jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Anis Attiaoui | Shunda Chen | J. C. Woicik | J. Zach Lentz | Liliane M. Vogl | Jarod Meyer | Kunal Mukherjee | Andrew Minor | Tianshu Li | Paul C. McIntyre
Source: arXiv (Cornell University) (preprint, 2026-03-29)
Field: Materials Science — Materials Chemistry

Shining light on short-range atomic ordering in semiconductors alloys The functional properties of semiconductors are typically controlled by tailoring their chemical composition and their state of strain, and by controlling their long-range structural order, including the presence of extended defects such as dislocations. In addition to these approaches, theoretical predictions suggest that short-range order (SRO) of atoms in group-IV semiconductor alloys can modify the bandgap, a defining property of any semiconductor. Herein, a new machine learning enabled, computation-guided methodology for extended X-ray absorption fine structure (EXAFS) analysis of SRO is used to quantify the effects of local atomic order on the bandgap of germanium-tin (GeSn) alloy single crystal nanostructures with well-controlled strain and composition. Correlative analysis of EXAFS and photoluminescence (PL) establishes the relationship between bandgap and the Warren-Cowley short-range order (WC-SRO) parameter of the GeSn alloys. It is further demonstrated that SRO can be tuned over a broad range by post-deposition annealing of the alloy crystals. This work establishes control of SRO as an important design parameter for semiconducting properties and suggests the potential for quantitative measurement and tuning of SRO in other semiconductor alloy systems. Semiconductor ; Alloy ; Photoluminescence ; Band gap ; Annealing (glass) ; Nanostructure ; Extended X-ray absorption fine structure ; Wide-bandgap semiconductor Machine Learning in Materials Science ; Photonic and Optical Devices ; Nanowire Synthesis and Applications
Authors: Samir Darouich | Vinh Tong | Tanja Bien | Johannes Kästner | Mathias Niepert
Source: Journal of Chemical Information and Modeling (article, 2026-02-02)
Field: Materials Science — Materials Chemistry

Adaptive Transition-State Refinement with Learned Equilibrium Flows Identifying transition states (TSs), the high-energy configurations that molecules pass through during chemical reactions, is essential for understanding and designing chemical processes. However, accurately and efficiently identifying these states remains one of the most challenging problems in computational chemistry. In this work, we introduce a new generative AI approach that improves the quality of initial guesses for TS structures. Our method can be combined with a variety of existing techniques, including both machine-learning models and fast, approximate quantum methods, to refine their predictions and bring them closer to chemically accurate results. Applied to TS guesses from a state-of-the-art machine-learning model, our approach reduces the median structural error to 0.077 Å and lowers the median absolute error in reaction barrier heights to 0.40 kcal mol<sup>-1</sup>. When starting from a widely used tight-binding approximation, it increases the success rate of locating valid TSs by 41% and speeds up high-level quantum optimization by a factor of 3. By making TS searches more accurate, robust, and efficient, this method could accelerate reaction mechanism discovery and support the development of new materials, catalysts, and pharmaceuticals. Quantum chemical ; Variety (cybernetics) ; Quantum ; Quality (philosophy) ; Mechanism (biology) ; Generative grammar ; Generative model Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Computational Drug Discovery Methods
Authors: Tianyu Gao | Bin Wang | Ran Jia | Haiming Zhang | Miao Xie | W. A. III GODDARD | Lifeng Chi
Source: The Journal of Physical Chemistry Letters (article, 2026-01-15)
Field: Materials Science — Materials Chemistry

Predicting Activation Energy of Hydrocarbon Dehydrogenation on Au(111) via Machine Learning Activation of C-H bonds in hydrocarbons is fundamental for synthesizing organic functional materials, yet traditional density functional theory (DFT) methods for determining reaction energy barriers are computationally intensive and often suffer from convergence challenges. We report here the construction of a comprehensive DFT-based data set of hydrocarbon dehydrogenation reactions on the Au(111) surface and propose a feature-enhanced graph neural network (F-GNN) that integrates eight chemically informed descriptors with molecular graph representations. This F-GNN model accurately predicts reaction activation energies, outperforming conventional approaches such as the Brønsted-Evans-Polanyi relationship and standalone machine learning models. Our findings demonstrate that combining chemical prior knowledge with data-driven features enables efficient and precise energy barrier prediction, offering a promising strategy to accelerate reaction path screening and mechanistic understanding in surface-catalyzed hydrocarbon transformations. Dehydrogenation ; Density functional theory ; Hydrocarbon ; Artificial neural network ; Graph ; Energy (signal processing) ; Convergence (economics) ; Activation energy Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Electrocatalysts for Energy Conversion
Authors: Ragnar Bjornsson
Source: Journal of Computational Chemistry (article, 2026-03-29)
Field: Materials Science — Materials Chemistry

<scp>ASH</scp> : A Multi‐Scale, Multi‐Theory Modeling Program We introduce ASH, a multi-scale, multi-theory modeling program for quantum mechanics (QM), molecular mechanics (MM), and hybrid calculations, written in the Python programming language. ASH is written in response to the increasingly diverse computational chemistry software landscape that features more QM and MM programs than ever before, and with machine learning interatomic potentials (MLIP) further changing the way modern computational chemistry is being performed. ASH is a Python library that intentionally separates computational chemistry jobs (geometry optimizations, frequency calculations, molecular dynamics, surface scans, reaction paths, etc.) from the QM, MM, or ML method (calculated by the specialized QM or MM programs or ML libraries). By keeping the jobs separate from the Hamiltonian, a highly flexible computational chemistry environment emerges that can be used in workflows involving QM methods (using interfaces to many different QM programs), classical MM methods with multiple force fields (via an interface to the OpenMM library), machine-learning potentials, or hybrid methods. ASH is especially powerful as a program for performing hybrid simulations: including QM/MM, QM/ML, ML/MM, QM + ML, or ONIOM calculations for proteins, solvated molecules, or molecular crystals. Molecular dynamics and enhanced sampling can be performed using any level of theory allowing for highly flexible free-energy simulations (such as metadynamics) enabled by interfaces to algorithms in OpenMM and Plumed. There are flexible interfaces to many QM programs such as ORCA, xTB, pySCF, CP2K, MRCC, Turbomole, CFour, and many others. Python (programming language) ; ONIOM ; Workflow ; Molecular dynamics ; Software ; Computational mechanics ; Interface (matter) ; Quantum chemistry Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Advanced Physical and Chemical Molecular Interactions
Authors: Muyang Chen | Rui Tan
Source: Cronfa (Swansea University) (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

High‐Conductivity Electrolytes Screened Using Fragment‐ and Composition‐Aware Deep Learning Deep learning ; Feature (linguistics) ; Electrolyte ; Pattern recognition (psychology) Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; CO2 Reduction Techniques and Catalysts
Authors: William Freitas
Source: arXiv (Cornell University) (article, 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: Raj Maddipati | Dhruthi Boddapati | Arunan, Elangannan | Phani Motamarri | Konduri Aditya
Source: ArXiv.org (article, 2026-01-13)
Field: Materials Science — Materials Chemistry

ChemXDyn: Dynamics-informed species and reaction detection methodology from atomistic simulations Accurate identification of chemical species and reaction pathways from molecular dynamics (MD) trajectories is a prerequisite for deriving predictive chemical-kinetic models and for mechanistic discovery in reactive systems. However, state-of-the-art trajectory analysis methods infer bonding from instantaneous distance thresholds, which can misclassify transient, nonreactive encounters as bonds and thereby introduce spurious intermediates, distorted reaction networks, and biased rate estimates. Here, we introduce ChemXDyn, a dynamics-aware computational methodology that leverages time-resolved interatomic distance signatures as a core principle to robustly identify chemically consistent bonded interactions and, consequently, extract meaningful reaction pathways. In particular, ChemXDyn propagates molecular connectivity through time while enforcing atomic valence and coordination constraints to distinguish genuine bond-breaking and bond-forming events from transient, nonreactive encounters. We evaluate ChemXDyn on ReaxFF MD simulations of hydrogen and ammonia oxidation and on neural-network potential MD simulations of methane oxidation, and benchmark its performance against widely used trajectory analysis methods. Across these cases, ChemXDyn suppresses unphysical species prevalent in static analyses, recovers experimentally consistent reaction pathways, and improves the fidelity of rate constant estimation. In ammonia oxidation, ChemXDyn removes unphysical intermediates and resolves key NOx- and N2O-forming and -consuming routes. In methane oxidation, it reconstructs the canonical progression from CH4 to CO2. By linking atomistic dynamics to chemically consistent reaction identification, ChemXDyn provides a transferable foundation for MD-derived reaction networks and kinetics, with potential utility spanning combustion, catalysis, plasma chemistry, and electrochemical environments. Molecular dynamics ; Spurious relationship ; ReaxFF ; Chemical reaction ; Reaction dynamics ; Reaction rate ; Trajectory ; Methane ; Reaction rate constant Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Electrocatalysts for Energy Conversion
Authors: Roger Monreal-Corona | Anna Pla‐Quintana | Albert Poater
Source: Digital Discovery (article, 2026-01-01)
Field: Chemistry — Inorganic Chemistry

Reaction optimization through mechanistic insight and predictive modelling The optimization of chemical reactions lies at the heart of synthetic chemistry. We review how DoE, computational models, and machine learning drive progress in efficiency and selectivity. Model predictive control ; Control (management) ; Chemical reaction engineering ; Chemical reaction kinetics ; Mathematical model ; Chemical reaction Asymmetric Hydrogenation and Catalysis ; Innovative Microfluidic and Catalytic Techniques Innovation ; Process Optimization and Integration
Authors: Yuchen Wang
Source: Atoms (article, 2026-03-08)
Field: Materials Science — Materials Chemistry

Diabatic Potential Energy Matrices at the Interface of Nonadiabatic Dynamics, Machine Learning, and Quantum Computing The accurate description of nonadiabatic quantum molecular dynamics represents one of the most significant challenges in modern computational chemistry, serving as a gateway to understanding complex phenomena ranging from photochemistry and electron transfer to surface scattering and biological exciton transport. A key difficulty lies in bridging high-level electronic structure theory for ground and excited states with accurate quantum dynamics theory. Although on-the-fly semiclassical approaches are increasingly viable, most quantum dynamics simulations still rely on pre-constructed potential energy surfaces, or in the nonadiabatic context, diabatic potential energy matrices (DPEMs). This perspective paper addresses the theoretical foundations, construction methodologies, and emerging frontiers of DPEMs. We examine the mathematical framework of the adiabatic-to-diabatic transformation, addressing the inherent topological challenges imposed by the geometric phase and the curl condition. We further analyze the transformative impact of machine learning, detailing how machine learning algorithms, such as permutation invariant polynomial neural networks and deep learning architectures, are reshaping the construction of global, high-dimensional DPEMs. Finally, we explore the disruptive potential of quantum computing, discussing how quantum algorithms are automating the direct simulation of nonadiabatic dynamics. In emerging quantum-centric workflows, DPEMs will continue to provide the critical bridge which enables the mapping of realistic, time-dependent molecular Hamiltonians onto quantum hardware. Diabatic ; Potential energy ; Quantum ; Quantum dynamics ; Semiclassical physics ; Quantum computer ; Hamiltonian (control theory) ; Excited state ; Potential energy surface Machine Learning in Materials Science ; Quantum many-body systems ; Quantum Computing Algorithms and Architecture
Authors: Youcai Zhu | Yue Mu | Xiaoke Shi | Long Chen | Shu Yang | Li Sun | Zhen Liu
Source: Chemical Science (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Decoding polyethylene formation in Cr/PNP catalyzed ethylene oligomerization <i>via</i> experimentally guided machine learning Polyethylene formation remains a critical side reaction in ethylene selective oligomerization, lowering α-olefin yields and lacking reliable predictive strategies. Here, an automated workflow incorporating structural parsing and indexing was developed to construct a Cr/PNP catalysts library, extract comprehensive molecular descriptors, and integrate them with machine learning models trained on experimentally measured PE values. A combined classification-regression strategy enabled accurate identification of low-PE catalysts and quantitative prediction of PE contents, establishing a broadly applicable framework for catalyst design and side-product control in homogeneous catalysis. Analysis of SHAP feature importance and DFT calculations revealed that the intrinsic properties of the ligand primarily determine whether it produces low or high PE. In contrast, optimizing experimental conditions plays a pivotal role in further reducing PE in low-PE ligands. Ethylene ; Polyethylene ; Catalysis ; Feature (linguistics) ; Ligand (biochemistry) ; Parsing ; Homogeneous ; Workflow Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Organometallic Complex Synthesis and Catalysis
Authors: Z. Q. Zhu | Rui Wang | Ning Li | Zhaoyu Pan | Kai Miao | Sheng Huang | Qingyu Li | Dichen Li
Source: Virtual and Physical Prototyping (article, 2026-04-05)
Field: Materials Science — Materials Chemistry

Physics-informed machine learning for mapping of composition and mechanical properties in 316L/AlCrFeNi multi-material alloys fabricated by directed energy deposition Deposition (geology) ; Energy (signal processing) ; Composition (language) ; Component (thermodynamics) Machine Learning in Materials Science ; Additive Manufacturing Materials and Processes ; High Entropy Alloys Studies
Authors: Ayaki Sunaga | Albert P. Bartók | Edit Mátyus
Source: arXiv (Cornell University) (article, 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: Xue-Wei Zhang | Gong-Xiang Qi | Yu Han | Yuxing Wang | Weilin Song | Zi-Qiang Wang | Jinhui Liu | Chun-Guang Liu
Source: ACS Sensors (article, 2026-02-05)
Field: Materials Science — Materials Chemistry

Interpretable Multimodal Graph Learning Platform for Rational Design of AIEgens: From Molecular Structure and Microenvironments to Photophysical Properties Aggregation-induced emission luminogens (AIEgens) exhibit significant application potential in materials science due to their unique photophysical properties. However, systematically elucidating their structure-property relationships remains challenging due to the high dispersion of data, the complex correlations of features, and the limited interpretability of traditional machine learning models. Herein, we constructed a data-driven and interpretable deep learning model (referred to as GATM) that integrates multisource data from the literature, including molecular structures, photophysical parameters, and solvent environments. By integrating graph neural networks with machine learning algorithms, this multimodal predictive framework successfully deciphers the intricate relationships between molecular structural features, solvent environments, and photophysical properties. The visualization of solvent-solute interaction mechanisms was achieved through multilevel attention capture and feature quantification analysis utilizing the graph attention network (GAT). Furthermore, the GAT also provided deep insights into the influence of key structural features-such as atomic type and hybridization state-on the luminescence mechanisms of AIEgens. The results demonstrate that GATM achieves high predictive accuracy (mean <i>R</i><sup>2</sup> > 0.90) for key parameters of AIEgens, including fluorescence lifetime, quantum yield, and maximum absorption/emission wavelengths. Subsequent molecular synthesis experiments further validated the model's predictive accuracy. Furthermore, the synthesized molecules underwent organic pesticide detection and discrimination experiments, achieving a low detection limit (0.4 nM) and 100% discrimination accuracy. This intelligent prediction platform provides a novel paradigm for the rational design of new AIEgens and paves the way for future inverse design research for other functional materials. Interpretability ; Rational design ; Deep learning ; Visualization ; Graph ; Virtual screening ; Artificial neural network ; Molecular descriptor Luminescence and Fluorescent Materials ; Carbon and Quantum Dots Applications ; Covalent Organic Framework Applications
Authors: Manoj Settem | Emanuele Telari | Antonio Tinti | Riccardo Ferrando | Alberto Giacomello
Source: ArXiv.org (article, 2026-03-24)
Field: Materials Science — Materials Chemistry

Structural Chart of Copper-Silver Nanoalloys through machine learning Nanoalloys (or alloy nanoparticles) are an important class of materials that are promising for their functional properties. However, designing synthesis protocols to control their structure and chemical ordering is rather challenging. Part of this difficulty stems from the lack of information on their metastable and stable structures. Here, we develop a general computational framework to construct a structural chart of nanoalloys using 38-atom AgCu nanoalloys as a model system. Initially, the equilibrium structural distribution is sampled using parallel tempering combined with molecular dynamics (PTMD). Using a machine learning (ML) based approach, the vast number of sampled configurations are classified into various structural classes. This ML approach produces a single three-dimensional map in which all structures and compositions can be visualized and discriminated. Finally, a finite-temperature structural chart is constructed which provides information on the dominant structures across the entire range of compositions and temperatures. In addition, the structural chart reveals significant differences in thermal stability between nanoalloys and bulk alloys. The presented framework provides an effective route to compute and map the vast structural and chemical space of multicomponent nanoparticles, paving the way to the rational design of functional nanoalloys. Chart ; Stability (learning theory) ; Construct (python library) ; Metastability ; Class (philosophy) ; Structural stability ; Range (aeronautics) Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Electrocatalysts for Energy Conversion
Authors: Akira Kusaba | Tetsuji Kuboyama | Yoshihiro Kangawa
Source:  (article, 2026-03-05)
Field: Materials Science — Materials Chemistry

Material configurations informatics for semiconductor materials research Even when the crystal structure and alloy composition are fixed, the arrangement of different atoms on crystal sites can significantly affect material properties and formation processes. To address this, we have been developing a research framework called Material Configurations Informatics (MCI), which focuses on the degrees of freedom in atomic configurations through computational science and machine learning approaches. With increasing computational power, large-scale first-principles calculations (DFT) have become feasible. However, the biggest challenge in MCI is the enormous number of possible atomic configurations. We tackle this problem using techniques such as efficient sampling via Bayesian optimization and interaction modeling with approaches like the Ising model. In this presentation, we introduce applications of MCI to issues such as the mixing of different surface reconstructions—an important factor in discussions of crystal growth processes—and the study of semiconductor properties in 2D alloy materials. Materials informatics ; Informatics ; Semiconductor materials ; Electronic materials ; Materials testing Machine Learning in Materials Science ; Metallurgical and Alloy Processes ; Advanced Materials and Semiconductor Technologies