jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sathya R. Chitturi | Akash Ramdas | Yue Wu | Brian A. Rohr | Stefano Ermon | Jennifer A. Dionne | Felipe H. da Jornada | Mike Dunne | Christopher J. Tassone | Willie Neiswanger | Daniel Ratner
Source: arXiv (Cornell University) (preprint, 2023-12-26)
Field: Materials Science — Materials Chemistry

Targeted materials discovery using Bayesian algorithm execution Rapid discovery and synthesis of new materials requires intelligent data acquisition strategies to navigate large design spaces. A popular strategy is Bayesian optimization, which aims to find candidates that maximize material properties; however, materials design often requires finding specific subsets of the design space which meet more complex or specialized goals. We present a framework that captures experimental goals through straightforward user-defined filtering algorithms. These algorithms are automatically translated into one of three intelligent, parameter-free, sequential data acquisition strategies (SwitchBAX, InfoBAX, and MeanBAX). Our framework is tailored for typical discrete search spaces involving multiple measured physical properties and short time-horizon decision making. We evaluate this approach on datasets for TiO$_2$ nanoparticle synthesis and magnetic materials characterization, and show that our methods are significantly more efficient than state-of-the-art approaches. Computer science ; Bayesian optimization ; Bayesian probability ; Characterization (materials science) ; Algorithm ; Machine learning ; Data mining ; Artificial intelligence Machine Learning in Materials Science ; Geochemistry and Geologic Mapping
Authors: Ji Wei Yoon | Adithya Kumar | Pawan Kumar | Kedar Hippalgaonkar | J. Senthilnath | Vijila Chellappan
Source: arXiv (Cornell University) (preprint, 2023-08-08)
Field: Materials Science — Materials Chemistry

Explainable machine learning to enable high-throughput electrical conductivity optimization and discovery of doped conjugated polymers The combination of high-throughput experimentation techniques and machine learning (ML) has recently ushered in a new era of accelerated material discovery, enabling the identification of materials with cutting-edge properties. However, the measurement of certain physical quantities remains challenging to automate. Specifically, meticulous process control, experimentation and laborious measurements are required to achieve optimal electrical conductivity in doped polymer materials. We propose a ML approach, which relies on readily measured absorbance spectra, to accelerate the workflow associated with measuring electrical conductivity. The classification model accurately classifies samples with a conductivity &gt; 25 to 100 S/cm, achieving a maximum of 100 % accuracy rate. For the subset of highly conductive samples, we employed a regression model to predict their conductivities, yielding an impressive test R2 value of 0.984. We tested the models with samples of the two highest conductivities (498 and 506 S/cm) and showed that they were able to correctly classify and predict the two extrapolative conductivities at satisfactory levels of errors. The proposed ML-assisted workflow results in an improvement in the efficiency of the conductivity measurements by 89 % of the maximum achievable using our experimental techniques. Furthermore, our approach addressed the common challenge of the lack of explainability in ML models by exploiting bespoke mathematical properties of the descriptors and ML model, allowing us to gain corroborated insights into the spectral influences on conductivity. Through this study, we offer an accelerated pathway for optimizing the properties of doped polymer materials while showcasing the valuable insights that can be derived from purposeful utilization of ML in experimental science. Workflow ; Conductivity ; Computer science ; Throughput ; Artificial intelligence ; Machine learning ; Doping ; Biological system ; Materials science ; Absorbance ; Process (computing) ; Process engineering ; Optoelectronics ; Physics ; Optics ; Engineering ; Database Machine Learning in Materials Science ; Explainable Artificial Intelligence (XAI) ; Adversarial Robustness in Machine Learning
Authors: Sumner B. Harris | Arpan Biswas | Seok Joon Yun | Christopher M. Rouleau | Alexander A. Puretzky | Rama K. Vasudevan | David B. Geohegan | Kai Xiao
Source: arXiv (Cornell University) (preprint, 2023-08-16)
Field: Materials Science — Materials Chemistry

Autonomous synthesis of thin film materials with pulsed laser deposition enabled by in situ spectroscopy and automation Synthesis of thin films has traditionally relied upon slow, sequential processes carried out with substantial human intervention, frequently utilizing a mix of experience and serendipity to optimize material structure and properties. With recent advances in autonomous systems which combine synthesis, characterization, and decision making with artificial intelligence (AI), large parameter spaces can be explored autonomously at rates beyond what is possible by human experimentalists, greatly accelerating discovery, optimization, and understanding in materials synthesis which directly address the grand challenges in synthesis science. Here, we demonstrate autonomous synthesis of a contemporary 2D material by combining the highly versatile pulsed laser deposition (PLD) technique with automation and machine learning (ML). We incorporated in situ and real-time spectroscopy, a high-throughput methodology, and cloud connectivity to enable autonomous synthesis workflows with PLD. Ultrathin WSe2 films were grown using co-ablation of two targets and showed a 10x increase in throughput over traditional PLD workflows. Gaussian process regression and Bayesian optimization were used with in situ Raman spectroscopy to autonomously discover two distinct growth windows and the process-property relationship after sampling only 0.25% of a large 4D parameter space. Any material that can be grown with PLD could be autonomously synthesized with our platform and workflows, enabling accelerated discovery and optimization of a vast number of materials. Bayesian optimization ; Workflow ; Characterization (materials science) ; Computer science ; Materials science ; Automation ; Nanotechnology ; Throughput ; Thin film ; Artificial intelligence ; Deposition (geology) ; Laser ablation ; Laser ; Mechanical engineering ; Engineering ; Physics ; Wireless ; Optics Machine Learning in Materials Science ; 2D Materials and Applications ; Electronic and Structural Properties of Oxides
Authors: Seonghwan Kim | Charles M. Schroeder | Nicholas E. Jackson
Source: ChemRxiv (preprint, 2023-01-24)
Field: Materials Science — Materials Chemistry

Open Macromolecular Genome: Generative Design of Synthetically Accessible Polymers A grand challenge in polymer science lies in the predictive design of new polymeric materials with targeted functionality. However, de novo design of functional polymers is challenging due to the vast chemical space and an incomplete understanding of structure-property relations. Recent advances in deep generative modeling have facilitated the efficient exploration of molecular design space, but data sparsity in polymer science is a major obstacle hindering progress. In this work, we introduce a vast polymer database known as the Open Macromolecular Genome (OMG), which contains synthesizable polymer chemistries compatible with known polymerization reactions and commercially available reactants selected for synthetic feasibility. The OMG is used in concert with a synthetically aware generative model known as Molecule Chef to identify property-optimized constitutional repeating units, constituent reactants, and reaction pathways of polymers, thereby advancing polymer design into the realm of synthetic relevance. As a proof-of-principle demonstration, we show that polymers with targeted octanol-water solubilities are readily generated together with monomer reactant building blocks and associated polymerization reactions. Suggested reactants are further integrated with Reaxys polymerization data to provide hypothetical reaction conditions (e.g. temperature, catalysts, solvents). Broadly, the OMG is a polymer design approach capable of enabling data intensive generative models for synthetic polymer design. Overall, this work represents a significant advance enabling the property targeted design of synthetic polymers subject to practical synthetic constraints. Polymer ; Polymerization ; Chemical space ; Computer science ; Synthetic biology ; Nanotechnology ; Materials science ; Biochemical engineering ; Chemistry ; Organic chemistry ; Bioinformatics ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Yifan Wu | Aron Walsh | Alex M. Ganose
Source: ChemRxiv (preprint, 2023-12-05)
Field: Materials Science — Materials Chemistry

Race to the bottom: Bayesian optimisation for chemical problems What is the minimum number of experiments, or calculations, required to find an optimal solution? Relevant chemical problems range from identifying a compound with target functionality within a given phase space to controlling materials synthesis and device fabrication conditions. A common feature in this application domain is that both the dimensionality of the problems and the cost of evaluations are high. The selection of an appropriate optimisation technique is key, with standard choices including iterative (e.g. steepest descent) and heuristic (e.g. simulated annealing) approaches, which are complemented by a new generation of statistical machine learning methods. Here we pay attention to progress in Bayesian optimisation. We highlight recent success cases and discuss the challenges of using machine learning with automated research workflows that produce small and noisy data sets. Finally, we outline opportunities for developments in hybrid algorithms for robust and efficient searches. Computer science ; Bayesian optimization ; Curse of dimensionality ; Simulated annealing ; Workflow ; Bayesian probability ; Machine learning ; Heuristic ; Artificial intelligence ; Domain (mathematical analysis) ; Key (lock) ; Feature selection ; Data mining ; Mathematics Machine Learning in Materials Science ; Advanced Multi-Objective Optimization Algorithms ; Machine Learning and Algorithms
Authors: Peichen Zhong | Bowen Deng | Tanjin He | Zhengyan Lun | Gerbrand Ceder
Source: arXiv (Cornell University) (preprint, 2023-04-11)
Field: Materials Science — Materials Chemistry

Deep learning of experimental electrochemistry for battery cathodes across diverse compositions Artificial intelligence (AI) has emerged as a tool for discovering and optimizing novel battery materials. However, the adoption of AI in battery cathode representation and discovery is still limited due to the complexity of optimizing multiple performance properties and the scarcity of high-fidelity data. In this study, we present a machine-learning model (DRXNet) for battery informatics and demonstrate the application in the discovery and optimization of disordered rocksalt (DRX) cathode materials. We have compiled the electrochemistry data of DRX cathodes over the past five years, resulting in a dataset of more than 19,000 discharge voltage profiles on diverse chemistries spanning 14 different metal species. Learning from this extensive dataset, our DRXNet model can automatically capture critical features in the cycling curves of DRX cathodes under various conditions. Illustratively, the model gives rational predictions of the discharge capacity for diverse compositions in the Li--Mn--O--F chemical space as well as for high-entropy systems. As a universal model trained on diverse chemistries, our approach offers a data-driven solution to facilitate the rapid identification of novel cathode materials, accelerating the development of next-generation batteries for carbon neutralization. Cathode ; Battery (electricity) ; Computer science ; Fidelity ; High fidelity ; Electrochemistry ; Artificial intelligence ; Materials science ; Nanotechnology ; Machine learning ; Electrical engineering ; Engineering ; Chemistry ; Electrode ; Physics Machine Learning in Materials Science ; Advancements in Battery Materials ; Electron and X-Ray Spectroscopy Techniques
Authors: Alexander E. Siemenn | Eunice Aissi | Sheng Fang | Armi Tiihonen | Hamide Kavak | Basita Das | Tonio Buonassisi
Source: Research Square (preprint, 2023-10-31)
Field: Materials Science — Materials Chemistry

Autocharacterization: Automated and Scalable Semiconductor Property Estimation from High-throughput Experiments using Computer Vision <title>Abstract</title> High-throughput materials synthesis methods have risen in popularity due to their potential to accelerate the design and discovery of novel functional materials, such as solution-processed semiconductors. After synthesis, key material properties must be measured and characterized to validate discovery and provide feedback to optimization cycles. However, with the boom in development of high-throughput synthesis tools that champion production rates up to 10<sup>4</sup> samples per hour with flexible form factors, most sample characterization methods are either slow (conventional rates of 10<sup>1</sup> samples per hour, approximately 1000x slower) or rigid (<italic>e.g.</italic>, designed for standard-size microplates), resulting in a bottleneck that impedes the materials-design process. To overcome this challenge, we propose a set of automated material property characterization (autocharacterization) tools that leverage the adaptive, parallelizable, and scalable nature of computer vision to accelerate the throughput of characterization by 85x compared to the non-automated workflow. We demonstrate a generalizable composition mapping tool for high-throughput synthesized binary material systems as well as two scalable autocharacterization algorithms that (1) autonomously compute the band gap of 200 unique compositions in 6 minutes and (2) autonomously compute the degree of degradation in 200 unique compositions in 20 minutes, generating ultra-high compositional resolution trends of band gap and stability. We demonstrate that the developed band gap and degradation detection autocharacterization methods achieve 98.5% accuracy and 96.9% accuracy, respectively, on the FA<sub>1-x</sub>MA<sub>x</sub>PbI<sub>3</sub>, 0 ≤ x ≤ 1 perovskite semiconductor system. Bottleneck ; Scalability ; Throughput ; Computer science ; Workflow ; Nanomanufacturing ; Leverage (statistics) ; Parallelizable manifold ; Algorithm ; Artificial intelligence ; Materials science ; Nanotechnology ; Embedded system Machine Learning in Materials Science ; Quantum Dots Synthesis And Properties ; Advanced Photocatalysis Techniques
Authors: Xing Han | Zihui Zhou | Kaiyu Wang | Zhiling Zheng | Silvio Neumann | Tianqiong Ma | Omar M. Yaghi
Source: ChemRxiv (preprint, 2023-11-06)
Field: Materials Science — Materials Chemistry

Crystalline Polyphenylene Covalent Organic Frameworks The synthesis of crystalline polyphenylene covalent organic frameworks (COFs) was accomplished by linking fluorinated tris(4-acetylphenyl)benzene building units using aldol cyclotrimerization. The structures of two COFs, reported here, were confirmed by powder X-ray diffraction techniques, Fourier-transform infrared, and solid-state 13C CP/MAS NMR spectroscopy. The results showed that the COFs were porous and chemically stable in corrosive, harsh environments for at least 1 week. Accordingly, post-synthetically modified derivatives of these COFs using primary amines showed CO2 uptake from air and flue gas. Covalent bond ; Benzene ; Fourier transform infrared spectroscopy ; Materials science ; Covalent organic framework ; Porosity ; Solid-state ; Chemical engineering ; Infrared spectroscopy ; Polymer chemistry ; Organic chemistry ; Chemistry ; Physical chemistry ; Composite material Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies
Authors: Amin Kazemi | Mohammad Zargartalebi | David Sinton
Source: Energy & Environmental Science (article, 2023-12-20)
Field: Chemistry — Physical and Theoretical Chemistry

Differential microthermometry enables high-throughput calorimetry Thermal balance between the sample and reference channels (Δ T = 0) enables rapid and accurate specific heat capacity measurement. Throughput ; Differential scanning calorimetry ; Sample (material) ; Calorimetry ; Environmental science ; Materials science ; Chemistry ; Computer science ; Thermodynamics ; Physics ; Chromatography ; Telecommunications thermodynamics and calorimetric analyses ; Thermal properties of materials ; Chemical Thermodynamics and Molecular Structure
Authors: Vinod G. Landge | Ankita Mishra | Waruna Thotamune | Audrey L. Bonds | Indunil Alahakoon | Ajith Karunarathne | Michael C. Young
Source: ChemRxiv (preprint, 2023-07-26)
Field: Chemistry — Organic Chemistry

Selective C–H Activation of Unprotected Allylamines by Control of Catalyst Speciation An outstanding challenge in the Pd-catalyzed functionalization of allylamines is the control of stereochemistry. Terminal alkenes preferentially undergo Heck-type reactions, while internal alkenes may undergo a mixture of Heck and C–H activation reactions that give mixtures of stereochemical products. In the case of unprotected allylamines, the challenge in achieving C–H activation is that facile in situ formation of Pd nanoparticles leads to preferential formation of trans rather than cis-substituted products. In this study we have demonstrated the feasibility of using mono-protected amino acid (MPAA) ligands as metal protecting groups to prevent aggregation and reduction, allowing the selective synthesis of free cis-arylated allylamines. This method compliments Heck-selective methods, allowing complete stereochemical control over the synthesis of cinnamylamines, an important class of amine that can serve as therapeutics directly or as advanced intermediates. To highlight the utility of the methodology, we have demonstrated rapid access to mu opioid receptor ligands. Chemistry ; Heck reaction ; Catalysis ; Amine gas treating ; Combinatorial chemistry ; Palladium ; Stereochemistry ; Organic chemistry Catalytic C–H Functionalization Methods ; Chemical Synthesis and Analysis ; Catalytic Cross-Coupling Reactions
Authors: Shehtab Zaman | Denis Akhiyarov | Mauricio Araya‐Polo | Kenneth Chiu
Source: arXiv (Cornell University) (preprint, 2023-11-06)
Field: Materials Science — Materials Chemistry

STRIDE: Structure-guided Generation for Inverse Design of Molecules Machine learning and especially deep learning has had an increasing impact on molecule and materials design. In particular, given the growing access to an abundance of high-quality small molecule data for generative modeling for drug design, results for drug discovery have been promising. However, for many important classes of materials such as catalysts, antioxidants, and metal-organic frameworks, such large datasets are not available. Such families of molecules with limited samples and structural similarities are especially prevalent for industrial applications. As is well-known, retraining and even fine-tuning are challenging on such small datasets. Novel, practically applicable molecules are most often derivatives of well-known molecules, suggesting approaches to addressing data scarcity. To address this problem, we introduce $\textbf{STRIDE}$, a generative molecule workflow that generates novel molecules with an unconditional generative model guided by known molecules without any retraining. We generate molecules outside of the training data from a highly specialized set of antioxidant molecules. Our generated molecules have on average 21.7% lower synthetic accessibility scores and also reduce ionization potential by 5.9% of generated molecules via guiding. Set (abstract data type) ; Molecule ; Computer science ; Pruning ; Small molecule ; Generative grammar ; Hypervalent molecule ; Generative model ; Drug discovery ; Workflow ; Artificial intelligence ; Chemistry ; Database Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Mohammad H. Samha | Lucas J. Karas | David B. Vogt | Emmanuel Odogwu | Jennifer M. Elward | Jennifer M. Crawford | Janelle E. Steves | Matthew S. Sigman
Source: ChemRxiv (preprint, 2023-10-24)
Field: Materials Science — Materials Chemistry

Predicting Success in Cu-Catalyzed C–N Coupling Reactions using Data Science Data science is assuming a pivotal role in guiding reaction optimization and streamlining experimental workloads in the evolving landscape of synthetic chemistry. A discipline-wide goal is the development of workflows that integrate computational chemistry and data science tools with high-throughput experimentation as it provides experimentalists the ability to maximize success in expensive synthetic campaigns. Herein, we report an end-to-end data driven process to effectively predict how structural features of coupling partners and ligands impact Cu-catalyzed C–N coupling reactions. The established workflow underscores the limitations posed by substrates and ligands, while also providing a systematic ligand prediction tool that uses probability to assess when a ligand will be successful. This platform is strategically designed to confront the intrinsic unpredictability frequently encountered in synthetic reaction deployment. Workflow ; Software deployment ; Computer science ; Coupling (piping) ; Ligand (biochemistry) ; Throughput ; Process (computing) ; Chemistry ; Catalysis ; Biochemical engineering ; Combinatorial chemistry ; Data science ; Nanotechnology ; Software engineering ; Engineering ; Materials science ; Database ; Organic chemistry ; Programming language Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Asymmetric Hydrogenation and Catalysis
Authors: Daniel Wigh | Matthieu Tissot | Patrick Pasau | Jonathan M. Goodman | Alexei A. Lapkin
Source: The Journal of Physical Chemistry A (article, 2023-03-14)
Field: Chemistry — Inorganic Chemistry

Quantitative <i>In Silico</i> Prediction of the Rate of Protodeboronation by a Mechanistic Density Functional Theory-Aided Algorithm Computational reaction prediction has become a ubiquitous task in chemistry due to the potential value accurate predictions can bring to chemists. Boronic acids are widely used in industry; however, understanding how to avoid the protodeboronation side reaction remains a challenge. We have developed an algorithm for <i>in silico</i> prediction of the rate of protodeboronation of boronic acids. A general mechanistic model devised through kinetic studies of protodeboronation was found in the literature and forms the foundation on which the algorithm presented in this work is built. Protodeboronation proceeds through 7 distinct pathways, though for any particular boronic acid, only a subset of mechanistic pathways are active. The rate of each active mechanistic pathway is linearly correlated with its characteristic energy difference, which in turn can be determined using Density Functional Theory. We validated the algorithm using leave-one-out cross-validation on a data set of 50 boronic acids and made a further 50 rate predictions on academically and industrially important boronic acids out of sample. We believe this work will provide great assistance to chemists performing reactions that feature boronic acids, such as Suzuki-Miyaura and Chan-Evans-Lam couplings. Boronic acid ; In silico ; Computer science ; Set (abstract data type) ; Work (physics) ; Chemistry ; Biochemical engineering ; Algorithm ; Combinatorial chemistry Asymmetric Hydrogenation and Catalysis ; Catalytic Cross-Coupling Reactions ; Organoboron and organosilicon chemistry