jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zhichao Zhang | Jun Zhou | Jinhui Wang | Guotao Chu | Xuerui You | Chunping Liang | Jifan Hu
Source: New Journal of Chemistry (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

A compositional feature-driven prediction model for the emission wavelength of Eu <sup>2+</sup> -doped whitlockite-type phosphors coupled with a material design framework A composition-only machine learning model was developed and integrated into a materials design framework, enabling the targeted synthesis of the solid-solution phosphor Ca 8− x Sr x MgLa(PO 4 ) 7 (0 ≤ x ≤ 8). Phosphor ; Luminescence ; Rational design ; Material Design ; Wavelength Luminescence Properties of Advanced Materials ; Lanthanide and Transition Metal Complexes ; Magnesium Oxide Properties and Applications
Authors: Zhengzheng Dang | Lei Cheng | Zhichen Tang | Zeyu Zhang | Yu Tian | Jixin Wu | Yide Chang | Chi Chen | Yanming Wang
Source: Journal of Chemical Theory and Computation (article, 2026-03-16)
Field: Materials Science — Materials Chemistry

Uncertainty-Driven Deep-Ensemble Temporal Convolutional Networks for Predicting Chemical Reaction Dynamics Chemical reaction dynamics, critical to advancing technologies in energy, environment, and materials, can in principle be captured via reactive molecular dynamics (RMD). Time-series machine learning algorithms enable prediction of species evolution information hidden in these simulations, but long-horizon autoregressive accuracy often degrades due to error accumulation, and generating sufficiently large and diverse RMD data sets across operating conditions is computationally expensive. Here, we propose DEAL-TCN (deep-ensemble active learning with temporal convolutional networks), an active-learning framework that effectively disentangles the high-dimensional complexity of reaction dynamics spanning species, time, and operating conditions. It adopts the query-by-committee strategy to select informative conditions and leverages one-dimensional temporal convolutions to capture interspecies and long timescale couplings, enabling efficient modeling and long-term prediction of species evolution. In a prototypical case study of Mo-O-S precursors, DEAL-TCN robustly identifies and accurately predicts the concentration evolution of all involved chemical species across orthogonally designed test sets spanning a broad parameter range (1100-1500 K, 2-6 atm, and a feed ratio of 1/25-1/15). Given only the first 50 ps of each trajectory as input, the model attains a mean prediction error of 4.8% at the picosecond level while maintaining a mean error of 18.2% over the subsequent 0.45 ns, representing improvements of ∼29% and 24% over baseline LSTM and Transformer architectures, respectively. Meanwhile, DEAL-TCN outperforms random sampling in 98.8% of active-learning iterations with the same labeling budget. These results underscore DEAL-TCN's potential as a scalable and generalizable approach for mechanistic discovery, reaction design, and optimization. Dynamics (music) ; Chemical Dynamics ; Convolutional neural network ; Reaction dynamics ; Chemical reaction ; Pattern recognition (psychology) Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference ; Advanced Graph Neural Networks
Authors: V. I. Rimshin | S. V. Usanov | A. N. Vydrin | A. E. Kern | E. S. Makarova
Source: Modern Trends in Construction Urban and Territorial Planning (article, 2026-01-12)
Field: Materials Science — Materials Chemistry

Neural Network Modeling of the Strength of Normal Sections of Prefabricated Reinforced Concrete Ribbed Slabs Introduction. Precast reinforced concrete ribbed slabs are broadly used as floors and coverings for industrial, residential and public buildings. Their use in this capacity is due to the high technological efficiency of manufacturing, efficient use of concrete and the possibility of automating factory production. One of the critical tasks in designing such structures is to calculate the bearing capacity of normal cross sections. Traditional calculation methods are reliable, but they are outdated. Machine learning methods are increasingly being employed in engineering, where researchers are opting for artificial neural networks (ANNs). The use of traditional methods in processing structured data such as tables and databases has its limitations. Neural networks are capable of analyzing unstructured data such as text, images, and videos, which opens up new prospects for analyzing and comprehending information. The article sets forth an approach to neural network modeling of the bearing capacity of normal sections of prefabricated reinforced concrete ribbed slabs. Materials and Methods. A structured and processed data array (dataset) includes 20 samples for which a computational model based on a multilayer perceptron has been developed and verified. The input parameters are the geometric as well as physical and mechanical characteristics of the slabs and the applied load, the output parameter is the limiting bending moment calculated using the limit state method. Research Results . Training on a limited sample did not lead to retraining of the model due to the correct division of data into test, training and control batches and the use of the quasi-Newton optimization method. The model has displayed a high level accuracy and reliability. Artificial neural networks are capable of identifying nonlinear dependencies between the parameters with no a priori assumptions. Discussion and Conclusion. The suggested model is not a substitute for the existing calculations, but it serves as an efficient digital tool for quick verification of design solutions, optimization of reinforcement and improvement of structural reliability. Its implementation into BIM systems and digital construction platforms is in compliance with the requirements of Industry 4.0 and creates new opportunities for designing prefabricated reinforced concrete structures. Artificial neural network ; Perceptron ; Precast concrete ; Factory (object-oriented programming) ; Moment (physics) ; Slab ; Test data ; Bearing capacity ; Reinforced concrete Structural mechanics and materials ; Heat Transfer and Mathematical Modeling ; Machine Learning in Materials Science
Authors: S. Srinivasan | G. Gokilakrishnan | S. Thirumurugaveerakumar | D. Prabha
Source: Innovative Infrastructure Solutions (article, 2026-03-14)
Field: Materials Science — Materials Chemistry

Mechanical properties prediction in austenitic stainless steels: inelastic constitutive artificial neural networks Artificial neural network ; Austenitic stainless steel ; Ultimate tensile strength ; Mean squared error ; MATLAB ; Backpropagation ; Coefficient of determination ; Sensitivity (control systems) ; Casting Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Microstructure and Mechanical Properties of Steels
Authors: Chanwoo Park | WonJoo Lee | Howon Lee | Seong-Hoon Kang | Jonghun Yoon
Source: Materials & Design (article, 2026-03-07)
Field: Materials Science — Materials Chemistry

Machine learning–driven prediction of mechanical properties in heat treated L-PBF Ti-6Al-4V from microstructural images • A machine learning framework predicts stress–strain curve of heat-treated L-PBF Ti-6Al-4V. • Quantitative microstructural features extracted from as-built OM images using 2-point correlation and autoencoder. • Microstructure-based prediction accounts for effects beyond process parameters, including geometry and environment. • Machine learning framework enables optimization of manufacturing process without repeated heat treatment or tensile testing. In the L-PBF process of Ti-6Al-4V, rapid solidification leads to the formation of acicular martensitic microstructures, which result in high strength but significantly reduced elongation. To overcome this limitation, heat treatment is applied, and the resulting mechanical properties vary greatly depending on the conditions. In this study, we propose an artificial neural network (ANN)-based model that predicts the entire stress–strain curve, including tensile properties, based on the initial microstructure images of Ti-6Al-4V alloy. Microstructural features were quantitatively extracted using a two-point correlation function and an autoencoder. The developed model reconstructed the full stress–strain curve by predicting the fitting parameters of the Voce equation, thereby capturing plastic behavior. This approach demonstrates that mechanical properties can be predicted using initial microstructure information and heat treatment conditions, underscoring the potential of data-driven models to optimize processes without repeated tensile testing. Microstructure ; Deformation (meteorology) ; Thermography ; Finite element method Titanium Alloys Microstructure and Properties ; Machine Learning in Materials Science ; Metallurgy and Material Forming
Authors: Jason W. Rocks | Dat P. Truong | Dmitrij Rappoport | Samuel Maddrell-Mander | Daniel A. Martin-Alarcon | Toni M. Lee | Steve Crossan | Joshua E. Goldford
Source: Proceedings of the National Academy of Sciences (article, 2026-03-17)
Field: Materials Science — Materials Chemistry

Dual-encoder contrastive learning accelerates enzyme discovery The ability to engineer enzymes for desired reactions is a cornerstone of modern biotechnology, yet identifying suitable starting proteins remains a critical bottleneck. Although contrastive learning offers a compelling computational approach for enzyme discovery, these models have yet to be implemented at scale or proven effective in real-world experimental settings. Here, we present <i>Horizyn-1</i>, a computationally efficient deep learning framework that enables large-scale reaction-to-enzyme recommendation validated through comprehensive experimental testing. Leveraging a combination of reaction fingerprints and protein language models, we trained <i>Horizyn-1</i> on millions of reaction-enzyme pairs to achieve state-of-the-art performance, recovering an enzyme with correct activity within the top 100 hits for over 75% of test reactions. We experimentally validate <i>Horizyn-1</i> across three enzyme discovery scenarios: identifying enzymes for orphan reactions, predicting enzyme promiscuity for both characterized and uncharacterized enzymes, and discovering enzymes for nonnatural biochemical reactions including lysine-driven transaminations that enable efficient synthesis of noncanonical amino acids. On underrepresented reaction classes, we find that fine-tuning with fewer than 10 additional reactions can dramatically improve performance. Furthermore, a logarithmic scaling of model performance with training dataset size suggests continued improvement with larger and more diverse reaction datasets. <i>Horizyn-1</i> addresses the critical bottleneck of sourcing initial enzymes for optimization campaigns, enabling efficient and scalable in silico screening for enzymes with desired activities and promising to accelerate future efforts in biocatalysis and metabolic engineering. In silico ; Scalability ; Bottleneck ; Enzyme ; Benchmark (surveying) ; Biocatalysis ; Synthetic biology Machine Learning in Materials Science ; Protein Structure and Dynamics ; Enzyme Catalysis and Immobilization
Authors: Xueming He | Yuanji Xiao | Yongnan Lu | Xiaoyun Yang | Fang Zhang
Source: Food Control (article, 2026-02-27)
Field: Chemistry — Organic Chemistry

Improved prediction for AFB1 in naturally contaminated peanut oil by fusing fluorescence with visible and near-infrared absorption and scattering properties Contamination ; Mean squared error ; Linear regression ; Support vector machine ; Absorption (acoustics) ; Analytical Chemistry (journal) ; Interference (communication) ; Aflatoxin ; Coefficient of determination ; Fusion Edible Oils Quality and Analysis ; Oil Spill Detection and Mitigation ; Sulfur Compounds in Biology
Authors: Yang Xiao | Xuan Xia | Q. Ye | Pei Zhao | Ke Wen | Chenao Yan | Bingran Xin | Xinhong Qiu
Source: Chemosphere (article, 2026-04-11)
Field: Materials Science — Materials Chemistry

Calcined coal gangue-derived Mg- and Ca-based layered double hydroxides for Cr(VI) soil remediation: Experiments and machine learning Layered double hydroxides ; Calcination ; Coal ; Adsorption Layered Double Hydroxides Synthesis and Applications ; Magnesium Oxide Properties and Applications ; Concrete and Cement Materials Research
Authors: P. Bernát Szabó | Zeno Schätzle | Frank Noé
Source: arXiv (Cornell University) (preprint, 2026-03-26)
Field: Materials Science — Materials Chemistry

Enabling ab initio geometry optimization of strongly correlated systems with transferable deep quantum Monte Carlo A faithful description of chemical processes requires exploring extended regions of the molecular potential energy surface (PES), which remains challenging for strongly correlated systems. Transferable deep-learning variational Monte Carlo (VMC) offers a promising route by efficiently solving the electronic Schrödinger equation jointly across molecular geometries at consistently high accuracy, yet its stochastic nature renders direct exploration of molecular configuration space nontrivial. Here, we present a framework for highly accurate ab initio exploration of PESs that combines transferable deep-learning VMC with a cost-effective estimation of energies, forces, and Hessians. By continuously sampling nuclear configurations during VMC optimization of electronic wave functions, we obtain transferable descriptions that achieve zero-shot chemical accuracy within chemically relevant distributions of molecular geometries. Throughout the subsequent characterization of molecular configuration space, the PES is evaluated only sparsely, with local approximations constructed by estimating VMC energies and forces at sampled geometries and aggregating the resulting noisy data using Gaussian process regression. Our method enables accurate and efficient exploration of complex PES landscapes, including structure relaxation, transition-state searches, and minimum-energy pathways, for both ground and excited states. This opens the door to studying bond breaking, formation, and large structural rearrangements in systems with pronounced multi-reference character. Ab initio ; Monte Carlo method ; Excited state ; Energy minimization ; Potential energy surface ; Gaussian ; Electronic structure ; Configuration space ; Space (punctuation) ; Quantum Monte Carlo Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Quantum many-body systems
Authors: Ruishu Xu | Yiting Xu | Zhixian Liu | Haiyan Li | Houen Zou | Wanqiang Liu | Haowen Huang
Source: Chemical Engineering Journal (article, 2026-03-10)
Field: Materials Science — Materials Chemistry

Light-gated electron gating in lanthanide-doped carbon dots for programmable molecular logic and intelligent diagnosis Gating ; Logic gate ; Carbon fibers ; Nanoelectronics ; Power gating Carbon and Quantum Dots Applications ; Electrochemical sensors and biosensors ; Analytical Chemistry and Sensors
Authors: Rui Liu | Conghe Zhang | Weiweili Bai | Qiyixin Han
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Chemistry — Analytical Chemistry

Lightweight Identification of Angelica sinensis via Deep Learning-Guided Spectral Feature Reduction Discriminative model ; Pattern recognition (psychology) ; Hyperspectral imaging ; Interpretability ; Linear discriminant analysis ; Angelica sinensis ; Convolutional neural network ; Identification (biology) Spectroscopy and Chemometric Analyses ; Remote-Sensing Image Classification ; Traditional Chinese Medicine Analysis
Authors: Luis H. Delgado-Granados | David A. Mazziotti
Source: Open MIND (preprint, 2026-02-23)
Field: Materials Science — Materials Chemistry

Direct Variational Calculation of Two-Electron Reduced Density Matrices via Semidefinite Machine Learning We introduce a data-driven framework for approximating the convex set of $N$-representable two-electron reduced density matrices (2-RDMs). Traditional approaches characterize this set through linear matrix inequalities that define its supporting hyperplanes. Here, we instead learn a vertex-based approximation to its boundary from molecular data and use this information to improve the set defined by low-order positivity constraints, without explicitly constructing higher-order conditions. The resulting semidefinite machine learning approach -- combining an input convex neural network with semidefinite programming -- drives a direct variational calculation of the 2-RDM with enhanced accuracy at computational cost comparable to two-positivity calculations. Applications to the potential energy curves of ${\rm C}_2^{2-}$, ${\rm N}_2$, and ${\rm O}_2^{2+}$ demonstrate these systematic improvements as well as close agreement with complete active space configuration interaction results. Overall, semidefinite machine learning interweaves data-driven boundary information with semidefinite positivity constraints to yield more accurate energies and 2-RDMs without explicit higher-order positivity conditions. Semidefinite programming ; Semidefinite embedding ; Convex optimization ; Boundary (topology) ; Positive-definite matrix ; Linear matrix inequality ; Set (abstract data type) ; Regular polygon ; Matrix (chemical analysis) Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Advanced Physical and Chemical Molecular Interactions
Authors: Hailong Guo | Kai Li | Ziang Wu | Xuchen Yang | Yuzuo Wang | Zhijun Qiao | Dianbo Ruan | Dazhi Wang | Yang Lu
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine learning enhanced electrohydrodynamic printing of heteroatom-doped composite electrodes for high-performance flexible supercapacitors Supercapacitor ; Capacitance ; Electrode ; Composite number ; Capacitive sensing ; Electrohydrodynamics ; Fabrication Supercapacitor Materials and Fabrication ; Anodic Oxide Films and Nanostructures ; Electrospun Nanofibers in Biomedical Applications
Authors: Qingping Sun | Mengjia Wang | Junshuai Wu | Xianbin Xia | Jingying Li | Yixin Guo | Haolong Liu | Wenxuan Qi | Tao Wu | Zhenqing Wang | Yingdong Song
Source: Advanced Functional Materials (article, 2026-02-15)
Field: Materials Science — Polymers and Plastics

Advanced Multifunctional Vitrimer‐Based Composites for the Next Generation: Innovative Design, Performance Breakthroughs, and Intelligent Application Frontiers ABSTRACT Motivated by the global carbon neutrality objective and the need for sustainable development, the advancement of materials exhibiting self‐healing properties, recyclability, and multifunctional integration has emerged as a prominent area of research. This paper systematically reviews recent advancements in third‐generation polymer Vitrimer and fiber reinforced Vitrimer‐based composites (V‐FRP), which exhibit both thermoplastic and thermosetting properties, focusing on structural design, performance optimization, and intelligent applications. Initially, the environmental aging mechanisms and failure behaviors of traditional fiber‐reinforced polymer composites under various coupled service conditions are analyzed. Subsequently, an in‐depth analysis of the design principles governing various dynamic covalent bonds within Vitrimer is presented. Furthermore, by leveraging molecular dynamics simulation and machine learning techniques, the structure–activity relationships are revealed, endowing the material with self‐repairing, reshaping, and recycling capabilities. The discussion also encompasses strategies aimed at improving the comprehensive performance of V‐FRP through nanomaterial reinforcement, interface self‐repair engineering, and multifunctional integration strategies. Finally, the challenges currently encountered by V‐FRP in large‐scale applications and sustainable recycling were summarized. Additionally, future research directions were proposed, offering theoretical support for the advancement of the next generation of high‐performance, long‐lasting, and adaptive composite materials. Thermosetting polymer ; Interface (matter) ; Thermoplastic ; Nanomaterials ; Biomimetics ; Biomimetic materials ; Smart material ; Composite number Polymer composites and self-healing ; Dielectric materials and actuators ; Polymer crystallization and properties
Authors: Shogo Wakazaki | Masaki Azuma
Source: Journal of the Ceramic Society of Japan (article, 2026-01-15)
Field: Materials Science — Materials Chemistry

Screening and mechanism analysis of negative thermal expansion in Bi&lt;sub&gt;0.85&lt;/sub&gt;Ca&lt;sub&gt;0.15&lt;/sub&gt;Fe&lt;sub&gt;0.85&lt;/sub&gt;&lt;i&gt;M&lt;/i&gt;&lt;sub&gt;0.15&lt;/sub&gt;O&lt;sub&gt;3&lt;/sub&gt; (&lt;i&gt;M&lt;/i&gt;: tetravalent cations) via machine learning force fields Negative thermal expansion (NTE) is a key property for achieving dimensional stability in advanced technologies,13 yet the discovery of new NTE materials is often hindered by the vast compositional space and the complexity of 14 phase transition mechanisms.In this study, we applied large-scale molecular dynamics simulations using the 15 Crystal Hamiltonian Graph Network (CHGNet), a pretrained universal machine learning force field (MLFF), to 16 screen for NTE candidates in the Bi 0.85 Ca 0.15 Fe 0.85 M 0.15 O 3 system (M = Ti, Zr, Sn, Hf, Ir, Pb, Ru).Our systematic 17 simulations reveal that the co-substitution of Ca and tetravalent cations effectively modulates the phase stability of 18 BiFeO 3 , with the transition temperature varying depending on the substituting element.Notably, the Ir-substituted 19 compound exhibits a sharp volume-collapsing first-order phase transition at lower temperature (~380 K) with a 20 substitution level of 15% in good agreement with the experimental result.These findings demonstrate that 21 universal MLFFs are powerful tools for accelerating the exploration of functional materials and clarifying atomic-22 level mechanisms of complex phase transitions.23 Mechanism (biology) ; Thermal expansion ; Thermal ; Field (mathematics) ; Support vector machine Thermal Expansion and Ionic Conductivity ; Catalysis and Oxidation Reactions ; Chemical Looping and Thermochemical Processes