jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ignacio Arretche (19255089) | Jacob J. Lessard (5629013) | Parmeet Kaur (2068066) | Mya G. Mills (21077743) | Abbie J. Kim (18106373) | Hannah J. Liu (21077746) | Julian C. Cooper (5921975) | Randy H. Ewoldt (548110) | Jeffrey S. Moore (1385685) | Sameh Tawfick (1965277)
Source: Figshare (article, 2025-04-11)
Field: Chemistry — Organic Chemistry

Active LearningGuided Optimization of Frontal Ring-OpeningMetathesis Polymerization via Alkylidene Modification Frontal ring-opening metathesis polymerization (FROMP) offers an energy-efficient method for manufacturing high-performance thermoset resins. However, the background reaction attributed to ring-opening metathesis polymerization (ROMP) results in a complex trade-off between the resin shelf lifenecessary for practical manufacturabilityand the front velocity. Here, we study the influence of alkylidene ligand selection in Grubbs’ second-generation Ru-initiators on the kinetics of FROMP and background ROMP. We reveal that ligand identity differentially affects FROMP and background ROMP reactivity, enabling tunable control over pot life and front speed. Leveraging this insight, we use active learning with multiobjective Bayesian optimization to efficiently explore the FROMP resin design space and identify superior resin formulations. This work advances the rational design of FROMP resins, expanding the range of accessible formulations and accelerating the discovery of high-performance materials for energy-efficient manufacturing applications. Polymerization ; ROMP ; Ring-opening metathesis polymerisation ; Ligand (biochemistry) ; Metathesis ; Bayesian optimization Photopolymerization techniques and applications ; Advanced Polymer Synthesis and Characterization ; Synthesis and properties of polymers
Authors: Yosuke Harashima | Takashi Miyake | Ryuto Baba | Tomoaki Takayama | Shogo Takasuka | Yasuteru Shigeta | Yuichi Yamaguchi | Akihiko Kudo | Mikiya Fujii
Source: Physical Review Materials (article, 2025-09-09)
Field: Materials Science — Materials Chemistry

Covariance linkage assimilation method for unobserved data exploration This study proposes a materials search method combining a data assimilation technique based on a multivariate Gaussian distribution with Bayesian optimization. The efficiency of the search using this method was demonstrated using a pair of example functions. By combining Bayesian optimization with the data assimilation technique, the maximum value of the example function was found more efficiently compared to ordinary Bayesian optimization without the data assimilation. A practical demonstration was also conducted by constructing a data assimilation model for the band gap of $({\mathrm{Sr}}_{1\ensuremath{-}{x}_{1}\ensuremath{-}{x}_{2}}{\mathrm{La}}_{{x}_{1}}{\mathrm{Na}}_{{x}_{2}})({\mathrm{Ti}}_{1\ensuremath{-}{x}_{1}\ensuremath{-}{x}_{2}}{\mathrm{Ga}}_{{x}_{1}}{\mathrm{Ta}}_{{x}_{2}}){\mathrm{O}}_{3}$. The concentration dependence of the band gap was analyzed, and synthesis was performed with chemical compositions in the sparse region of the training data points to validate the predictions. Data assimilation ; Bayesian optimization ; Covariance ; Bayesian probability ; Gaussian ; Multivariate statistics ; Multivariate normal distribution ; Gaussian process Machine Learning in Materials Science
Authors: Trevor Hastings | Mrinalini Mulukutla | Danial Khatamsaz | Daniel Salas | Wenle Xu | Daniel Lewis | Nicole Person | Matthew Skokan | Braden Miller | James D. Paramore | Brady G. Butler | Douglas Allaire | Vahid Attari | İbrahim Karaman | George M. Pharr | Ankit Srivastava | R. Arroyave
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Accelerated Multi-Objective Alloy Discovery Through Efficient Bayesian Methods: Application to the FCC High Entropy Alloy Space Alloy ; Bayesian probability ; Entropy (arrow of time) ; Computer science ; Materials science ; Metallurgy ; Artificial intelligence ; Physics ; Thermodynamics Crystallization and Solubility Studies
Authors: Bo Song (255492) | Tao Liu (10785) | Mingyan Zhao (201041) | Yan Cui (9885) | Junghui Chen (1602457) | Zoltan K. Nagy (1515985) | Rolf Findeisen (124207)
Source: Figshare (article, 2025-04-22)
Field: Materials Science — Materials Chemistry

Surrogate Modelingwith Sensitivity-Based ExperimentDesign and Process Optimization of Seeded Cooling Crystallization:A Case Study on β‑Form LGA To facilitate quality-by-design (QbD) of seeded cooling crystallization, a novel surrogate modeling and process optimization method is proposed in this paper, based on the design of experiments (DoE) with sensitivity analysis on the process operating conditions. To overcome the deficiency of the crystal growth kinetic model related to the population balance equation, which could not reflect the explicit relationship between the process operating conditions (e.g., initial solution supersaturation and cooling rate) and product crystal size distribution (CSD), a surrogate model is established by using the Gaussian process regression (GPR) approach, based on experimental data from a permitted range of operating conditions. Correspondingly, a swarm-based metaheuristic algorithm named beluga whale optimization (BWO) is adopted to determine proper hyperparameters in the surrogate model. By analyzing the global sensitivity analysis (GSA) of product CSD with respect to these operation conditions, a sensitivity-based DoE is developed to reduce the number of batch experiments required for implementation. Based on the established surrogate model, a comprehensive quality criterion is introduced to optimize these operating conditions, which takes into account the information entropy of product CSD together with the desired product yield and size range. The seeded cooling crystallization process of the β-form l-glutamic acid is tested to verify the effectiveness and merits of the proposed modeling and optimization method. Surrogate model ; Design of experiments ; Sensitivity (control systems) ; Seeding ; Multi-objective optimization ; Process optimization ; Process (computing) ; Process variable ; Population ; Gaussian process Crystallization and Solubility Studies ; Process Optimization and Integration ; Advanced Multi-Objective Optimization Algorithms
Authors: Johanna J. Schwartz | Jayvic C. Jimenez | Michell Marufu | Micah Silverman | Santiago Tzintzun | Brian Au | Robert Cerda | Chelsea Igtanloc | Katherine Elena Rivadeneira Velasco | Maged Elshatoury | Chloe Lau | Zeyuan Hu | Nishant Ojal | Marissa Wood | Yiran Xiao | Seongkoo Cho | Aldair E. Gongora | Jung-Pil Noh | Travis L. Massey | Maxwell A. T. Marple
Source: Research Square (preprint, 2025-03-24)
Field: Materials Science — Materials Chemistry

Studying-Polymers-On a-Chip (SPOC): High-throughput screening of polymers for battery applications Throughput ; Battery (electricity) ; Polymer ; Chip ; High-throughput screening ; Nanotechnology ; Computer science ; Materials science ; Chemistry ; Telecommunications ; Physics ; Wireless ; Composite material Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Electron and X-Ray Spectroscopy Techniques
Authors: Omri Lesser | Yanjun Liu | Natalie Maus | Aaditya Panigrahi | Krishnanand Mallayya | Leslie M. Schoop | J.A. Gardner | Eun-Ah Kim | Chatterjee, Sudipta | Merino, Amira | Weinberger, Kilian Q.
Source: ArXiv.org (preprint, 2025-10-08)
Field: Materials Science — Materials Chemistry

Electron affinity difference distributions guide the discovery of the superconductor PtPb$_3$Bi Predicting the superconducting transition temperature ($T_c$) from crystal structure and composition remains a central challenge in condensed-matter physics, reflecting the absence of a broadly predictive framework connecting microscopic bonding to macroscopic quantum behavior. Here, we introduce a structure- and chemistry-aware approach implemented in an interpretable Gaussian process model, which we call GP-$T_c$ (Gaussian Process $T_c$), that enables uncertainty-quantified prediction of superconductivity from experimentally accessible inputs. By encoding local bonding environments and geometry as graphlet histograms and learning within a probabilistic framework, we find that the predictive space collapses to a compact set of descriptors: the distribution of electron-affinity differences between neighboring atoms, together with simple elemental features and interatomic distances, provides an informative basis for predicting $T_c$ across disparate superconducting families. This result identifies an overlooked chemical control parameter while emphasizing the essential role of local structure beyond composition-only approaches. We demonstrate the framework through two complementary tests: validation against a recently established superconducting family and discovery of a previously unknown material. GP-$T_c$ reproduces the experimentally reported $T_c$ range of the infinite-layer nickelate Nd0.8Sr0.2NiO2. We further predict superconductivity in stoichiometric PtPb$_3$Bi and experimentally confirm it through synthesis and bulk measurements, establishing PtPb$_3$Bi as a new superconductor with $T_c$~3 K. GP-$T_c$ identifies additional high-priority superconducting candidates -- including SrNiO2, K(PRh)2, and Ho2C3 -- that provide concrete targets for ongoing and future experimental exploration. Superconductivity ; Histogram ; Classifier (UML) ; Pattern recognition (psychology) ; Support vector machine ; Feature extraction ; Feature (linguistics) ; Kernel (algebra) Machine Learning in Materials Science ; Topic Modeling
Authors: Ivan A. Bespalov (21491561) | Nikolai V. Krivoshchapov (9234734) | Alexey A. Lisov (14600288) | Vasiliy A. Chaliy (19851505) | Michael G. Medvedev (3589598)
Source: Figshare (article, 2025-06-05)
Field: Materials Science — Materials Chemistry

Physics-Informed Bayesian Optimization for ConformationalEnsemble Augmentation Conformational search is a key part of reaction modeling, molecular docking, and other fields of computational chemistry, where it is important to take molecules’ flexibility into account. However, modern conformational search approaches provide no guarantee that they did not miss any important conformation. Thus, identifying missing conformations from an existing ensemble is of broad importance for computational chemistry. In this paper, we introduce a Bayesian optimization algorithm for conformational ensemble augmentation, that is, locating missing conformers in an existing ensemble, which employs Bayesian optimization with physics-informed torsion-potential-based kernel function and a novel acquisition function that prioritizes potential energy surface exploration for increased conformer diversity. The devised method demonstrates high efficiency on a test set of biologically relevant molecules. Bayesian optimization ; Conformational ensembles ; Conformational isomerism ; Bayesian probability ; Flexibility (engineering) ; Set (abstract data type) ; Kernel (algebra) ; Function (biology) ; Key (lock) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Gaussian Processes and Bayesian Inference
Authors: Yuehui Xian | Xiangdong Ding | Xue Jiang | Yumei Zhou | Jun Sun | Dezhen Xue | Turab Lookman
Source: Research Square (preprint, 2025-04-28)
Field: Materials Science — Materials Chemistry

Unlocking the Black Box beyond Bayesian Global Optimization for Materials Design using Reinforcement Learning Bayesian optimization ; Reinforcement learning ; Black box ; Reinforcement ; Bayesian probability ; Computer science ; Artificial intelligence ; Machine learning ; Engineering ; Structural engineering Machine Learning in Materials Science ; Injection Molding Process and Properties ; Topology Optimization in Engineering
Authors: Mayukh Kundu | Mark W. Bush | Chris A. Kieslich | Michael P. Howard
Source: The Journal of Chemical Physics (article, 2025-12-10)
Field: Materials Science — Materials Chemistry

Inverse design of drying-induced assembly of multicomponent colloidal-particle films using surrogate models The properties of films assembled by drying colloidal-particle suspensions depend sensitively on both the particles and the processing conditions, making them challenging to engineer. In this work, we develop and test an inverse-design strategy based on surrogate modeling to identify conditions that yield a target film structure. We consider a two-component hard-sphere colloidal suspension whose designable parameters are the particle sizes, the initial composition of particles, and the drying rate. Film drying is simulated approximately using Brownian dynamics. Surrogate models based on Gaussian process regression (GPR) and Chebyshev polynomial interpolation are trained on a loss function, computed from the simulated film structures, that guides the design process. We find the surrogate models to be effective for both approximation and optimization using only a small number of samples of the loss function. The GPR models are typically slightly more accurate than polynomial interpolants trained using comparable amounts of data, but the polynomial interpolants are more computationally convenient. This work has important implications not only for designing colloidal materials but also more broadly as a strategy for engineering nonequilibrium assembly processes. Surrogate model ; Interpolation (computer graphics) ; Polynomial ; Kriging ; Suspension (topology) ; Polynomial interpolation ; Gaussian process ; Brownian motion ; Chebyshev polynomials ; Diffusion Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Advanced Multi-Objective Optimization Algorithms
Authors: Derick E. Ober | Sesha Sai Behara | Anton Van der Ven
Source: ArXiv.org (preprint, 2025-09-09)
Field: Materials Science — Materials Chemistry

Bayesian Prior Construction for Uncertainty Quantification in First-Principles Statistical Mechanics First-principles statistical mechanics enables the prediction of thermodynamic and kinetic properties of materials, but is computationally expensive. Many approaches require surrogate models to calculate energies within Monte Carlo or molecular dynamics simulations. Inexpensive surrogates such as cluster expansions enable otherwise intractable calculations by interpolating data from higher accuracy methods, such as Density Functional Theory (DFT). Surrogate models introduce uncertainty into downstream calculations, in addition to any uncertainty inherent to DFT calculations. Bayesian frameworks address this by quantifying uncertainty and incorporating expert knowledge through priors. However, constructing effective priors remains challenging. This work introduces and describes practical strategies for building Bayesian cluster expansions, focusing on basis truncation, hyperparameter selection, and ground state replication. We analyze multiple basis truncation schemes, compare cross-validation to the evidence-approximation for hyperparameter optimization, and provide methods to find and enforce ground-state-preserving models through priors. Additionally, we compare the uncertainties between different approximations to DFT (LDA, PBE, SCAN) against the uncertainty introduced with the use of cluster expansion surrogate models. These approaches are demonstrated on the BCC Li$_x$Mg$_{1-x}$ and Li$_x$Al$_{1-x}$ alloys, which are both of interest for solid-state Li batteries. Our results provide guidelines for constructing and utilizing Bayesian cluster expansions, thereby improving the transparency of materials modeling. The approaches and insights developed in this work can be transferred to a wide range of cluster expansion surrogate models, including the atomic cluster expansion and related machine-learned interatomic potential architectures. Uncertainty quantification ; Bayesian probability ; Hyperparameter ; Bayesian inference ; Monte Carlo method ; Surrogate model ; Cluster expansion ; Prior probability ; Uncertainty analysis Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Inorganic Chemistry and Materials
Authors: Erfan Mohammadipour | Mohammad Ghorbani
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Biomaterials

Small-Sample Machine Learning for PEO Process–Microstructure Mapping: Minimizing Pore Size via an MLP and Bayesian Design Overfitting ; Extrapolation ; Kriging ; Surrogate model ; Bayesian probability ; Gaussian process ; Bayesian optimization ; Cross-validation ; Pattern recognition (psychology) Magnesium Alloys: Properties and Applications ; Titanium Alloys Microstructure and Properties ; Nuclear Materials and Properties
Authors: Shuntaro Yamazaki (21668377) | Tomoya Horide (3643246) | Yutaka Yoshida (67904)
Source: Figshare (article, 2025-07-07)
Field: Materials Science — Materials Chemistry

Accelerated ProcessDevelopment of YBa<sub>2</sub>Cu<sub>3</sub>O<sub>7</sub> film withBayesian Optimization Augmentedby Transferred Data Efficient process optimization is a key to the development of YBa<sub>2</sub>Cu<sub>3</sub>O<sub>7</sub> films, whose properties are influenced by many factors such as substrate, nanostructure, and composition. The simplest optimization is exhaustive search, which requires many experiments. Bayesian optimization is more efficient but requires initial data. Here we propose the Bayesian optimization augmented by domain-aligned sampling, data transfer, and peak screening (transfer-based Bayesian optimization), which efficiently acquires the initial data. In this method, the preliminary data in a system with a consistent process nature is used as the initial data. The preliminary data of the critical current density (<i>J</i><sub>c</sub>) were obtained in the YBa<sub>2</sub>Cu<sub>3</sub>O<sub>7</sub>/SrTiO<sub>3</sub> by exhaustive search, and the <i>J</i><sub>c</sub> of YBa<sub>2</sub>Cu<sub>3</sub>O<sub>7</sub>/LaAlO<sub>3</sub> was optimized. A Gaussian process regression model was constructed from the preliminary data of YBa<sub>2</sub>Cu<sub>3</sub>O<sub>7</sub>/SrTiO<sub>3</sub> and the domain-aligned sampling data of the YBa<sub>2</sub>Cu<sub>3</sub>O<sub>7</sub>/LaAlO<sub>3</sub>. The initial local maxima were included as candidates for the global maximum in the peak screening to avoid overlooking potentially high <i>J</i><sub>c</sub>. The transfer-based Bayesian optimization found an optimal <i>J</i><sub>c</sub> as high as that by the exhaustive search with a smaller number of iterations. Thus, the optimal property was obtained efficiently by transfer-based Bayesian optimization, making the optimization of coated conductors and advanced materials more efficient. Bayesian optimization ; Bayesian probability ; Gaussian process ; Process (computing) ; Key (lock) ; Process optimization ; Optimization problem ; Sampling (signal processing) ; Gaussian Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Physics of Superconductivity and Magnetism
Authors: Felix Thelen | Rico Zehl | Ridha Zerdoumi | Jan Lukas Bürgel | Lars Banko | Wolfgang Schuhmann | Alfred Ludwig
Source: ArXiv.org (preprint, 2025-02-22)
Field: Materials Science — Materials Chemistry

Accelerating Combinatorial Electrocatalyst Discovery with Bayesian Optimization: A Case Study in the Quaternary System Ni-Pd-Pt-Ru for the Oxygen Evolution Reaction The discovery of high-performance electrocatalysts is crucial for advancing sustainable energy technologies. Compositionally complex solid solutions comprising multiple metals offer promising catalytic properties, yet their exploration is challenging due to the combinatorial explosion of possible compositions. In this work, we combine combinatorial sputtering of thin-film materials libraries and their high-throughput characterization with Bayesian optimization to efficiently explore the quaternary composition space Ni-Pd-Pt-Ru for the oxygen evolution reaction in alkaline media. Using this method, the global activity optimum of pure Ru was identified after covering less than 20% of the complete composition space with six materials libraries. Six additional libraries were fabricated to validate the activity trend. The resulting dataset is used to formulate general guidelines for the efficient composition space exploration using combinatorial synthesis paired with Bayesian optimization. Oxygen evolution ; Bayesian optimization ; Bayesian probability ; Electrocatalyst ; Space (punctuation) ; Chemical space ; Catalysis Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysis and Oxidation Reactions
Authors: Chaofan Huang | Sunday C. Aduloju | John Fritz | V. Roshan Joseph | Mark Messner | Timothy J. Truster
Source: Journal of Computing and Information Science in Engineering (article, 2025-01-09)
Field: Materials Science — Materials Chemistry

Calibration of RAFM Micromechanical Model for Creep Using Bayesian Optimization for Functional Output Abstract A Bayesian optimization procedure is presented for calibrating a multimechanism micromechanical model for creep to experimental data of F82H steel. Reduced activation ferritic martensitic (RAFM) steels based on Fe(8–9)%Cr are the most promising candidates for some fusion reactor structures. Although there are indications that RAFM steel could be viable for fusion applications at temperatures up to 600∘C, the maximum operating temperature will be determined by the creep properties of the structural material and the breeder material compatibility with the structural material. Due to the relative paucity of available creep data on F82H steel compared to other alloys such as Grade 91 steel, micromechanical models are sought for simulating creep based on relevant deformation mechanisms. As a point of departure, this work recalibrates a model form that was previously proposed for Grade 91 steel to match creep curves for F82H steel. Due to the large number of parameters (9) and cost of the nonlinear simulations, an automated approach for tuning the parameters is pursued using a recently developed Bayesian optimization for functional output (BOFO) framework (Huang et al., 2021, “Bayesian optimization of functional output in inverse problems,” Optim. Eng., 22, pp. 2553–2574). Incorporating extensions such as batch sequencing and weighted experimental load cases into BOFO, a reasonably small error between experimental and simulated creep curves at two load levels is achieved in a reasonable number of iterations. Validation with an additional creep curve provides confidence in the fitted parameters obtained from the automated calibration procedure to describe the creep behavior of F82H steel. Creep ; Materials science ; Calibration ; Structural material ; Martensite ; Compatibility (geochemistry) ; Blanket ; Structural engineering ; Nuclear engineering ; Composite material ; Microstructure ; Engineering ; Mathematics Fusion materials and technologies ; Nuclear Materials and Properties ; High Temperature Alloys and Creep