tc-python toolkit (Thermo Fisher)
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tc-python toolkit
Tc Python Toolkit, supplied by Thermo Fisher, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/python+toolkits/tc+python/10__1038_slash_s44334___025___00028___x-198-29-32
Average 90 stars, based on 1 article reviews
Tc Python Toolkit, supplied by Thermo Fisher, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/python+toolkits/tc+python/10__1038_slash_s44334___025___00028___x-198-29-32
Average 90 stars, based on 1 article reviews
tc-python toolkit - by Bioz Stars,
2026-10
90/100 stars
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Software:Article Title: Triplex steel powder design to avoid hot cracking in laser-powder bed fusion using computational thermodynamics Article Snippet: Thermodynamic calculations were carried out using the TC-Python module of the Article Title: Insights on phase formation from thermodynamic calculations and machine learning of 2436 experimentally measured high entropy alloys Article Snippet: Both CALPHAD (CALculation of PHAse Diagrams) and machine learning (ML) approaches were employed to analyze the phase formation in 2436 experimentally measured high entropy alloy (HEA) compositions consisting of various quinary mixtures of Al, Co, Cr, Cu, Fe, Mn, and Ni.. CALPHAD was found to have good capabilities in predicting the BCC/B2 and FCC phase formation for the 1761 solid-solution-only compositions, excluding HEAs containing an amorphous phase (AM) or/and intermetallic compound (IM).. Phase selection rules were examined systematically using several parameters and it was revealed that valence electron concentration (VEC) < 6.87 and VEC > 9.16 are the conditions for the formation of single-phase BCC/B2 and FCC, respectively; and CALPHAD could predict this with essentially 100% accuracy. Article Title: Rapid data acquisition and machine learning-assisted composition design of functionally graded alloys via wire arc additive manufacturing Article Snippet: The phase fractions, thermal expansion coefficient (TEC), heat capacity (Cp), freezing range (difference between solidus and liquidus temperatures), and latent heat at the solidus temperature were calculated using the Article Title: Revisit the VEC rule in high entropy alloys (HEAs) with high-throughput CALPHAD approach and its applications for material design-A case study with Al–Co–Cr–Fe–Ni system Article Snippet: In this work, Article Title: Uncertainty quantification and composition optimization for alloy additive manufacturing through a CALPHAD-based ICME framework Article Snippet: ICME model framework All the models were implemented using the Article Title: Revisit the VEC rule in high entropy alloys (HEAs) with high-throughput CALPHAD approach and its applications for material design-A case study with Al–Co–Cr–Fe–Ni system Article Snippet: TC-Python is a PythonTM language-based SDK available with Thermo-Calc giving Article Title: Computational approach to grain boundary segregation engineering of nickel-base superalloys. Article Snippet: TC-Python was employed to couple the High Throughput Screening Assay:Article Title: Triplex steel powder design to avoid hot cracking in laser-powder bed fusion using computational thermodynamics Article Snippet: Thermodynamic calculations were carried out using the TC-Python module of the Article Title: Insights on phase formation from thermodynamic calculations and machine learning of 2436 experimentally measured high entropy alloys Article Snippet: Both CALPHAD (CALculation of PHAse Diagrams) and machine learning (ML) approaches were employed to analyze the phase formation in 2436 experimentally measured high entropy alloy (HEA) compositions consisting of various quinary mixtures of Al, Co, Cr, Cu, Fe, Mn, and Ni.. CALPHAD was found to have good capabilities in predicting the BCC/B2 and FCC phase formation for the 1761 solid-solution-only compositions, excluding HEAs containing an amorphous phase (AM) or/and intermetallic compound (IM).. Phase selection rules were examined systematically using several parameters and it was revealed that valence electron concentration (VEC) < 6.87 and VEC > 9.16 are the conditions for the formation of single-phase BCC/B2 and FCC, respectively; and CALPHAD could predict this with essentially 100% accuracy. Article Title: Rapid data acquisition and machine learning-assisted composition design of functionally graded alloys via wire arc additive manufacturing Article Snippet: The phase fractions, thermal expansion coefficient (TEC), heat capacity (Cp), freezing range (difference between solidus and liquidus temperatures), and latent heat at the solidus temperature were calculated using the Article Title: Revisit the VEC rule in high entropy alloys (HEAs) with high-throughput CALPHAD approach and its applications for material design-A case study with Al–Co–Cr–Fe–Ni system Article Snippet: In this work, Article Title: Uncertainty quantification and composition optimization for alloy additive manufacturing through a CALPHAD-based ICME framework Article Snippet: ICME model framework All the models were implemented using the Article Title: Revisit the VEC rule in high entropy alloys (HEAs) with high-throughput CALPHAD approach and its applications for material design-A case study with Al–Co–Cr–Fe–Ni system Article Snippet: TC-Python is a PythonTM language-based SDK available with Thermo-Calc giving Article Title: Computational approach to grain boundary segregation engineering of nickel-base superalloys. Article Snippet: TC-Python was employed to couple the |