jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Akshay Subramanian | James Damewood | Juno Nam | Kevin P. Greenman | A. Singhal | Rafael Gómez‐Bombarelli
Source: arXiv (Cornell University) (preprint, 2024-10-11)
Field: Materials Science — Materials Chemistry

Symmetry-Constrained Generation of Diverse Low-Bandgap Molecules with Monte Carlo Tree Search Organic optoelectronic materials are a promising avenue for next-generation electronic devices due to their solution processability, mechanical flexibility, and tunable electronic properties. In particular, near-infrared (NIR) sensitive molecules have unique applications in night-vision equipment and biomedical imaging. Molecular engineering has played a crucial role in developing non-fullerene acceptors (NFAs) such as the Y-series molecules, which have significantly improved the power conversion efficiency (PCE) of solar cells and enhanced spectral coverage in the NIR region. However, systematically designing molecules with targeted optoelectronic properties while ensuring synthetic accessibility remains a challenge. To address this, we leverage structural priors from domain-focused, patent-mined datasets of organic electronic molecules using a symmetry-aware fragment decomposition algorithm and a fragment-constrained Monte Carlo Tree Search (MCTS) generator. Our approach generates candidates that retain symmetry constraints from the patent dataset, while also exhibiting red-shifted absorption, as validated by TD-DFT calculations. Symmetry (geometry) ; Monte Carlo method ; Tree (set theory) ; Statistical physics ; Molecule ; Physics ; Band gap ; Molecular physics ; Mathematics ; Condensed matter physics ; Combinatorics ; Quantum mechanics ; Statistics ; Geometry Machine Learning in Materials Science
Authors: Zi Li | Wenbo Fu | Bochen Li | Yao Jia | Jiuchuang Yuan | Michael A. Bellucci | Guangxu Sun | Zhengtian Song | Liu Shi | Zhu Lang | Jian Ma | Shuhao Wen | Qun Zeng
Source: ChemRxiv (preprint, 2024-07-15)
Field: Materials Science — Materials Chemistry

Machine Learning-Based Recommendation of Optimal Crystallization Conditions for Organic Small Molecules Crystallization is an important process in a broad range of industries, though studies on this topic remain complicated. Recently, machine learning has been applied to resolve complex issues in chemistry and material science. Here we present a machine learning model to propose crystallization experiments for organic small molecules. This model has been integrated into a robotic platform that performs experiments automatically. To improve applicability and accuracy, the model was trained on both simulated and experimental data. In comparative case studies, polymorph screening experiments by the platform yielded a high rate of solid products, and the number of forms obtained by the platform equaled those obtained by human researchers. The model provides a data-based perspective of the promoting and inhibiting influences to crystallization from molecular and interaction features. This work demonstrates the feasibility of applying machine learning techniques to solid-state studies to boost efficiency and deepen understanding. Crystallization ; Organic molecules ; Computer science ; Molecule ; Artificial intelligence ; Biochemical engineering ; Chemistry ; Process engineering ; Organic chemistry ; Engineering Crystallization and Solubility Studies ; Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Bumju Kwak | Jeonghee Jo
Source: arXiv (Cornell University) (preprint, 2024-06-01)
Field: Chemistry — Spectroscopy

Neural Polarization: Toward Electron Density for Molecules by Extending Equivariant Networks Recent SO(3)-equivariant models embedded a molecule as a set of single atoms fixed in the three-dimensional space, which is analogous to a ball-and-stick view. This perspective provides a concise view of atom arrangements, however, the surrounding electron density cannot be represented and its polarization effects may be underestimated. To overcome this limitation, we propose \textit{Neural Polarization}, a novel method extending equivariant network by embedding each atom as a pair of fixed and moving points. Motivated by density functional theory, Neural Polarization represents molecules as a space-filling view which includes an electron density, in contrast with a ball-and-stick view. Neural Polarization can flexibly be applied to most type of existing equivariant models. We showed that Neural Polarization can improve prediction performances of existing models over a wide range of targets. Finally, we verified that our method can improve the expressiveness and equivariance in terms of mathematical aspects. Equivariant map ; Polarization (electrochemistry) ; Electron ; Molecule ; Physics ; Electron density ; Computer science ; Mathematics ; Pure mathematics ; Chemistry ; Quantum mechanics Molecular spectroscopy and chirality ; Photoreceptor and optogenetics research ; Machine Learning in Materials Science
Authors: V. Diana Rakotonirina | Marco Bragato | Stefan Heinen | O. Anatole von Lilienfeld
Source: arXiv (Cornell University) (preprint, 2024-05-13)
Field: Materials Science — Materials Chemistry

Combining Hammett $σ$ constants for $Δ$-machine learning and catalyst discovery We study the applicability of the Hammett-inspired product (HIP) Ansatz to model relative substrate binding within homogenous organometallic catalysis, assigning $σ$ and $ρ$ to ligands and metals, respectively. Implementing an additive combination (c) rule for obtaining $σ$ constants for any ligand pair combination results in a cHIP model that enhances data efficiency in computational ligand tuning. We show its usefulness (i) as a baseline for $Δ$-machine learning (ML), and (ii) to identify novel catalyst candidates via volcano plots. After testing the combination rule on Hammett constants previously published in the literature, we have generated numerical evidence for the Suzuki-Miyaura (SM) C-C cross-coupling reaction using two synthetic datasets of metallic catalysts (including (10) and (11)-metals Ni, Pd, Pt, and Cu, Ag, Au as well as 96 ligands such as N-heterocyclic carbenes, phosphines, or pyridines). When used as a baseline, $Δ$-ML prediction errors of relative binding decrease systematically with training set size and reach chemical accuracy ($\sim$1 kcal/mol) for 20k training instances. Employing the individual ligand constants obtained from cHIP, we report relative substrate binding for a novel dataset consisting of 720 catalysts (not part of training data), of which 145 fall into the most promising range on the volcano plot accounting for oxidative addition, transmetalation, and reductive elimination steps. Multiple Ni-based catalysts, e.g. Aphos-Ni-P($t$-Bu)$_3$, are included among these promising candidates, potentially offering dramatic cost savings in experimental applications. Sigma ; Catalysis ; Chemistry ; Computational chemistry ; Thermodynamics ; Econometrics ; Economics ; Physics ; Organic chemistry ; Quantum mechanics Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Catalysis and Oxidation Reactions
Authors: Yunlong SUN
Source: Institutional Repositories DataBase (IRDB) (article, 2024-09-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Application of machine learning to hexagonal rare-earth ferrite (h-RFeO₃,R=Tb, Eu, Yb) thin film fabrication UTokyo Repositoryは本学で生産されたさまざまな学術成果を電子的形態で集中的に蓄積・保存し、世界に発信することを目的としたインターネット上の発信拠点です。 The UTokyo Repository is the system to store and provide digital resources created by members of the University of Tokyo. Its main purpose is to develop digital collections, make them available online, and preserve them for long-term access. Fabrication ; Ferrite (magnet) ; Hexagonal crystal system ; Thin film Multiferroics and related materials ; Machine Learning in Materials Science ; Magnetic Properties and Synthesis of Ferrites
Authors: В. И. Колесников | O. V. Kudryakov | A. I. Voropaev | И. В. Колесников | В. Н. Варавка | Mikhail Lifar | Alexander A. Guda | D. S. Manturov | Е. С. Новиков
Source: Trenie i Iznos (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Formation and Prediction of Tribotechnical Properties of Ion-Plasma Diamond-Like Coatings under Stabilization with Nitrogen - - . . - DLC, ., DLC 1,0-1,5 , . L %N Materials science ; Nitrogen ; Plasma ; Ion ; Diamond-like carbon ; Diamond ; Chemical engineering ; Composite material ; Nanotechnology ; Chemistry ; Thin film ; Engineering ; Organic chemistry Diamond and Carbon-based Materials Research ; Metal and Thin Film Mechanics ; Advanced materials and composites
Authors: Mackenzie C. R. Denton | Natasha P. Murphy | Brenna Norton‐Baker | Mauro Lua | Harrison Steel | Gregg T. Beckham
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-03-06)
Field: Materials Science — Biomaterials

Integration of pH control into Chi.Bio reactors and demonstration with small-scale enzymatic poly(ethylene terephthalate) hydrolysis Abstract Small-scale bioreactors that are affordable and accessible would be of major benefit to the research community. In previous work, an open-source, automated bioreactor system was designed to operate up to the 30 mL scale with online optical monitoring, stirring, and temperature control, and this system, dubbed Chi.Bio, is now commercially available at a cost that is typically 1-2 orders of magnitude less than commercial bioreactors. In this work, we further expand the capabilities of the Chi.Bio system by enabling continuous pH monitoring and control through hardware and software modifications. For hardware modifications, we sourced low-cost, commercial pH circuits and made straightforward modifications to the Chi.Bio head plate to enable continuous pH monitoring. For software integration, we introduced closed-loop feedback control of the pH measured inside the Chi.Bio reactors and integrated a pH-control module into the existing Chi.Bio user interface. We demonstrated the utility of pH control through the small-scale depolymerization of the synthetic polyester, poly(ethylene terephthalate) (PET), using a benchmark cutinase enzyme, and compared this to 250 mL bioreactor hydrolysis reactions. The results in terms of PET conversion and rate, measured both by base addition and product release profiles, are statistically equivalent, with the Chi.Bio system allowing for a 20-fold reduction of purified enzyme required relative to the 250 mL bioreactor setup. Through inexpensive modifications, the ability to conduct pH control in Chi.Bio reactors widens the potential slate of biochemical reactions and biological cultivations for study in this system, and may also be adapted for use in other bioreactor platforms. Poly ethylene ; Ethylene ; Enzymatic hydrolysis ; Hydrolysis ; Scale (ratio) ; Enzyme ; Chemistry ; Control (management) ; Materials science ; Chemical engineering ; Organic chemistry ; Catalysis ; Computer science ; Engineering ; Physics ; Artificial intelligence biodegradable polymer synthesis and properties ; Microplastics and Plastic Pollution ; Enzyme Catalysis and Immobilization
Authors: Philipp J. Vetter | Thomas Reisser | Maximilian G. Hirsch | Tommaso Calarco | Felix Motzoi | Fedor Jelezko | Matthias M. Müller
Source: arXiv (Cornell University) (preprint, 2024-03-01)
Field: Materials Science — Materials Chemistry

Gate-set evaluation metrics for closed-loop optimal control on nitrogen-vacancy center ensembles in diamond A recurring challenge in quantum science and technology is the precise control of their underlying dynamics that lead to the desired quantum operations, often described by a set of quantum gates. These gates can be subject to application-specific errors, leading to a dependence of their controls on the chosen circuit, the quality measure and the gate-set itself. A natural solution would be to apply quantum optimal control in an application-oriented fashion. In turn, this requires the definition of a meaningful measure of the contextual gate-set performance. Therefore, we explore and compare the applicability of quantum process tomography, linear inversion gate-set tomography, randomized linear gate-set tomography, and randomized benchmarking as measures for closed-loop quantum optimal control experiments, using a macroscopic ensemble of nitrogen-vacancy centers in diamond as a test-bed. Our work demonstrates the relative trade-offs between those measures and how to significantly enhance the gate-set performance, leading to an improvement across all investigated methods. Diamond ; Vacancy defect ; Center (category theory) ; Nitrogen-vacancy center ; Loop (graph theory) ; Set (abstract data type) ; Nitrogen ; Control (management) ; Computer science ; Materials science ; Mathematics ; Physics ; Condensed matter physics ; Artificial intelligence ; Chemistry ; Crystallography ; Combinatorics ; Quantum mechanics Catalytic Processes in Materials Science ; Diamond and Carbon-based Materials Research ; Advanced Materials Characterization Techniques
Authors: Yingming Pu | Liping Huang | Tao Lin | Hongyu Chen
Source: arXiv (Cornell University) (preprint, 2024-07-12)
Field: Materials Science — Materials Chemistry

Leveraging large language models for nano synthesis mechanism explanation: solid foundations or mere conjectures? With the rapid development of artificial intelligence (AI), large language models (LLMs) such as GPT-4 have garnered significant attention in the scientific community, demonstrating great potential in advancing scientific discovery. This progress raises a critical question: are these LLMs well-aligned with real-world physicochemical principles? Current evaluation strategies largely emphasize fact-based knowledge, such as material property prediction or name recognition, but they often lack an understanding of fundamental physicochemical mechanisms that require logical reasoning. To bridge this gap, our study developed a benchmark consisting of 775 multiple-choice questions focusing on the mechanisms of gold nanoparticle synthesis. By reflecting on existing evaluation metrics, we question whether a direct true-or-false assessment merely suggests conjecture. Hence, we propose a novel evaluation metric, the confidence-based score (c-score), which probes the output logits to derive the precise probability for the correct answer. Based on extensive experiments, our results show that in the context of gold nanoparticle synthesis, LLMs understand the underlying physicochemical mechanisms rather than relying on conjecture. This study underscores the potential of LLMs to grasp intrinsic scientific mechanisms and sets the stage for developing more reliable and effective AI tools across various scientific domains. Mechanism (biology) ; Nano- ; Computer science ; Epistemology ; Cognitive science ; Psychology ; Philosophy ; Engineering ; Chemical engineering Machine Learning in Materials Science ; Machine Learning in Bioinformatics ; Computational Drug Discovery Methods
Authors: Dimitris Gkoumas
Source: arXiv (Cornell University) (preprint, 2024-05-14)
Field: Materials Science — Materials Chemistry

ALMol: Aligned Language-Molecule Translation LLMs through Offline Preference Contrastive Optimisation The field of chemistry and Artificial Intelligence (AI) intersection is an area of active research that aims to accelerate scientific discovery. The integration of large language models (LLMs) with scientific modalities has shown significant promise in this endeavour. However, challenges persist in effectively addressing training efficacy and the out-of-distribution problem, particularly as existing approaches rely on larger models and datasets. In this context, we focus on machine language-molecule translation and deploy a novel training approach called contrastive preference optimisation, which avoids generating translations that are merely adequate but not perfect. To ensure generalisability and mitigate memorisation effects, we conduct experiments using only 10% of the data. Our results demonstrate that our models achieve up to a 32% improvement compared to counterpart models. Finally, we introduce a fine-grained, domain-agnostic evaluation method to assess hallucination in LLMs and promote responsible use. Translation (biology) ; Preference ; Linguistics ; Computer science ; Natural language processing ; Chemistry ; Economics ; Philosophy ; Microeconomics ; Gene ; Biochemistry ; Messenger RNA Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Natural Language Processing Techniques
Authors: Sebastian Bauer | Peter Benner | Tristan Bereau | Volker Blüm | Mario Boley | Christian Carbogno | C. Richard A. Catlow | Gerhard Dehm | Sebastian Eibl | Ralph Ernstorfer | Ádám Fekete | Lucas Foppa | Peter Fratzl | Christoph Freysoldt | Baptiste Gault | Luca M. Ghiringhelli | Sajal Kumar Giri | Anton Gladyshev | Pawan Goyal | Jason Hattrick‐Simpers | Lara Kabalan | Petr Karpov | Mohammad S. Khorrami | Christoph T. Koch | Sebastian Kokott | Thomas Kosch | Igor Kowalec | Kurt Kremer | Andreas Leitherer | Yue Li | Christian H. Liebscher | Andrew J. Logsdail | Zhongwei Lu | Phuc Luong | Andreas Marek | F. Merz | Jaber Rezaei Mianroodi | Jörg Neugebauer | Pei, Zongrui | Thomas A. R. Purcell | Dierk Raabe | Markus Rampp | Mariana Rossi | Jan M. Rost | Saal, James | Ulf Saalmann | Sasidhar, Kasturi Narasimha | Alaukik Saxena | Luigi Sbailò | Markus Scheidgen | Marcel Schloz | Daniel F. Schmidt | Simon Teshuva | Annette Trunschke | Ye Wei | Gerhard Weikum | R. Patrick Xian | Yiyu Yao | Yin, Junqi | Meng Zhao | Matthias Scheffler
Source: arXiv (Cornell University) (preprint, 2024-02-01)
Field: Materials Science — Materials Chemistry

Roadmap on Data-Centric Materials Science Science is and always has been based on data, but the terms "data-centric" and the "4th paradigm of" materials research indicate a radical change in how information is retrieved, handled and research is performed. It signifies a transformative shift towards managing vast data collections, digital repositories, and innovative data analytics methods. The integration of Artificial Intelligence (AI) and its subset Machine Learning (ML), has become pivotal in addressing all these challenges. This Roadmap on Data-Centric Materials Science explores fundamental concepts and methodologies, illustrating diverse applications in electronic-structure theory, soft matter theory, microstructure research, and experimental techniques like photoemission, atom probe tomography, and electron microscopy. While the roadmap delves into specific areas within the broad interdisciplinary field of materials science, the provided examples elucidate key concepts applicable to a wider range of topics. The discussed instances offer insights into addressing the multifaceted challenges encountered in contemporary materials research. Data science ; Political science ; Regional science ; Engineering ethics ; Computer science ; Geography ; Engineering Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; X-ray Diffraction in Crystallography
Authors: Keisuke Ozawa | Teppei Suzuki | Shunsuke Tonogai | Tomoya Itakura
Source: ChemRxiv (preprint, 2024-04-15)
Field: Materials Science — Materials Chemistry

Graph-Text Contrastive Learning of Inorganic Crystal Structure toward a Foundation Model of Inorganic Materials Developing foundation models for materials science has attracted attention. However, there is a lack of studies on inorganic materials due to the difficulty in the comprehensive representation of geometric concepts composing crystals: local atomic environments, their connections, and the global symmetries. We present a contrastive learning of inorganic crystal structure (CLICS) for embedding the geometric concepts, which contrasts texts representing the contextual patterns of geometries with the crystal graphs. We demonstrate that the geometric concepts are integrally embedded on the CLICS feature space, through experiments of concept retrieval from crystal graphs, similar structure search, and few-shot/imbalanced crystal structure classification. Embedding ; Foundation (evidence) ; Representation (politics) ; Crystal (programming language) ; Graph ; Space (punctuation) ; Computer science ; Feature (linguistics) ; Artificial intelligence ; Theoretical computer science ; Linguistics ; Geography Machine Learning in Materials Science ; Topic Modeling ; Natural Language Processing Techniques
Authors: Matthias Scheffler
Source: ChemRxiv (preprint, 2024-02-14)
Field: Materials Science — Materials Chemistry

Roadmap on Data-Centric Materials Science Science is and always has been based on data, but the terms ‘data-centric’ and the ‘4th paradigm’ of materials research indicate a radical change in how information is retrieved, handled and research is performed. It signifies a transformative shift towards managing vast data collections, digital repositories, and innovative data analytics methods. The integration of Artificial Intelligence (AI) and its subset Machine Learning (ML), has become pivotal in addressing all these challenges. This Roadmap on Data-Centric Materials Science explores fundamental concepts and methodologies, illustrating diverse applications in electronic-structure theory, soft matter theory, microstructure research, and experimental techniques like photoemission, atom probe tomography, and electron microscopy. While the roadmap delves into specific areas within the broad interdisciplinary field of materials science, the provided examples elucidate key concepts applicable to a wider range of topics. The discussed instances offer insights into addressing the multifaceted challenges encountered in contemporary materials research. Data science ; Computer science ; Engineering ethics ; Engineering Machine Learning in Materials Science ; Advanced Materials Characterization Techniques
Authors: Henry Kelbrick Pentz | Thomas Warford | Ivan Timokhin | Qian Yang | Anupam Bhattacharya | Artem Mishchenko
Source: arXiv (Cornell University) (preprint, 2024-06-17)
Field: Materials Science — Materials Chemistry

Elf autoencoder: unsupervised exploration of flat-band materials using electronic band structure fingerprints Two-dimensional materials with flat electronic bands are promising for realizing exotic quantum phenomena such as unconventional superconductivity and nontrivial topology, but exploring their vast chemical space remains challenging. Here, we introduce an unsupervised convolutional autoencoder agent (elf) that operates on electronic band structure images and is capable of mapping band features and extracting the latent space representation as a fingerprint, enabling autonomous clustering of materials with common electronic properties beyond traditional chemical paradigms. Unsupervised visualisation of the latent space then helps to uncover hidden chemical trends and identify promising candidates based on similarities to well-studied exemplars. Our framework paves the way for the accelerated discovery of novel flat-band materials with desirable electronic characteristics. It complements high-throughput ab initio methods by rapidly screening candidates and guides further investigations into the mechanisms governing the emergence of flat-band physics. We believe the elf autoencoder will be a valuable tool for the autonomous discovery of previously unexplored flat-band materials, aiding in the unbiased identification of compounds with desirable electronic properties in vast 2D chemical space. Autoencoder ; Artificial intelligence ; Pattern recognition (psychology) ; Computer science ; Remote sensing ; Geology ; Materials science ; Deep learning Material Selection and Properties ; Advanced ceramic materials synthesis
Authors: Ahlem BOUSMID
Source: URBAN ART BIO (article, 2024-10-07)
Field: Materials Science — Biomaterials

COURSE This course booklet is validated by the scientific council of our University Center. It explores in detail the fundamental principles, techniques, and applications in the field of biotechnologies. It serves as an educational resource designed to enhance understanding in various areas impacted by biotechnological advancements. Computer science biodegradable polymer synthesis and properties