jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zhiling Zheng | Federico Florit | Brooke Jin | Haoyang Wu | Shih‐Cheng Li | Kakasaheb Y. Nandiwale | Chase A. Salazar | Jason Mustakis | William H. Green | Klavs F. Jensen
Source: ChemRxiv (preprint, 2024-08-28)
Field: Materials Science — Materials Chemistry

Integrating Machine Learning and Large Language Models to Advance Exploration of Electrochemical Reactions Electrochemical C-H oxidation reactions offer a sustainable route to functionalize hydrocarbons, yet the identification of competent substrates and their synthesis optimization remains challenging. Here, we report an integrated approach combining machine learning (ML) and large language models (LLMs) to streamline the exploration of electrochemical C-H oxidation reactions. Utilizing a batch rapid screening electrochemical platform, we evaluated a wide range of reactions, initially classifying substrates by their reactivity, while LLMs text-mined literature data to augment the training set. The resulting ML models, one for reactivity prediction and the other one for site selectivity, both achieved high accuracy (>90%) and enabled virtual screening of a large set of commercially available molecules. To optimize reaction conditions of substrates of interest upon the screening, LLMs were prompted to generate code to iteratively improve yield, lowering the barrier for scientists to access ML programs, and this strategy efficiently identified high-yield conditions for eight drug-like substances or intermediates. Notably, we benchmarked the accuracy and reliability of 10 different LLMs, including llama, Claude, and GPT-4, on generating and executing codes related to ML based on natural language prompts given by chemists to showcase their tool-making and tool-using capabilities and potentials for accelerating research across four diverse tasks. In addition, we collected an experimental benchmark dataset comprising 1071 reaction conditions and yields for electrochemical C-H oxidation reactions, and our findings revealed that integrating LLMs and ML outperformed using either method alone. We envision that this combined approach offers a robust and generalizable pathway for advancing synthetic chemistry research Benchmark (surveying) ; Set (abstract data type) ; Computer science ; Reactivity (psychology) ; Artificial intelligence ; Machine learning ; Identification (biology) ; Yield (engineering) ; Materials science ; Biology ; Programming language Machine Learning in Materials Science ; Radical Photochemical Reactions ; CO2 Reduction Techniques and Catalysts
Authors: Chottanahalli S. Pavan Kumar | Koustuv Ray
Source: Process Safety and Environmental Protection (article, 2024-11-22)
Field: Materials Science — Materials Chemistry

Accelerating catalytic experimentation of water gas shift reaction using machine learning models Water-gas shift reaction ; Catalysis ; Chemistry ; Computer science ; Process engineering ; Engineering ; Environmental science ; Organic chemistry Machine Learning in Materials Science ; Topic Modeling ; Neural Networks and Applications
Authors: Jan‐Michael Y. Carrillo | Vijith Parambil | Tarak K. Patra | Zhan Chen | Thomas P. Russell | Subramanian K. R. S. Sankaranarayanan | Bobby G. Sumpter | Rohit Batra
Source: The Journal of Physical Chemistry B (article, 2024-04-22)
Field: Materials Science — Materials Chemistry

Accelerated Sequence Design of Star Block Copolymers: An Unbiased Exploration Strategy via Fusion of Molecular Dynamics Simulations and Machine Learning Star block copolymers (s-BCPs) have potential applications as novel surfactants or amphiphiles for emulsification, compatibilization, chemical transformations, and separations. s-BCPs have chain architectures where three or more linear diblock copolymer arms comprised of two chemically distinct linear polymers, e.g., solvophobic and solvophilic chains, are covalently joined at one point. The chemical composition of each of the subunit polymer chains comprising the arms, their molecular weights, and the number of arms can be varied to tailor the surface and interfacial activity of these architecturally unique molecules. This makes identification of the optimal s-BCP design nontrivial as the total number of plausible s-BCP architectures is experimentally or computationally intractable. In this work, we use molecular dynamics (MD) simulations coupled with a reinforcement learning-based Monte Carlo tree search (MCTS) to identify s-BCP designs that minimize the interfacial tension between polar and nonpolar solvents. We first validate the MCTS approach for the design of small- and medium-sized s-BCPs and then use it to efficiently identify sequences of copolymer blocks for large-sized s-BCPs. The structural origins of interfacial tension in these systems are also identified by using the configurations obtained from MD simulations. Chemical insights into the arrangement of copolymer blocks that promote lower interfacial tension were mined using machine learning (ML) techniques. Overall, this work provides an efficient approach to solve design problems via fusion of simulations and ML and provides important groundwork for future experimental investigation of s-BCPs for various applications. Copolymer ; Solvophobic ; Surface tension ; Molecular dynamics ; Monte Carlo method ; Materials science ; Computer science ; Polymer ; Amphiphile ; Molecule ; Biological system ; Chemistry ; Physics ; Computational chemistry ; Thermodynamics ; Mathematics ; Organic chemistry Block Copolymer Self-Assembly ; Advanced Polymer Synthesis and Characterization ; Machine Learning in Materials Science
Authors: Ngoc Hung Phi | Huu Nguyen Bui | Seong‐Yeon Moon | Jong‐Wook Lee
Source: Advanced Intelligent Systems (article, 2024-02-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Controlled Synthesis of Space–Time Modulated Metamaterial for Enhanced Nonreciprocity by Machine Learning Nonreciprocity plays a fundamental role in governing direction‐dependent asymmetric wave propagation. Previous approaches to nonreciprocity involve ferrite‐based devices with bulky systems. Herein, the controlled synthesis of a space–time modulation (STM) metamaterial for enhanced nonreciprocity using machine learning (ML) is investigated. The design of STM metamaterial poses great challenges due to the nonlinear nature of time‐periodic Floquet harmonics, which are inefficiently handled in traditional methods such as numerical optimization. To deal with the challenge, an ML approach is proposed that learns from the accumulated training data using the guided objective function and generates high‐quality designs by leveraging the learned features. This approach first trains a residual neural network (ResNet) to learn the nonlinear relationships between the STM parameters and nonreciprocal responses. The trained ResNet achieves a high testing accuracy, with 96.7% of the 9000 instances having a mean square error less than 0.6 × 10 −4 . For the synthesis of STM metamaterial, a customized Wasserstein generative adversarial network (WGAN) is proposed, which leverages the discovered nonlinearity and synthesizes large‐scale datasets using small computational costs. The histogram obtained using 90 000 data samples shows that WGAN generates designs with an average normalized nonreciprocity of 0.83, four times higher than the measured data. Metamaterial ; Computer science ; Nonlinear system ; Artificial neural network ; Harmonics ; Topology (electrical circuits) ; Electronic engineering ; Algorithm ; Computer engineering ; Artificial intelligence ; Physics ; Optics ; Electrical engineering ; Engineering Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Acoustic Wave Phenomena Research
Authors: Lalit Yadav
Source: AIP Advances (article, 2024-04-01)
Field: Materials Science — Materials Chemistry

Atoms as words: A novel approach to deciphering material properties using NLP-inspired machine learning on crystallographic information files (CIFs) In condensed matter physics and materials science, predicting material properties necessitates understanding intricate many-body interactions. Conventional methods such as density functional theory and molecular dynamics often resort to simplifying approximations and are computationally expensive. Meanwhile, recent machine learning methods use handcrafted descriptors for material representation, which sometimes neglect vital crystallographic information and are often limited to single property prediction or a sub-class of crystal structures. In this study, we pioneer an unsupervised strategy, drawing inspiration from natural language processing to harness the underutilized potential of Crystallographic Information Files (CIFs). We conceptualize atoms and atomic positions within a crystallographic information file similarly to words in textual content. Using a Word2Vec-inspired technique, we produce atomic embeddings that capture intricate atomic relationships. Our model, CIFSemantics, trained on the extensive material project dataset, adeptly predicts 15 distinct material properties from the CIFs. Its performance rivals that of specialized models, marking a significant step forward in material property predictions. Natural language processing ; Artificial intelligence ; Computer science ; Materials science Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Zheng Shaojie | Xu Huang | Jijiang Hu | Zhen Yao
Source: Reaction Chemistry & Engineering (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Machine learning for revealing the relationship between the process–structure–properties of polypropylene in-reactor alloys Polypropylene reactor alloys with distinct structures were synthesized, and machine learning models were developed to reveal the relationship between the process–structure–properties and optimize process conditions. Polypropylene ; Process (computing) ; Materials science ; Process engineering ; Composite material ; Chemical engineering ; Computer science ; Engineering Machine Learning in Materials Science ; Injection Molding Process and Properties ; Mineral Processing and Grinding
Authors: Saientan Bag | Melissa K. Meinel | Florian Müller‐Plathe
Source: Journal of Chemical Theory and Computation (article, 2024-04-09)
Field: Materials Science — Materials Chemistry

Synthetic Force-Field Database for Training Machine Learning Models to Predict Mobility-Preserving Coarse-Grained Molecular-Simulation Potentials Balancing accuracy and efficiency is a common problem in molecular simulation. This tradeoff is evident in coarse-grained molecular dynamics simulation, which prioritizes efficiency, and all-atom molecular simulation, which prioritizes accuracy. Despite continuous efforts, creating a coarse-grained model that accurately captures both the system's structure and dynamics remains elusive. In this article, we present a data-driven approach for constructing coarse-grained models that aim to describe both the structure and dynamics of the system equally well. While the development of machine learning models is well-received in the scientific community, the significance of dataset creation for these models is often overlooked. However, data-driven approaches cannot progress without a robust dataset. To address this, we construct a database of synthetic coarse-grained potentials generated from unphysical all-atom models. A neural network is trained with the generated database to predict the coarse-grained potentials of real liquids. We evaluate their quality by calculating the combined loss of structural and dynamical accuracy upon coarse-graining. When we compare our machine learning-based coarse-grained potential with the one from iterative Boltzmann inversion, the machine learning prediction turns out better for all eight hydrocarbon liquids we studied. As all-atom surfaces turn more nonspherical, both ways of coarse-graining degrade. Still, the neural network outperforms iterative Boltzmann inversion in constructing good quality coarse-grained models for such cases. The synthetic database and the developed machine learning models are freely available to the community, and we believe that our approach will generate interest in efficiently deriving accurate coarse-grained models for liquids. Computer science ; Granularity ; Machine learning ; Artificial intelligence ; Artificial neural network ; Boltzmann machine ; Molecular dynamics ; Inversion (geology) ; Field (mathematics) ; Force field (fiction) ; Algorithm ; Data mining ; Chemistry Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Protein Structure and Dynamics
Authors: Alessio Alexiadis
Source: Frontiers in Nanotechnology (article, 2024-04-10)
Field: Materials Science — Materials Chemistry

Designing a set of criteria for evaluating artificial neural networks trained with physics-based data to replicate molecular dynamics and other particle method trajectories This article presents an in-depth analysis and evaluation of artificial neural networks (ANNs) when applied to replicate trajectories in molecular dynamics (MD) simulations or other particle methods. This study focuses on several architectures—feedforward neural networks (FNNs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), time convolutions (TCs), self-attention (SA), graph neural networks (GNNs), neural ordinary differential equation (ODENets), and an example of physics-informed machine learning (PIML) model—assessing their effectiveness and limitations in understanding and replicating the underlying physics of particle systems. Through this analysis, this paper introduces a comprehensive set of criteria designed to evaluate the capability of ANNs in this context. These criteria include the minimization of losses, the permutability of particle indices, the ability to predict trajectories recursively, the conservation of particles, the model’s handling of boundary conditions, and its scalability. Each network type is systematically examined to determine its strengths and weaknesses in adhering to these criteria. While, predictably, none of the networks fully meets all criteria, this study extends beyond the simple conclusion that only by integrating physics-based models into ANNs is it possible to fully replicate complex particle trajectories. Instead, it probes and delineates the extent to which various neural networks can “understand” and interpret aspects of the underlying physics, with each criterion targeting a distinct aspect of this understanding. Replicate ; Artificial neural network ; Set (abstract data type) ; Data set ; Dynamics (music) ; Artificial intelligence ; Computer science ; Experimental data ; Particle (ecology) ; Machine learning ; Physics ; Biology ; Mathematics ; Statistics ; Ecology Machine Learning in Materials Science ; Nuclear Physics and Applications ; Nuclear reactor physics and engineering
Authors: Yu Liu | Zhenyi Jiang | Xiaodong Zhang | Wen-Xuan Wang | Zhiyong Zhang
Source: Ceramics International (article, 2024-07-14)
Field: Materials Science — Materials Chemistry

Deep-learning molecular dynamics simulation of pressure-driven transformation for bulk TiO2 Molecular dynamics ; Materials science ; Transformation (genetics) ; Chemical physics ; Dynamics (music) ; Statistical physics ; Nanotechnology ; Engineering physics ; Computational chemistry ; Psychology ; Physics ; Chemistry Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; X-ray Diffraction in Crystallography
Authors: Ki Sung Jung | Bruno S. Soriano | Jacqueline H. Chen | Mohammad Khalil
Source: Proceedings of the Combustion Institute (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

A Hessian-based transfer learning approach for artificial neural networks based chemical kinetics with a sparse dataset One of the primary challenges in high-fidelity numerical simulations of turbulent reacting flows stems from the prohibitive computational cost of evaluating chemical reaction source terms of the thermo-chemical variables. Although data-driven approaches are increasingly adopted in chemically reactive flow simulations to accelerate the evaluation of the chemical source terms, the data acquisition process is often challenging due to the inherent case-specific and high-dimensional nature of combustion systems. In the present study, we adopt regularization-based transfer learning methods to leverage pre-trained knowledge obtained from a previous task to train the target neural network model with a sparse training dataset. This study demonstrates that the overall performance of a neural network model with a sparse dataset can be remarkably enhanced by using regularization-based transfer learning methods demonstrated for both zero-and two-dimensional chemically reactive flow simulations. Moreover, a Hessian-based transfer learning method is introduced illustrating that additional performance gains can be achieved by transferring curvature-based knowledge obtained from a previous task to the target neural network model. The efficiency of the Hessian-based transfer learning method is analyzed for various scenarios and is found to outperform the baseline transfer learning method when task similarity and data sparsity in the target task are high. Hessian matrix ; Transfer of learning ; Artificial intelligence ; Computer science ; Artificial neural network ; Kinetics ; Biological system ; Machine learning ; Mathematics ; Applied mathematics ; Physics ; Biology Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference ; Nuclear reactor physics and engineering
Authors: Jiaxin Lin | Zhe Chen | Yinwu Li | Zhuofeng Ke
Source: Journal of Catalysis (article, 2024-07-22)
Field: Chemistry — Inorganic Chemistry

Mechanistic investigation and machine learning exploration of structure-activity relationship of NHC-Mn catalyzed ester hydrosilylation reaction Chemistry ; Hydrosilylation ; Catalysis ; Organic chemistry Asymmetric Hydrogenation and Catalysis ; Carbon dioxide utilization in catalysis ; Organoboron and organosilicon chemistry
Authors: Anwar Shafe | Pouria Nourian | Xiyuan Liu | Guoqiang Li | Collin D. Wick | Andrew J. Peters
Source: Macromolecules (article, 2024-10-21)
Field: Materials Science — Polymers and Plastics

Identification and Design of Better Diamine-Hardened Epoxy-Based Thermoset Shape Memory Polymers: Simulation and Machine Learning An approach for designing thermoset shape memory polymers (TSMPs) with improved shape memory properties through the integration of molecular dynamics (MD) simulation, machine learning (ML), and chemical intuition is presented. We identified key molecular features correlated with desired shape memory properties, and used MD simulations to create an initial data set of TSMPs consisting of commercially available and manually designed monomers. Our prediction set was prepared by employing four different approaches for modifying existing monomers based on chemical intuition and insights gleaned from the literature. We trained our ML model on the initial data set, used it to identify the most promising candidates, evaluated their properties, and added them to our initial data set. To further speed up the process, we identified the most promising candidate after a few cycles and modified its structure to obtain a variant with better properties. Our approach, which capitalizes on the synergy between computational methodologies and human expertise to enable efficient exploration of vast chemical space, resulted in the design of a monomer exhibiting more than 60% increase in the desired recovery stress compared to the highest experimentally validated one. Thermosetting polymer ; Epoxy ; Diamine ; Polymer ; Materials science ; Identification (biology) ; Shape-memory polymer ; Polymer science ; Polymer chemistry ; Composite material Polymer composites and self-healing ; Photopolymerization techniques and applications ; Photochromic and Fluorescence Chemistry
Authors: Amirhossein Kardoost | Robert Schönherr | Carsten Deiter | Lars Redecke | Kristina Lorenzen | Joachim Schulz | Iñaki de Diego
Source: Journal of Applied Crystallography (article, 2024-02-23)
Field: Materials Science — Materials Chemistry

Convolutional neural network approach for the automated identification of <i>in cellulo</i> crystals <i>In cellulo</i> crystallization is a rare event in nature. Recent advances that have made use of heterologous overexpression can promote the intracellular formation of protein crystals, but new tools are required to detect and characterize these targets in the complex cell environment. The present work makes use of Mask R-CNN, a convolutional neural network (CNN)-based instance segmentation method, for the identification of either single or multi-shaped crystals growing in living insect cells, using conventional bright field images. The algorithm can be rapidly adapted to recognize different targets, with the aim of extracting relevant information to support a semi-automated screening pipeline, in order to aid the development of the intracellular protein crystallization approach. Convolutional neural network ; Pipeline (software) ; Identification (biology) ; Computer science ; Crystallization ; Segmentation ; Protein crystallization ; Artificial intelligence ; Field (mathematics) ; Pattern recognition (psychology) ; Computational biology ; Chemistry ; Biology Enzyme Structure and Function ; Crystallization and Solubility Studies ; Cell Image Analysis Techniques
Authors: GengHao Xu | H.R. Chen | HaoSen Lin | XuYang Liu | Bobo Li | Wei Chen | Dan Wu | Lei Ma
Source: Optical Materials (article, 2024-09-06)
Field: Materials Science — Materials Chemistry

Synthesis guidance of PbS colloidal quantum dots with neural network model for short wave infrared photodetector Photodetector ; Infrared ; Quantum dot ; Optoelectronics ; Quantum ; Colloid ; Materials science ; Artificial neural network ; Nanotechnology ; Optics ; Physics ; Chemistry ; Computer science ; Quantum mechanics ; Physical chemistry ; Artificial intelligence Quantum Dots Synthesis And Properties ; Chalcogenide Semiconductor Thin Films ; Gold and Silver Nanoparticles Synthesis and Applications