jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Liangshuai Guo | Yuanbin Liu | Zekun Chen | Hong-Ao Yang | Davide Donadio | Bing Cao
Source: npj Computational Materials (article, 2025-04-11)
Field: Materials Science — Materials Chemistry

Generative deep learning for predicting ultrahigh lattice thermal conductivity materials Developing materials with ultrahigh thermal conductivity is crucial for thermal management and energy conversion. The recent development of generative models and machine learning (ML) holds great promise for predicting new functional materials. However, these data-driven methods are not tailored to identifying energetically stable structures and accurately predicting their thermal properties, as they lack physical constraints and information about the complexity of atomic many-body interactions. Here, we show how combining deep generative models of crystal structures with quantum-accurate, fast ML interatomic potentials can accelerate the prediction of materials with ultrahigh lattice thermal conductivity while ensuring energy optimality. We exploit structural symmetry and similarity metrics derived from atomic coordination environments to enable fast exploration of the structural space produced by the generative model. Additionally, we propose an active-learning-based protocol for the on-the-fly training of ML potentials to achieve high-fidelity predictions of stability and lattice thermal conductivity in prospective materials. Applying this method to carbon materials, we screen 100,000 candidates and identify 34 carbon polymorphs, approximately a quarter of which had not been previously predicted, to have lattice thermal conductivity above 800 W m−1 K−1, reaching up to 2,400 W m−1 K−1 aside from diamond. These findings provide a viable pathway toward the ML-assisted prediction of periodic materials with exceptional thermal properties. Thermal conductivity ; Generative grammar ; Materials science ; Lattice (music) ; Artificial intelligence ; Deep learning ; Machine learning ; Computer science ; Physics ; Composite material Machine Learning in Materials Science ; Thermal properties of materials ; Advanced Thermoelectric Materials and Devices
Authors: Wataru Kobayashi | Takuma Otsuka | Yuki K. Wakabayashi | Gensai Tei
Source: npj Computational Materials (article, 2025-02-15)
Field: Materials Science — Materials Chemistry

Physics-informed Bayesian optimization suitable for extrapolation of materials growth This paper describes a novel physics-informed Bayesian optimization approach that leverages prior physics knowledge, specifically Vegard’s law and the linear relationship between gas flow rate and composition in compound semiconductors. The methodology was applied to metal-organic chemical vapor deposition for III–V semiconductor growth. It resulted in the successful synthesis of III–V semiconductors with tailored band gap wavelengths and lattice constants in the region of growth conditions not included in the training data. Furthermore, it predicted hidden trends that Ga composition would be smaller than In composition in As-rich growth regions. This trend is not described by prior physics, demonstrating that statistical machine learning is effective not only for optimization but also for gaining a physical understanding of crystal growth mechanisms. The study demonstrates the potential to develop extrapolable machine learning models by integrating robust physics knowledge, which significantly enhances the efficiency of high-throughput and autonomous material synthesis. Extrapolation ; Bayesian optimization ; Bayesian probability ; Materials science ; Statistical physics ; Computer science ; Biology ; Physics ; Mathematics ; Artificial intelligence ; Statistics Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Advancements in Photolithography Techniques
Authors: Divya Suman | Jigyasa Nigam | Sandra Saade | Paolo Pegolo | Hanna Türk | Xing Zhang | Garnet Kin‐Lic Chan | Michele Ceriotti
Source: Journal of Chemical Theory and Computation (article, 2025-06-25)
Field: Materials Science — Materials Chemistry

Exploring the Design Space of Machine Learning Models for Quantum Chemistry with a Fully Differentiable Framework Traditional atomistic machine learning (ML) models serve as surrogates for quantum mechanical (QM) properties, predicting quantities such as dipole moments and polarizabilities directly from compositions and geometries of atomic configurations. With the emergence of ML approaches to predict the "ingredients" of a QM calculation, such as the ground-state charge density or the effective single-particle Hamiltonian, it has become possible to obtain multiple properties through analytical physics-based operations on these intermediate ML predictions. We present a framework that seamlessly integrates the prediction of an effective electronic Hamiltonian, for both molecular and condensed-phase systems, with PySCFAD, a differentiable QM workflow. This integration facilitates training models indirectly against functions of the Hamiltonian, such as electronic energy levels, dipole moments, polarizability, etc. We then use this framework to explore various possible choices within the design space of hybrid ML/QM models, examining the influence of incorporating multiple targets on model performance and learning a reduced-basis ML Hamiltonian that can reproduce targets computed on a much larger basis. Our benchmarks evaluate the accuracy and transferability of these hybrid models, compare them against predictions of atomic properties from their surrogate models, and provide indications to guide the design of the interface between the ML and QM components of the model. Hamiltonian (control theory) ; Polarizability ; Dipole ; Computer science ; Workflow ; Differentiable function ; Quantum ; Quantum chemistry ; Statistical physics ; Physics ; Quantum mechanics ; Mathematics ; Mathematical optimization Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Hayri Yıldırım
Source: Journal of Materials Science (article, 2025-03-01)
Field: Materials Science — Polymers and Plastics

Prediction of weight change of glass fiber reinforced polymer matrix composites with SiC nanoparticles after artificial aging by artificial neural network-based model Abstract In this study, the weights of SiC (silicon carbide) nanoparticle-filled and unfilled glass fiber reinforced polymer matrix composites (PMC) after artificial aging were estimated using an artificial neural network (ANN) model. Composite samples with different SiC nanoparticle weight fractions (0%, 0.5%, 1%, 1.5%, 2%) were produced by vacuum infusion method and subjected to artificial aging at 70 ºC and 85% relative humidity for 0, 250, 500, 750, 1000, 1250, and 1500 h. The weights of the samples were measured and recorded periodically during the aging process. The developed ANN model was trained to estimate the sample weight using SiC nanoparticle weight fraction and aging time as input parameters. The network with four neurons in a single hidden layer was trained with the Levenberg–Marquardt feedforward backpropagation algorithm, and a total of 35 datasets were used for training, testing, and validation. The weights predicted by the model overlapped with the experimentally obtained data with high accuracy. The mean square error (MSE) value calculated to evaluate the accuracy and adequacy of the model was determined as 0.001225 in the 256th iteration. It was concluded that the trained artificial neural network model was able to predict the weights of SiC nanoparticle-filled and unfilled glass fiber reinforced PMCs with high accuracy and efficiency. Graphical abstract Materials science ; Composite material ; Solid mechanics ; Nanoparticle ; Artificial neural network ; Polymer ; Matrix (chemical analysis) ; Glass fiber ; Fiber ; Nanotechnology ; Machine learning Natural Fiber Reinforced Composites ; Additive Manufacturing and 3D Printing Technologies ; Innovative concrete reinforcement materials
Authors: Xinyi Fang | Elizabeth Murphy | Phillip Kohl | Youli Li | Craig J. Hawker | Christopher M. Bates | Mengyang Gu
Source: Journal of Polymer Science (article, 2025-01-25)
Field: Materials Science — Materials Chemistry

Universal Phase Identification of Block Copolymers From Physics‐Informed Machine Learning ABSTRACT Block copolymers play a vital role in materials science due to their diverse self‐assembly behavior. Traditionally, exploring the block copolymer self‐assembly and associated structure–property relationships involve iterative synthesis, characterization, and theory, which is labor‐intensive both experimentally and computationally. Here, we introduce a versatile, high‐throughput workflow toward materials discovery that integrates controlled polymerization and automated chromatographic separation with a novel physics‐informed machine‐learning algorithm for the rapid analysis of small‐angle X‐ray scattering data. Leveraging the expansive and high‐quality experimental data sets generated by fractionating polymers using automated chromatography, this machine‐learning method effectively reduces data dimensionality by extracting chemical‐independent features from SAXS data. This new approach allows for the rapid and accurate prediction of morphologies without repetitive and time‐consuming manual analysis, achieving out‐of‐sample predictive accuracy of around 95% for both novel and existing materials in the training data set. By focusing on a subset of samples with large predictive uncertainty, only a small fraction of the samples needs to be inspected to further improve accuracy. Collectively, the synergistic combination of controlled synthesis, automated chromatography, and data‐driven analysis creates a powerful workflow that markedly expedites the discovery of structure–property relationships in advanced soft materials. Copolymer ; Identification (biology) ; Block (permutation group theory) ; Phase (matter) ; Materials science ; Computer science ; Nanotechnology ; Physics ; Mathematics ; Quantum mechanics ; Biology ; Geometry ; Polymer ; Composite material Block Copolymer Self-Assembly ; Machine Learning in Materials Science ; Nanopore and Nanochannel Transport Studies
Authors: Xingkang Diao | Guohua Qi | Xinli Li | Yu Tian | Jing Li | Yongdong Jin
Source: Analytical Chemistry (article, 2025-02-25)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Label-Free Exosomal SERS Detection Assisted by Machine Learning for Accurately Discriminating Cell Cycle Stages and Revealing the Molecular Mechanisms during the Mitotic Process Cell cycle analysis is crucial for disease diagnosis and treatment, especially for investigating cell heterogeneity and regulating cell behaviors. Exosomes are highly appealing as noninvasive biomarkers for monitoring real-time changes in the cell cycle due to their abundant molecular information inherited from their metrocyte cells and reflecting the state of these cells to some extent. However, to our knowledge, the relationship between exosomes and the cell cycle has not been reported. Herein, we successfully monitored the variation of exosomal surface-enhanced Raman spectroscopy (SERS) spectra to discriminate different cell cycle stages (G<sub>0</sub>/G<sub>1</sub>, S, and G<sub>2</sub>/M phases) based on label-free surface-enhanced Raman spectroscopy (SERS) combined with the machine learning method of linear discriminant analysis (LDA). An average accuracy of 85% based on the trained SERS spectra of exosomes from different cell cycle stages confirmed the high reliability of the support vector machine (SVM) algorithm for analyzing dynamic changes in the cell cycle at different time points. Importantly, the related molecular mechanisms among mitotic processes (prometaphase, metaphase, and anaphase/telophase) and unique biomolecular events between cancerous (HeLa) and normal (H8) cells were also revealed by the present label-free SERS detection platform. Based on SERS analysis, the content of phenylalanine (Phe) within HeLa cells increased, and some structures of proteins containing Phe and tryptophan (Trp) residues may be transformed during the mitotic process. Notably, the α-helix and β-sheet of proteins coexisted in HeLa cells; meanwhile, the α-helix of the proteins was more dominant in H8 cells than in HeLa cells. The strategy is effective for discriminating cell cycle stages and elucidating the associated molecular events during the cell mitotic process and will provide potential application value for guiding the cell cycle treatment strategies of cancer in the future. Chemistry ; Process (computing) ; Mitosis ; Nanotechnology ; Computational biology ; Artificial intelligence ; Biological system ; Cell biology ; Computer science Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Extracellular vesicles in disease
Authors: Luis E. Castro-Anaya | Eduardo Marese | Jaime A. Lozano | Guilherme Fidelis Peixer | Jader R. Barbosa | Sergio Yesid Gómez González
Source: Journal of Chemical Information and Modeling (article, 2025-02-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine Learning Methodologies Applied to Magnetocaloric Perovskites Discovery Traditionally, designing novel materials involves exploring new compositions guided by insights from previous work, relying on a trial-and-error approach, where continuous synthesis and characterization proceed until the properties meet the improvements. This method is inefficient due to the challenges of exploring vast chemical spaces. In this study, a machine-learning-based methodology is developed to assist the design from available data in the literature, allowing us to test in silico more than 1.2 million compositions. Two databases with 1227 inputs were created from published studies. Four machine learning (ML) models were trained over the feature sets using 517 compositional features (generated from 58 atomic properties) to predict magnetocaloric properties of perovskites: Curie temperature (<i>T</i><sub>C</sub>), magnetic entropy change (ME), and relative cooling power (RCP). The best model-feature combinations were used to explore the chemical space of lanthanum, praseodymium, and neodymium manganites, identifying composition trends for different temperature applications, including room temperature refrigeration, where the most suitable combinations of doping elements were highlighted. The study offers valuable guidelines for future research insights on magnetocaloric materials, and the methodology can be transferred to other perovskite related material areas, such as catalysts and solar cell materials. Magnetic refrigeration ; Machine learning ; Praseodymium ; Computer science ; Perovskite (structure) ; Characterization (materials science) ; Neodymium ; Materials science ; Artificial intelligence ; Curie temperature ; Process engineering ; Nanotechnology ; Chemistry ; Physics ; Magnetization ; Magnetic field ; Ferromagnetism ; Condensed matter physics Magnetic and transport properties of perovskites and related materials ; Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices
Authors: Jiayu Peng
Source: The Journal of Chemical Physics (article, 2025-07-28)
Field: Materials Science — Materials Chemistry

Toward data-driven predictive modeling of electrocatalyst stability and surface reconstruction Catalyst dissolution and surface restructuring are ubiquitous in electrocatalysis, often leading to formidable activity-stability trade-offs and obscure electrochemically induced surface species that severely hinder the understanding and optimization of electrocatalysts under diverse harsh operating conditions. As even state-of-the-art characterization techniques lack the resolution and efficiency for the unambiguous elucidation of decomposition kinetics and reconstruction dynamics at electrocatalytic interfaces, many atomistic modeling approaches-following the recent advances in physics-driven machine learning-have been widely used to facilitate the atom-by-atom understanding and rational engineering of electrocatalyst stability and dynamics. This Perspective systematically assesses classical and data-driven approaches in theoretical surface science and computational catalysis, recognizing their achievements and highlighting their limitations in throughput, efficiency, accuracy, bias, transferability, and scalability toward enabling realistic and predictive modeling of electrocatalyst degradation and reconstruction. By examining different methods spanning first-principle simulations, surface sampling, neural network interatomic potentials, and generative deep learning models, it is underscored how such data-driven computational techniques help elucidate the precise nature of various key interfacial atomistic processes to address existing technical challenges in surface modeling and provide a new paradigm to optimize dissolution kinetics and restructuring dynamics for electrocatalyst design. Electrocatalyst ; Stability (learning theory) ; Surface (topology) ; Materials science ; Computer science ; Environmental science ; Chemistry ; Mathematics ; Machine learning ; Geometry ; Physical chemistry ; Electrochemistry ; Electrode Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced Photocatalysis Techniques
Authors: Domantas Kuryla | Gábor Cśanyi | Adri C. T. van Duin | Angelos Michaelides
Source: The Journal of Chemical Physics (article, 2025-03-21)
Field: Materials Science — Materials Chemistry

Efficient exploration of reaction pathways using reaction databases and active learning The fast and accurate simulation of chemical reactions is a major goal of computational chemistry. Recently, the pursuit of this goal has been aided by machine learning interatomic potentials (MLIPs), which provide energies and forces at quantum mechanical accuracy but at a fraction of the cost of the reference quantum mechanical calculations. Assembling the training set of relevant configurations is key to building the MLIP. Here, we demonstrate two approaches to training reactive MLIPs based on reaction pathway information. One approach exploits reaction datasets containing reactant, product, and transition state structures. Using an SN2 reaction dataset, we accurately locate reaction pathways and transition state geometries of up to 170 unseen reactions. In another approach, which does not depend on data availability, we present an efficient active learning procedure that yields an accurate MLIP and converged minimum energy path given only the reaction end point structures, avoiding quantum mechanics driven reaction pathway search at any stage of training set construction. We demonstrate this procedure on an SN2 reaction in the gas phase and with a small number of solvating water molecules, predicting reaction barriers within 20 meV of the reference quantum chemistry method. We then apply the active learning procedure on a more complex reaction involving a nucleophilic aromatic substitution and proton transfer, comparing the results against the reactive ReaxFF force field. Our active learning procedure, in addition to rapidly finding reaction paths for individual reactions, provides an approach to building large reaction path databases for training transferable reactive machine learning potentials. SN2 reaction ; Chemistry ; Computer science ; Quantum ; ReaxFF ; Computational chemistry ; Molecular dynamics ; Physics ; Interatomic potential ; Quantum mechanics ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Juno Nam | Jiayu Peng | Rafael Gómez‐Bombarelli
Source: Nature Communications (article, 2025-05-10)
Field: Materials Science — Materials Chemistry

Interpolation and differentiation of alchemical degrees of freedom in machine learning interatomic potentials Machine learning interatomic potentials (MLIPs) have become a workhorse of modern atomistic simulations, and recently published universal MLIPs, pre-trained on large datasets, have demonstrated remarkable accuracy and generalizability. However, the computational cost of MLIPs limits their applicability to chemically disordered systems requiring large simulation cells or to sample-intensive statistical methods. Here, we report the use of continuous and differentiable alchemical degrees of freedom in atomistic materials simulations, exploiting the fact that graph neural network MLIPs represent discrete elements as real-valued tensors. The proposed method introduces alchemical atoms with corresponding weights into the input graph, alongside modifications to the message-passing and readout mechanisms of MLIPs, and allows smooth interpolation between the compositional states of materials. The end-to-end differentiability of MLIPs enables efficient calculation of the gradient of energy with respect to the compositional weights. With this modification, we propose methodologies for optimizing the composition of solid solutions towards target macroscopic properties, characterizing order and disorder in multicomponent oxides, and conducting alchemical free energy simulations to quantify the free energy of vacancy formation and composition changes. Computer science ; Degrees of freedom (physics and chemistry) ; Differentiable function ; Graph ; Interpolation (computer graphics) ; Statistical physics ; Artificial neural network ; Algorithm ; Theoretical computer science ; Physics ; Artificial intelligence ; Mathematics ; Mathematical analysis Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; X-ray Diffraction in Crystallography
Authors: Muhammad Azam Qamar | Majed Y. Almashnowi | Mohsenah H. J. Mashniwi | Syed Kashif Ali | Neelam Shahadat
Source: Ionics (review, 2025-01-31)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Advancing the future: a mini review of developments and prospects in potassium-ion capacitors and potassium-ion hybrid capacitors Capacitor ; Potassium ; Ion ; Materials science ; Electrochemistry ; Nanotechnology ; Chemistry ; Electrical engineering ; Voltage ; Metallurgy ; Engineering ; Electrode Supercapacitor Materials and Fabrication ; Advancements in Battery Materials ; Advanced Battery Technologies Research
Authors: Balaji Wamanrao Matore | Anjali Murmu | Purusottam Banjare | Naveen Kumar Vishvakarma | Partha Pratim Roy | Jagadish Singh
Source: Computational Biology and Chemistry (article, 2025-04-26)
Field: Chemistry — Organic Chemistry

Discovery of newer 1,3,4-Oxadiazole clubbed Isoindoline-1,3-dione derivatives as potential anticancer agents: Design, machine learning, synthesis, molecular docking, ADMET, DFT and MD simulation Isoindoline ; Oxadiazole ; Docking (animal) ; Chemistry ; Combinatorial chemistry ; Computer science ; Organic chemistry ; Medicine Synthesis and biological activity ; Computational Drug Discovery Methods ; Click Chemistry and Applications