jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: William Freitas
Source: arXiv (Cornell University) (preprint, 2026-03-16)
Field: Materials Science — Materials Chemistry

Introduction to the artificial neural network-based variational Monte Carlo method In this self-contained tutorial, the variational Monte Carlo method with trial wave functions based on artificial neural networks is detailed. Unfolding the historical background we illustrate how machine learning interacts with physics research. The mathematical tools for both, artificial neural networks and variational Monte Carlo, are introduced. We demonstrate, how the algorithm operates with generic examples from chemical physics, including the harmonic, the Morse, the Poschl-Teller and the Yukawa potential, as well as the simplest molecules, the hydrogen molecular ion and the hydrogen molecule. Artificial neural network ; Monte Carlo method ; Variational Monte Carlo ; Hybrid Monte Carlo ; Yukawa potential ; Monte Carlo molecular modeling ; Monte Carlo method in statistical physics ; Variational method Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Quantum Mechanics and Non-Hermitian Physics
Authors: Tao yang | Qiuyi Wu | Jie Liang | Yao Qin | Xiao Wei | Yuan Wang | Daheng Wen | Pengfei Jia | Yuanqin Zhu | Shuangliang Zhao | Zengxi Wei
Source: Journal of Catalysis (article, 2026-03-13)
Field: Materials Science — Materials Chemistry

Multimodal descriptor integration and transfer learning for rapid yet accurate prediction metallocene ethylene-insertion activity Metallocene ; Transfer of learning ; Transfer (computing) ; Catalysis Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning in Bioinformatics
Authors: Ryan Trice | Mintyu Yu | Eric Welp | Morgan Applegate | Wesley Reinhart | Law, Stephanie
Source: ArXiv.org (article, 2026-01-19)
Field: Materials Science — Materials Chemistry

Machine Learning Guided Polymorph Selection in Molecular Beam Epitaxy of In2Se3 Indium selenide (In2Se3), a layered chalcogenide with multiple polymorphs, is a promising material for optoelectronic and ferroelectric applications. However, achieving polymorph-pure thin films remains a major challenge due to the complex growth space. In this work, Bayesian Optimization (BO) is successfully leveraged to guide the molecular beam epitaxy (MBE) growth of In2Se3 on Al2O3 substrates. By training a predictive Gaussian Process Regressor with sequential learning, we efficiently explored substrate temperature, indium flux, selenium flux, and cracker temperature, reducing experimental trials required for successful synthesis. A γ-In2Se3 film with 91% phase purity was achieved after fewer than ten trials. Attempts to isolate α-In2Se3 were limited by amorphous film formation at low temperatures, indicating that single-step co-deposition is unsuitable for crystalline α-In2Se3 on Al2O3. Overall, this study validates Bayesian Optimization as a powerful approach for phase-selective growth in complex materials systems. Bayesian optimization ; Molecular beam epitaxy ; Substrate (aquarium) ; Epitaxy ; Indium ; Chalcogenide ; Amorphous solid ; Thin film 2D Materials and Applications ; Machine Learning in Materials Science ; Chalcogenide Semiconductor Thin Films
Authors: Stefan P. Schmid | Henrik Seng | Thibault Kläy | Kjell Jorner
Source: ChemRxiv (article, 2026-02-02)
Field: Materials Science — Materials Chemistry

Rapid generation of transition-state conformer ensembles via constrained distance geometry The consideration of transition state (TS) conformer ensembles is required to accurately model a reaction, and thus plays a key role in computational catalyst design. While CREST and GOAT are established methods for TS conformer ensemble generation, the associated computational cost remains a major bottleneck in computational chemistry pipelines, including for the generation of large machine learning datasets for catalyst design. To this end, we present racerTS (RApid Conformer Ensembles with Rdkit for Transition States), a method for efficient TS conformer ensemble generation. In this work, we describe the algorithm behind racerTS, which is based on constrained distance geometry. To benchmark the performance of racerTS against CREST and GOAT, we create conformer ensembles for transition states of 20 diverse reactions. To assess the utility of each conformer generator in computational chemistry workflows, we optimize selected low-energy and diverse conformers at the DFT level. We use the generated conformer ensembles and the results of this pipeline to assess conformer generators according to the following metrics: computational cost, exhaustiveness, validity, and accuracy in low-energy regions. Considering the generated ensembles, wefind that racerTS covers the conformer space similarly to CREST and slightly less comprehensively than GOAT, while the validity of the DFT-optimized TSs is better and the accuracy in the low-energy region is sufficient for computational chemistry applications (median error 0.17 kcal/mol). Remarkably, racerTS achieves these results with a significant reduction in required wall-time. Our results thus demonstrate that racerTS is a highly efficient TS conformer ensemble generator, allowing for rapid TS conformer sampling in computational chemistry pipelines. Additionally, racerTS paves the way to create meaningful TS datasets to advance machine learning methods for the discovery of novel and sustainable catalysts. Conformational isomerism ; Bottleneck ; Generator (circuit theory) ; Initialization ; Transition state ; Solver ; Theory of computation Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Chenlu Wang | Qisheng Wu
Source: Research Square (preprint, 2026-02-06)
Field: Materials Science — Materials Chemistry

Transferable Drude-Based Polarizable Force Field Framework for Predictive Modeling of Battery Electrolytes Polarizability ; Solvation ; Force field (fiction) ; Electrolyte ; Molecular dynamics ; Field (mathematics) ; Langevin dynamics ; Stability (learning theory) ; Ion Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Electrocatalysts for Energy Conversion
Authors: Jaakko Kähärä | Katsiaryna Haitsiukevich | Hanna Vehkamäki | Theo Kurtén
Source:  (article, 2026-03-14)
Field: Materials Science — Materials Chemistry

Sampling of clusters of oxygenated organic molecules enhanced with machine learning models Oxygenated organic molecules (OOMs), formed in the atmosphere by oxidation of volatile organic compounds, are expected to take part in new particle formation (NPF). To determine their contribution to NPF, it is necessary to sample global minima of OOM clusters. However, the complexity of potential energy surfaces and the requirement of expensive of quantum calculations makes modelling of OOM cluster formation extremely time consuming. We have previously addressed these bottlenecks by assuming that the minimum cluster energy is likely to found by maximizing the hydrogen bonds between the monomers. Thus, we initially perform a constrained sampling to force random hydrogen bond formation. Additional local minima are found by utilizing metadynamics simulations.We further improve upon cluster sampling by replacing the costly DFT methods with significantly faster UMA and Orb-v3 neural network potentials (NNP). The pretrained models allow us optimize clusters geometries and predict cluster binding energies at near quantum chemical accuracy. We study the efficacy of the NNPs by generating dimer clusters of selected C10 sized OOMs. We find that the ability of OOMs to bind strongly is often hindered by the tendency of monomers to form intramolecular hydrogen bonds. Additionally, we show that C20 sized alpha-pinene accretion production may form cluster without the involvement of inorganic acids or ions, and their clustering ability with sulfuric acid is comparable to that of ammonia.While our approach is more efficient, the sampling become less likely find the true global minima as cluster complexity increases. To further reduce the number of structures to needed optimize, we use the previously generated OOM cluster data to train a graph neutral network (GNN) model to predict energies of the configurations from graph-based descriptions. GNNs allow us to very quickly find a subset of hydrogen bond pairings most likely to optimize towards a new global energy minima though the prediction accuracy is significantly reduced compared to NNPs. Our goal is to train a general model which may also extrapolate to molecules and clusters not included in the training set. Maxima and minima ; Cluster (spacecraft) ; Metadynamics ; Cluster analysis ; Sampling (signal processing) ; Artificial neural network ; Quantum ; Molecule Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Advanced Graph Neural Networks
Authors: Rakshit Kumar Singh | Aryan Amit Barsainyan | Bharath Ramsundar
Source: arXiv (Cornell University) (article, 2026-02-17)
Field: Materials Science — Materials Chemistry

A fully differentiable framework for training proxy Exchange Correlation Functionals for periodic systems Density Functional Theory (DFT) is widely used for first-principles simulations in chemistry and materials science, but its computational cost remains a key limitation for large systems. Motivated by recent advances in ML-based exchange-correlation (XC) functionals, this paper introduces a differentiable framework that integrates machine learning models into density functional theory (DFT) for solids and other periodic systems. The framework defines a clean API for neural network models that can act as drop in replacements for conventional exchange-correlation (XC) functionals and enables gradients to flow through the full self-consistent DFT workflow. The framework is implemented in Python using a PyTorch backend, making it fully differentiable and easy to use with standard deep learning tools. We integrate the implementation with the DeepChem library to promote the reuse of established models and to lower the barrier for experimentation. In initial benchmarks against established electronic structure packages (GPAW and PySCF), our models achieve relative errors on the order of 5-10%. Differentiable function ; Python (programming language) ; Artificial neural network ; Density functional theory ; Reuse ; Key (lock) ; Deep learning Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Quantum many-body systems
Authors: Ayaki Sunaga | Albert P. Bartók | Edit Mátyus
Source: Open MIND (preprint, 2026-02-19)
Field: Materials Science — Materials Chemistry

Vibrational infrared and Raman spectra of the methanol molecule with equivariant neural-network property surfaces Electric dipole and polarizability surfaces are developed for the methanol molecule using {\it ab initio} electronic structure data, computed at the CCSD/aug-cc-pVTZ level of theory, and equivariant neural networks. These property surfaces are used to compute vibrational infrared and Raman intensities up to the OH stretching fundamental vibration. The intensity computations use the vibrational energies and wave functions obtained in continued variational vibrational computations from earlier work [J. Chem. Phys. 163, 064101 (2025)]. The vibrational representation accounts for the large-amplitude torsion and uses curvilinear normal coordinates for the small-amplitude modes, allowing truncation of the vibrational basis set and the integration grid. Raman spectroscopy ; Hot band ; Curvilinear coordinates ; Molecule ; Overtone band ; Infrared ; Polarizability ; Molecular vibration ; Dipole ; Basis set Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Advanced Chemical Physics Studies
Authors: Luca Binci | KyuJung Jun | Bowen Deng | Gerbrand Ceder
Source: ArXiv.org (article, 2026-01-10)
Field: Materials Science — Materials Chemistry

Mechanisms of alkali ionic transport in amorphous oxyhalides solid state conductors Amorphous oxyhalides have attracted significant attention due to their relatively high ionic conductivity ($>$1 mS cm$^{-1}$), excellent chemical stability, mechanical softness, and facile synthesis routes via standard solid-state reactions. These materials exhibit an ionic conductivity that is almost independent of the underlying chemistry, in stark contrast to what occurs in crystalline conductors. In this work, we employ an accurately fine-tuned machine learning interatomic potential to construct large-scale molecular dynamics trajectories encompassing hundreds of nanoseconds to obtain statistically converged transport properties. We find that the amorphous state consists of chain fragments of metal-anion tetrahedra of various lenght. By analyzing the residence time of alkali cations migrating around tetrahedrally-coordinated trivalent metal ions, we find that oxygen anions on the metal-anion tetrahedra limit alkali diffusion. By computing the full Einstein expression of the ionic conductivity, we demonstrate that the alkali transference number of these materials is strongly influenced by distinct-particles correlations, while at the same time they are characterized by an alkali Haven ratio close to one, implying that ionic transport is largely dictated by uncorrelated self-diffusion. Finally, by extending this analysis to chemical compositions $AMX_{2.5}\textsf{O}_{0.75}$, spanning different alkaline ($A$ = Li, Na, K), metallic ($M$ = Al, Ga, In), and halogen ($X$ = Cl, Br, I) species, we clarify why the diffusion properties of these materials remain largely insensitive to variations in atomic chemistry. Alkali metal ; Ionic conductivity ; Amorphous solid ; Ionic bonding ; Conductivity ; Diffusion ; Fast ion conductor ; Molecular dynamics Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Advancements in Solid Oxide Fuel Cells
Authors: Luca Binci | KyuJung Jun | Bowen Deng | Gerbrand Ceder
Source: arXiv (Cornell University) (preprint, 2026-01-10)
Field: Materials Science — Materials Chemistry

Mechanisms of alkali ionic transport in amorphous oxyhalides solid state conductors Amorphous oxyhalides have attracted significant attention due to their relatively high ionic conductivity ($&gt;$1 mS cm$^{-1}$), excellent chemical stability, mechanical softness, and facile synthesis routes via standard solid-state reactions. These materials exhibit an ionic conductivity that is almost independent of the underlying chemistry, in stark contrast to what occurs in crystalline conductors. In this work, we employ an accurately fine-tuned machine learning interatomic potential to construct large-scale molecular dynamics trajectories encompassing hundreds of nanoseconds to obtain statistically converged transport properties. We find that the amorphous state consists of chain fragments of metal-anion tetrahedra of various lenght. By analyzing the residence time of alkali cations migrating around tetrahedrally-coordinated trivalent metal ions, we find that oxygen anions on the metal-anion tetrahedra limit alkali diffusion. By computing the full Einstein expression of the ionic conductivity, we demonstrate that the alkali transference number of these materials is strongly influenced by distinct-particles correlations, while at the same time they are characterized by an alkali Haven ratio close to one, implying that ionic transport is largely dictated by uncorrelated self-diffusion. Finally, by extending this analysis to chemical compositions $AMX_{2.5}\textsf{O}_{0.75}$, spanning different alkaline ($A$ = Li, Na, K), metallic ($M$ = Al, Ga, In), and halogen ($X$ = Cl, Br, I) species, we clarify why the diffusion properties of these materials remain largely insensitive to variations in atomic chemistry. Alkali metal ; Ionic conductivity ; Amorphous solid ; Ionic bonding ; Conductivity ; Diffusion ; Fast ion conductor ; Molecular dynamics Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Thermal Expansion and Ionic Conductivity
Authors: M. D. Zhang | Wen Tian | Lingtao Wang | Hao Gong
Source: ACS Catalysis (article, 2026-01-13)
Field: Materials Science — Materials Chemistry

Insights into Ethylene Deep Oxidation on Pd–Au Bimetallic Catalysts: From Surface Dynamics to Pathway Selectivity Deep oxidation of ethylene represented the primary side reaction during vinyl acetate synthesis over the Pd–Au catalysts. Because of the complexity of the deep oxidation process and the uncertain surface structures of bimetallic catalysts, the process was not well understood. We developed a robust Pd–Au–C–H-O machine-learning force field (MLFF) by employing a pretraining and fine-tuning strategy. Deep-potential molecular dynamics (DPMD) simulations were then used to elucidate the dynamic surface reconstruction of Pd–Au nanoparticles (NPs) under operando conditions and the heterogeneous reaction pathways on Pd–Au bimetallic surfaces. By integration of density functional theory (DFT) calculations with our MLFF framework, the accessible time and length scales of conventional atomistic simulations were well extended. Our results demonstrated that dynamic reconstruction of Pd–Au NPs enriched the Pd content on the surface, forming contiguous Pd ensembles that served as active sites for ethylene deep oxidation. Surface Pd concentration dictated the selectivity between dehydrogenation and oxidation pathways: high Pd coverage favored dehydrogenation to low H/C intermediates and carbon deposition, whereas Au moderated the Pd activity by balancing dehydrogenation and oxidation. This complex problem involving the coupling of adsorbents, active surfaces, and catalytic reactions can potentially be solved through these multiscale simulation strategies. This work provides mechanistic insights into the deep oxidation of ethylene and offers guidelines for the rational design of bimetallic catalysts. Moreover, it highlights the broad potential of MLFF-assisted molecular dynamics to advance studies in heterogeneous catalysis and reveal dynamic adsorbate-catalyst coupling mechanisms. Bimetallic strip ; Dehydrogenation ; Catalysis ; Ethylene ; Selectivity ; Molecular dynamics ; Density functional theory ; Nanoparticle Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Authors: Y. Lu | Jian Zhou | Ján Brndiar | I. Štich | Yongda Huang | Zhimei Sun
Source: Chinese Chemical Letters (article, 2026-04-01)
Field: Materials Science — Materials Chemistry

Approaches for strong electron correlation beyond DFT: Conventional and neural network quantum Monte Carlo Quantum Monte Carlo ; Wave function ; Monte Carlo method ; Backflow ; Artificial neural network ; Diffusion Monte Carlo ; Plane wave ; Variational Monte Carlo ; Function (biology) Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Quantum, superfluid, helium dynamics
Authors: Qingmei Hu | Daniel Hedman | Ya Feng | Wanyu Dai | Daisuke Asa | Aina Fitó-Parera | Yixi Yao | Yongjia Zheng | Kaoru Hisama | Gunjan Auti | Hirofumi Daiguji | Shohei Chiashi | Dmitry Levshov | Wim Wenseleers | Keigo Otsuka | Yan Li | Christophe Bichara | Sofie Cambré | Rong Xiang | Shigeo Maruyama
Source: ArXiv.org (article, 2026-02-04)
Field: Materials Science — Materials Chemistry

Structures of iron and cobalt bimetallic clusters for optimized chemical vapor deposition growth of single-walled carbon nanotubes We investigate iron-cobalt (Fe-Co) alloys as a representative high-performance catalyst system for SWCNT growth in a systematic manner by combining chemical vapor deposition (CVD) experiments with chirality-resolved spectroscopic analysis, as well as molecular dynamics (MD) simulations based on density functional theory-derived machine learning force fields while varying the Fe-Co ratio. Using zeolite-based SWCNTs prepared by alcohol CVD, absorption and photoluminescence spectroscopy, together with two-dimensional excitation-emission fitting was employed to quantify chirality-specific growth efficiency. Two distinct growth regimes were identified. At a relatively low temperature of 600 C, pure Co exhibits the highest catalytic activity, promoting efficient growth of small-diameter (0.7-0.9 nm) SWCNTs. In contrast, at 850 C, the Fe0.75Co0.25 alloy shows a pronounced enhancement in growth efficiency compared with pure Fe, pure Co, and other Fe-Co compositions, also yielding larger diameter tubes (0.9-1.1nm). Similar growth behavior was observed on SiO2 substrates, enabling detailed transmission electron microscopy analysis of catalyst nanoparticles. Electron microscopy and energy-dispersive X-ray spectroscopy reveal that high SWCNT yields correlate with the formation of small, uniform Fe-Co nanoparticles with Co-enriched surfaces, in excellent agreement with MD simulations. Lastly, MD results are summarized in a composition-diameter phase diagram that rationalizes the experimentally observed growth trends. The exceptional performance of the Fe0.75Co0.25 catalyst at high temperature is attributed to the stabilization of small and uniform catalyst clusters, providing mechanistic insight into the synergistic roles of alloy composition and temperature in SWCNT growth. Bimetallic strip ; Catalysis ; Transmission electron microscopy ; Chemical vapor deposition ; Carbon nanotube ; Cobalt ; Alloy ; Evaporation Carbon Nanotubes in Composites ; Electrocatalysts for Energy Conversion ; Graphene research and applications
Authors: Tomasz Gancarz | Stephan Handschuh-Wang
Source: Metallurgical and Materials Transactions A (article, 2026-02-21)
Field: Materials Science — Materials Chemistry

Properties vs Application How to Design New Liquid Alloys Based on Ga Discovery of new materials is based on knowledge of the properties and the precognition of the impact of changes in chemical composition on these properties for future sustainable materials and devices with advanced performance. Data-driven materials research, i.e., by artificial intelligence (AI) and machine learning, is a hot topic as these approaches can predict the structure–property relationship and may find use in elucidating the (thermophysical/chemical) properties–application (performance) relationship. This review focuses on the thermophysical properties, such as surface tension, viscosity, thermal and electrical conductivity, of liquid metals and alloys based on gallium and their influence on performance in various applications. The rise of Ga-based liquid metals stems from gallium’s fluidity and low toxicity, electrical and thermal conductivity, catalytic/reactive properties, and the ability to pattern and print it controllably. These properties are often exploited in stretchable electronics, soft sensors, e-skins, heat management, and reaction environment, but are not limited to these applications. In this regard, four case studies of applications are provided that necessitate careful design of alloy composition for high performance. The goal is to aid researchers identify appropriate alloy composition, which hinge on the desired application. The identified properties–application relationships may serve as starting points for machine learning approaches, reducing the need for time-consuming and labor-intensive experiments. Alloy ; Liquid metal ; Thermal ; Gallium ; Material properties ; Hinge Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Advanced Sensor and Energy Harvesting Materials
Authors: Vineet Kumar | Akashdeep Negi | Adhirath Mandal | Gopal Ji | Gaurav Gautam | Rohit Saxena | Manvandra Kumar Singh
Source: AIP conference proceedings (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Neural networks in predicting mechanical properties of Al-Zn alloys: A review and model development Artificial neural network ; Development (topology) ; Feature (linguistics) ; Field (mathematics) ; Identification (biology) Machine Learning in Materials Science ; Metallurgy and Material Forming ; Aluminum Alloy Microstructure Properties