jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ilya Balabin | Thomas M. Kaiser
Source: Open MIND (preprint, 2026-02-26)
Field: Materials Science — Materials Chemistry

Inferential Mechanics Part 1: Causal Mechanistic Theories of Machine Learning in Chemical Biology with Implications Machine learning techniques are now routinely encountered in research laboratories across the globe. Impressive progress has been made through ML and AI techniques with regards to large data set processing. This progress has increased the ability of the experimenter to digest data and make novel predictions regarding phenomena of interest. However, machine learning predictors generated from data sets taken from the natural sciences are often treated as black boxes which are used broadly and generally without detailed consideration of the causal structure of the data set of interest. Work has been attempted to bring causality into discussions of machine learning models of natural phenomena; however, a firm and unified theoretical treatment is lacking. This series of three papers explores the union of chemical theory, biological theory, probability theory and causality that will correct current causal flaws of machine learning in the natural sciences. This paper, Part 1 of the series, provides the formal framework of the foundational causal structure of phenomena in chemical biology and is extended to machine learning through the novel concept of focus, defined here as the ability of a machine learning algorithm to narrow down to a hidden underpinning mechanism in large data sets. Initial proof of these principles on a family of Akt inhibitors is also provided. The second paper containing Part 2 will provide a formal exploration of chemical similarity, and Part 3 will present extensive experimental evidence of how hidden causal structures weaken all machine learning in chemical biology. This series serves to establish for chemical biology a new kind of mathematical framework for modeling mechanisms in Nature without the need for the tools of reductionism: inferential mechanics. Causal inference ; Set (abstract data type) ; Causality (physics) ; Algorithmic learning theory ; Mechanism (biology) ; Computational learning theory ; Natural (archaeology) ; Causal model Machine Learning in Materials Science ; Machine Learning in Bioinformatics ; Gene Regulatory Network Analysis
Authors: Lan-Ping Qian | Xinyu Li | Yu-Lei Bai | Kan Guo | Jiapu Wang | Di Chen | Yi Li | Yue Li | Xiuli Du
Source: Journal of Building Engineering (article, 2026-03-16)
Field: Materials Science — Materials Chemistry

Deep learning-driven microstructural image analysis: Recent advances in cementitious materials Deep learning ; Cementitious ; Segmentation ; Focus (optics) ; Feature (linguistics) ; Image segmentation ; Generative grammar ; Transformative learning Machine Learning in Materials Science ; Innovative concrete reinforcement materials ; Rock Mechanics and Modeling
Authors: Haiyang Zhang | Zhongying Xue | Pai Li | Xing Wei
Source: Small (article, 2026-03-20)
Field: Materials Science — Materials Chemistry

Asynchronous Transition Across the Crystal‐Melt Interface Revealed by Machine Learning Potentials Understanding the atomic-scale structure and thermodynamics of crystal-melt interfaces is crucial in materials science. Here, we employ a DFT-based machine learning potential to investigate the crystal-melt interface, taking silicon as an example of a single-component system. We reveal that the transition is not abrupt, but spans a broad (> 12 Å) region where structural, dynamic, and thermodynamic properties evolve asynchronously. Using the local order parameter <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:msub><mml:mover><mml:mi>q</mml:mi> <mml:mo>¯</mml:mo></mml:mover> <mml:mn>6</mml:mn></mml:msub> <mml:annotation>${\bar q_6}$</mml:annotation></mml:semantics> </mml:math> , we identify a relative sharp structural change, sharing a common transition area with entropy/enthalpy change. The local density and self-diffusivity, however, exhibit a distinct transition area closer to the liquid side. This spatial decoupling suggests a staged melting mechanism. Furthermore, we compute the interfacial free energy via mold integration, revealing pronounced anisotropy: γ<sub>(111)</sub> < γ<sub>(110)</sub> < γ<sub>(100)</sub>. This explains the prevalence of (111) facets in silicon crystal growth. The interfacial energy decreases with supercooling, lowering the nucleation barrier. Wulff construction based on our calculated energies predicts equilibrium crystal shapes consistent with experimental observations. Our work provides a comprehensive, multi-dimensional picture of the crystal-melt interface, linking atomic-scale dynamics to macroscopic crystal morphology. Decoupling (probability) ; Nucleation ; Silicon ; Crystal (programming language) ; Asynchronous communication ; Work (physics) ; Energy (signal processing) ; Interface (matter) Machine Learning in Materials Science ; Solidification and crystal growth phenomena ; Nanowire Synthesis and Applications
Authors: Manas Sajjan | Vinit Singh | Sabre Kais
Source: Research Square (preprint, 2026-03-14)
Field: Materials Science — Materials Chemistry

Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Quantum ; Quantum Monte Carlo ; Monte Carlo method ; Robustness (evolution) ; Quantum algorithm ; Boltzmann machine ; Quantum computer ; Quantum state ; Quantum network ; Artificial neural network Machine Learning in Materials Science ; Quantum many-body systems ; Quantum Computing Algorithms and Architecture
Authors: Tao Liang | Eric A. Lass | Haochen Zhu | Carla Joyce C. Nocheseda | Philip D. Rack | Stephen Puplampu | Dayakar Penumadu | Haixuan Xu
Source: arXiv (Cornell University) (preprint, 2026-04-07)
Field: Materials Science — Materials Chemistry

Composition design of refractory compositionally complex alloys using machine learning models Refractory compositionally complex alloys (RCCAs) are considered the next generation high-temperature materials. However, their high-dimensional composition spaces are too large to explore by traditional density functional theory or experimental means, making new RCCA discovery slow and cumbersome. This work has addressed these challenges with an integrated composition design framework that can efficiently and exhaustively explore the relationship between the compositions and two fundamental aspects: 1) the phase stability, including the target body-centered cubic (BCC) phase and its competing phases (hexagonal closed-pack (HCP) structures, Laves and B2 intermetallic phases), and 2) the mechanical properties. This framework is demonstrated with RCCAs within nine refractory metals (Ti, V, Cr, Zr, Nb, Mo, Hf, Ta, and W). Theory-guided machine learning (ML) models were employed to find the composition-mechanical property relationship of RCCAs, where the established theory is used to supplement the yield strength data at ultra-high temperature, and a forward sequential feature selection (SFS) is used to determine feature selection. The resulting ML model for temperature-dependent yield strength was found to have an R_squared value of 0.98 over the entire temperature range (from 0 to 2000 K). The impact of each constituent element on the six key properties is evaluated. The addition of Nb tends to stabilize the BCC phase and the addition of Ti improves the ductility of RCCAs. Combined with all methods involved in this framework, the on-demand designer allows the alloy designers to have all properties for any RCCA compositions and narrow down the composition space by applying custom screening criteria. The output from the predictor and screener provides valuable guidance for our experimental study of RCCAs and accelerates the pace of materials discovery. Intermetallic ; Ductility (Earth science) ; Phase (matter) ; Composition (language) ; Yield (engineering) ; Refractory (planetary science) ; Feature (linguistics) ; Alloy Machine Learning in Materials Science ; High Entropy Alloys Studies ; Advanced materials and composites
Authors: Stephen E. Farr | Stefan Doerr | Antonio Mirarchi | Francesc Sabanés Zariquiey | Gianni De Fabritiis
Source: ArXiv.org (article, 2026-01-02)
Field: Materials Science — Materials Chemistry

AceFF: A State-of-the-Art Machine Learning Potential for Small Molecules We introduce AceFF, a pre-trained machine learning interatomic potential (MLIP) optimized for small molecule drug discovery. While MLIPs have emerged as efficient alternatives to Density Functional Theory (DFT), generalizability across diverse chemical spaces remains difficult. AceFF addresses this via a refined TensorNet2 architecture trained on a comprehensive dataset of drug-like compounds. This approach yields a force field that balances high-throughput inference speed with DFT-level accuracy. \mbox{AceFF} fully supports the essential medicinal chemistry elements (H, B, C, N, O, F, Si, P, S, Cl, Br, I) and is explicitly trained to handle charged states. Validation against rigorous benchmarks, including complex torsional energy scans, molecular dynamics trajectories, batched minimizations, and tests of force and energy accuracy, demonstrates that AceFF is state-of-the-art for organic molecules in the accuracy and speed regime important for drug discovery. The AceFF-2 model weights and inference code are available at https://huggingface.co/Acellera/AceFF-2.0. Generalizability theory ; Inference ; Field (mathematics) ; Force field (fiction) ; Energy (signal processing) ; Density functional theory ; Small molecule ; Code (set theory) ; Statistical inference Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Xinze Li | Ruitao Ma | Chen Qu | Dong H. Zhang | Qi Yu
Source: arXiv (Cornell University) (article, 2026-02-15)
Field: Materials Science — Materials Chemistry

Monomeric machine learning potential for general covalent molecules: linear alkanes as an example Machine-learning potentials (MLPs) have become important tools for modern molecular simulations. However, developing models that simultaneously achieve high accuracy and high computational efficiency remains a significant challenge. In this work, we extend the recently proposed MB-PIPNet framework to general covalently bonded molecular systems by combining monomer-based energy decomposition, permutationally invariant polynomial (PIP) descriptors, and neural networks within a fragmentation-based strategy. Within this framework, the total potential energy is represented as a sum of effective monomeric contributions, where PIPs provide compact and chemically motivated descriptions of both monomer internal structures and their local chemical environments. As a proof-of-concept application, we apply the MB-PIPNet framework to linear alkanes, using n-Tetradecane as a representative system, and benchmark its performance against established atomistic machine-learning models. The resulting MB-PIPNet potential accurately reproduces reference ab initio electronic energies and reliably captures key molecular properties, including torsional potential energy profiles, harmonic vibrational frequencies, and vibrational power spectra obtained from molecular dynamics simulations. Importantly, MB-PIPNet demonstrates a substantial advantage in computational efficiency over other MLP models for combined energy and force evaluations. These results establish MB-PIPNet as a scalable and efficient framework for constructing MLPs, providing an additional route for large-scale quantum and classical simulations of complex molecular systems. Ab initio ; Molecular dynamics ; Potential energy ; Invariant (physics) ; Quantum ; Monomer ; Benchmark (surveying) ; Potential energy surface ; Quantum chemistry Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Crystallography and molecular interactions
Authors: Tao Liang | Eric A. Lass | Haochen Zhu | Carla Joyce C. Nocheseda | Philip D. Rack | Stephen Puplampu | Dayakar Penumadu | Haixuan Xu
Source: arXiv (Cornell University) (article, 2026-04-07)
Field: Materials Science — Materials Chemistry

Composition design of refractory compositionally complex alloys using machine learning models Refractory compositionally complex alloys (RCCAs) are considered the next generation high-temperature materials. However, their high-dimensional composition spaces are too large to explore by traditional density functional theory or experimental means, making new RCCA discovery slow and cumbersome. This work has addressed these challenges with an integrated composition design framework that can efficiently and exhaustively explore the relationship between the compositions and two fundamental aspects: 1) the phase stability, including the target body-centered cubic (BCC) phase and its competing phases (hexagonal closed-pack (HCP) structures, Laves and B2 intermetallic phases), and 2) the mechanical properties. This framework is demonstrated with RCCAs within nine refractory metals (Ti, V, Cr, Zr, Nb, Mo, Hf, Ta, and W). Theory-guided machine learning (ML) models were employed to find the composition-mechanical property relationship of RCCAs, where the established theory is used to supplement the yield strength data at ultra-high temperature, and a forward sequential feature selection (SFS) is used to determine feature selection. The resulting ML model for temperature-dependent yield strength was found to have an R_squared value of 0.98 over the entire temperature range (from 0 to 2000 K). The impact of each constituent element on the six key properties is evaluated. The addition of Nb tends to stabilize the BCC phase and the addition of Ti improves the ductility of RCCAs. Combined with all methods involved in this framework, the on-demand designer allows the alloy designers to have all properties for any RCCA compositions and narrow down the composition space by applying custom screening criteria. The output from the predictor and screener provides valuable guidance for our experimental study of RCCAs and accelerates the pace of materials discovery. Intermetallic ; Ductility (Earth science) ; Phase (matter) ; Composition (language) ; Yield (engineering) ; Refractory (planetary science) ; Feature (linguistics) ; Alloy Machine Learning in Materials Science ; High Entropy Alloys Studies ; Advanced materials and composites
Authors: Ziduo Yang | Wei Zhuo | Huiqiang Xie | Xiaoqing Liu | Lei Shen
Source: arXiv (Cornell University) (preprint, 2026-03-25)
Field: Materials Science — Materials Chemistry

An Efficient High-Degree, High-Order Equivariant Graph Neural Network for Direct Crystal Structure Optimization Crystal structure optimization is fundamental to materials modeling but remains computationally expensive when performed with density-functional theory (DFT). Machine-learning (ML) approaches offer substantial acceleration, yet existing methods face three key limitations: (i) most models operate solely on atoms and treat lattice vectors implicitly, despite their central role in structural optimization; (ii) they lack efficient mechanisms to capture high-degree angular information and higher-order geometric correlations simultaneously, which are essential for distinguishing subtle structural differences; and (iii) many pipelines are multi-stage or iterative rather than truly end-to-end, making them prone to error accumulation and limiting scalability. Here we present E$^{3}$Relax-H$^{2}$, an end-to-end high-degree, high-order equivariant graph neural network that maps an initial crystal directly to its relaxed structure. The key idea is to promote both atoms and lattice vectors to graph nodes, enabling a unified and symmetry-consistent representation of structural degrees of freedom. Building on this formulation, E$^{3}$Relax-H$^{2}$ introduces two message-passing mechanisms: (i) a high-degree, high-order message-passing module that efficiently captures high-degree angular representations and high-order many-body correlations; and (ii) a lattice-atom message-passing module that explicitly models the bidirectional coupling between lattice deformation and atomic displacement. In addition, we propose a differentiable periodicity-aware Cartesian displacement loss tailored for one-shot structure prediction under periodic boundary conditions. Lattice (music) ; Equivariant map ; Differentiable function ; Topology (electrical circuits) ; Cartesian coordinate system ; Graph ; Data structure ; Artificial neural network Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Quantum many-body systems
Authors: Xinze Li | Ruitao Ma | Chen Qu | Dong H. Zhang | Qi Yu
Source: Open MIND (preprint, 2026-02-15)
Field: Materials Science — Materials Chemistry

Monomeric machine learning potential for general covalent molecules: linear alkanes as an example Machine-learning potentials (MLPs) have become important tools for modern molecular simulations. However, developing models that simultaneously achieve high accuracy and high computational efficiency remains a significant challenge. In this work, we extend the recently proposed MB-PIPNet framework to general covalently bonded molecular systems by combining monomer-based energy decomposition, permutationally invariant polynomial (PIP) descriptors, and neural networks within a fragmentation-based strategy. Within this framework, the total potential energy is represented as a sum of effective monomeric contributions, where PIPs provide compact and chemically motivated descriptions of both monomer internal structures and their local chemical environments. As a proof-of-concept application, we apply the MB-PIPNet framework to linear alkanes, using n-Tetradecane as a representative system, and benchmark its performance against established atomistic machine-learning models. The resulting MB-PIPNet potential accurately reproduces reference ab initio electronic energies and reliably captures key molecular properties, including torsional potential energy profiles, harmonic vibrational frequencies, and vibrational power spectra obtained from molecular dynamics simulations. Importantly, MB-PIPNet demonstrates a substantial advantage in computational efficiency over other MLP models for combined energy and force evaluations. These results establish MB-PIPNet as a scalable and efficient framework for constructing MLPs, providing an additional route for large-scale quantum and classical simulations of complex molecular systems. Ab initio ; Molecular dynamics ; Potential energy ; Invariant (physics) ; Quantum ; Monomer ; Benchmark (surveying) ; Potential energy surface ; Quantum chemistry Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Crystallography and molecular interactions
Authors: Fernando Garcia-Escobar | Tomoya Tashiro | Naotoshi Miyasaka | Rensuke Koiwai | Lauren Takahashi | K. Takahashi
Source: ACS Applied Energy Materials (article, 2026-01-05)
Field: Materials Science — Materials Chemistry

Designing Anode Materials for Proton Exchange Membrane Electrolysis via Literature Data and Machine Learning Proton exchange membrane (PEM) electrolysis is a crucial technology for sustainable hydrogen production, yet the discovery of efficient and durable anode materials remains a challenge. In this work, literature data on PEM electrolysis are systematically collected and analyzed to identify trends in material selection, synthesis methods, and key experimental conditions. Statistical analysis revealed that a limited number of anode and cathode materials dominate the field primarily due to their catalytic activity and stability in acidic environments. Supervised machine learning is then employed to predict anode materials aimed at reducing Ir content in the anode material by using compositional and experimental descriptors to model the cell performance. Experimental validation of Ir0.9M0.1O2 (M = Cr, Zn, Sb, Ho) demonstrated that incorporating these predicted elements preserved cell performance while enabling a reduction in the Ir content at the anode. This study demonstrates that integrating data-driven approaches with literature analysis can effectively guide the discovery of anode materials, advancing the PEM electrolysis technology. Anode ; Electrolysis ; Cathode ; Polymer electrolyte membrane electrolysis ; Electrolysis of water ; Proton exchange membrane fuel cell Machine Learning in Materials Science ; Hybrid Renewable Energy Systems ; Electrocatalysts for Energy Conversion
Authors: Zimeng Yang | William V. Rocheleau | Yu-Chen Sun | Alain Tchagang | Ta‐Ya Chu | Hani E. Naguib
Source:  (article, 2026-03-13)
Field: Materials Science — Materials Chemistry

Machine learning classification in synthesis and dispersion of MXene materials Dispersion (optics) ; Support vector machine ; Feature (linguistics) ; Pattern recognition (psychology) ; Stability (learning theory) MXene and MAX Phase Materials ; Machine Learning in Materials Science ; Ammonia Synthesis and Nitrogen Reduction
Authors: M. Lebeda | Jan Drahokoupil | Veronika Mazáčová | Petr Vlčák
Source: Journal of Materials Research and Technology (article, 2026-02-26)
Field: Materials Science — Materials Chemistry

Revealing interstitial energetics in Ti-23Nb-0.7Ta-2Zr gum metal base alloy via universal machine learning interatomic potentials Understanding the behavior of light interstitial elements in multicomponent alloys remains challenging due to the complexity of local chemical environments and the high computational cost of first-principles calculations. Here we demonstrate that three universal machine-learning interatomic potentials (uMLIPs) – MACE-MATPES-PBE-0, Orb-v3, and SevenNet-0 can efficiently map the energetics of C, N, O, and H interstitials in a Ti-23Nb-0.7Ta-2Zr (at.%) gum metal base alloy while being several orders of magnitude faster than density functional theory (DFT). All uMLIPs predict broad energy distributions (∼1-3 eV) across the four interstitial elements, reflecting their strong sensitivity to local lattice chemistry. Despite alloy disorder, MACE-MATPES-PBE-0 and Orb-v3 reproduce the expected site preferences of the bcc structure: C, N, and O relax into octahedral sites, whereas H stabilizes in tetrahedral positions. In contrast, SevenNet-0 predicts H to be most stable in octahedral coordination, indicating a limitation of this model. Correlation analysis reveals two dominant chemical trends: Ti-rich environments strongly stabilize interstitials, whereas close proximity to Nb is destabilizing. Zr and Ta show no statistically significant influence, likely due to their low concentrations. Benchmarking representative O interstitial configurations against DFT confirms that the uMLIPs reasonably reproduce the energetic ordering of chemically distinct environments. Additional DFT validation confirms that tetrahedral configurations are energetically more favorable than octahedral sites for H interstitials, further illustrating the SevenNet-0 limitation. Overall, these results demonstrate that uMLIPs enable computationally efficient, statistically broad characterization of defect energetics in Ti-23Nb-0.7Ta-2Zr gum metal base alloy and provide insight into how local chemical environments govern interstitial stability. Octahedron ; Alloy ; Interstitial defect ; Tetrahedron ; Energetics ; Density functional theory ; Interatomic potential ; Lattice (music) Machine Learning in Materials Science ; Titanium Alloys Microstructure and Properties ; Nuclear Materials and Properties
Authors: Marco Barnfield | O. L. Polyansky | Sergei  N. Yurchenko | J. Tennyson
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Chemistry — Spectroscopy

&lt;p&gt;Machine Learning Isotope Shifts in Molecular Energy Levels&lt;/p&gt; Isotopologue ; Extrapolation ; Line (geometry) ; Residual ; Carbon monoxide ; Energy (signal processing) ; Spectroscopy Spectroscopy and Laser Applications ; Astrophysics and Star Formation Studies ; Advanced Chemical Physics Studies