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Chemical Software Market Size 2025-2029

The chemical software market size is forecast to increase by USD 561 million at a CAGR of 11.4% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing adoption of Industry 4.0 and big data analytics in the chemical industry. Industry 4.0, also known as the Fourth Industrial Revolution, is transforming the chemical sector through automation, digitalization, and interconnectivity. This technological shift is enabling real-time process optimization, predictive maintenance, and improved supply chain visibility, leading to increased efficiency and productivity. Simultaneously, the market is faced with challenges stemming from stringent norms associated with the use of chemicals. Regulatory compliance is a critical concern for chemical companies, with growing emphasis on environmental sustainability and safety.
Adhering to these regulations requires significant investment in software solutions to manage complex chemical processes, ensure regulatory compliance, and mitigate risks. These challenges present opportunities for chemical software providers to offer innovative solutions that help companies navigate the regulatory landscape while optimizing their operations. Companies that can effectively address these trends and challenges will be well-positioned to capitalize on the growing demand for advanced chemical software solutions.

What will be the Size of the Chemical Software Market during the forecast period?

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The market continues to evolve, driven by the integration of advanced technologies and their applications across various sectors. Chemical databases, cloud computing, process simulation, structure-activity relationships (SAR), molecular dynamics, Data Analytics, predictive modeling, Artificial Intelligence (AI), quantum chemistry, and other solutions are transforming the way the industry operates. Cloud computing enables on-demand access to vast amounts of data, enabling process optimization and environmental impact assessment. Chemical process simulation software utilizes advanced algorithms to model complex systems, enhancing efficiency and reducing costs. SAR and molecular dynamics solutions aid in drug discovery and materials science research. Data analytics and predictive modeling leverage AI and machine learning to uncover hidden patterns and trends, while quantum chemistry provides insights into the molecular behavior of chemicals.
Big data and workflow management tools facilitate process automation and regulatory compliance. Collaboration tools enable seamless communication and data sharing among teams, while scientific visualization and data visualization solutions facilitate the interpretation of complex data. specialty chemicals, chemical manufacturing, and reaction engineering also benefit from these technological advancements. The ongoing unfolding of market activities reveals a dynamic landscape, with continuous innovation and integration of new technologies shaping the future of the market.

How is this Chemical Software Industry segmented?

The chemical software industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Deployment

  On-premises
  Cloud-based


Product

  Chemical process simulation
  Inventory management
  ISO management
  Others


End-User

  Pharmaceuticals
  Chemical Manufacturing
  Academic Research
  Environmental Testing


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  Middle East and Africa

    Egypt
    Oman
    UAE


  APAC

    China
    India
    Japan


  South America

    Argentina
    Brazil


  Rest of World (ROW)

By Deployment Insights

The on-premises segment is estimated to witness significant growth during the forecast period.

The market is characterized by the adoption of advanced technologies such as monte carlo simulations, experimental design, high-throughput screening, collaboration tools, data analysis, machine learning, scientific visualization, materials science, and chemical process simulation. These solutions enable organizations to optimize chemical manufacturing processes, improve product quality, and enhance research and development efforts. Chemical databases and cloud computing facilitate easy access to vast amounts of data for data analytics, predictive modeling, and artificial intelligence applications. Structure-activity relationships (SAR), molecular dynamics, and quantum chemistry help in understanding the behavior of chemicals at a molecular level. Process optimization, environmental impact assessment, and regulatory compliance are essential aspects of the chemical industry.

Computational chemistry, wo

The inherently high computational cost of iterative self-consistent field (SCF) methods proves to be a critical issue delaying visual and haptic feedback in real-time quantum chemistry. In this work, we introduce two schemes for SCF acceleration. They provide a guess for the initial density matrix of the SCF procedure generated by extrapolation techniques. SCF optimizations then converge in fewer iterations, which decreases the execution time of the SCF optimization procedure. To benchmark the proposed propagation schemes, we developed a test bed for performing quantum chemical calculations on sequences of molecular structures mimicking real-time quantum chemical explorations. Explorations of a set of six model reactions employing the semi-empirical methods PM6 and DFTB3 in this testing environment showed that the proposed propagation schemes achieved speedups of up to 30% as a consequence of a reduced number of SCF iterations.

Supplementary data for the article "Quantum state tracking and control of a single molecular ion in a thermal environment" by Yu Liu, Julian Schmidt, Zhimin Liu, David R. Leibrandt, Dietrich Leibfried, Chin-wen Chou, submitted to Science in 2024. The manuscript describes a quantum state-specific investigation of the molecular state evolution of a single CaH+ ion in a thermal environment. The molecular state can be tracked in real time with single quantum-state resolution and the thermal radiation-induced transitions can be reversed with coherent molecular state manipulation according to the outcomes of state measurements. Results on the transition rates are used to infer the properties of the thermal environment. The data may be used to reproduce the plots shown in the figures.

Quantum chemical studies of reactivity involve calculations on a large number of molecular structures and the comparison of their energies. Already the setup of these calculations limits the scope of the results that one will obtain, because several system-specific variables such as the charge and spin need to be set prior to the calculation. For a reliable exploration of reaction mechanisms, a considerable number of calculations with varying global parameters must be taken into account, or important facts about the reactivity of the system under consideration can remain undetected. For example, one could miss crossings of potential energy surfaces for different spin states or might not note that a molecule is prone to oxidation. Here, we introduce the concept of molecular propensity to account for the predisposition of a molecular system to react across different electronic states in certain nuclear configurations or with other reactants present in the reaction liquor. Within our real-time quantum chemistry framework, we developed an algorithm that automatically detects and flags such a propensity of a system under consideration.

The ab initio molecular dynamics (AIMD) method provides a computational route for the real-time simulation of reactive chemistry. An often-overlooked capability of this approach is the opportunity to examine the electronic evolution of a chemical system. For AIMD trajectories based on Hartree–Fock or density functional theory methods, the real-time evolution of single-particle molecular orbitals (MOs) can provide detailed insights into the time-dependent electronic structure of molecules. The evolving electronic Hamiltonians at each MD step pose problems for tracking and visualizing a given MO’s character, ordering, and associated phase throughout an MD trajectory, however. This report presents and assesses a simple algorithm for correcting these deficiencies by exploiting similarity projections of the electronic structure between neighboring MD steps. Two aspects bring this analysis beyond a simple step-to-step projection scheme. First, the challenging case of coincidental orbital degeneracies is resolved via a quadrupole-field perturbation that nonetheless rigorously preserves energy conservation. Second, the resulting orbitals are shown to evolve adiabatically, in spite of the “preservation of character” concept that undergirds a projection of neighboring steps’ MOs. The method is tested on water clusters, which exhibit considerable dynamic degeneracies, as well as a classic organic nucleophilic substitution reaction, in which the adiabatic evolution of the bonding orbitals clarifies textbook interpretations of the electronic structure during this reactive collision.

The Weather Research and Forecasting (WRF) model coupled with Chemistry version 3.9.1 is used to generate a 48-hour air quality forecast daily at 12 km grid spacing over the CONUS. The first 24 forecast files are available here. This WRF-Chem model simulates the emission, transport, mixing, and chemical transformation of trace gases and aerosols simultaneously with the meteorology. These forecasts are used to support air quality decision-making, field campaign planning, early identification of model errors and biases, and support the atmospheric science community in their research. This system aims to complement the operational air quality forecasts produced by the National Oceanic and Atmospheric Administration (NOAA), not to replace them. A publicly available information dissemination system has been established that displays various air quality products, including a near-real-time evaluation of the model forecasts at the WRF Chemistry Forecast Maps [https://www.acom.ucar.edu/firex-aq/forecast.shtml].

SoftwareSoftware solutions for molecular modelling encompass a wide range of tools, including molecular dynamics simulations, quantum chemistry calculations, and docking studies. Key players in this segment include Dassault Systems, Schrodinger LLC, Chemical Computing Group, and Cresset Biomolecular Discovery. These software solutions offer advanced capabilities for simulating and analyzing molecular systems, enabling researchers to study complex biological processes and design new drugs and materials.ServicesServices providers offer expert guidance and support for molecular modelling projects, ranging from data preparation to interpretation of results. Leading service providers include Optibrium, Biosolve-IT, and Simulations Plus Inc. These service providers offer a comprehensive range of services, including molecular modelling consulting, training, and data analysis, helping researchers maximize the value of their molecular modelling studies. Recent developments include: July 2022 Cadence Design Systems purchased OpenEye Scientific Software in July 2022 to capitalize on Cadence's analytical software competencies in molecular modeling and prediction., March 2022 PerkinElmer released ChemDraw V21 technology in March 2022, allowing academics to quickly develop scientifically sophisticated MS PowerPoint presentations with a single click., April 2021 Accelera Ltd released a new edition of its chemical dynamics modeling program, ACEMD, in April 2021.. Key drivers for this market are: Increasing complexity of drug development

Government initiatives supporting research and innovation

Advances in computational power and algorithms

Adoption of AI and quantum computing. Potential restraints include: High computational costs for sophisticated simulations

Limited availability of skilled workforce

Data privacy and ethical concerns

Regulatory compliance challenges. Notable trends are: Convergence of AI and quantum computing

Cloud-based platforms

Machine learning-driven optimization

Predictive analytics for drug discovery

Real-time simulation and visualization.

Optimized structure, energy levels and bond lengths of (CdS)6-CdAc2 by spin-polarized calculations and interatomic distances at time = 0, time = 500 fs and after relaxation, optical absorption, bond orders, atomic net charges and Fukui functions of (CdS)6-XAc2, as well as optimized structure, atomic net charges and DOS of (CdS)13-CdAc2.

Chemical Engineering Software Market Outlook

The global chemical engineering software market size was valued at USD 1.8 billion in 2023 and is projected to reach USD 3.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 7.5% during the forecast period. This remarkable growth can be attributed to the increasing demand for efficient and cost-effective chemical processes, driven by advancements in digital technology and industrial automation.

One of the primary growth factors for the chemical engineering software market is the exponential increase in digital transformation initiatives across various industries. Companies are increasingly adopting sophisticated software solutions to streamline their operations, improve efficiency, and reduce costs. These software solutions enable precise process simulation, equipment design, and process optimization, which are crucial in the highly competitive chemical engineering sector. Additionally, the integration of artificial intelligence and machine learning algorithms into these software solutions further enhances their capability to predict outcomes and optimize processes.

Another significant driver for market growth is the rising focus on safety and compliance. As regulatory frameworks become more stringent globally, companies are compelled to adopt advanced software solutions to ensure they meet safety and compliance standards. This is particularly vital in industries like pharmaceuticals, oil & gas, and chemicals, where non-compliance can lead to severe financial and reputational damage. The ability to run simulations and stress tests through software helps in identifying potential risks and mitigating them proactively.

The increasing complexity of chemical processes and the necessity for innovation also contribute to market growth. With the advent of new materials and processes, there is a growing need for advanced software that can handle complex calculations and provide accurate models. This need is particularly pronounced in research and development departments where innovative solutions are crucial for staying ahead of the competition. The adoption of cloud-based solutions further facilitates the collaboration and sharing of data across different geographies, which is essential for multinational corporations.

The integration of Cloud Infrastructure in Chemical industries is revolutionizing how companies manage their operations and data. By leveraging cloud technology, chemical companies can enhance their computational capabilities, enabling more complex simulations and faster processing times. This shift not only facilitates real-time data access and collaboration across global teams but also significantly reduces the need for costly on-premises infrastructure. As a result, companies can allocate resources more efficiently, focusing on innovation and development rather than maintenance and hardware upgrades. Furthermore, cloud infrastructure supports scalability, allowing businesses to adjust their computational resources according to demand, which is particularly beneficial for handling large-scale projects or sudden increases in workload.

The regional outlook of the market indicates a strong growth potential in the Asia Pacific region, driven by rapid industrialization and urbanization. Countries like China and India are investing heavily in infrastructure and manufacturing, leading to increased demand for chemical engineering software. North America and Europe also show significant growth, driven by technological advancements and stringent regulatory standards. The Middle East & Africa region is expected to witness moderate growth, mainly fueled by the oil & gas sector. Latin America is also emerging as a potential market due to increasing investments in the chemical and petrochemical sectors.

Deployment Mode Analysis

The chemical engineering software market is segmented by deployment mode into on-premises and cloud-based solutions. On-premises deployment has traditionally been the go-to choice for many companies, primarily due to concerns about data security and control. Organizations prefer to have their software installed on their local servers, which allows them to maintain full control over their data. This is especially important for companies dealing with sensitive information and intellectual property.

However, the market is witnessing a significant shift towards cloud-based solutions. The benefits o

Cloud‑Based Chemoinformatics Suites Market Outlook

According to our latest research, the global market size for Cloud-Based Chemoinformatics Suites reached USD 1.27 billion in 2024, reflecting robust adoption across the pharmaceutical, chemical, and research sectors. The market is projected to grow at a CAGR of 12.6% during the forecast period, reaching USD 3.72 billion by 2033. This growth is primarily driven by the increasing demand for scalable, cost-effective, and collaborative data analysis tools in drug discovery, chemical analysis, and predictive modeling. As per our most recent analysis, the proliferation of cloud technologies and the surge in digital transformation initiatives within life sciences and chemical industries are pivotal factors accelerating market expansion.

The growth trajectory of the Cloud-Based Chemoinformatics Suites Market is underpinned by the escalating complexity of chemical and biological data generated in the pharmaceutical and biotechnology sectors. As organizations increasingly focus on personalized medicine and high-throughput screening, the need for advanced informatics solutions to manage, analyze, and interpret vast datasets has become paramount. Cloud-based platforms enable seamless integration of disparate data sources, facilitate real-time collaboration among geographically dispersed teams, and provide scalable computational resources. This is particularly crucial for drug discovery, where the rapid identification and optimization of lead compounds can significantly reduce time-to-market and R&D costs. The flexibility and accessibility offered by cloud-based chemoinformatics suites are thus transforming the operational landscape of research-driven industries.

Another critical growth factor is the rising adoption of artificial intelligence (AI) and machine learning (ML) algorithms within cloud-based chemoinformatics platforms. These technologies enable predictive modeling, virtual screening, and molecular property prediction with unprecedented speed and accuracy. AI-driven chemoinformatics tools are empowering researchers to uncover novel insights, optimize chemical processes, and predict biological activities, thereby enhancing innovation and productivity. Furthermore, cloud infrastructures support continuous software updates, integration of new analytical tools, and real-time data sharing, which are essential for maintaining competitive advantage in fast-paced research environments. The convergence of AI, big data analytics, and cloud computing is expected to further fuel the adoption of chemoinformatics suites across multiple verticals.

Regulatory compliance and data security are also significant drivers for market growth. The pharmaceutical and chemical industries operate in highly regulated environments, where data integrity, traceability, and compliance with standards such as GxP, HIPAA, and GDPR are non-negotiable. Cloud-based chemoinformatics suites are increasingly being designed with robust security protocols, role-based access controls, and audit trails to ensure compliance and safeguard sensitive intellectual property. Additionally, the pay-as-you-go pricing models and reduced IT infrastructure costs associated with cloud deployments are making these solutions attractive to small and medium enterprises (SMEs) and academic institutions, further broadening the market base.

From a regional perspective, North America continues to dominate the Cloud-Based Chemoinformatics Suites Market, owing to its mature pharmaceutical sector, strong presence of leading cloud service providers, and high R&D investments. Europe follows closely, driven by government support for digital transformation in healthcare and chemical industries. Asia Pacific is emerging as a high-growth region, fueled by expanding biotech hubs, increasing research collaborations, and rising awareness about the benefits of cloud-based informatics. However, regional disparities in cloud infrastructure and regulatory frameworks persist, influencing adoption rates and market dynamics across different geographies.

Computational Biology Software Market Outlook

The global computational biology software market size was valued at approximately USD 5.8 billion in 2023 and is projected to reach USD 13.2 billion by 2032, exhibiting a robust CAGR of 9.5% over the forecast period. This impressive growth can be primarily attributed to the increasing integration of computational tools in biological research and the growing demand for personalized medicine, which relies heavily on computational biology for analyzing genetic information. The market is being propelled by advancements in bioinformatics, which enable more efficient data analysis and modeling in fields like genomics and proteomics, leading to breakthroughs in drug discovery and the development of personalized medicine.

The rising prevalence of chronic diseases and the corresponding need for effective treatments are key growth factors driving the computational biology software market. As the global population ages, the incidence of diseases such as cancer, diabetes, and cardiovascular disorders is increasing, necessitating the development of innovative therapeutic approaches. Computational biology software plays a crucial role in drug discovery and development processes, enabling pharmaceutical and biotechnology companies to analyze biological data and simulate molecular interactions, thus facilitating the identification of potential drug targets. Additionally, the advent of next-generation sequencing technologies and the decreasing cost of sequencing have generated vast amounts of biological data, which require sophisticated computational tools for analysis, further fueling market growth.

Another significant growth driver is the expanding scope of personalized medicine, which aims to tailor medical treatments to individual patients based on their genetic makeup. Computational biology software is essential for analyzing genomic, proteomic, and metabolomic data to identify biomarkers and develop personalized treatment plans. This approach not only enhances the efficacy of therapies but also reduces adverse effects, making it a key focus area for healthcare providers and researchers. Moreover, government initiatives and funding for genomic research and precision medicine are creating a conducive environment for the growth of the computational biology software market. Collaboration between academic institutions, research organizations, and industry players is also fostering innovation in the field.

The growing adoption of cloud-based solutions is another factor contributing to the market's expansion. Cloud-based computational biology software offers several advantages over traditional on-premises solutions, including scalability, cost-effectiveness, and ease of access. The ability to store and analyze large datasets in the cloud allows researchers and healthcare providers to collaborate more effectively and facilitates real-time data sharing and analysis. Furthermore, the COVID-19 pandemic has accelerated the shift towards digital solutions in healthcare, with increased reliance on computational tools for research and drug development. As the demand for remote access to computational resources continues to rise, the market for cloud-based computational biology software is expected to witness significant growth.

Chemoinformatics is an emerging field that plays a pivotal role in the advancement of computational biology software. By combining chemical data with computational techniques, chemoinformatics facilitates the analysis and interpretation of complex chemical and biological data. This integration is crucial for drug discovery and development, as it allows researchers to predict the behavior of chemical compounds and their interactions with biological targets. As the demand for more efficient and targeted therapies grows, chemoinformatics is becoming an indispensable tool for pharmaceutical and biotechnology companies, enabling them to streamline the drug development process and reduce time-to-market for new therapeutics.

Regionally, North America holds the largest share in the computational biology software market, driven by the presence of leading pharmaceutical and biotechnology companies, advanced healthcare infrastructure, and significant investment in research and development. The region's strong focus on precision medicine and personalized healthcare further supports market growth. However, the Asia Pacific region is anticipated to exhibit the highest growth rate during the forecast period, fueled by increasing government initiatives to promote biotechnology research, gro

To explore the science behind excited-state dynamics in high-complexity chemical systems, a scalable nonadiabatic molecular dynamics (MD) technique is indispensable. In this study, by treating the electronic degrees of freedom at the density-functional tight-binding level, we developed and implemented a reduced scaling and multinode-parallelizable Ehrenfest MD method. To achieve this goal, we introduced a concept called patchwork approximation (PA), where the effective Hamiltonian for real-time propagation of the electronic density matrix is partitioned into a set of local parts. Numerical results for giant icosahedral fullerenes, which comprise up to 6000 atoms, suggest that the scaling of the present PA-based method is less than quadratic, which yields a significant advantage over the conventional cubic scaling method in terms of computational time. The acceleration by the parallelization on multiple nodes was also assessed. Furthermore, the electronic and structural dynamics resulting from the perturbation by the external electric field were accurately reproduced with the PA, even when the electronic excitation was spatially delocalized.

Density functional tight binding (DFTB) is a quantum chemical simulation method based on an approximate density functional theory (DFT), known for its low computational cost and comparable accuracy to DFT. For several years, the application of DFTB in organic photovoltaics (OPV) has been limited by the absence of an appropriate set of parameters that adequately account for the relevant elements and necessary corrections. Here we have developed new parametrizations using hybrid functionals, including B3LYP and CAM-B3LYP, for OPV applications within the DFTB method in order to overcome the self-interaction error present in DFT functionals lacking long-range correction. These parametrizations encompass electronic and repulsive parameters for the elements H, C, N, O, F, S, and Cl. A Bayesian optimization approach was employed to optimize the free atom eigenenergies of unoccupied shells. The effectiveness of these new parametrizations was evaluated by a data set of 12 OPV donor and acceptor molecules, showing consistent performance when compared with their corresponding DFT references. Frontier molecular orbitals and optimized geometries were examined to evaluate the performance of the new parametrizations in predicting ground-state properties. Furthermore, the excited-state properties of monomers and dimers were investigated by means of real-time time-dependent DFTB (real-time TD-DFTB). The appearance of charge-transfer (CT) excitations in the dimers was observed, and the influence of alkyl side-chains on the photoinduced CT process was explored. This work paves the way for studying ground- and excited-state properties, including band alignments and CT mechanisms at donor–acceptor interfaces, in realistic OPV systems.

IaaS in Chemical Market Outlook

The global Infrastructure as a Service (IaaS) market size in the chemical industry was valued at roughly $4.9 billion in 2023 and is projected to reach approximately $13.6 billion by 2032, exhibiting a compound annual growth rate (CAGR) of 12.1% over the forecast period. This growth is primarily driven by the increasing adoption of digital transformation strategies and the need for scalable and cost-efficient IT infrastructure solutions within the chemical industry.

One of the primary growth factors contributing to the expansion of the IaaS market in the chemical industry is the rising demand for enhanced operational efficiency. As chemical companies face increasing global competition, there is a mounting need to optimize supply chains, improve production management, and enhance quality control processes. IaaS offers the flexibility and scalability required to support these improvements, enabling businesses to adapt quickly to changing market conditions and regulatory requirements. Furthermore, the growing adoption of advanced technologies, such as artificial intelligence (AI) and the Internet of Things (IoT), necessitates robust cloud-based infrastructure, further fueling the demand for IaaS solutions.

Another significant factor driving the market's growth is the increasing reliance on data-driven decision-making. In the chemical industry, vast amounts of data are generated daily, ranging from raw material procurement to end-product distribution. IaaS provides the computational power and storage capacity to handle and analyze this data effectively. By leveraging cloud-based infrastructure, chemical companies can gain valuable insights into their operations, identify inefficiencies, and make informed decisions that enhance productivity and profitability. This growing emphasis on data analytics and real-time monitoring is expected to continue propelling the adoption of IaaS in the chemical sector.

The shift towards sustainability and environmental compliance is also a key driver for the IaaS market in the chemical industry. With increasing regulatory pressure to reduce carbon footprints and minimize waste, chemical companies are seeking innovative solutions to achieve their sustainability goals. IaaS enables the integration of advanced monitoring and control systems that can track emissions, energy consumption, and waste management processes in real-time. This real-time data facilitates proactive measures to enhance sustainability efforts and ensure compliance with ever-evolving environmental regulations. As the focus on sustainability intensifies, the demand for IaaS solutions in the chemical industry is expected to rise accordingly.

From a regional perspective, North America and Europe currently dominate the IaaS market in the chemical industry. These regions are characterized by the presence of established chemical companies with substantial IT budgets and a strong inclination towards digital transformation. Additionally, the advanced technological infrastructure and regulatory frameworks in these regions support the adoption of IaaS solutions. However, Asia Pacific is anticipated to exhibit the highest growth rate during the forecast period. Rapid industrialization, expanding chemical production capacities, and increasing investments in digital infrastructure are driving the adoption of IaaS in countries like China, India, and Japan. Latin America and the Middle East & Africa are also expected to witness significant growth, albeit at a slightly slower pace, as chemical companies in these regions gradually embrace cloud-based infrastructure solutions.

Deployment Type Analysis

The IaaS market in the chemical industry can be segmented by deployment type into public cloud, private cloud, and hybrid cloud. Public cloud deployment is projected to hold a significant share of the market due to its cost-effectiveness and ease of scalability. Public cloud services allow chemical companies to access a vast pool of computing resources over the internet, reducing the need for substantial capital investment in IT infrastructure. This model is particularly attractive for small and medium enterprises (SMEs) that may lack the financial resources to invest in extensive on-premises hardware. Moreover, public cloud providers often offer robust security measures, ensuring data protection and compliance with industry standards.

Private cloud deployment, on the other hand, provides enhanced control and customization options, making it a preferred choice for large enterprises with specific security and compliance requirements. In the chemical i

Computational Fluid Dynamics (CFD) simulations for combustion do not currently have the predictive capability typically found for non-reacting flows due to the prohibitively high computational cost incurred when one introduces detailed chemical kinetics. In this SBIR project, we propose a novel method, Adaptive Chemistry, to enable such detailed modeling. This method adapts the reaction mechanism used in CFD to local reaction conditions. Instead of a single comprehensive reaction mechanism throughout the computation, smaller, locally valid reduced models are used to accurately capture the chemical kinetics at a smaller cost. Our Adaptive Chemistry approach seeks to obtain a reduced model guaranteed to be valid within the variable range for each grid point, and controls errors rigorously without evaluating the very expensive full model. Adaptive Chemistry also dynamically constructs a reduced model library based on real-time reaction conditions to prevent large memory overhead for arbitrary solution trajectories. This also allows Adaptive Chemistry to be easily extendable to transient problems. Finally, Adaptive Chemistry allows users to set a constraint on the largest model size by using a skeletal model, but selects each reduced model based on the full, detailed chemistry, which obtains a guaranteed optimal solution more efficiently compared to the traditional skeletal model methods.

In this project, we will develop an error-controlled reduced-species Adaptive Chemistry software package that can be easily interfaced with any CFD solver. The first objective of this work is to continue developing needed methods for error-controlled reduced-species Adaptive Chemistry for steady-state reacting flow simulations. The second objective is to implement the available methods into a modular package that can be easily interfaced with any CFD solver. We will also develop an Adaptive Chemistry module that can be coupled with the PREMIX program for commercialization.

The development of robust odor navigation strategies for automated environmental monitoring applications requires realistic simulations of odor time series for agents moving across large spatial scales. Traditional approaches that rely on computational fluid dynamics (CFD) methods can capture the spatiotemporal dynamics of odor plumes, but are impractical for large-scale simulations due to their computational expense. On the other hand, puff-based simulations, although computationally tractable for large scales and capable of capturing the stochastic nature of plumes, fail to reproduce naturalistic odor statistics. Here, we present COSMOS (Configurable Odor Simulation Model over Scalable Spaces), a data-driven probabilistic framework that synthesizes realistic odor time series from spatial and temporal features of real datasets. COSMOS generates similar distributions of key statistical features such as whiff frequency, duration, and concentration as observed in real data, while dramatic..., , # COSMOS: A Data-Driven Probabilistic Time Series Simulator for Chemical Plumes Across Spatial Scales

The development of robust odor navigation strategies for automated environmental monitoring applications requires realistic simulations of odor time series for agents moving across large spatial scales. Traditional approaches that rely on computational fluid dynamics (CFD) methods can capture the spatiotemporal dynamics of odor plumes, but are impractical for large-scale simulations due to their computational expense. On the other hand, puff-based simulations, although computationally tractable for large scales and capable of capturing the stochastic nature of plumes, fail to reproduce naturalistic odor statistics. Here, we present COSMOS (Configurable Odor Simulation Model over Scalable Spaces), a data-driven probabilistic framework that synthesizes realistic odor time series from spatial and temporal features of real datasets. COSMOS generates similar distributions of key statistical...,

Automotive Quantum Computing Market Outlook

The global automotive quantum computing market size is projected to grow from USD 145 million in 2023 to USD 2.85 billion by 2032, exhibiting a compound annual growth rate (CAGR) of 40.2% over the forecast period. This exponential growth can be attributed to the rising need for advanced computational technologies in the automotive industry, driven by the increasing complexity of vehicular systems and the push towards autonomous driving.

One of the primary growth factors for the automotive quantum computing market is the burgeoning demand for autonomous and connected vehicles. As the automotive industry gears up for a future dominated by self-driving cars, the computational power required to process immense amounts of data in real-time is becoming critical. Quantum computing offers the ability to solve complex algorithms and optimize routes, traffic management, and decision-making processes much faster than classical computers, thereby significantly enhancing the functionality and safety of autonomous vehicles.

Another significant growth driver is the automotive industry's shift towards electric vehicles (EVs). As the world moves away from fossil fuels, EVs are becoming more prominent, prompting the need for sophisticated battery management systems, energy optimization, and materials science research. Quantum computing can provide unprecedented insights into battery chemistry, leading to the development of more efficient and longer-lasting batteries. Additionally, it can optimize manufacturing processes and supply chains, reducing costs and improving sustainability in EV production.

Moreover, the integration of quantum computing in cybersecurity is playing a crucial role in market expansion. With connected vehicles becoming more common, the risk of cyberattacks has increased. Quantum computing can enhance cybersecurity measures by developing advanced encryption techniques that are virtually impenetrable, safeguarding vehicle systems and user data from potential threats. This capability is particularly essential as vehicles become more connected to the internet and other external networks.

Regionally, North America is expected to dominate the automotive quantum computing market during the forecast period. The presence of leading automotive manufacturers and technology companies, coupled with substantial investments in quantum computing research, is propelling the market in this region. Furthermore, favorable government policies and initiatives supporting the development of advanced automotive technologies are also contributing to market growth. Europe and Asia Pacific are also poised for significant growth, driven by the strong automotive sector and increasing research and development activities in quantum technologies.

Component Analysis

Hardware Analysis

The hardware segment of the automotive quantum computing market includes quantum processors, qubits, and ancillary devices required for quantum computations. Quantum processors, which are the core of any quantum computer, are being developed rapidly, with significant advancements in qubit technology. Companies like IBM, Google, and D-Wave are at the forefront of developing quantum processors that can handle complex automotive applications. The demand for high-performance quantum processors is expected to soar as automotive manufacturers seek to leverage quantum computing for various applications, including simulation and optimization.

Qubits, the fundamental units of quantum information, are critical to the hardware segment. Advances in qubit technology, including superconducting qubits, trapped ions, and topological qubits, are driving the market forward. Each qubit type offers unique advantages and challenges, and the ongoing research is focused on improving qubit coherence, error rates, and scalability. The automotive industry is particularly interested in scalable quantum processors that can handle large-scale computations required for autonomous driving and other advanced applications.

Ancillary devices such as quantum memory, error correction units, and quantum interconnects are also essential components of the hardware segment. These devices ensure the efficient functioning of quantum computers by providing reliable storage, error correction, and communication between qubits. The development of robust ancillary devices is crucial for the practical implementation of quantum computing in the automotive sector, as they help maintain the integrity and accuracy of quantum computa

China Import Quantum Index: MoM: SITC3: Live Animals Other than Animals of Division 03 data was reported at 28.700 Average 12 Mths PY=100 in Feb 2025. This records a decrease from the previous number of 77.100 Average 12 Mths PY=100 for Jan 2025. China Import Quantum Index: MoM: SITC3: Live Animals Other than Animals of Division 03 data is updated monthly, averaging 88.900 Average 12 Mths PY=100 from Jan 2018 (Median) to Feb 2025, with 85 observations. The data reached an all-time high of 217.100 Average 12 Mths PY=100 in Mar 2020 and a record low of 22.027 Average 12 Mths PY=100 in Apr 2022. China Import Quantum Index: MoM: SITC3: Live Animals Other than Animals of Division 03 data remains active status in CEIC and is reported by General Administration of Customs. The data is categorized under China Premium Database’s International Trade – Table CN.JE: Quantum Index: MoM: SITC3 Classification.

China Import Quantum Index: MoM: HS2: Live Animals (LA): Animal Products (AP) data was reported at 98.800 Average 12 Mths PY=100 in Mar 2025. This records an increase from the previous number of 89.600 Average 12 Mths PY=100 for Feb 2025. China Import Quantum Index: MoM: HS2: Live Animals (LA): Animal Products (AP) data is updated monthly, averaging 104.750 Average 12 Mths PY=100 from Jan 2018 (Median) to Mar 2025, with 86 observations. The data reached an all-time high of 186.200 Average 12 Mths PY=100 in Dec 2019 and a record low of 70.283 Average 12 Mths PY=100 in Mar 2018. China Import Quantum Index: MoM: HS2: Live Animals (LA): Animal Products (AP) data remains active status in CEIC and is reported by General Administration of Customs. The data is categorized under China Premium Database’s International Trade – Table CN.JE: Quantum Index: MoM: HS2 Classification.

Least-squares tensor hypercontraction (LS-THC) has received some attention in recent years as an approach to reduce the significant computational costs of wave function-based methods in quantum chemistry. However, previous work has demonstrated that LS-THC factorization performs disproportionately worse in the description of wave function components (e.g., cluster amplitudes T̂2) than Hamiltonian components (e.g., electron repulsion integrals (pq|rs)). This work develops novel theoretical methods to study the source of these errors in the context of the real-space T̂2 kernel, and reports, for the first time, the existence of a “correlation feature” in the errors of the LS-THC representation of the “exchange-like” correlation energy EX and T̂2 that is remarkably consistent across ten molecular species, three correlated wave functions, and four basis sets. This correlation feature portends the existence of a “pair point kernel” missing in the usual LS-THC representation of the wave function, which critically depends upon pairs of grid points situated close to atoms and with interpair distances between one and two Bohr radii. These findings point the way for future LS-THC developments to address these shortcomings.