN), to predict plasmon dynamics in Ag nanoparticles. The model takes as a minimum as three time steps of the reference real-time time-dependent density functional theory (rt-TDDFT) calculated charges as history and predicts trajectories for 5 fs in great agreement with the reference simulation. Further, we show that a multistep training approach in which the loss function includes errors from future time-step predictions can stabilize the model predictions for the entire simulated trajectory (∼25 fs). This extends the model's capability to accurately predict plasmon dynamics in large nanoparticles of up to 561 atoms, not present in the training data set. More importantly, with machine learning models on GPUs, we gain a speed-up factor of ∼10<sup>3</sup> as compared with the rt-TDDFT calculations when predicting important physical quantities such as dynamic dipole moments in Ag<sub>55</sub> and a factor of ∼10<sup>4</sup> for extended nanoparticles that are 10 times larger. This underscores the promise of future machine learning accelerated electron/nuclear dynamics simulations for understanding fundamental properties of plasmon-driven hot carrier devices. Plasmon ; Nanoparticle ; Dynamics (music) ; Computer science ; Plasmonic nanoparticles ; Nanotechnology ; Materials science ; Artificial intelligence ; Physics ; Optoelectronics Machine Learning in Materials Science ; Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy and Quantum Chemical Studies
Authors: Thies Jansen | Geert Brocks | Menno Bokdam
Source: Physical review. B./Physical review. B (article, 2023-12-05)
Field: Materials Science — Materials Chemistry

Phase transitions of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>LaMnO</mml:mi><mml:mn>3</mml:mn></mml:msub></mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>SrRuO</mml:mi><mml:mn>3</mml:mn></mml:msub></mml:math> from <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>DFT</mml:mi><mml:mo>+</mml:mo><mml:mi>U</mml:mi></mml:mrow></mml:math> based machine learning force fields simulations Perovskite oxides are known to exhibit many magnetic, electronic, and structural phases as function of doping and temperature. These materials are theoretically frequently investigated by the $\mathrm{DFT}+U$ method, typically in their ground state structure at $T=0$. We show that by combining machine learning force fields (MLFFs) and $\mathrm{DFT}+U$ based molecular dynamics, it becomes possible to investigate the crystal structure of complex oxides as function of temperature and $U$. Here, we apply this method to the magnetic transition metal compounds ${\mathrm{LaMnO}}_{3}$ and ${\mathrm{SrRuO}}_{3}$. We show that the structural phase transition from orthorhombic to cubic in ${\mathrm{LaMnO}}_{3}$, which is accompanied by the suppression of a Jahn-Teller distortion, can be simulated with an appropriate choice of $U$. For ${\mathrm{SrRuO}}_{3}$, we show that the sequence of orthorhombic to tetragonal to cubic crystal phase transitions can be described with great accuracy. We propose that the $U$ values that correctly capture the temperature-dependent structures of these complex oxides can be identified by comparison of the MLFF simulated and experimentally determined structures. Orthorhombic crystal system ; Tetragonal crystal system ; Perovskite (structure) ; Crystal structure ; Jahn–Teller effect ; Materials science ; Phase transition ; Crystal (programming language) ; Phase (matter) ; Crystallography ; Condensed matter physics ; Physics ; Algorithm ; Chemistry ; Computer science ; Ion ; Quantum mechanics Machine Learning in Materials Science ; Magnetic and transport properties of perovskites and related materials ; Advanced Condensed Matter Physics
Authors: Kenji Nawa | Katsuyuki Hagiwara | Kohji Nakamura
Source: Computational Materials Science (article, 2023-01-19)
Field: Materials Science — Materials Chemistry

Prediction-accuracy improvement of neural network to ferromagnetic multilayers by Gaussian data augmentation and ensemble learning In materials informatics using machine learning and density functional theory (DFT) calculations, it is often hard to obtain enough database due to extremely large costs of DFT. Therefore, it is required a machine learning technique that learns a complex target relationship from a limited dataset. In the present work, to overcome this issue, we built a neural network by implementing two techniques: Gaussian data augmentation (GDA) method, which injects Gaussian noises into the training dataset, and ensemble learning, which employs multiple models to train and make prediction by averaging their outputs. With typical examples of magnetic moment and formation energy as a function of atomic-layer configuration in CoFe multilayers, the prediction accuracy can be greatly improved, e.g., by using a training dataset consisting of 10∼30% of all data. We found that the use of GDA substantially increases the prediction accuracy for unknown test dataset where the improvement is attributed to a smoothing effect of a fitting curve of NN, and a combination with the ensemble learning brings further improvement with reducing the variance error originating from the selection of training sampling dataset in addition to a similar smoothing effect. The present approach, thus, can be generalized widely to materials informatics for which database is limited. Computer science ; Smoothing ; Artificial neural network ; Gaussian ; Machine learning ; Artificial intelligence ; Gaussian function ; MNIST database ; Gaussian process ; Variance (accounting) ; Data mining ; Algorithm ; Physics Machine Learning in Materials Science ; Magnetic properties of thin films ; Theoretical and Computational Physics
Authors: Kin Long Kelvin Lee | Carmelo Gonzales | Matthew Spellings | Mikhail Galkin | Santiago Miret | Nalini Kumar
Source:  (preprint, 2023-11-10)
Field: Materials Science — Materials Chemistry

Towards Foundation Models for Materials Science: The Open MatSci ML Toolkit Artificial intelligence and machine learning have shown great promise in their ability to accelerate novel materials discovery. As researchers and domain scientists seek to unify and consolidate chemical knowledge, the case for models with potential to generalize across different tasks within materials science—so-called “foundation models”—grows with ambitions. This manuscript reviews our recent progress with development of Open MatSci ML Toolkit, and details experiments that lay the groundwork for foundation model research and development with our framework. First, we describe and characterize a new pretraining task that uses synthetic data generated from symmetry operations, and reveal complex training dynamics at large scales. Using the pretrained model, we discuss a number of use cases relevant to foundation model development: semantic architecture of datasets, and fine-tuning for property prediction and classification. Our key results show that for simple applications, pretraining appears to provide worse modeling performance than training models from random initialization. However, for more complex instances, such as when a model is required to learn across multiple datasets and types of targets simultaneously, the inductive bias from pretraining provides significantly better performance. This insight will hopefully inform subsequent efforts into creating foundation models for materials science applications. Foundation (evidence) ; Computer science ; Open science ; Software engineering ; Data science ; Archaeology ; Physics ; History Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; X-ray Diffraction in Crystallography
Authors: Mohammad Reza Babaei | Ryan Stone | Thomas A. Knotts | John D. Hedengren
Source: Journal of Chemical Theory and Computation (article, 2023-06-09)
Field: Materials Science — Materials Chemistry

Physics-Informed Neural Networks with Group Contribution Methods Thermophysical properties of organic compounds are used in countless scientific, engineering, and industrial settings in developing theories, designing new systems and devices, analyzing costs and risks, and improving existing infrastructure. Often, due to costs, safety, prior interest, or procedural difficulties, experimental values for desired properties are not available and must be predicted. The literature is filled with prediction techniques, but even the best traditional methods have significant errors compared to what is possible considering experimental uncertainty. Recently, machine learning and artificial intelligence techniques have been applied to the property prediction problem, but the examples to date do not extrapolate well outside the data set used for training the model. This work demonstrates a solution to this problem by combining chemistry and physics when training the model and builds upon prior traditional and machine learning methods. Two case studies are presented. The first is for parachor which is used for surface tension prediction. Surface tensions are needed to design distillation columns, adsorption processes, gas-liquid reactors, liquid-liquid extractors, improve oil reservoir recovery, and undertake environmental impact studies or remediation actions. A set of 277 compounds is divided into training, validation, and test sets, and a multilayered physics-informed neural network (PINN) is developed. The results demonstrate that better extrapolation by deep learning models can be developed by adding in physics-based constraints. Second, a set of 1600 compounds is utilized for training, validating, and testing a PINN to improve normal boiling point predictions based on group contribution methods and physics-based constraints. The results show that the PINN performs better than any other method with a normal boiling point mean absolute error of 6.95 °C on training and 11.2 °C on test data. Key observations are that (1) a balanced split by compound type is important to have representative compound families in each of the train, validation, and test sets and (2) constraining group contributions being positive improves predictions on the test set. While this work demonstrates improvements for only surface tension and normal boiling point, the results offer significant hope that PINNs can improve prediction of other relevant thermophysical properties over existing approaches. Extrapolation ; Set (abstract data type) ; Artificial neural network ; Computer science ; Artificial intelligence ; Test set ; Machine learning ; Mathematics Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Heat Transfer and Boiling Studies
Authors: Anna Zabelina | A. Trelin | Anastasia Skvortsova | Denis Zabelin | Vasilii Burtsev | Elena Miliutina | V. Svorcik | Oleksiy Lyutakov
Source: Analytica Chimica Acta (article, 2023-08-11)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Bioinspired superhydrophobic SERS substrates for machine learning assisted miRNA detection in complex biomatrix below femtomolar limit Analyte ; Chemistry ; Nanotechnology ; Surface-enhanced Raman spectroscopy ; Detection limit ; Substrate (aquarium) ; Raman spectroscopy ; Surface modification ; Plasmon ; Aptamer ; Raman scattering ; Chromatography ; Materials science ; Optoelectronics ; Optics Gold and Silver Nanoparticles Synthesis and Applications ; Advanced biosensing and bioanalysis techniques ; Extracellular vesicles in disease
Authors: Annalisa Cardellini | Martina Crippa | Chiara Lionello | Syed Pavel Afrose | Dibyendu Das | Giovanni M. Pavan
Source: The Journal of Physical Chemistry B (article, 2023-03-09)
Field: Chemistry — Organic Chemistry

Unsupervised Data-Driven Reconstruction of Molecular Motifs in Simple to Complex Dynamic Micelles The reshuffling mobility of molecular building blocks in self-assembled micelles is a key determinant of many their interesting properties, from emerging morphologies and surface compartmentalization, to dynamic reconfigurability and stimuli-responsiveness. However, the microscopic details of such complex structural dynamics are typically nontrivial to elucidate, especially in multicomponent assemblies. Here we show a machine-learning approach that allows us to reconstruct the structural and dynamic complexity of mono- and bicomponent surfactant micelles from high-dimensional data extracted from equilibrium molecular dynamics simulations. Unsupervised clustering of smooth overlap of atomic position (SOAP) data enables us to identify, in a set of multicomponent surfactant micelles, the dominant local molecular environments that emerge within them and to retrace their dynamics, in terms of exchange probabilities and transition pathways of the constituent building blocks. Tested on a variety of micelles differing in size and in the chemical nature of the constitutive self-assembling units, this approach effectively recognizes the molecular motifs populating them in an exquisitely agnostic and unsupervised way, and allows correlating them to their composition in terms of constitutive surfactant species. Micelle ; Molecular dynamics ; Biological system ; Cluster analysis ; Reconfigurability ; Computer science ; Nanotechnology ; Chemistry ; Chemical physics ; Materials science ; Artificial intelligence ; Computational chemistry ; Biology Surfactants and Colloidal Systems ; Spectroscopy and Quantum Chemical Studies ; Protein Structure and Dynamics
Authors: Yuanpeng Deng | Shubin Fu | Jingran Guo | Xiang Xu | Hui Li
Source: ACS Nano (article, 2023-07-17)
Field: Materials Science — Materials Chemistry

Anisotropic Collective Variables with Machine Learning Potential for <i>Ab Initio</i> Crystallization of Complex Ceramics Enhanced sampling molecular dynamics (MD) simulations have been extensively used in the phase transition study of simple crystalline materials, such as aluminum, silica, and ice. However, MD simulation of the crystallization process for complex crystalline materials still faces a formidable challenge due to their multicomponent induced multiphase problem. Here, we realize the ab initio accuracy MD crystallization simulations of complex ceramics by using anisotropic collective variables (CVs) and machine learning (ML) potential. The anisotropic X-ray diffraction intensity CVs provide precise identification of complex crystal structures with detailed crystallography information, while the ML potential makes it feasible to further perform enhanced sampling simulations with ab initio accuracy. We verify the universality and accuracy of this method through complex ceramics with three kinds of representative structures, i.e., Ti3SiC2 for the MAX structure, zircon for the mineral structure, and lead zirconate titanate for the perovskite structure. It demonstrates exceptional efficiency and ab initio quality in achieving crystallization and generating free energy surfaces of all these ceramics, facilitating the analysis and design of complex crystalline materials. Crystallization ; Ab initio ; Materials science ; Chemical physics ; Crystal structure ; Anisotropy ; Molecular dynamics ; Ceramic ; Crystallography ; Thermodynamics ; Computational chemistry ; Physics ; Chemistry ; Optics Machine Learning in Materials Science ; MXene and MAX Phase Materials ; Inorganic Chemistry and Materials