jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sadman Sadeed Omee | Lai Wei | Sourin Dey | Jianjun Hu
Source: Advanced Science (article, 2025-09-26)
Field: Materials Science — Materials Chemistry

Polymorphism Crystal Structure Prediction with Adaptive Space Group Diversity Control Crystalline materials can form different structural arrangements (i.e., polymorphs) with the same chemical composition, exhibiting distinct physical properties depending on how they are synthesized or the conditions under which they operate. For example, carbon can exist as graphite (soft, conductive) or diamond (hard, insulating). Computational methods that can predict these polymorphs are vital in materials science, which help understand stability relationships, guide synthesis efforts, and discover new materials with desired properties without extensive trial-and-error experimentation. However, effective crystal structure prediction (CSP) algorithms for inorganic polymorph structures remain limited. ParetoCSP2 is proposed, a multi-objective genetic algorithm for polymorphism CSP that incorporates an adaptive space group diversity control technique and the sustainable age-fitness Pareto optimized evolutionary mechanism, preventing over-representation of any single space group in the population guided by a neural network interatomic potential. Using an improved population initialization method and performing iterative structure relaxation, ParetoCSP2 not only alleviates premature convergence but also achieves improved convergence speed. These results show that ParetoCSP2 achieves excellent performance in polymorphism prediction, including a nearly perfect space group and structural similarity accuracy for formulas with two polymorphs but with the same number of unit cell atoms. Evaluated on a benchmark dataset, it outperforms baseline algorithms by factors of 2.46-8.62 for these accuracies and improves by 44.8-87.04% across key performance metrics for regular CSP. This source code is freely available at https://github.com/usccolumbia/ParetoCSP2. Initialization ; Crystal structure prediction ; Population ; Convergence (economics) ; Benchmark (surveying) ; Group (periodic table) ; Premature convergence ; Crystal structure Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Cipriano Rángel | J. Espinosa-Garcı́a
Source: Physical Chemistry Chemical Physics (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Potential energy surfaces: Δ-machine learning from analytical functional forms Delta-machine learning (Δ-ML) is a highly cost-effective approach for developing high-level potential energy surfaces (PESs) from a large number of low-level configurations. In particular, the high flexibility of the analytical potential energy surface developed previously by our group is exploited to efficiently sample points from the low-level data set and, using information from the highly accurate permutation invariant polynomial neural network (PIP-NN) surface, construct the Δ-ML PES. This approach is applied to the well-known H + CH<sub>4</sub> hydrogen abstraction reaction. In order to test the validity and accuracy of the approach to describe this polyatomic system, kinetic studies using the variational transition state with multidimensional tunneling corrections and dynamic studies on the deuterated reaction, H + CD<sub>4</sub>, using quasiclassical trajectory calculations were performed on three surfaces. The delta-machine learning approach reproduces the kinetics and dynamics information of the high-level surface, showing its efficiency in describing multidimensional polyatomic systems. Flexibility (engineering) ; Energy (signal processing) ; Computer science ; Artificial intelligence ; Machine learning ; Mathematics ; Physics ; Quantum mechanics ; Statistics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Molecular Junctions and Nanostructures
Authors: Apri Wahyudi | Natthapong Sueviriyapan | Uthaiporn Suriyapraphadilok
Source: Separation and Purification Technology (article, 2025-12-05)
Field: Materials Science — Materials Chemistry

Generalizable physics-informed graph neural network for CO2 solubility prediction across amine classes Extrapolation ; Artificial neural network ; Mean squared error ; Molecular graph ; Consistency (knowledge bases) ; Flexibility (engineering) ; Amine gas treating ; Graph ; Logarithm Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Carbon Dioxide Capture Technologies
Authors: Do Young Kim | Duck Hyun Wang | Se-Han Lee | Woo Jong Kim | Taeil Yi
Source: Materials & Design (article, 2025-11-25)
Field: Materials Science — Materials Chemistry

Unifying mechanics and thermodynamics: The atomic-scale origin of stability in titanium alloys The thermomechanical stability of high-performance materials such as titanium alloys is essential to their functional reliability, yet predicting this stability is challenging due to the complex interplay between local stress and thermodynamic driving forces at the atomic scale. Here, using large-scale molecular dynamics simulations, we establish a direct, quantitative correlation between local von Mises stress, configurational entropy, and Helmholtz free energy in representative α - and β -phase titanium alloys. We reveal that β -phases exhibit broader stress-entropy distributions, indicating enhanced atomic mobility and reduced thermodynamic stability. Crucially, we resolve a common ambiguity by demonstrating that α -phases, despite having comparable per-atom energies, possess lower free energy per unit volume, which confirms their superior thermodynamic stability at low to moderate temperatures. We further show that the geometric constraints of the HCP α -phase lattice produce a much stronger coupling between local stress and free energy than in the more deformable BCC β -phase. These insights are integrated into a machine learning model that accurately predicts atomic-scale thermodynamic states of the alloy across a wide temperature range. Our findings provide an interpretable, atomistically resolved framework for predicting stress-driven disorder and phase stability in complex engineering alloys. • Volumetric free-energy analysis confirms α -phase titanium alloys are thermodynamically more stable than β -phases at moderate temperatures. • The constrained HCP crystal structure of the α -phase results in a significantly stronger coupling between free energy and mechanical stress compared to the more deformable β -phase. • Bivariate maps linking atomic entropy and local stress provide a quantitative fingerprint of a material’s thermomechanical state. • An interpretable machine-learning surrogate, trained on statistically sufficient MD data, accurately predicts atomic-scale states from temperature, enabling rapid alloy screening. • The established link between structure and stability provides a transferable framework for accelerating data-driven alloy design. Helmholtz free energy ; Chemical stability ; Titanium alloy ; von Mises yield criterion ; Pseudoelasticity ; Thermodynamic integration ; Titanium ; Stability (learning theory) ; Coupling (piping) ; Shape-memory alloy Machine Learning in Materials Science ; Titanium Alloys Microstructure and Properties ; High Entropy Alloys Studies
Authors: Ouail Zakary | Perttu Lantto
Source: The Journal of Physical Chemistry Letters (article, 2025-11-11)
Field: Chemistry — Organic Chemistry

Equivariant Neural Networks Reveal How Host–Guest Interactions Shape <sup>129</sup> Xe NMR in Porous Liquids Understanding host-guest interactions in porous liquids (PLs) formed from porous organic cages (POCs) is pivotal in tailoring their physicochemical properties, therefore providing an avenue for engineering new PLs with enhanced functionalities. In this work, we demonstrate, for the first time, the use of an accurate and efficient machine-learning-based approach for atomistic modeling of host-guest interactions in large-scale PLs. The approach uses <i>E</i>(3)-equivariant graph neural networks (EGNNs) to construct a machine learning interatomic potential and a nuclear magnetic resonance machine learning model. The former enables machine learning molecular dynamics (MLMD) simulations, while the latter computes the <sup>129</sup>Xe isotropic chemical shift, δ<sub>iso</sub>, from MLMD snapshots. Applied to a PL composed of CC3-<i>R</i> POC in 4-(trifluoromethoxy)benzyl alcohol (TBA) solvent loaded with high Xe concentration, this dual-model approach shows that host(CC3)-guest(Xe) interactions are best described by a three-site binding model comprising the CC3 intrinsic cavity, CC3 openings, and TBA solvent, with exchange events occurring between these sites. Good agreement between computed and experimental <sup>129</sup>Xe δ<sub>iso</sub> validates our approach, demonstrating EGNN-based simulations as transformative tools for advancing the understanding of PLs. Artificial neural network ; Isotropy ; Molecular dynamics ; Porosity ; Graph ; Construct (python library) ; Porous medium ; Minification Supramolecular Chemistry and Complexes ; Covalent Organic Framework Applications ; Supramolecular Self-Assembly in Materials
Authors: Thomas Gasevic | Marcel Müller | Jonathan Schöps | Stephanie Lanius | Jan Hermann | Stefan Grimme | Andreas Hansen
Source: ChemRxiv (preprint, 2025-06-13)
Field: Chemistry — Organic Chemistry

Chemical Space Exploration with Artificial ”Mindless” Molecules We introduce MindlessGen, a Python-based generator for creating chemically diverse, “mindless” molecules through random atomic placement and subsequent geometry optimization. Using this framework, we constructed the MB2061 benchmark set, containing 2061 molecules with high-level PNO-LCCSD(T)-F12 reference data for dissociation reactions. This set provides a challenging benchmark for testing, validation, and training of density functional approximations (DFAs), semiempirical methods, force fields, and machine learning potentials using molecular structures beyond the conventional chemical space. For DFAs, we initially hypothesized that highly parameterized functionals might perform poorly on this set. However, no consistent relationship between fitting strategy and accuracy was observed. A clear Jacob’s ladder trend emerges, with ωB97X-2 achieving the lowest mean absolute error (MAE) of 8.4 kcal·mol−1 and r²SCAN-3c offering a robust cost-efficient alternative (19.6 kcal·mol−1). Furthermore, we discuss the performance of selected semiempirical methods and contemporary machine learned interatomic potentials. Space (punctuation) ; Chemical space ; Molecule ; Psychology ; Nanotechnology ; Chemistry ; Materials science ; Computer science ; Organic chemistry ; Biochemistry Supramolecular Chemistry and Complexes ; Supramolecular Self-Assembly in Materials
Authors: Emilio Dorigatti | Jonathan Groß | Jonas Kühlborn | Robert Möckel | Frank J. Maier | Julian Keupp
Source: Digital Discovery (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Enhancing automated drug substance impurity structure elucidation from tandem mass spectra through transfer learning and domain knowledge We present a machine learning model that elucidates process impurities from synthesis via tandem mass spectra, and demonstrate how it becomes accurate enough to become a useful companion in a real-world chemical/analytical development setting. Tandem ; Impurity ; Drug ; Chemistry ; Domain (mathematical analysis) ; Combinatorial chemistry ; Materials science ; Pharmacology ; Medicine ; Organic chemistry ; Mathematics Spectroscopy and Chemometric Analyses ; Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods
Authors: Maxime Infuso | Samuel Del Fré | Gilberto A. Alou Angulo | M. Bertin | J.-H. Fillion | Alejandro Rivero Santamaría | Maurice Monnerville
Source: The Journal of Chemical Physics (article, 2025-08-25)
Field: Materials Science — Materials Chemistry

Deep potential-driven molecular dynamics of CO ice analogs: Investigating desorption following vibrational excitation We present a new deep learning-based machine learning potential (MLP) for molecular dynamics simulations of solid carbon monoxide (CO), capable of accurately describing CO vibrations both in the fundamental state and in highly excited vibrational states, up to approximately v = 40. The MLP is based on the combination of high-dimensional neural network atomic potentials using the DeePMD-kit package, trained on prior ab initio molecular dynamics data, with selective treatment of the excited molecule, allowing us to capture complex energy redistribution dynamics in condensed-phase environments. In particular, the MLP is capable of accurately describing the desorption process of a single CO molecule within an aggregate of 50 CO molecules, in excellent agreement with both previous theoretical predictions and experimental measurements. The MLP provides a much finer description of the translational and rotational energy distributions, capturing their character with high fidelity and allowing a more detailed comparison with experimental results. Furthermore, the analysis of the rotational energy, resolved over specific translational energies, revealed new insights into the coupling between translational and rotational degrees of freedom during the photodesorption process. This novel approach opens new perspectives for extensive statistical studies on desorption energies and detailed investigations of surface molecule excitations and the exploration of larger-scale models incorporating periodic boundary conditions to simulate more realistic CO aggregates. Excited state ; Molecular dynamics ; Degrees of freedom (physics and chemistry) ; Potential energy surface ; Excitation ; Chemistry ; Chemical physics ; Molecule ; Ab initio ; Desorption ; Potential energy ; Atomic physics ; Molecular physics ; Physics ; Computational chemistry ; Physical chemistry ; Quantum mechanics Machine Learning in Materials Science ; Quantum, superfluid, helium dynamics ; Catalysis and Oxidation Reactions
Authors: Mahesh Biyani | M. Matsumoto | Yasuo Yoshimi
Source: Nanomaterials (article, 2025-06-24)
Field: Chemistry — Analytical Chemistry

A Poly(methacrolein-co-methacrylamide)-Based Template Anchoring Strategy for the Synthesis of Fluorescent Molecularly Imprinted Polymer Nanoparticles for Highly Selective Serotonin Sensing Neurotransmitters such as serotonin regulate key physiological and cognitive functions, yet real-time detection remains challenging due to the limitations of conventional techniques like amperometry and microdialysis. Fluorescent molecularly imprinted polymer nanoparticles (fMIP-NPs) offer a promising alternative and are typically synthesized via solid-phase synthesis, in which template molecules are covalently immobilized on a solid support to enable site-specific imprinting. However, strong template-template interactions during this process can compromise selectivity. To overcome this, we incorporated a poly(methacrolein-<i>co</i>-methacrylamide)-based template anchoring strategy to minimize undesired template interactions and enhance imprinting efficiency. We optimized the synthesis of poly(methacrolein-<i>co</i>-methacrylamide) under three different conditions by varying the monomer compositions and reaction parameters. The poly(methacrolein-<i>co</i>-methacrylamide) synthesized under Condition 3 (5:1 methacrolein-to-methacrylamide molar ratio, 1:150 initiator-to-total monomer ratio, and 4.59 M total monomer concentration) yielded the most selective fMIP-NPs, whose fluorescence intensity increased with an increase in serotonin concentration, rising by up to 37% upon serotonin binding. This improvement is attributed to higher aldehyde functionality in the poly(methacrolein-<i>co</i>-methacrylamide) which enhances template immobilization and generates a rigid imprinted cavity to interact with serotonin. These findings suggest that the developed fMIP-NPs hold significant potential as imaging probes for neurotransmitter detection, contributing to advanced studies in neural network analysis. Methacrylamide ; Methacrolein ; Molecularly imprinted polymer ; Molecular imprinting ; Methacrylic acid ; Monomer ; Chemistry ; Materials science ; Polymer ; Organic chemistry ; Selectivity ; Acrylamide ; Catalysis Analytical chemistry methods development ; Forensic Toxicology and Drug Analysis ; Psychedelics and Drug Studies
Authors: Mahmudul Islam | Killian Sheriff | Rodrigo Freitas
Source: Acta Materialia (preprint, 2025-12-23)
Field: Materials Science — Materials Chemistry

Dislocation-mediated short-range order evolution during thermomechanical processing Dislocation ; Plasticity ; Thermomechanical processing ; Microstructure ; Thermal ; Deformation (meteorology) ; Work (physics) ; Annihilation Microstructure and mechanical properties ; Microstructure and Mechanical Properties of Steels ; Aluminum Alloy Microstructure Properties
Authors: Cristina Nuzzi | Simone Pasinetti | Irene Bassi | Valentina Bello
Source: Measurement (article, 2025-10-10)
Field: Chemistry — Analytical Chemistry

On the applicability of speckle pattern imaging combined with AI for raw milk classification This work demonstrates the high potential of an innovative technique for raw milk classification based on the integration of speckle pattern imaging and artificial intelligence. By exciting speckle patterns with a semiconductor laser and collecting experimental images with a CMOS camera, a total of 20 samples of raw cow milk with similar nutritional contents were tested during 4 Campaigns. Data analysis was conducted leveraging one common feature-based machine learning model and one state-of-the-art image-based deep learning model for speckle patterns. This study aims to provide in-depth insights to the community on how this measurement technique can be applied to raw cow milk samples and how the prediction models tested perform due to the similarity of the nutritional components of the samples. The machine learning model was trained on a set of 16 custom features, while the deep learning model used speckle pattern images as input. Both types of data were standardized dataset-wise beforehand using z-score. The best machine learning and deep learning models achieved 95% accuracy. The study highlights that the nutritional similarity of the samples highly impacts the models’ confusion in both cases, especially when Campaigns conducted at different sample temperatures were not included in the training. Overall, the analysis technique presented leveraging uncertainty metrics is a stepping stone toward relevant advances in the field of milk analysis. • Speckle pattern imaging and AI are combined to classify raw cow milk samples. • The method is evaluated by comparing the performance of a ML model and a CNN. • Geometry-based and homophily-based uncertainty are compared for both models. Speckle pattern ; Pattern recognition (psychology) ; Deep learning ; Sample (material) ; Raw data ; Raw milk ; Speckle noise Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Effects of Environmental Stressors on Livestock
Authors: Xiaoyu Xie | Qin Wang | Zhen Jia | Yilan Sun | Chao Qiu | Ning Qian | Jie Pang
Source: Journal of Future Foods (article, 2025-06-18)
Field: Chemistry — Analytical Chemistry

Application of convolutional neural network in the production and processing of flower and fruit tea • CNN reduces feature extraction time • CNN outperforms traditional and machine learning algorithms • The application of CNN promotes the transformation of the tea industry towards intelligence • Provide theoretical guidance for the sustainable development of the tea industry At present, the global tea industry is in a stage of transformation towards intelligent chemical development. Although traditional machine learning methods have achieved good results in the production and processing supervision of flower and fruit tea, it is difficult to improve supervision efficiency due to the limitations of manually extracting features. The automatic feature learning function of convolutional neural networks(CNN) solves this limitation and opens up a new perspective for the intelligent development of the flower and fruit tea industry. This article reviews the latest progress in the application advantages of CNN in the flower and fruit tea industry. A systematic review and meta-analysis were conducted on applying CNN in pest control, harvesting, and processing methods of flower and fruit tea raw materials (teas, flowers, fruits). Finally, an outlook was made on the relevant advanced progress and prospects. Compared with traditional machine learning methods, CNN has significant advantages in supervising flower and fruit tea production and processing. This review is expected to provide new insights into the application of intelligent technology in the tea industry. Production (economics) ; Convolutional neural network ; Computer science ; Horticulture ; Artificial intelligence ; Biology Spectroscopy and Chemometric Analyses ; Remote Sensing and Land Use
Authors: Andrea Corradini | Giovanni Marini | Matteo Calandra
Source: npj Computational Materials (article, 2025-05-26)
Field: Materials Science — Materials Chemistry

Scalable machine learning approach to light induced order disorder phase transitions with ab initio accuracy While machine learning excels in simulating material thermal properties, its application to order-disorder non-thermal phase transitions induced by visible light has been limited by challenges in accurately describing potential energy surfaces, forces, and vibrational properties in the presence of a photoexcited electron-hole plasma. Here, we present a novel approach that combines constrained density functional theory with machine learning, yielding highly reliable interatomic potentials capable of capturing electron-hole plasma effects on structural properties. Applied to photoexcited silicon, our potential accurately reproduces the phonon dispersion of the crystal phase and allows for molecular dynamics simulations of tens of thousands of atoms. We show that, at low enough temperatures, the non-thermal melting transition is driven by a soft phonon and the formation of a double-well potential, at odds with thermal melting being strictly first order. Our method paves the way to large-scale, long-time simulations of light-induced order-disorder phase transitions with ab initio accuracy. Ab initio ; Phonon ; Phase transition ; Materials science ; Crystal structure prediction ; Phase (matter) ; Molecular dynamics ; Crystal (programming language) ; Thermal ; Statistical physics ; Condensed matter physics ; Chemical physics ; Molecular physics ; Crystal structure ; Physics ; Computer science ; Chemistry ; Thermodynamics ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Electronic and Structural Properties of Oxides